Natural ecosystems are comprised of diverse species and their interspecific interactions, in contrast to an ecological theory that predicts the instability of large ecological communities. This apparent gap has led ecologists to explore the mechanisms that allow complex communities to stabilize, even via environmental changes. A standard approach to tackling this complexity-stability problem is starting with a description of the ecological network of species and their interaction links, exemplified by a food web. This traditional description is based on the view that each species is in an active state; that is, each species constantly forages and reproduces. However, in nature, species’ activities can virtually stop when hiding, resting, and diapausing or hibernating, resulting in overlooking another situation where they are inactive. Here I theoretically demonstrate that adaptive phenotypic change in active and inactive modes may be the key to understanding food web dynamics. Accurately switching activity modes can greatly stabilize otherwise unstable communities in which coexistence is impossible, further maintaining strong stabilization, even in a large complex community. I hypothesize that adaptive plastic change in activity modes may play a key role in maintaining ecological communities. May  mathematIn nature, organisms should have both active and inactive modes –18. AnimN species, any pair of which is connected with the probability C (connectance). Population dynamics  are driven by interspecific prey-predator interactions (see G controls the speed of plastic changes in activity modes. θ denotes the plastic sensitivity to differences in fitness between activity modes (accuracy), and may be interpreted as the ability to catch environmental information; a larger θ represents a higher certainty of environmental information and θ  =  0 indicates that no environmental information is available. Hence, when θ = 0, phenotypic changes are random and when θ is high, the model approaches a step function of differences in each fitness (G and θ) to examine how activity mode affects the stability of ecological communities as evaluated by community persistence, with the probability that all species persist for a given time [Here, using a food web model, I demonstrate that switching activity modes in organisms greatly affects community dynamics stability and plays a key role in maintaining complex communities. The model proposed in this study is based on a food web comprising ions see . A casca fitness . I contrven time ,25  are connected by a trophic interaction with a probability of C, which is defined as the proportion of realized interaction links L in the possible maximum interaction links Lmax of a given network model (L = CLmax). For each pair of species, i, j = 1,…, N with i < j, species i never consumes species j, whereas species j may consume species i. To examine the generalization of the main result, random food webs were also tested /2 in both the random and cascade models. The food web model is defined by the following ordinary differential equation:Xki is the abundance of species i, rki is the intrinsic rate of change in species i, si is the density-dependent self-regulation of species i, and Mkij is the interaction coefficient between species i and j. Interaction coefficients are defined as Mkij = ekijakij and Mkji = −akij, where aij is the consumption rate of resource species j by species i and ekij (<1) is the conversion efficiency. The subscript k represents the activity modes, where 1 is active and 2 is inactive. The last two terms in r.h.s. of Eq (1) represent the dynamics due to plastic changes between the active and inactive modes. G is the constant parameter that controls the speed of plastic changes in activity modes, γi is the species-specific phenotypic change rate, and Pi is the switching probability from an active to inactive mode, described by the following non-linear function:Fki (k = 1 or 2) is the fitness of the active and inactive modes, defined as Fki = rki–siXki + ΣjMijkXjk. θ denotes the plastic sensitivity to differences in fitness between the activity modes, and may be interpreted as the ability to catch the information on environments. A larger θ represents a higher certainty of environmental information, while θ  =   0 indicates that no environmental information is available. Hence, when θ = 0, phenotypic changes are random and when θ increases, the model approaches a step function of differences in each fitness.A cascade food web  is consio tested . The maxri1> ri2 ; this reflects a cost, due to the phenotypic change to the inactive modes and/or a reduction of the utilization of extra resources. (ii) eij1> eij2 ; this also reflects a cost due to the phenotypic change to the inactive modes. (iii) aij1> aij2 ; this reflects that phenotypic change to the inactive modes makes each species more defensive, but reduces foraging activity. This considers a special case where the refuge in inactive mode is common among each species and very safety against active predators. If they use different habitats in active and inactive individuals, the interaction between active and inactive individuals would be rare. This strong assumption will be relaxed afterward.The active and inactive models are defined as follows: (i) ri1 = cir, aij1 = ciA1, and γi = ci, where ci is a constant randomly determined from a uniform distribution (0.0–1.0), r is the absolute growth rate of the active mode (assumed as r = 5), and A1 is the absolute interaction strength (consumption rate) of the active mode. In this study, for convenience purposes, A1 and A2 are referred to as the interaction strength of the active and inactive modes, respectively.It is assumed that Xki, 0 to 1.0; ci, 0 to 1.0). The values for si and eij1 were set to constants of 1  and 0.2 [In each iterated simulation, the initial species abundance and parameters were randomly selected from a uniform distribution  after a sufficiently long time  in 500 runs.Community persistence ,25 was cG = 0 and Xi2 = 0 for all i). Then, in a complex community with diverse species, virtually no community persists (white circle in Consider an extreme case without inactive mode (G > 0) or switch between active and inactive modes, the community can persist. The stabilization effects critically depend on the fitness sensitivity θ and adaptation speed G. Under slow adaptation (G≪1), the community becomes highly stable and virtually all communities persist, regardless of fitness sensitivity (θ = 0), stability decreases as the speed of adaptation increases  is considered. Here, an extreme case, where the inactive mode does not interact with active other species, is assumed first. In this situation, it is expected that inactive individuals remain completely at rest in a safe refuge. Once each species can change their activity mode  destabilizes community dynamics and can cause species extinctions in food webs without inactive modes . Howeveraij11> aij12, aij21 and aij12> aij21, aij22, where aklij is the consumption rate of active prey j by active species i if k = l = 1, that of inactive prey j by active species i if k = 1 and l = 2, that of active prey j by inactive species i if k = 2 and l = 1, and that of inactive prey j by inactive species i if k = l = 2, respectively. This is because active individuals encounter other individuals more frequently. In particular, active predators are likely to find active prey more frequently than inactive prey, while inactive predators are less likely to find inactive prey than active prey. The abovementioned novel assumptions corroborate with the main results.Here, the model is extended into more general situations. The above analysis assumed no interactions between the active and inactive modes. In addition, intraspecific interaction (self-regulation) does not change among the different activity modes. These strong assumptions are relaxed, and it is first assumed that intraspecific interaction becomes weak in the inactive mode. Although intraspecific competition among the inactive modes should not weaken if species compete for a refuge, it should do so if the inactive mode is a defensive phenotype, such as inducible defense, because species do not use the refuge and activity is low. Second, the following relations between consumption rates are assumed: θ and G) is likely to stabilize the system . Other parameters are identical to those in (TIFF)Click here for additional data file.S2 Figθ did not affect the results . Parameters are identical to those in Note that in (a) and (f), (TIFF)Click here for additional data file.S3 Figθ. Parameters are R = 0.04, E = 0.01, A1 = 0.1, A2 = 0.01, and G = 1.(a) Effects of species richness (N). I assume C = 0.3. (b) Effects of connectance (C). I assume N = 40. Black lines represent cases without inactive modes. Color represents deferent values of (TIFF)Click here for additional data file.S4 FigA12 = 0.01. (b) A12 = 0.02. (c) A12 = 0.05. (d) A12 = 0.1. Akl is redefined as: aij11 = ciA11, aij12 = A12aij11, aij21 = A21aij11, and aij22 = A22aij11, where A11 is the absolute interaction strength (consumption rate) of the active predator to active prey , and A12, A21 and A22 are constant parameters that controls the degree of reduction of interaction strengths from normal to the other three cases. Different colors represent different values of A21. In (a), blue, red, yellow, and green are A21 = 0.001, 0.002, 0.005, and 0.01, respectively; in (b), blue, red, yellow, and green are A21 = 0.001, 0.002, 0.005, and 0.02, respectively; in (c), blue, red, yellow, and green are A21 = 0.001, 0.002, 0.01, and 0.05, respectively; and in (d), blue, red, yellow, and green are A21 = 0.001, 0.002, 0.01, and 0.1, respectively. N = 40 and C = 0.3. Parameters are s1 = 1, s2 = 0.5, A11 = 0.1, R = 0.04, E = 0.01, θ = 20, and G = 1.(a) (TIFF)Click here for additional data file.S5 Figθi values are randomly chosen by a uniform distribution with a mean shown on the horizontal axis. Parameters are the same as those in (TIFF)Click here for additional data file.S6 Figs1 = 1, s2 = 0.5, A11 = 0.1, A12 = 0.01, A21 = 0.005, A22 = 0.001. r = 1, R = 0.2, E = 0.01, and G = 1.(a) Effects of species richness (N), C = 0.3; (b) Effects of connectance (C), N = 40. Parameters are (TIFF)Click here for additional data file.S7 Figθ = 5, G = 0.001; (b) θ = 5, G = 10; (c) θ = 20, G = 0.001; (d) θ = 20, G = 10. Each color represents a species. Other parameters are the same as those in (a) (TIFF)Click here for additional data file.S8 Fig(a), (b), (c) and (d) correspond to (TIFF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


LCT and MHC loci. We also apply it to a data set of brown rats sampled in NYC and identify genes related to olfactory perception. To facilitate use of this method, we have implemented it in user-friendly open source software.The inference of positive selection in genomes is a problem of great interest in evolutionary genomics. By identifying putative regions of the genome that contain adaptive mutations, we are able to learn about the biology of organisms and their evolutionary history. Here we introduce a composite likelihood method that identifies recently completed or ongoing positive selection by searching for extreme distortions in the spatial distribution of the haplotype frequency spectrum along the genome relative to the genome-wide expectation taken as neutrality. Furthermore, the method simultaneously infers two parameters of the sweep: the number of sweeping haplotypes and the “width” of the sweep, which is related to the strength and timing of selection. We demonstrate that this method outperforms the leading haplotype-based selection statistics, though strong signals in low-recombination regions merit extra scrutiny. As a positive control, we apply it to two well-studied human populations from the 1000 Genomes Project and examine haplotype frequency spectrum patterns at the  LCT and MHC, in a European and an African human population that show strong evidence for selection. We also apply this statistic to the genomes of an urban brown rat population, where we uncover evidence for adaptation in olfactory perception genes. We release user-friendly software implementing this statistic.Identifying regions of the genome that contain adaptive variation is of fundamental interest in evolutionary biology, providing insight into an organism’s history and biology. When positive selection is recent or ongoing, we expect to find genomic patterns such as high frequency haplotypes and low genetic diversity in the vicinity of the adaptive locus. Here we develop a statistic to identify these regions based on distortions of the haplotype frequency spectrum from a background distribution. We evaluate the performance of this statistic under numerous realistic settings of interest to empiricists and demonstrate its superior performance relative to other haplotype-based selection statistics. We also apply this statistic to real population-genetic data. As a positive control, we explore two well-studied loci,  The identification and classification of genomic regions undergoing positive selection in populations has been of long standing interest for studying organisms across the tree of life. By investigating regions containing putative adaptive variation, one can begin to shed light on a population’s evolutionary history and the biological changes well-suited to cope with various selection pressures.The genomic footprint of positive selection is generally characterized by long high-frequency haplotypes and low nucleotide diversity in the vicinity of the adaptive locus, the result of linked genetic material “sweeping” to high frequency faster than mutation and recombination can introduce novel variation. These selective sweeps are often described by two paradigms—“hard sweeps” and “soft sweeps”. Whereas a hard sweep is the result of a beneficial mutation that brings a single haplotype to high frequency , soft swT statistic implemented in LASSI i∈W, thersaltiLASSI method, we compute Λ at each window in the genome, where each window is considered the target window i⋆ in turn, and the likelihood is maximized independently for each target window. That is, all parameters  are optimized at each target window i⋆, thereby permitting the footprint size A of the sweep to vary across the genome, adjusting for initial linkage disequilibrium and local recombination rates that could impact sweep signals. Similar to the way SweepFinder , and A ∈ {Amin, …, Amax}. Here, Amin = −ln 0.99999/dmin, representing a value of A with a slow decay with distance; Amax = −ln 0.00001/dmin, representing a value of A with a fast decay with distance; and dmin is the smallest distance between any two windows genome-wide. We make 100 equally spaced (in log-space) steps between Amin and Amax. Furthermore, in order to reduce computational burden, we pre-compute To apply the epFinder , SweepFiepFinder , and LASr s, the ag of [nSL  statisti of [nSL  for unphT, H12, nSL, and iHS under a constant-size demographic history with diploid effective size of N = 104 diploid individuals. The Λ, T, and H12 statistics were computed for different window sizes, consisting of 51, 101, or 201 SNPs per window. ν ∈ {1, 2, 4, 8, 16} distinct haplotypes) and for sweeps of varying per-site per-generation strengths of s ∈ {0.01, 0.1}, the method with highest power regardless of time of selection  is Λ, thereby outperforming the competing methods. Interestingly, Λ applied to 51 SNP windows has generally higher power than with 101 and 201 SNP windows. Furthermore, smaller window sizes enable Λ to achieve high power even for old sweeps—with this elevated power often substantially higher than the closest competing method. This result recapitulates a finding of [T statistic was used within a machine learning framework, computing the T statistic in a greater number of small windows yielded higher power for ancient sweeps than when a smaller number of large windows was used. This is an intriguing result, because smaller windows have poorer estimates of the distortion of the HFS, yet it appears that for detecting ancient sweeps what matters is capturing the overall spatial trend of the distortion of the HFS. That is, when using too large of windows, Λ is averaging the HFS across too large of a region, which has likely been broken up over time due to recombination for ancient sweeps. Instead, smaller windows focus on genomic segments with less shuffling of haplotype variation due to recombination events, such that distortions in the HFS are due to the effect of a sweep at a nearby selected site.To begin, we compare the performance of Λ to nding of , where ts = 0.1 are recent, whereas for sweeps of strength s = 0.01, highest power for each method is shifted farther in the past toward more ancient sweep. This pattern was also found previously for H12 [T [s = 0.01 require more time for the beneficial allele to reach high frequency and leave a conspicuous genomic footprint, with this greater time to reach high frequency associated with increased chance that recombination and mutation act to break up high-frequency haplotypes. In contrast, sweeps of strength s = 0.1 create an immediate selection signature to appear in the genome due to the rapid rise in frequency of a beneficial mutation, but traces of this sweep pattern erode over time due to recombination, mutation, and drift. However, regardless, the Λ statistic paired with a small window size yields uniformly better or comparable sweep detection ability than the other approaches we examined. We also found that all methods performed poorly when selection strength was s = 0.001. for H12  and T [1r H12 [T . The likm and the footprint size of the sweep A, leading to respective estimates s = 0.01, the estimated number of sweeping haplotypes ν, regardless of window size used or age of the sweep. In contrast, ν = 1) of strength s = 0.1, the estimate of the number of sweeping haplotypes when using 51 SNP windows is often consistent with hard sweeps  haplotypes plotypes , the estt), sweep softness (ν), and sweep strength (s). These results suggest that the estimate of the sweep footprint size  [T and G123, with the best performance deriving from Λ with a small window size of 51 SNPs, and with substantially higher power for old sweeps compared to other approaches. An exception is that for recent (t ≤ 1000 generations) and highly soft (ν = 16) sweeps, using a window size of 101 SNPs for Λ had substantially higher power than using the smaller 51 SNP window. Moreover, for highly soft (ν = 16) and ancient (t ≥ 2000) sweeps with strength s = 0.1, the power of Λ is much lower with unphased multilocus genotypes compared to phased haplotypes (compare ν) than for softer sweeps (larger ν).Obtaining phased haplotypes for input to Λ represents an error-prone step that, without sufficient reference panels or high-enough quality genotypes, may make identification of sweeps difficult or potentially impossible for a number of diverse study systems. It is therefore beneficial if the favorable performance of Λ transfers to datasets that have not been phased. Similar to prior studies  , both of compare  Figs. Inplotypes  Figs shoWhile adaptive processes generally affect variation locally in the genome, neutral processes such as demographic history influence overall levels of genome diversity. Specifically, it is common to consider that demographic processes impact the mean value of genetic diversity, and numerous likelihood approaches for detecting sweeps , 16–24 aT, H12, nSL, and iHS when applied to haplotype data (T and G123 when applied to unphased multilocus genotype data (T statistic in a machine learning framework (referred to as T-Trendsetter), power to detect recent sweeps is higher for larger windows and power to detect ancient sweeps is higher for smaller windows under the bottleneck history considered here.The Λ statistic generally exhibits superior power to ype data  or to T ype data . Moreoveype data  Figs holype data  Figs, wiype data , under type data . In partIn addition to demographic history, a pervasive force acting to reduce variation across the genome is background selection –43, whicr = 10−8 for each simulation replicate. However, recombination rate is known to vary across the genome [−8 , nSL, and iHS under phased haplotypes and unphased multilocus genotypes settings. These results further highlight the robustness of the Λ statistic to realistic genomic characteristics often encountered in empirical studies.Throughout our experiments, we have considered a per-site per-generation recombination rate of e genome , and it  [−8 see  for reacn of sampled individuals as well the number K of haplotypes used to truncate the HFS should affect the resolution at which we can model the distortion of the HFS due to a sweep, and thus would likely result in alterations of power of Λ to detect sweeps. As expected, n and smallest window size combination  and the lowest power with the smallest n and largest window size combination . Moreover, as sample size increases, Λ is better able to detect sweeps of older age, and for extremely small samples , the estimates ν of sweeping haplotypes are poor. In contrast to changing sample size n, changing the number of haplotypes K to truncate the HFS does not have a substantial effect on the power of Λ to detect sweeps  and an African population (n = 108). Furthermore, as patterns of recent selection have been extensively studied in these populations, the results will allow us to confirm that the method returns sensible results.The 1000 Genomes Project Phase 3  publisheWe plot the genome-wide Λ statistics for the CEU population in LCT [APOL1 [As this statistic is a composite likelihood ratio test that ignores recombination, we expected that Λ values may be negatively correlated with recombination rate. And, indeed, we find that the max Λ observed in a window across all replicates tends to be larger for low-recombination regions LCT , 59–61),LCT ), and thT [APOL1 ). We nexT [APOL1  for eachT [APOL1 , 65.LCT and MHC loci (LCT) or both (MHC) populations. The LCT locus has been previously identified as under selection in some northern European populations and eastern African populations [LCT in CEU but not in YRI. Indeed, this is what we see in LCT locus on Chromosome 2. Furthermore, we examine the truncated HFS among eleven windows spanning LCT in both YRI  in these two populations’ empirical data and all replicates of simulated demography-matched whole-genome data using two distance measures other than physical distance (number of windows and centiMorgans) and find high correlation between Λ values calculated with these alternative distance measures and physical distance .n = 29) from across the island of Manhattan, New York City, USA to study adaptation to urban environment. In this study, they note that haplotype phase is unknown and that the demographic history for brown rats was not well-calibrated in this population. As such, they chose to use the G123 [saltiLASSI framework to illustrate its use in the context of unphased data and a poorly understood demographic history that requires an outlier approach. publishethe G123  and otheWe plot the genome-wide Λ statistics for the NYC rats in Olr23, Olr24, Olr25, Olr27, Olr29, Olr30, Olr32, and Olr34), and the region on chromosome 2 contains a cluster of calcium-activated chloride channel genes . Notably, calcium-activated chloride channel genes are expressed in the olfactory nerve layer of mouse brains [We note that the two strongest signals in the genome are on chromosomes 1 and 2 . The rege brains . If thesTaking the collection of annotated genes present in m and footprint size A, which is correlated with age and strength of the sweep. These additional parameters have the potential to further characterize well-supported sweep signals from large Λ values.In this study, we developed a new likelihood ratio test statistic Λ that examines the spatial distribution of the HFS for evidence of sweeps. We demonstrated that this statistic has high power to detect both hard and soft sweeps, with performance substantially better than competing haplotype-based approaches for the same task. Moreover, while optimizing the model parameters of Λ we obtain estimates of sweep softness LCT gene in Europeans due to lactase persistence [In addition to lending exceptional performance on simulated data, application of Λ to whole-genome variant calls from central European and sub-Saharan African individuals recapitulated the well-established signal at the sistence , as wellsistence , 68, 69.Though our identification of the MHC locus in both human empirical scans as a sweep is not novel, it is important to address that the MHC locus comes with a number of technical challenges when assessing genetic variation. Specifically, the MHC locus is known to harbor extensive structural variation, which makes it difficult to assemble  and may As the human populations have a well-characterized demographic history, we were able to perform demography-matched neutral simulations to aid in identifying regions of the genome likely affected by selection. When analyzing the New York City brown rat dataset, we had to take an outlier approach as the brown rat demographic history was previously noted to be mis-calibrated for this population . HoweverT statistic using a different modeling approach. Therefore, choice of window size may be informed by the time frame of selective events that is being investigated. As highlighted in e.g., Figs LCT. In addition to using simulation results to aid in selecting appropriate window sizes, an alternate method such as choosing sizes based on the expected decay of linkage disequilibrium in the genome has been demonstrated to also work well in practice ..T statisWe note that this approach is a composite likelihood statistic, and as such it treats windows as independent, ignoring the effects of recombination. This means that Λ values are likely to be larger in low-recombination regions , and extT statistic of [T statistic within a machine learning framework enhanced its detection ability, specifically for ancient sweeps. However, machine learning frameworks require extensive simulations to train , 28T stan  in the CEU, with inferred soft sweeps (estimated m > 1) in the MHC region, which are consistent with the number of prominent high-frequency haplotypes at these regions .ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes, where each of these haplotypes harbored a beneficial allele in the center of the simulated genomic segment with strength s ∈ {0.001, 0.01, 0.1} per generation that immediately appeared and became beneficial at time t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling. To ensure that a sweep signature had the potential to be uncovered , we required that the beneficial allele established in the population by reaching a frequency of 0.1 in the population. Simulation replicates for which the beneficial allele did not reach a frequency of 0.1 in the population were repeated until the beneficial allele established in the population. All neutral and selection simulations were run for 11N generations, where the first 10N generations were used as burn-in and n = 50 diploid individuals were sampled from the population after 11N generations . Because forward-time simulations are computationally intensive, as is commonly-practiced [2 in computing time, such that the constant-size simulations run roughly 100 times faster than without scaling and the CEU model simulations run approximately 400 times faster, making a large-scale simulation study feasible.In addition to these genetic and demographic parameters, for selection simulations, we modeled sweeps on racticed , 90 we sT statistic [nSL [T, and H12, we considered their applications in central windows of 51, 101, and 201 SNPs, and analyzed windows every 25 SNPs across a simulated sequence. We chose SNP-delimited windows rather than windows based on physical length as they should be more robust to variation in recombination and mutation rate across the genome, as well as random missing genomic segments due to poor mappability, alignability, or sequence quality. That is, we expect SNP-delimited to be more conservative than windows based on the physical length of an analyzed genomic segment. We also examined the application of Λ, T, and G123 , and the selscan software [nSL.When analyzing each simulated replicate, we examined the performance of Λ with the likelihood tatistic  that doetatistic  that wastatistic  and nSL tic [nSL  methods e of H12 . We applsoftware  to compuWe extracted the phased genomes of CEU (99 diploids) and YRI (108 diploids) populations, separately, from the full 1000 Genomes Project Phase 3 dataset (2504 diploids) . For eacsaltiLASSI Λ statistics for both phased (haplotype-based) and unphased (multilocus-genotype-based) analyses with lassip. We use physical distance as the distance measure, and we set --winsize 201, --winstep 100, and --k 20 to use the ranked HFS for the top K = 20 most frequent haplotypes. By default lassip assumes phased data and computes haplotype-based statistics, when the --unphased flag is set, all statistics are computed using multilocus genotypes.We compute stdpopsim [msprime [OutOfAfrica_2T12 demographic history [HapMapII_GRCh37 genetic map [stdpopsim, we simulate 100 replicates of all 22 autosomes for each population separately, sampling 99 diploid individuals for CEU simulations and 108 diploid individuals for YRI simulations. For each replicate, we then compute saltiLASSI Λ statistics for both phased and unphased analyses with lassip, setting --winsize 201, --winstep 100, and --k 20. As simulated genomes do not simulate variants at the same sites, the windows within which Λ is calculated will not perfectly align with each other or our real-data analysis. In order to compare neutral and real Λ values at local regions of the genome, for each neutral replicate, separately, we align the simulated windows to the windows of our real-data analysis, and then for each real-data window we calculate a weighted mean of all overlapping windows to get a neutral-simulation Λ for that window associated with our real-data. In this way we are able to compute 100 neutral-simulated Λ values for each window in our real-data analyses. We then compute the max Λ, the top-0.1% Λ, and the top-1% Λ across all windows in all replicates for each population and each analysis (phased/unphased), which are given in To determine significance thresholds, we simulated neutral whole genomes with a realistic recombination map and demographic history using tdpopsim  and mspr[msprime . Using t history  and the etic map  in stdpoHapMapII_GRCh37 genetic map [HapMapII_GRCh37 genetic map are linearly interpolated based on the nearest surrounding sites. We compare these results to the results calculated using physical distance using Spearman’s rank correlation . We set --winsize 201 and --winstep 100, and we choose --k 20 to use the ranked HFS for the top K = 20 most frequent haplotypes.We extracted the genetic data of 29 rats sampled in New York City , retainisaltiLASSI method on these data. We compute the top-0.1% Λ, the top-1% Λ, and the top-5% Λ across all windows genome-wide, getting 389.839, 88.080, and 22.724, respectively. Putatively selected regions were identified by concatenating consecutive windows with Λ greater than the top-1% Λ observed . Results are based on a sample of n = 50 diploid individuals and the haplotype frequency spectra for the Λ and T statistics truncated at K = 10 haplotypes.As a function of selection start time for applications of Λ, (EPS)Click here for additional data file.S2 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated number of sweeping haplotypes ((EPS)Click here for additional data file.S3 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated genomic size influenced by the sweep ((EPS)Click here for additional data file.S4 FigT, and G123 with windows of size 51, 101, and 201 SNPs to unphased multilocus genotype input data under simulations of a constant-size demographic history for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals and the multilocus genotype frequency spectra for the Λ and T statistics truncated at K = 10 multilocus genotypes.As a function of selection start time for applications of Λ, (EPS)Click here for additional data file.S5 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated number of sweeping haplotypes ((EPS)Click here for additional data file.S6 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated genomic size influenced by the sweep ((EPS)Click here for additional data file.S7 FigT, and H12 with windows of size 51, 101, and 201 SNPs, as well nSL and iHS under simulations of the human central European (CEU) demographic history of [s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals and the haplotype frequency spectra for the Λ and T statistics truncated at K = 10 haplotypes.As a function of selection start time for applications of Λ, story of  for per-(EPS)Click here for additional data file.S8 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated number of sweeping haplotypes (story of  for per-(EPS)Click here for additional data file.S9 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated genomic size influenced by the sweep (story of  for per-(EPS)Click here for additional data file.S10 FigT, and G123 with windows of size 51, 101, and 201 SNPs to unphased multilocus genotype input data under simulations of the human central European (CEU) demographic history of [s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals and the multilocus genotype frequency spectra for the Λ and T statistics truncated at K = 10 multilocus genotypes.As a function of selection start time for applications of Λ, story of  for per-(EPS)Click here for additional data file.S11 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated number of sweeping haplotypes (story of  for per-(EPS)Click here for additional data file.S12 Figs ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated genomic size influenced by the sweep (story of  for per-(EPS)Click here for additional data file.S13 FigT, and H12 with windows of size 51, 101, and 201 SNPs, as well nSL and iHS under simulations of the human central European (CEU) demographic history of [−8 for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals and the haplotype frequency spectra for the Λ and T statistics truncated at K = 10 haplotypes. Plots displaying patterns in estimated sweep softness and footprint size can be found in As a function of selection start time for applications of Λ, story of  with per(EPS)Click here for additional data file.S14 Fig−8 for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated number of sweeping haplotypes (story of  with per(EPS)Click here for additional data file.S15 Fig−8 for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated genomic size influenced by the sweep (story of  with per(EPS)Click here for additional data file.S16 FigT, and G123 with windows of size 51, 101, and 201 SNPs to unphased multilocus genotype input data under simulations of the human central European (CEU) demographic history of [−8 for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals and the multilocus genotype frequency spectra for the Λ and T statistics truncated at K = 10 multilocus genotypes. Plots displaying patterns in estimated sweep softness and footprint size can be found in As a function of selection start time for applications of Λ, story of  with per(EPS)Click here for additional data file.S17 Fig−8 for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated number of sweeping haplotypes (story of  with per(EPS)Click here for additional data file.S18 Fig−8 for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Gray solid, dashed, and dotted horizontal lines are the corresponding mean n = 50 diploid individuals and the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated genomic size influenced by the sweep (story of  with per(EPS)Click here for additional data file.S19 FigT, H12, and G123 with windows of size 51, 101, and 201 SNPs, as well nSL and iHS under simulations of a constant-size demographic history and the human central European (CEU) demographic history of [T, H12, nSL, and iHS) or unphased multilocus genotype input data . Proportion of false signals is computed as the fraction of background selection simulations in which the score computed for Λ, T, H12, G123, nSL, or iHS exceeded the corresponding score threshold defined by a particular false positive rate. Results are based on a sample of n = 50 diploid individuals and haplotype and multilocus genotype frequency spectra for the Λ and T statistics truncated at K = 10 haplotypes or multilocus genotypes.As a function of false positive rate for applications of Λ, story of  (bottlen(EPS)Click here for additional data file.S20 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the haplotype frequency spectra for the Λ statistics truncated at K = 10 haplotypes.as a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs under simulations of a constant-size demographic history and sample size of (EPS)Click here for additional data file.S21 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated number of sweeping haplotypes ((EPS)Click here for additional data file.S22 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated genomic size influenced by the sweep ((EPS)Click here for additional data file.S23 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.As a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs to unphased multilocus genotype input data under simulations of a constant-size demographic history and sample size of (EPS)Click here for additional data file.S24 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated number of sweeping haplotypes ((EPS)Click here for additional data file.S25 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated genomic size influenced by the sweep ((EPS)Click here for additional data file.S26 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the haplotype frequency spectra for the Λ statistics truncated at K = 10 haplotypes.As a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs under simulations of the human central European (CEU) demographic history of  and samp(EPS)Click here for additional data file.S27 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated number of sweeping haplotypes (story of  and samp(EPS)Click here for additional data file.S28 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the haplotype frequency spectrum for the Λ statistic truncated at K = 10 haplotypes.Illustrated by mean estimated genomic size influenced by the sweep (story of  and samp(EPS)Click here for additional data file.S29 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.As a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs to unphased multilocus genotype input data under simulations of the human central European (CEU) demographic history of  and samp(EPS)Click here for additional data file.S30 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated number of sweeping haplotypes (story of  and samp(EPS)Click here for additional data file.S31 Fign ∈ {10, 25, 50} diploid individuals for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on the multilocus genotype frequency spectrum for the Λ statistic truncated at K = 10 multilocus genotypes.Illustrated by mean estimated genomic size influenced by the sweep (story of  and samp(EPS)Click here for additional data file.S32 FigK ∈ {5, 10, 20} haplotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.As a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs under simulations of a constant-size demographic history and the haplotype frequency spectra for the Λ statistic truncated at (EPS)Click here for additional data file.S33 FigK ∈ {5, 10, 20} haplotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated number of sweeping haplotypes ((EPS)Click here for additional data file.S34 FigK ∈ {5, 10, 20} haplotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated genomic size influenced by the sweep ((EPS)Click here for additional data file.S35 FigK ∈ {5, 10, 20} multilocus genotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.As a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs to unphased multilocus genotype input data under simulations of a constant-size demographic history and the multilocus genotype frequency spectra for the Λ statistic truncated at (EPS)Click here for additional data file.S36 FigK ∈ {5, 10, 20} multilocus genotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated number of sweeping haplotypes ((EPS)Click here for additional data file.S37 FigK ∈ {5, 10, 20} multilocus genotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated genomic size influenced by the sweep ((EPS)Click here for additional data file.S38 FigK ∈ {5, 10, 20} haplotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.As a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs under simulations of the human central European (CEU) demographic history of  and the (EPS)Click here for additional data file.S39 FigK ∈ {5, 10, 20} haplotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated number of sweeping haplotypes (story of  and the (EPS)Click here for additional data file.S40 FigK ∈ {5, 10, 20} haplotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated genomic size influenced by the sweep (story of  and the (EPS)Click here for additional data file.S41 FigK ∈ {5, 10, 20} multilocus genotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Classification ability demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.As a function of selection start time for applications of Λ with windows of size 51, 101, and 201 SNPs to unphased multilocus genotype input data under simulations of the human central European (CEU) demographic history of  and the (EPS)Click here for additional data file.S42 FigK ∈ {5, 10, 20} multilocus genotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated softness demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated number of sweeping haplotypes (story of  and the (EPS)Click here for additional data file.S43 FigK ∈ {5, 10, 20} multilocus genotypes for per-generation selection coefficients of s ∈ {0.001, 0.01, 0.1} on the rows. Mean estimated genomic size influenced by sweeps demonstrated for selection start times of t ∈ {500, 1000, 1500, 2000, 2500, 3000} generations prior to sampling for ν ∈ {1, 2, 4, 8, 16} initially-selected haplotypes (columns). Results are based on a sample of n = 50 diploid individuals.Illustrated by mean estimated genomic size influenced by the sweep (story of  and the (EPS)Click here for additional data file.S44 FigFor the (A) CEU and (B) YRI populations from the 1000 Genomes Project. Each point represents a single 201-SNP window along the genome. Horizontal lines represent the top 1%, top 0.1%, and maximum observed Λ statistic across all windows in demography-matched neutral simulations. Red line indicates the maximum observed Λ among 100 replicate simulations at that location in the genome.(EPS)Click here for additional data file.S45 FigLCT region, vertical dotted lines indicate zoomed region shown in (B) and (C). (B) YRI empirical HFS for 11 windows in the LCT region. (C) CEU empirical HFS for 11 windows in the LCT region. (D) Λ plotted in the MHC region, vertical dotted lines indicate zoomed region shown in (E) and (F). (E) YRI empirical HFS for 11 windows in the MHC region. (F) CEU empirical HFS for 11 windows in the MHC region. In (B), (C), (E), and (F), numbers above HFS are Λ values for the window rounded to the nearest whole number, and the genome-wide average HFS is highlighted in grey. ith most common MLG truncated to K = 20.(A) Λ plotted in the (EPS)Click here for additional data file.S46 FigFor the (A) CEU and (B) YRI populations.(EPS)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableFrom demography-matched neutral whole genome simulations with variable recombination rate (mean across 100 replicates) and from empirical data.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.


We have previously observed substantially higher oxygen uptake in soldiers walking on terrain at night than when performing the same walk in bright daylight. The aims of the present study were to investigate the influence of vision on mechanical efficiency during slow, horizontal, constant-speed walking, and to determine whether any vision influence is modified by load carriage.n = 15) walked (3.3 km/h) for 10 min on a treadmill in four different conditions: (1) full vision, no carried load, (2) no vision, no carried load, (3) full vision with a 25.5-kg rucksack, (4) no vision with a 25.5-kg rucksack.Each subject (Oxygen uptake was 0.94 ± 0.12 l/min in condition (1), 1.15 ± 0.20 l/min in (2), 1.15 ± 0.12 l/min in (3) and 1.35 ± 0.19 l/min in (4). Thus, lack of vision increased oxygen uptake by about 19%. Analyses of movement pattern, by use of optical markers attached to the limbs and torso, revealed considerably shorter step length (12 and 10%) in the no vision (2 and 4) than full vision conditions (1 and 3). No vision conditions (2 and 4) increased step width by 6 and 6%, and increased vertical foot clearance by 20 and 16% compared to full vision conditions (1 and 3).The results suggest that vision has a marked influence on mechanical efficiency even during entrained, repetitive movements performed on an obstacle-free horizontal surface under highly predictable conditions. The present study concerns effects of vision on energy expenditure during slow constant-pace walking. Army soldiers commonly conduct foot-borne operations that include heavy load carriage at night. Typically, the reasons to perform such operations at night are tactical considerations, but during missions in hot climates, another reason may be to reduce heat strain. However, in conjunction with training exercises, we have occasionally observed substantially higher oxygen uptake, and hence endogenous heat production, in soldiers walking on terrain at night whilst using night-vision goggles, than when performing the same task in bright daylight (unpublished observations). Several factors might have contributed to the increased oxygen uptake. For instance, it is conceivable that under conditions of restricted peripheral vision, soldiers walk with higher foot clearance from the ground to reduce the risk of tripping  and conformed to the declaration of Helsinki.full vision and no carried load,no vision and no carried load,full vision carrying a 25.5-kg rucksack,no vision carrying a 25.5-kg rucksack.Each subject walked on a treadmill  at a constant velocity of 3.3 km/h for 10 min in four different conditions:Among subjects, the four trials were performed in an alternating counter-balanced order, each trial being interspersed by a 10-min period, during which the subject was resting motionless in a seated, upright position. For safety reasons, the subject wore a loosely fitting harness, attached at the ceiling by a strap, and the treadmill was provided with handle bars on both sides as well as in the front and back of the tread belt. Additionally, approximately 20 cm in front of the back-side handle bar, the treadmill was equipped with a laser beam, which, if broken, activated an acoustic signal alerting the subject that he was approaching the rear border of the treadmill walking belt.Before the experiments, the subjects went through a 45-min standardized training procedure, during which they walked with full vision as well as blindfolded on the treadmill (3.3 km/h), getting acquainted with the pace, the handle bars, the laser-beam warning mechanism and the rucksack. During the experiment, the subject wore shorts, t-shirt, long socks and hiking boots (1.3 kg). After donning the harness, he was equipped with 15 reflective markers, taped on the top of the head, and bilaterally on the following locations: the fifth metatarsal joint, the lateral malleolus, the lateral condyle of the tibia, the iliac crest, the dorsal side of the wrist, the lateral epicondyle of the humerus and the greater tuberosity of the humerus. Four more markers were placed at the four corners of the treadmill. Three-dimensional marker trajectory data were collected using a six-camera motion capturing system , during two 1-min periods in each 10-min trial, namely, min 3–4 (Start) and 8–9 (End) of the trial. All kinematic variables, except for cadence see “” section4b2, Cosmed, Italy) for measurement of oxygen uptake  was evenly distributed in the bag; its weight was measured before the first experiment.Heart rate (HR) was monitored continuously using a carry-on system . After the positioning of the reflective markers, the subject was equipped with a mobile standalone metabolic cart system , was developed to determine all kinematic variables from the motion capturing data. The computer identifications of reflective markers during the walking trials were always checked manually, and if need be, corrected/intrapolated, before allowing the program to estimate the following kinematic variables:A two-way ANOVA with repeated measures was used to define the effects of weight bearing and vision on the variables measured . A Bonferonni post hoc test was employed to assign the specific differences in the analysis of variance in case of a significant main effect. For the cardiorespiratory variables, the last 8 min of each treadmill trial was included in the analyses, and for the kinematic variables, both 1-min sample periods (see above) were included in the analyses. Values are presented as means (SD). The significance level was set at 0.01. Note that the statistical analyses are based on absolute values, which are also presented in figures and table, whereas, for comparison, relative values (% changes based on absolute means) are given in the narrative of “All experiments were performed without any adverse events. Several of the subjects were, on one or a few occasions, alerted by the acoustic warning signal that they were approaching the rear end of the tread belt, whereas on no occasion did any of the subjects make use of the handle bars or safety harness.F = 22.4, p < 0.01; Fig. F = 26.3, p < 0.01] and HR by 8%   = 233.2, p < 0.01], F = 93.1, p < 0.01] and HR by 7%   = 0.1, p = 0.7; for F = 0.9, p = 0.3; and HR: F = 2.7, p = 0.1).Carrying the rucksack increased mentclass2pt{minimmentclass2pt{minimF values between 0.1 and 2.7, p > 0.05).No significant changes over time were observed in the cardiorespiratory responses  = 14.9 p < 0.01; Fig. F = 9.5, p = 0.01] and the vertical foot clearance by 18%   = 32.2, p < 0.01; Fig. F = 0.1, p = 0.9].Blindfolding increased walking cadence by 11% [01] Fig. . Blindfop > 0.05) and no significant interaction between load carriage and vision was observed  = 0.9, p = 0.3; for step width: F = 0.4, p = 0.5; for foot clearance: F = 0.1, p = 0.7; and for hip vertical amplitude: F = 0.1, p = 0.9 Fig. The 25.5-kg rucksack had no effect on any of the kinematic variables  = 0.9, p = 0.3], the step width  or the stride length , whereas the foot clearance was reduced during the course of the 10-min treadmill walk by 4% [F = 21, p < 0.01; interaction effect: F = 0.1, p = 0.7; Fig. No significant changes over time were observed in the walking cadence [The aims of the present study were to investigate the influence of vision on energy expenditure and kinematics during slow, horizontal, constant-speed walking; and whether preserved vision is more important for minimizing energy expenditure if the walker is carrying a heavy backpack. The results demonstrated that lack of vision increased oxygen uptake by 19%, which incidentally corresponded approximately to the added oxygen consumption induced by carrying the 25.5-kg rucksack with vision. Lack of vision increased energy expenditure similarly whether or not the walker was carrying a 25.5-kg rucksack. Lack of vision reduced stride length and hence, increased step cadence and increased foot clearance as well as step width. Gait kinematics were not affected by carrying the rucksack at the slow, pre-determined treadmill speed.Our observation that the blindfolding increased oxygen uptake during the treadmill walking by 19% is in agreement with the finding that blind individuals exhibited a 25% higher oxygen uptake during level treadmill walking at 4.8 km/h than did their sighted controls . It appears that, during level walking, the choice of stride length is a critical factor for such minimization , and it therefore cannot be excluded that under more realistic conditions, for instance whilst walking on a horizontal road, the blindfolded subjects might have chosen a different, less oxygen consuming pace. Notably, at a given pace between 2.9 and 5.9 km/h, the metabolic demand has been found identical whether the walking is conducted on a treadmill or on a horizontal hard surface Ralston .Furthermore, considering the original context of the present study, namely nighttime patrol missions, neither obstacle-free horizontal terrain nor a complete lack of visual cues appear very realistic. Thus, the present results need to be supplemented by controlled experiments regarding the effects of night-vision goggles and differing terrain on energy expenditure during nighttime walking.In conclusion, blindfolding increased oxygen consumption during slow, constant-speed walking on a horizontal treadmill by 17% and 22%, respectively, with and without carrying a 25.5-kg rucksack. This suggests that vision has a marked influence on mechanical efficiency even during entrained, repetitive movements performed on an obstacle-free horizontal surface under highly predictable conditions.


Vibrio parahaemolyticus phage, vB_VpaP_DE10, was isolated from sewage samples collected in Guangzhou city, China. Transmission electron microscopy revealed that phage vB_VpaP_DE10 has an icosahedral head (52.4 ± 2.5 nm) and a short non-contracted tail (21.9 ± 1.0 nm). Phage vB_VpaP_DE10 lysed approximately 31% (8/26) of the antibiotic-resistant V. parahaemolyticus strains tested. A one-step growth curve showed that phage vB_VpaP_DE10 has a relatively long latency time of 25 min and a burst size of ~19 PFU per cell. The genome of phage vB_VpaP_DE10 is a 42,871-bp-long dsDNA molecule with a G + C content of 49.19% and is predicted to contain 46 open reading frames, 26 of which are predicted to be related to functions such as phage structure, packaging, host lysis, and DNA metabolism. Sequence comparisons suggested that vB_VpaP_DE10 is a member of the genus Maculvirus within the family Autographiviridae. Morphological and genomic analysis indicated that vB_VpaP_DE10 is a novel V. parahaemolyticus phage.In the present study, a novel lytic  Vibrio parahaemolyticus is a Gram-negative bacterium that exists in marine, riverine, and aquaculture environments [V. parahaemolyticus was first discovered in Osaka, Japan, and has been prevalent ever since [V. parahaemolyticus infection are reported each year with an estimated annual health cost of more than 30 million dollars [V. parahaemolyticus has been the main cause of foodborne disease outbreaks and infectious diarrhea cases in coastal areas [V. parahaemolyticus were isolated from 16,504 fecal specimens of patients with acute diarrhea in southeastern China; the annual isolation rate fluctuated between 6.1 and 8.7% [V. parahaemolyticus are mainly related to two virulence factors, namely thermostable direct hemolysin (TDH) and TDH-related hemolysin (TRH) [ronments . Infectironments . V. paraer since . Accordi dollars . In Chinal areas . From 20V. parahaemolyticus isolated from various sources is highly resistant to single or multiple antibiotics, particularly ampicillin [V. parahaemolyticus, but they are non-specific, easily destroy the natural ecology of aquaculture water, and the resulting residues pose a threat to the health of consumers [V. parahaemolyticus.The use of antibiotics is a common practice in treating the disease caused by infection with this bacterium; however, over time, an increasing number of reports have shown that picillin . Disinfeonsumers . TherefoV. parahaemolyticus [V. parahaemolyticus phage strains in total. Therefore, obtaining sufficient phage resources is a top priority. Moreover, it is necessary to clear the genetic information of phages before application. Considering the high genetic diversity and mutation rate of phages, traditional or single microbial analysis methods are no longer applicable. Therefore, it is very important to continuously update research methods and use multiple methods to verify each other.In the past decade, studies have suggested that phage therapy may be a promising strategy to control olyticus . HoweverV. parahaemolyticus phage, vB_VpaP_DE10, from the sewage of the Huangsha Aquatic Product Market in Guangzhou, China. We assessed the basic biological properties of its lytic activity, namely host range and one-step growth curve, and performed a multi-angle comprehensive analysis of its whole genome.In this study, we isolated a V. parahaemolyticus strains provided by the Institute of Microbiology, Guangdong Academy of Sciences, were used in this study. The serotype, resistance, and origin of each strain were confirmed by previous studies [V. parahaemolyticus O4-12 was used as a host for phage isolation and propagation. Strains were stored at −80 °C in 30% (v/v) glycerol. All V. parahaemolyticus strains were cultured in 5 mL of tryptic soy broth (TSB) and incubated at 37 °C for 12 h with shaking.Twenty-six  studies  (Table 1g for 10 min), and the supernatant was filtered using a 0.45 μm filter . Thereafter, MgSO4 was added to the filtrate to a final concentration of 50 mM and allowed to stand for 10 min before filtering using a 0.22-μm filter . The filter membranes were cut and eluted with a broth containing 3% (w/v) Bacto beef extract, 3% (v/v) Tween 80, and 50 mM NaCl. Subsequently, 2 mL of eluent was mixed with 2 mL of sterile double-strength TSB (4 mM CaCl2) and an 80-µL V. parahaemolyticus O4-12 culture (OD600 = 0.2). The mixtures were incubated overnight at 37 °C with shaking. After incubation, the mixed cultures were centrifuged at 8000× g for 5 min, and the supernatant was filtered through a 0.45 μm syringe filter. The purified phages were incubated with V. parahaemolyticus again at least twice to enrich the phage particles. A spot test assay was performed to test for the presence of lytic phages. The double-layer agar method was used to obtain single plaques and a plaque was purified four times to ensure the isolated phage was pure [Phage vB_VpaP_DE10 was isolated from sewage samples collected in the Huangsha aquatic product market in Guangzhou city, China, by a previously described method . Sewage was pure .g for 10 min and filtered through a 0.45-μm-pore membrane. Thereafter, polyethylene glycol  was added to it and placed at 4 °C overnight.Phage concentration was performed as previously described, with modifications . First, g for 20 min at 4 °C. The sediment was resuspended in SM buffer. The CsCl density gradient centrifugation method was used to purify and concentrate phages [The overnight phage concentrate was centrifuged at 12,000× e phages . Phage p2), poured onto tryptic soy agar plates, and placed at room temperature for 5 min or until the agar solidified. Then, 2 µL of tenfold diluted phage stock were pipetted on each plate. Thereafter, the plates were incubated at 37 °C for 6 h. Based on the clarity of plaques, the results were divided into two categories: clear (+) and no plaques (-) in the spotting area.First, 100 μL of bacterial culture in the mid-log phase was mixed with 5 mL TSB (0.4% agar and 2 mM CaClV. parahaemolyticus isolates which were sensitive to the phage vB_VpaP_DE10 in the spot test were selected for the determination of EOP by the double-agar layer method [V. parahaemolyticus culture (108 CFU/mL). The mixture was poured onto TSA plates and incubated at 37 °C for 6 h. The EOP values were calculated as the ratio of lysis plaques produced in each susceptible strain divided by the number of the plaques produced in V. parahaemolyticus O4-12 and then were ranked as “high efficiency” (EOP ≥ 0.5), “medium efficiency” (0.1 ≤ EOP < 0.5), “low efficiency” (0.001 < EOP < 0.1), or “inefficient” (EOP ≤ 0.001).All the r method . Brieflyg for 20 min at 4 °C, and the supernatant was discarded. Subsequently, 1 mL SM buffer was added to fully wash the tube wall and precipitate DNA, following which DNase I and RNase A were added to final concentrations of 0.1 units/µL and 3 µg/mL, respectively. The mixture was incubated at 37 °C for 1 h. Next, purified phages were treated with 10% SDS, EDTA, and proteinase K for 30 min at 65 °C. Then, an equal volume of Tris-saturated phenol solution was added and the mixture was centrifuged at 12,000× g for 5 min. The upper aqueous phase was transferred to another centrifuge tube, an equal volume of premixed phenol-chloroform-isoamyl alcohol (25:24:1) was added to it, and the mixture was centrifuged at 12,000× g for 5 min. The upper aqueous phase was again transferred to a fresh centrifuge tube, and an equal volume of chloroform was added (this step was repeated thrice). Thereafter, the upper aqueous phase was pipetted into a fresh centrifuge tube, an equal volume of isopropanol was added to it, and the tube was then incubated at −20 °C for 30 min. Next, the DNA sediment was collected by centrifugation at 12,000× g for 5 min at 4 °C, washed with 70% ethanol, and centrifuged at 12,000× g for 5 min again (this step was repeated twice). Subsequently, the DNA sediment was dried at room temperature and 50 μL deionized water (preheated to 65 °C) was added to it. The extracted DNA was stored at −20 °C.Phage whole-genome extraction was performed as described by Xing S et al. . The phahttp://lowelab.ucsc.edu/tRNAscan-SE/ accessed on 18 November 2021). The virulence factors and antibiotic genes were predicted using the virulence factor database [http://www.mgc.ac.cn/VFs/ accessed on 18 November 2021) and antibiotic resistance gene database [http://ardb.cbcb.umd.edu/ accessed on 18 November 2021), respectively. A circular map of the phage vB_VpaP_DE10 genome was generated using CGView [https://itol.embl.de accessed on 18 June 2022). Finally, the whole phage genome was visualized using Easyfig2.2.3 [The phage DNA was sequenced using Ion Torrent S5 platform . High-quality reads were assembled using SPAdes v. 3.12.0 . The whodatabase   was centrifuged at 8000× g for 5 min, the sediment was resuspended in 1 mL SM buffer, and 100 µL phage at multiplicity of infection (MOI) = 0.1 was added to it. This mixture was incubated at 37 °C for 10 min and the mixture was then centrifuged and resuspended in 1 mL TSB. Next, 0.2 mL of this mixture was added to 19.8 mL of TSB and incubated at 37 °C and 200 r/min for 50 min. Samples were taken every 5 min to determine the phage titer from 0 min. The experiment was repeated thrice. The burst size was calculated as follows:The one-step growth curve of phage vB_VpaP_DE10 was tested according to a previous method with minor modifications . BrieflyThe whole-genome sequence of phage vB_VpaP_DE10 was deposited in GenBank under the accession number MZ516827.p < 0.05.The data were expressed as mean ± standard deviation (SD) and the differences were analyzed with two-way ANOVA using GraphPad Prism 8.4.2. Significance was considered at V. parahaemolyticus O4-12 as the host cell. It was able to form clear plaques approximately 1–2 mm in diameter  with genomes of eight distinct species of the genus culvirus . Among tVibrio phages of the genus Maculvirus was performed. As shown in Due to the high genetic diversity of viruses, ANI analysis was suggested for their taxonomy in recent years. Thus, in order to accurately classify the phage vB_VpaP_DE10, ANI analysis between Vibrio phages of the family Autographiviridae family. In the DNA polymerase-based phylogenetic tree, phage vB_VpaP_DE10 was most closely related to the phage vB_VpP_DE18 of the genus Maculvirus (Maculvirus phages (vB_VpaP_GHSM17, H256D1, vB_Vc_SrVc2, and vB_Vc_SrVc9 (Maculvirus, which belongs to the family Autographiviridae.For phylogenetic analysis of phage vB_VpaP_DE10, the DNA polymerase and RNA polymerase were analyzed with all the culvirus . When inVc_SrVc9 . TherefoVibrio phages were isolated by our group at a different time. Although originating from the same place, genomes of six Vibrio phages showed high genetic diversities, and taxonomy varied. For example, even within the group of short-tail phages, the genome of vB_VpP_BA6 was completely different from vB_VpP_DE17, vB_VpP_FE11, and vB_VpaP_DE10.In this study, phage vB_VpaP_DE10 was obtained from sewage in the aquatic market, from where several Autographiviridae family. Several Autographiviridae phages specific for V. parahaemolyticus have been studied, such as phage vB_VpP_DE17 [V. parahaemolyticus phages of Autographiviridae family showed a latency period of 5–30 min and burst size of 37–244 PFU per cell [Based on the virionic morphology and genome analysis, phage vB_VpaP_DE10 was a member of the VpP_DE17 , phage vVpP_DE17 , phage vVpP_DE17 , phage vVpP_DE17 , phage VVpP_DE17 , phage OVpP_DE17 , phage vVpP_DE17 . Each phper cell ,15,16,32V. parahaemolyticus O3-11 and O11-31 were higher than the indicated host V. parahaemolyticus O4-12, whereas others were lower. Changes of surface molecules that serve as phage receptors would significantly affect EOP and reduce phage adsorption [In addition, the phage was also tested for the ability to lyse other bacteria for assessment of host range ,36. Bothsorption ,38. TherMaculvirus genus of the family Autographiviridae, and have short tails and high sequence similarity; however, the phage vB_VpaP_FE11 could infect V. parahaemolyticus O6-20, O8-21, and O11–30 strains whereas vB_VpaP_DE10 could not. The host range of phages has been reported to be associated with tail fiber or receptor-binding proteins [V. parahaemolyticus phage, vB_VpaP_DE10, having a short tail, and belonging to the genus Maculvirus within the family Autographiviridae.For the host range difference, phages vB_VpaP_DE10 and vB_VpaP_FE11 both belong to the proteins . Mutatioproteins . In the