Mixed Venous and Arterial Pco2

The rebreathing method of measuring oxygenated mixed venous Pco₂ (P$\bar{v}$co₂) was originally introduced as a bloodless way to estimate arterial Pco₂ (Paco₂). It has become common practice to subtract 6 mm Hg from the P$\bar{v}$co₂ to obtain the Paco₂ but there are many circumstances in which this leads to an overestimate of the Paco₂. Measurements of P$\bar{v}$co₂ and Paco₂ in 19 patients have shown that a better approximation to Paco₂ under normal conditions of cardiac output and arterial O₂ saturation is Paco₂ = 0·8 P$\bar{v}$co₂. These studies also showed that the P$\bar{v}$co₂ — Paco₂ difference may be much wider, particularly in the presence of arterial unsaturation and a low cardiac output.The factors governing the venoarterial Pco₂ difference are reviewed and their magnitude is calculated to emphasize the complementary roles of measurements of P$\bar{v}$co₂ and Paco₂ in the assessment of patients with cardiorespiratory disease.     

Comparison of breeding value prediction for two traits in a Nellore-Angus crossbred population using different Bayesian modeling methodologies

The objectives of this study were to 1) compare four models for breeding value prediction using genomic or pedigree information and 2) evaluate the impact of fixed effects that account for family structure. Comparisons were made in a Nellore-Angus population comprising F₂, F₃ and half-siblings to embryo transfer F₂ calves with records for overall temperament at weaning (TEMP; n = 769) and Warner-Bratzler shear force (WBSF; n = 387). After quality control, there were 34,913 whole genome SNP markers remaining. Bayesian methods employed were BayesB ($\tilde{\pi }$ = 0.995 or 0.997 for WBSF or TEMP, respectively) and BayesC (π = 0 and $\tilde{\pi }$), where $\tilde{\pi }$ is the ideal proportion of markers not included. Direct genomic values (DGV) from single trait Bayesian analyses were compared to conventional pedigree-based animal model breeding values. Numerically, BayesC procedures (using $\tilde{\pi }$) had the highest accuracy of all models for WBSF and TEMP ($\widehat{\rho }$_gĝ = 0.843 and 0.923, respectively), but BayesB had the least bias (regression of performance on prediction closest to 1, $\widehat{\beta }$_y,x = 2.886 and 1.755, respectively). Accounting for family structure decreased accuracy and increased bias in prediction of DGV indicating a detrimental impact when used in these prediction methods that simultaneously fit many markers.     

Sediment microbial communities in Great Boiling Spring are controlled by temperature and distinct from water communities

Great Boiling Spring is a large, circumneutral, geothermal spring in the US Great Basin. Twelve samples were collected from water and four different sediment sites on four different dates. Microbial community composition and diversity were assessed by PCR amplification of a portion of the small subunit rRNA gene using a universal primer set followed by pyrosequencing of the V8 region. Analysis of 164 178 quality-filtered pyrotags clearly distinguished sediment and water microbial communities. Water communities were extremely uneven and dominated by the bacterium Thermocrinis. Sediment microbial communities grouped according to temperature and sampling location, with a strong, negative, linear relationship between temperature and richness at all taxonomic levels. Two sediment locations, Site A (87–80 °C) and Site B (79 °C), were predominantly composed of single phylotypes of the bacterial lineage GAL35 ($\widehat{p}$=36.1%), Aeropyrum ($\widehat{p}$=16.6%), the archaeal lineage pSL4 ($\widehat{p}$=15.9%), the archaeal lineage NAG1 ($\widehat{p}$=10.6%) and Thermocrinis ($\widehat{p}$=7.6%). The ammonia-oxidizing archaeon ‘Candidatus Nitrosocaldus'' was relatively abundant in all sediment samples <82 °C ($\widehat{p}$=9.51%), delineating the upper temperature limit for chemolithotrophic ammonia oxidation in this spring. This study underscores the distinctness of water and sediment communities in GBS and the importance of temperature in driving microbial diversity, composition and, ultimately, the functioning of biogeochemical cycles.     

Rhinos in the Parks: An Island-Wide Survey of the Last Wild Population of the Sumatran Rhinoceros

In the 200 years since the Sumatran rhinoceros was first scientifically described (Fisher 1814), the range of the species has contracted from a broad region in Southeast Asia to three areas on the island of Sumatra and one in Kalimantan, Indonesia. Assessing population and spatial distribution of this very rare species is challenging because of their elusiveness and very low population number. Using an occupancy model with spatial dependency, we assessed the fraction of the total landscape occupied by Sumatran rhinos over a 30,345-km² survey area and the effects of covariates in the areas where they are known to occur. In the Leuser Landscape (surveyed in 2007), the model averaging result of conditional occupancy estimate was ψ^(SE[ψ^])=0.151(0.109) or 2,371.47 km², and the model averaging result of replicated level detection probability p^(SE[p^])=0.252(0.267); in Way Kambas National Park—2008: ψ^(SE[ψ^])=0.468(0.165) or 634.18 km², and p^(SE[p^])=0.138(0.571); and in Bukit Barisan Selatan National Park—2010: ψ^(SE[ψ^])=0.322(0.049) or 819.67 km², and p^(SE[p^])=0.365(0.42). In the Leuser Landscape, rhino occurrence was positively associated with primary dry land forest and rivers, and negatively associated with the presence of a road. In Way Kambas, occurrence was negatively associated with the presence of a road. In Bukit Barisan Selatan, occurrence was negatively associated with presence of primary dryland forest and rivers. Using the probabilities of site occupancy, we developed spatially explicit maps that can be used to outline intensive protection zones for in-situ conservation efforts, and provide a detailed assessment of conserving Sumatran rhinos in the wild. We summarize our core recommendation in four points: consolidate small population, strong protection, determine the percentage of breeding females, and recognize the cost of doing nothing. To reduce the probability of poaching, here we present only the randomized location of site level occupancy in our result while retaining the overall estimation of occupancy for a given area.     

Modeling Caribou Movements: Seasonal Ranges and Migration Routes of the Central Arctic Herd

Migration is an important component of the life history of many animals, but persistence of large-scale terrestrial migrations is being challenged by environmental changes that fragment habitats and create obstacles to animal movements. In northern Alaska, the Central Arctic herd (CAH) of barren-ground caribou (Rangifer tarandus granti) is known to migrate over large distances, but the herd’s seasonal distributions and migratory movements are not well documented. From 2003–2007, we used GPS radio-collars to determine seasonal ranges and migration routes of 54 female caribou from the CAH. We calculated Brownian bridges to model fall and spring migrations for each year and used the mean of these over all 4 years to identify areas that were used repeatedly. Annual estimates of sizes of seasonal ranges determined by 90% fixed kernel utilization distributions were similar between summer and winter ($\bar{X}$ = 27,929 SE = 1,064 and $\bar{X}$ = 26,585 SE = 4912 km², respectively). Overlap between consecutive summer and winter ranges varied from 3.3–18.3%. Percent overlap between summer ranges used during consecutive years ($\bar{X}$ = 62.4% SE = 3.7%) was higher than for winter ranges ($\bar{X}$ = 42.8% SE = 5.9%). Caribou used multiple migration routes each year, but some areas were used by caribou during all years, suggesting that these areas should be managed to allow for continued utilization by caribou. Restoring migration routes after they have been disturbed or fragmented is challenging. However, prior knowledge of movements and threats may facilitate maintenance of migratory paths and seasonal ranges necessary for long-term persistence of migratory species.     

A Variable Sampling Interval Synthetic Xbar Chart for the Process Mean

The usual practice of using a control chart to monitor a process is to take samples from the process with fixed sampling interval (FSI). In this paper, a synthetic X¯ control chart with the variable sampling interval (VSI) feature is proposed for monitoring changes in the process mean. The VSI synthetic X¯ chart integrates the VSI X¯ chart and the VSI conforming run length (CRL) chart. The proposed VSI synthetic X¯ chart is evaluated using the average time to signal (ATS) criterion. The optimal charting parameters of the proposed chart are obtained by minimizing the out-of-control ATS for a desired shift. Comparisons between the VSI synthetic X¯ chart and the existing X¯, synthetic X¯, VSI X¯ and EWMA X¯ charts, in terms of ATS, are made. The ATS results show that the VSI synthetic X¯ chart outperforms the other X¯ type charts for detecting moderate and large shifts. An illustrative example is also presented to explain the application of the VSI synthetic X¯ chart.     

Angle of Inclination of Tank-Treading Red Cells: Dependence on Shear Rate and Suspending Medium

Red cells suspended in solutions much more viscous than blood plasma assume an almost steady-state orientation when sheared above a threshold value of shear rate. This orientation is a consequence of the motion of the membrane around the red cell called tank-treading. Observed along the undisturbed vorticity of the shear flow, tank-treading red cells appear as slender bodies. Their orientation can be quantified as an angle of inclination (θ) of the major axis with respect to the undisturbed flow direction. We measured θ using solution viscosities (η₀) and shear rates (γ˙) covering one and three orders of magnitude, respectively. At the lower values of η₀, θ was almost independent of γ˙. At the higher values of η₀, θ displayed a maximum at intermediate shear rates. The respective maximal values of θ increased by ∼10° from 10.7 to 104 mPas. After accounting for the absent membrane viscosity in models by using an increased cytoplasmic viscosity, their predictions of θ agree qualitatively with our data. Comparison of the observed variation of θ at constant γ˙ with model results suggests a change in the reference configuration of the shear stiffness of the membrane.     

The effects of fasting on swimming performance in juvenile qingbo (Spinibarbus sinensis) at two temperatures

We measured the following variables to investigate the effects of fasting and temperature on swimming performance in juvenile qingbo (Spinibarbus sinensis): the critical swimming speed (U_crit), resting metabolic rate ${(\dot{\mathrm{M}}O}_{\mathrm{2rest}})$ and active metabolic rate ${(\dot{\mathrm{M}}O}_{2active})$ of fish fasting for 0 (control), 1, 2 and 4 weeks at low and high acclimation temperatures (15 and 25 °C). Both fasting treatment and temperature acclimation had significant effects on all parameters measured (P<0.05). Fasting at the higher temperature had a negative effect on all measured parameters after 1 week (P<0.05). However, when acclimated to the lower temperature, fasting had a negative effect on U_crit until week 2 and on ${(\dot{\mathrm{M}}O}_{\mathrm{2rest}})$, ${(\dot{\mathrm{M}}O}_{\mathrm{2active}})$ and metabolic scope (MS, ${(\dot{\mathrm{M}}O}_{\mathrm{2active}})$–${(\dot{\mathrm{M}}O}_{\mathrm{2rest}})$) until week 4 (P<0.05). The values of all parameters at the lower temperature were significantly lower than those at the higher temperature in the identical fasting period groups except for ${(\dot{\mathrm{M}}O}_{\mathrm{2rest}})$ of the fish that fasted for 2 weeks. The relationship between fasting time (T) and U_crit was described as U_crit(15)=−0.302T²−0.800T+35.877 (r=0.781, n=32, P<0.001) and U_crit(25)=0.471T²−3.781T+50.097 (r=0.766, n=32, P<0.001₎ at 15 and 25 °C, respectively. The swimming performance showed less decrease in the early stage of fasting but more decrease in the later stage at the low temperature compared to the high temperature, which might be related to thermal acclimation time, resting metabolism, respiratory capacity, energy stores, enzyme activity in muscle tissue and energy substrate utilization changes with fasting between low and high temperatures. The divergent response of the swimming performance to fasting in qingbo at different temperatures might be an adaptive strategy to seasonal temperature and food resource variation in their habitat.     

A Global Analysis of Deforestation in Moist Tropical Forest Protected Areas

Protected areas (PAs) have been established to conserve tropical forests, but their effectiveness at reducing deforestation is uncertain. To explore this issue, we combined high resolution data of global forest loss over the period 2000–2012 with data on PAs. For each PA we quantified forest loss within the PA, in buffer zones 1, 5, 10 and 15 km outside the PA boundary as well as a 1 km buffer within the PA boundary. We analysed 3376 tropical and subtropical moist forest PAs in 56 countries over 4 continents. We found that 73% of PAs experienced substantial deforestation pressure, with >0.1% a⁻¹ forest loss in the outer 1 km buffer. Forest loss within PAs was greatest in Asia (0.25% a⁻¹) compared to Africa (0.1% a⁻¹), the Neotropics (0.1% a⁻¹) and Australasia (Australia and Papua New Guinea; 0.03% a⁻¹). We defined performance (P) of a PA as the ratio of forest loss in the inner 1 km buffer compared to the loss that would have occurred in the absence of the PA, calculated as the loss in the outer 1 km buffer corrected for any difference in deforestation pressure between the two buffers. To remove the potential bias due to terrain, we analysed a subset of PAs (n = 1804) where slope and elevation in inner and outer 1 km buffers were similar (within 1° and 100 m, respectively). We found 41% of PAs in this subset reduced forest loss in the inner buffer by at least 25% compared to the expected inner buffer forest loss (P<0.75). Median performance (P˜) of subset reserves was 0.87, meaning a reduction in forest loss within the PA of 13%. We found PAs were most effective in Australasia (P˜=0.16), moderately successful in the Neotropics (P˜=0.72) and Africa (P˜=0.83), but ineffective in Asia (P˜=1). We found many countries have PAs that give little or no protection to forest loss, particularly in parts of Asia, west Africa and central America. Across the tropics, the median effectiveness of PAs at the national level improved with gross domestic product per capita. Whilst tropical and subtropical moist forest PAs do reduce forest loss, widely varying performance suggests substantial opportunities for improved protection, particularly in Asia.     

The Impact of Prior Information on Estimates of Disease Transmissibility Using Bayesian Tools

The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R^0 = 1.36–1.46, μ^ = 2.0–2.7, μ^ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0–4.4 and 7.4–11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample.     

Kinetics and Energetics of Biomolecular Folding and Binding

The ability of biomolecules to fold and to bind to other molecules is fundamental to virtually every living process. Advanced experimental techniques can now reveal how single biomolecules fold or bind against mechanical force, with the force serving as both the regulator and the probe of folding and binding transitions. Here, we present analytical expressions suitable for fitting the major experimental outputs from such experiments to enable their analysis and interpretation. The fit yields the key determinants of the folding and binding processes: the intrinsic on-rate and the location and height of the activation barrier.Dynamic processes in living cells are regulated through conformational changes in biomolecules—their folding into a particular shape or binding to selected partners. The ability of biomolecules to fold and to bind enables them to act as switches, assembly factors, pumps, or force- and displacement-generating motors (1). Folding and binding transitions are often hindered by a free energy barrier. Overcoming the barrier requires energy-demanding rearrangements such as displacing water from the sites of native contacts and breaking nonnative electrostatic contacts, as well as loss of configurational entropy. Once the barrier is crossed, the folded and bound states are stabilized by short-range interactions: hydrogen bonds, favorable hydrophobic effects, and electrostatic and van der Waals attractions (2).Mechanistic information about folding and binding processes is detailed in the folding and binding trajectories of individual molecules: observing an ensemble of molecules may obscure the inherent heterogeneity of these processes. Single-molecule trajectories can be induced, and monitored, by applying force to unfold/unbind a molecule and then relaxing the force until folding or binding is observed (3–5) (Fig. 1). Varying the force relaxation rate shifts the range of forces at which folding or binding occurs, thus broadening the explorable spectrum of molecular responses to force and revealing conformational changes that are otherwise too fast to detect. The measured force-dependent kinetics elucidates the role of force in physiological processes (6) and provides ways to control the timescales, and even the fate, of these processes. The force-dependent data also provides a route to understanding folding and binding in the absence of force—by extrapolating the data to zero force via a fit to a theory.Open in a separate windowFigure 1Schematic of the output from a force-relaxation experiment. The applied force is continuously relaxed from the initial value F₀ until the biomolecule folds or binds, as signified by a sharp increase in the measured force. From multiple repeats of this experiment, distributions of the folding or binding forces are collected (inset). Fitting the force distributions with the derived analytical expression yields the key parameters that determine the kinetics and energetics of folding or binding.In this letter, we derive an analytical expression for the distribution of transition forces, the major output of force-relaxation experiments that probe folding and binding processes. The expression extracts the key determinants of these processes: the on-rate and activation barrier in the absence of force. The theory is first developed in the context of biomolecular folding, and is then extended to cover the binding of a ligand tethered to a receptor. In contrast to unfolding and unbinding, the reverse processes of folding and binding require a theory that accounts for the compliance of the unfolded state, as well as the effect of the tether, to recover the true kinetic parameters of the biomolecule of interest.In a force-relaxation experiment, an unfolded biomolecule or unbound ligand-receptor complex is subject to a stretching force, which is decreased from the initial value F₀ as the pulling device approaches the sample at speed V until a folding or binding transition is observed (Fig. 1) (3–5). Define S(t) as the probability that the molecule has not yet escaped from the unfolded (implied: or unbound) state at time t. When escape is limited by one dominant barrier, S(t) follows the first-order rate equationS˙(t)≡dS(t)dt=−k←(F(t))S(t),where k_←(F(t)) is the on-rate at force F at time t. Because, prior to the transition, the applied force decreases monotonically with time, the distribution of transition forces, p(F), is related to S(t) through p(F)dF=S˙(t)dt, yieldingp(F)=−k←(F)F˙(F)e−∫F0Fk←(F′)F˙(F′)dF′.(1)Here F˙(F)≡dF(t)/dt<0 is the force relaxation rate. The proper normalization of p(F) is readily confirmed by integrating Eq. 1 from the initial force F₀ to negative infinity, the latter accounting for transitions that do not occur by the end of the experiment. Note that the expression for the distribution of folding/binding forces in Eq. 1 differs from its analog for the unfolding process (7) by the limits of integration and a negative sign, reflecting the property of a relaxation experiment to decrease the survival probability S(t) by decreasing the force. Converting the formal expression in Eq. 1 into a form suitable for fitting experimental data requires establishing functional forms for k_←(F) and F˙(F) and analytically solving the integral. These steps are accomplished below.The on-rate k_←(F) is computed by treating the conformational dynamics of the molecule as a random walk on the combined free energy profile G(x,t) = G₀(x) + G_pull(x,t) along the molecular extension x. Here G₀(x) is the intrinsic molecular potential and G_pull(x,t) is the potential of the pulling device. When G(x,t) features a high barrier on the scale of k_BT (k_B is the Boltzmann constant and T the temperature), the dynamics can be treated as diffusive. The unfolded region of the intrinsic potential for a folding process, unlike that for a barrierless process (8), can be captured by the functionG0(x)=ΔG‡ν1−ν(xx‡)11−ν−ΔG‡ν(xx‡),which has a sharp (if ν = 1/2, Fig. 2, inset) or smooth (if ν = 2/3) barrier of height ΔG^‡ and location x^‡. The potential of a pulling device of stiffness κ_S is G_pull(x,t) = κ_S/2(X₀ – Vt – x)² with an initial minimum at X₀ (corresponding to F₀). Applying Kramers formalism (9) to the combined potential G(x,t), we establish the analytical form of the on-rate at force F(t),k←(F)=k0(1+κSκU(F))1ν−12(1+νFx‡ΔG‡)1ν−1×eβΔG‡[1−(1+κSκU(F))2ν1−ν−1(1+νFx‡ΔG‡)1ν],where k₀ is the intrinsic on-rate, β ≡ (k_BT)⁻¹, andκU(F)=ν(1−ν)2ΔG‡x‡2(1+νFx‡ΔG‡)2−1νis the stiffness of the unfolded biomolecule under force F (see the Supporting Material for details on all derivations). The full nonlinear form of G_pull(x,t) was necessary in the derivation because, in contrast to the typically stiff folded state, the unfolded state may be soft (to be exact, 1/2κ_S x^‡2(F) << k_BT may not be satisfied) and thus easily deformed by the pulling device. Because of this deformation, the folding transition faces an extra contribution (regulated by the ratio κ_S/κ_U(F)) to the barrier height, typically negligible for unfolding, that decreases the on-rate in addition to the applied force F.Open in a separate windowFigure 2Contributions to the free energy profile for folding (inset) and binding (main figure). The derived expression (Eq. 2) extracts the on-rate and the location and height of the activation barrier to folding. When applied to binding data, the expression extracts the parameters of the ligand-tether-receptor (LTR) potential G˜0 (x); the proposed algorithm (Eqs. 3 and 4) removes the contribution of the tether potential G_teth(x) to recover the parameters of the intrinsic ligand-receptor (LR) potential G₀(x).The last piece required for Eq. 1, the loading rate F˙(F), is computed as the time derivative of the force F(t) on the unfolded molecule at its most probable extension at time t:F˙(F)=−κSV1+κS/κU(F).Finally, we realize that the integral in Eq. 1 can be solved analytically exactly, both for ν = 1/2 and ν = 2/3, resulting in the analytical expression for the distribution of folding forces:p(F)=k←(F)|F˙(F)|e−k←(F)β|F˙(F)|x‡(1+κSκU(F))νν−1(1+νFx‡ΔG‡)1−1ν.(2)Equation 2 can be readily applied to (normalized) histograms from force-relaxation experiments to extract the parameters of the intrinsic kinetics and energetics of folding. Being exact for ν = 1/2 and ν = 2/3, Eq. 2 is also an accurate approximation for any ν in the interval 1/2 < ν < 2/3 as long as κ_S ≲ κ_U (F) (see Fig. S1 in the Supporting Material). For simplicity, in Eq. 2 we have omitted the term containing F₀ as negligible if F₀ is large enough to prevent folding events.The solution in Eq. 2 reveals properties of the distribution of folding forces that distinguish it from its unfolding counterpart (7):

• 1.The distribution has a positive skew (Fig. 3), as intuitively expected: the rare folding events occur at high forces when the barrier is still high.Open in a separate windowFigure 3Force histograms from folding (left) and binding (right) simulations at several values of the force-relaxation speed (in nanometers per second, indicated at each histogram). Fitting the histograms with the analytical expression in Eq. 2 (lines) recovers the on-rate and activation barrier for folding or binding (2.Increasing the relaxation speed shifts the distribution to lower forces (Fig. 3): faster force relaxation leaves less time for thermal fluctuations to push the system over a high barrier, causing transitions to occur later (i.e., at lower forces), when the barrier is lower.
• 3.The stiffness κ_S and speed V enter Eq. 2 separately, providing independent routes to control the range of folding forces and thus enhance the robustness of a fit.

The application of the above framework to binding experiments on a ligand and receptor connected by a tether (3) involves an additional step—decoupling the effect of the tether—to reconstruct the parameters of ligand-receptor binding. Indeed, the parameters extracted from a fit of experimental histograms to Eq. 2 characterize the ligand-tether-receptor (LTR) potential (k˜0, x˜‡, ΔG˜‡, ν) (Fig. 2). The parameters of the natural ligand-receptor (LR) potential (k₀, x^‡, ΔG^‡) can be recovered using three characteristics of the tether: contour length L; persistence length p; and extension Δℓ of the tether along the direction of the force in the LTR transition state. The values of L and p can be determined from the force-extension curve of the tether (10); these define the tether potential G_teth(x) (Fig. 2). The value of Δℓ can be found from an unbinding experiment (7) on LTR and the geometry of the tether attachment points (see Fig. S3). Approximating the region of the LR potential between the transition and unbound states as harmonic, with no assumptions about the shape of the potential beyond x^‡, the ligand-receptor barrier parameters are thenx‡=α−1α−2x˜‡,ΔG‡=(α−1)22(α−2)x˜‡Fteth(Δℓ+x˜‡),(3)and the intrinsic unimolecular association rate isk0≈k˜0(βΔG‡)32(βΔG˜‡)1ν−12(x˜‡x‡)2eβ(ΔG˜‡−ΔG‡).(4)Here, the force value Fteth(Δℓ+x˜‡) is extracted from the force-extension curve of the tether at extension Δℓ+x˜‡ andα=2(ΔG˜‡−Gteth(Δℓ)+Gteth(Δℓ+x˜‡))x˜‡Fteth(Δℓ+x˜‡),where G_teth(x) is the wormlike-chain potential (see Eq. S13 in the Supporting Material). Equations 3–4 confirm that a tether decreases the height and width of the barrier (see Fig. 2), thus increasing the on-rate.In Fig. 3, the developed analytical framework is applied to folding and binding force histograms from Brownian dynamics simulations at parameters similar to those in the analogous experimental and computational studies (3,5,11) (for details on simulations and fitting procedure, see the Supporting Material). For the stringency of the test, the simulations account for the wormlike-chain nature of the molecular unfolded and LTR unbound states that is not explicitly accounted for in the theory. With optimized binning (12) of the histograms and a least-squares fit, Eqs. 2–4 recover the on-rate, the location and the height of the activation barrier, and the value of ν that best captures how the kinetics scale with force (

1.Multiple relaxation speeds,

2.Folding/binding events at low forces, and

3.A large number of events at each speed.

Table 1

On-rate and the location and height of the activation barrier from the fit of simulated data to the theory in Eq. 2

A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation

The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θ_anc = 4N_eu) and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L₂-loss performs best. Applying that method to the ibex data, we estimate θ^anc≈1.288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 × 10⁻⁴ and 3.5 × 10⁻³ per locus per generation. The proportion of males with access to matings is estimated as ω^≈0.21, which is in good agreement with recent independent estimates.     

Enhancement of Chemotactic Cell Aggregation by Haptotactic Cell-To-Cell Interaction

The crawling of biological cell is a complex phenomenon involving various biochemical and mechanical processes. Some of these processes are intrinsic to individual cells, while others pertain to cell-to-cell interactions and to their responses to extrinsically imposed cues. Here, we report an interesting aggregation dynamics of mathematical model cells, when they perform chemotaxis in response to an externally imposed global chemical gradient while they influence each other through a haptotaxis-mediated social interaction, which confers intriguing trail patterns. In the absence of the cell-to-cell interaction, the equilibrium population density profile fits well to that of a simple Keller-Segal population dynamic model, in which a chemotactic current density J→chemo∼∇p competes with a normal diffusive current density J→diff∼∇ρ, where p and ρ refer to the concentration of chemoattractant and population density, respectively. We find that the cell-to-cell interaction confers a far more compact aggregation resulting in a much higher peak equilibrium cell density. The mathematical model system is applicable to many biological systems such as swarming microglia and neutrophils or accumulating ants towards a localized food source.     

Sampling Citrus Fibrous Roots and Tylenchulus semipenetrans

Sampling precision was investigated for Tylenchulus semipenetrans juveniles and males in soil and females from roots and for citrus fibrous root mass density. For the case of two composite samples of 15 cores each, counts of juvenile and male nematodes were estimated to be within 40% of μ, at P < 0.06 (α) in orchards where $\bar{x}$ > 1,500 nematodes/100 cm³ soil. A similar level of α was estimated for measurements of fibrous root mass density, but at a precision level of 25% of μ. Densities of female nematodes were estimated with less precision than juveniles and males. Precision estimates from a general sample plan derived from Taylor''s Power Law were in good agreement with estimates from individual orchards. Two aspects involved in deriving sampling plans for management advisory purposes were investigated. A minimum of five to six preliminary samples were required to appreciably reduce bias toward underestimation of σ. The use of a Student''s t value rather than a standard normal deviate in formulae to estimate sample size increased the estimates by an average of three units. Cases in which the use of z rather than Student''s t is appropriate for these formulae are discussed.     

The Projection of a Test Genome onto a Reference Population and Applications to Humans and Archaic Hominins

We introduce a method for comparing a test genome with numerous genomes from a reference population. Sites in the test genome are given a weight, w, that depends on the allele frequency, x, in the reference population. The projection of the test genome onto the reference population is the average weight for each x, w¯(x). The weight is assigned in such a way that, if the test genome is a random sample from the reference population, then w¯(x)=1. Using analytic theory, numerical analysis, and simulations, we show how the projection depends on the time of population splitting, the history of admixture, and changes in past population size. The projection is sensitive to small amounts of past admixture, the direction of admixture, and admixture from a population not sampled (a ghost population). We compute the projections of several human and two archaic genomes onto three reference populations from the 1000 Genomes project—Europeans, Han Chinese, and Yoruba—and discuss the consistency of our analysis with previously published results for European and Yoruba demographic history. Including higher amounts of admixture between Europeans and Yoruba soon after their separation and low amounts of admixture more recently can resolve discrepancies between the projections and demographic inferences from some previous studies.     

Reproductive biology of the horned viper,Cerastes cerastes gasperettii in the central region of Saudi Arabia

The reproductive biology of the horned viper, Cerastes cerastes gasperettii, in Riyadh region of Saudi Arabia was investigated over a period of one year. Study of reproductive cycle of male and female C. c. gasperettii revealed that the breeding season is relatively short (April and May). Thereafter females laid eggs by mid of July and hatching probably had taken place by the end of September. No activity was observed during winter, this may indicate just a single clutch per year. Relative testis weight to body weight was drastically increased (X¯ = 0.88%) during the peak of reproductive activity (May) where maximal expansion of seminiferous tubules was also attained during April and May (X¯ = 209 μm and 191 μm, respectively). Likewise, the ovarian activity was the highest during May where ovarian parameters were greater in terms of relative ovarian weight to body weight and ova diameter being 0.46% and 2.29 mm, respectively. Fat body weight was increased drastically just before the peak of reproductive activity then started to decline during June. It could be concluded that the harsh desert conditions and similar environments certainly affect reproductive activity of Saudi Arabian reptiles including snakes.