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1.
2.
The Stepanov equation, relating the intensity of emission, fe(v̄), at a given frequency, and that of absorption, k(v̄), at the same frequency, is applied, in its modified form (see equation 3 in text) to suspensions of Chlorella, Porphyridium, and Anacystis and to chlorophyll solutions. This application can reveal whether the yield of fluorescence, Φ(v̄), is constant, or changes with frequency. In Chlorella (green alga) a sharp drop of Φ(v̄) is indicated towards the lower frequencies (longer waves), beginning around v̄ = 1.48 × 104cm-1 (680 mμ); the Φ(v̄) function calculated from the Stepanov equation is in fair agreement with the directly determined action spectrum for the excitation of chlorophyll fluorescence in this organism. In Porphyridium (red alga) and Anacystis (blue-green alga) application of the Stepanov equation supports the conclusions, derived from direct measurements, of a much earlier “red drop” of the fluorescence excitation spectra. Direct measurements suggest that the drop in Porphyridium may begin at about 1.53 × 104cm-1 (654 mμ); in Anacystis, it may begin already above 1.57 × 104cm-1 (<637mμ). These results confirm the relation, postulated earlier by Duysens and others, between the action spectra of photosynthesis and of chlorophyll a fluorescence in algal cells. The relation of these findings to spectroscopic evidence, suggesting the existence of two main chlorophyll a components in vivo, in green as well as in red and blue-green algae, is discussed.  相似文献   

3.
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 (π̃ = 0.995 or 0.997 for WBSF or TEMP, respectively) and BayesC (π = 0 and π̃), where π̃ 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 π̃) had the highest accuracy of all models for WBSF and TEMP (ρ̂ = 0.843 and 0.923, respectively), but BayesB had the least bias (regression of performance on prediction closest to 1, β̂y,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.  相似文献   

4.
5.
The rebreathing method of measuring oxygenated mixed venous Pco2 (Pv̄co2) was originally introduced as a bloodless way to estimate arterial Pco2 (Paco2). It has become common practice to subtract 6 mm Hg from the Pv̄co2 to obtain the Paco2 but there are many circumstances in which this leads to an overestimate of the Paco2. Measurements of Pv̄co2 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 Pv̄co2. These studies also showed that the Pv̄co2 — 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 Pv̄co2 and Paco2 in the assessment of patients with cardiorespiratory disease.  相似文献   

6.
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 (p̂=36.1%), Aeropyrum (p̂=16.6%), the archaeal lineage pSL4 (p̂=15.9%), the archaeal lineage NAG1 (p̂=10.6%) and Thermocrinis (p̂=7.6%). The ammonia-oxidizing archaeon ‘Candidatus Nitrosocaldus'' was relatively abundant in all sediment samples <82 °C (p̂=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.  相似文献   

7.
The purpose of this study was to characterize responses in oxygen uptake ( V·O2), heart rate (HR), perceived exertion (OMNI scale) and integrated electromyogram (iEMG) readings during incremental Nordic walking (NW) and level walking (LW) on a treadmill. Ten healthy adults (four men, six women), who regularly engaged in physical activity in their daily lives, were enrolled in the study. All subjects were familiar with NW. Each subject began walking at 60 m/min for 3 minutes, with incremental increases of 10 m/min every 2 minutes up to 120 m/min V·O2 , V·E and HR were measured every 30 seconds, and the OMNI scale was used during the final 15 seconds of each exercise. EMG readings were recorded from the triceps brachii, vastus lateralis, biceps femoris, gastrocnemius, and tibialis anterior muscles. V·O2 was significantly higher during NW than during LW, with the exception of the speed of 70 m/min (P < 0.01). V·E and HR were higher during NW than LW at all walking speeds (P < 0.05 to 0.001). OMNI scale of the upper extremities was significantly higher during NW than during LW at all speeds (P < 0.05). Furthermore, the iEMG reading for the VL was lower during NW than during LW at all walking speeds, while the iEMG reading for the BF and GA muscles were significantly lower during NW than LW at some speeds. These data suggest that the use of poles in NW attenuates muscle activity in the lower extremities during the stance and push-off phases, and decreases that of the lower extremities and increase energy expenditure of the upper body and respiratory system at certain walking speeds.  相似文献   

8.

Background

Minute ventilation (V·E) during walking has been shown to be higher in older individuals than in young individuals, but the mechanisms underlying the higher ventilatory response is unclear. Central command and peripheral neural reflex are important neural control mechanisms underlying ventilatory response during exercise. Passive leg movement has been used to exclude the influence of central command due to the lack of voluntary activation of muscles. The aim of the present study was to compare the ventilatory response during and after passive walking-like leg movement (PWM) in young and older individuals.

Methods

Eight young subjects (20 ± 2 years) and seven older subjects (70 ± 1 years) participated in this study. Subjects spent 7 minutes in a quiet standing (QS) position. Thereafter, they performed 14-minute rhythmic PWM at 1 Hz and this was followed by 7 minutes of QS.

Results

V·E values during pre-PWM QS were calculated as 1-minute averages using data obtained between 5 and 6 minutes. V·E values at pre-PWM QS in the young and older groups were 8.4 ± 2.1 and 7.5 ± 1.2 l/minute, respectively. V·E values increased significantly at the first minute of PWM to 11.4 ± 2.2 and 10.4 ± 2.5 l/minute in the young and older groups, respectively (P <0.001). In the young group, V·E at the last minute of PWM (9.2 ± 2.0 l/minute) was not significantly different from that at pre-PWM QS due to a decline in V·E, whereas V·E at the last minute of PWM in the older group (9.4 ± 2.2 l/minute) was still significantly higher (P <0.01). On the other hand, V·E at the first minute of post-PWM QS (7.2 ± 1.8 l/minute) was significantly lower than that during pre-PWM QS in the young group (P <0.05) but not in the older group.

Conclusions

Ventilatory response during and after PWM is higher in older individuals than in young individuals. This may be associated with a mechanism(s) other than central command. Our findings may explain part of the higher V·E response while walking in older individuals.  相似文献   

9.
The second-order nonlinear polarization properties of fibrillar collagen in various rat tissues (vertebrae, tibia, tail tendon, dermis, and cornea) are investigated with polarization-dependent second-harmonic generation (P-SHG) microscopy. Three parameters are extracted: the second-order susceptibility ratio, R = χZZZ(2)/χZXX(2); a measure of the fibril distribution asymmetry, |A|; and the weighted-average fibril orientation, 〈δ〉. A hierarchical organizational model of fibrillar collagen is developed to interpret the second-harmonic generation polarization properties. Highlights of the model include: collagen type (e.g., type-I, type-II), fibril internal structure (e.g., straight, constant-tilt), and fibril architecture (e.g., parallel fibers, intertwined, lamellae). Quantifiable differences in internal structure and architecture of the fibrils are observed. Occurrence histograms of R and |A| distinguished parallel from nonparallel fibril distributions. Parallel distributions possessed low parameter values and variability, whereas nonparallel distributions displayed an increase in values and variability. From the P-SHG parameters of vertebrae tissue, a three-dimensional reconstruction of lamellae of intervertebral disk is presented.  相似文献   

10.
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 Jchemop competes with a normal diffusive current density Jdiffρ, 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.  相似文献   

11.
Kinase cascades, in which enzymes are sequentially activated by phosphorylation, are quintessential signaling pathways. Signal transduction is not always achieved by direct activation, however. Often, kinases activate pathways by deactivation of a negative regulator; this indirect mechanism, pervasive in Akt signaling, has yet to be systematically explored. Here, we show that the indirect mechanism has properties that are distinct from direct activation. With comparable parameters, the indirect mechanism yields a broader range of sensitivity to the input, beyond saturation of regulator phosphorylation, and kinetics that become progressively slower, not faster, with increasing input strength. These properties can be integrated in network motifs to produce desired responses, as in the case of feedforward loops.Phosphorylation of proteins and lipids, catalyzed by specific kinase enzymes, is ubiquitous in intracellular signal transduction. A classic example in eukaryotes is the canonical structure of the mitogen-activated protein kinase cascades, in which three kinases are sequentially activated by phosphorylation (1). Another example is the PI3K (phosphoinositide 3-kinase)/Akt pathway, which (like the mammalian mitogen-activated protein kinases) is prominently dysregulated in human cancers (2). Type-I PI3Ks phosphorylate a lipid substrate to produce the lipid second messenger, PIP3, which recruits the protein kinase Akt and mediates its activation by phosphorylation (3,4). In no small part because of these important pathways, we typically think of phosphorylation as a direct means of activating molecular interactions and reactions in signal transduction. This is not the only way to increase the flux through a signaling pathway, however. Consider signaling downstream of Akt, which phosphorylates a host of protein substrates to affect diverse functions. A survey of the Akt signaling hub shows that many of these reactions result in a decrease, rather than an increase, in activity/function of the substrates (3). And, among those substrates, the four listed in Fig. S1 in the Supporting Material). Whereas negative regulators are appreciated for their roles in feedback adaptation of signaling, the implications of deactivating a negative regulator as an indirect mechanism of pathway activation has yet to be explored.

Table 1

Survey of Akt substrates and downstream signaling
Substrate (site)Effect on substrateOutcome
TSC2 (T1462)GAP activity ↓Rheb, mTOR ↑
PRAS40 (T246)mTOR binding ↓mTOR ↑
GSK3α/β (S21/S9)kinase activity ↓β-catenin ↑
BAD (S136)Bcl-2/xL binding ↓Bcl-2/xL ↑
Open in a separate windowHere, we use simple kinetic models to elucidate the basic properties of pathway activation by deactivation of a negative regulator (hereafter referred to as mechanism II), as compared with the standard activation of a positive regulator (mechanism I). The analysis is presented in the context of protein phosphorylation, but the conclusions may be generalized to other reversible modifications or to allosteric binding interactions. The common first step is phosphorylation of the regulatory molecule by the kinase. The activity of the upstream kinase such as Akt may be represented by a dimensionless, time (t)-dependent input signal function, s(t). We assume that the total amount of regulator is constant and define its phosphorylated fraction as ϕ(t). Neglecting concentration gradients and saturation of the upstream kinase and of the opposing (constitutively active) phosphatase(s), the conservation of phosphorylated regulator is expressed as follows (see Text S1 in the Supporting Material):dϕdt=kp[s(1ϕ)ϕ];ϕ(0)=0.(1)The parameter kp is the pseudo-first-order rate constant of protein dephosphorylation. In the case of s = constant (i.e., subject to a step change at t = 0), the properties of this simplified kinetic equation are well known (5) and may be summarized as follows. As the magnitude of the signal strength s increases, the steady-state value of ϕ, ϕss, increases in a saturable fashion; when s >> 1, ϕss approaches its maximum value of 1 and is insensitive to further increases in s. The kinetics of ϕ(t) approaching ϕss become progressively faster as s increases, however.Next, we model the influence of the regulator on a downstream response. Defining the fractional response as ρ and following analogous assumptions as above, we formulate equations for mechanisms I and II as follows:dρdt={[ka,0+(ka,maxka,0)ϕ](1ρ)kd,0ρ(I)ka,0(1ρ)[kd,0(kd,0kd,min)ϕ]ρ(II).(2)In each equation, the first term on the right-hand side describes activation, and the second, deactivation. In mechanism I, the effective rate constant of activation increases linearly with ϕ, from a minimum value of ka,0 when ϕ = 0 up to a maximum value of ka,max when ϕ = 1; the deactivation rate constant is fixed at kd,0. Conversely, in mechanism II, the effective rate constant of deactivation decreases linearly with ϕ, from a maximum value of kd,0 when ϕ = 0 down to a minimum value of kd,min when ϕ = 1; in this mechanism, the activation rate constant is fixed at ka,0. The initial condition is assigned so that ρ is stationary when ϕ = 0. To further set the two mechanisms on a common basis, we define dimensionless parameters such that the maximum steady-state value of ρ (with ϕss = 1) is the same for both mechanisms I and II,gka,max/ka,0kd,0/kd,minKka,0/kd,0.(3)With these definitions, each conservation equation is reduced to the following dimensionless form:1kd,0dρdt={K[1+(g1)ϕ](1ρ)ρ(I)K(1ρ)[1(1g1)ϕ]ρ(II).(4)Mechanisms I and II (Fig. 1 a) are compared first at the level of their steady-state solutions, ρss, for stationary s. Equation 1 yields the familiar hyperbolic dependence of ϕss on s, and ρss(s) has the same shape for both mechanisms. However, whereas ρss of mechanism I shows saturation at a lower value of s than ϕss, the opposite is true of mechanism II (Fig. 1 b). Thus, mechanism II retains sensitivity to the input even while phosphorylation of the upstream regulator shows saturation. This is perhaps more readily seen when ϕss(s) is replaced with a sigmoidal Hill function (i.e., with s replaced by sn in Eq. 1) (Fig. 1 c). The key parameter that affects the relative sensitivities of mechanisms I and II and the disparity between them is the gain constant, g (see Text S1 in the Supporting Material). As this parameter is increased, ρss of mechanism I becomes increasingly saturable with respect to ϕss (Fig. 1 d), whereas ρss of mechanism II gains sensitivity as ϕss approaches 1 (Fig. 1 e). As an illustrative example, consider that when ϕss is increased from 0.90 to 0.95, or from 0.98 to 0.99, the amount of the negative regulator in the active state is reduced by a factor of 2 (see Fig. S2).Open in a separate windowFigure 1Steady-state properties of mechanisms I and II. (a) Schematics of direct (I) and indirect (II) activation. (b) Steady-state dose responses, ρss(s), of mechanisms I and II along with phosphorylation of the upstream regulator, ϕss(s) (Eq. 1 at steady state); K = 0.05, g = 100. (c) Same as panel b, except with a sigmoidal ϕss(s) (Hill function with n = 4). (d) Steady-state output, ρss, of mechanism I vs. ϕss for K = 0.05 and indicated values of the gain constant, g. (e) Same as panel d, but for mechanism II. To see this figure in color, go online.The two mechanisms also show distinct temporal responses. In the response of mechanism I to a step increase in s, ρ(t) approaches ρss with a timescale that generally becomes faster as s increases. Unless the kinetics of ϕ(t) are rate-limiting, the timescale is ∼kd,0–1(1–ρss) (Fig. 2 a; see also Text S1 and Fig. S3 in the Supporting Material). Conversely, the response of mechanism II generally becomes slower as s increases, inasmuch as the frequency of deactivation decreases whereas that of activation is constant, with a timescale of ∼ka,0–1ρss (Fig. 2 b). To approximate a transient input, we model s(t) as a step increase followed by a decay. For mechanism I, the response ρ(t) is such that the variation in the time of the peak, as a function of the step size, is modest. The subsequent decay is prolonged when ϕ(t) hovers close to saturation (Fig. 2 c). Such kinetic schemes have been analyzed in some detail previously (6,7). In contrast, the response of mechanism II to the transient input is such that the system retains sensitivity and consistent decay kinetics beyond the saturation of ϕ(t). The distinctive feature is that ρ(t) peaks noticeably later in time as the magnitude of the peak increases (Fig. 2 d).Open in a separate windowFigure 2Kinetic properties of mechanisms I and II. (a) Response of mechanism I to a step change in s from zero to the indicated s(0). Time is given in units of kpt; parameters are K = 0.05, g = 10, and kd,0 = 0.1kp. (b) Same as panel a, but for mechanism II. (c) Same as panel a, but for a transient input, s(t) = s(0)exp(–0.03kpt). d) Same as panel c, but for mechanism II. To see this figure in color, go online.Having established the basic steady state and kinetic properties of mechanism II as compared with the canonical mechanism I, we considered what outcomes could be achieved by linking these motifs in series or in parallel. Such schemes are identified in the Akt/mTOR signaling network, for example (see Fig. S4). In a standard kinase activation cascade, it is understood that the properties of saturation and sensitivity are compounded with each step of the cascade (8). Thus, two sequential steps of mechanism I yield progressive saturation of the steady-state output at lower s (Fig. 3 a), and the desaturating effect of mechanism II is likewise compounded (Fig. 3 b). By corollary it follows that a sequence of mechanisms I and II will show an intermediate dose response; that is, the mechanism II step offsets the saturation effect of mechanism I.Open in a separate windowFigure 3Serial and parallel schemes incorporating mechanism I or/and II. (a) Steady-state outputs of two response elements, ρ1 and ρ2, activated by mechanism I in series. At each level, K = 0.05, g = 100. (b) Same as panel a, but for mechanism II in series. (c) Incoherent feedforward loop (FFL) in which mechanisms I and II are activated in parallel to activate and inhibit, respectively, the terminal output. For both mechanisms I and II, K = 0.05, g = 100. The parameters for Eq. 5 are α = 2.5, β = 50. To see this figure in color, go online.A more complex scheme is to combine the two mechanisms in parallel, as in an incoherent feedforward loop (FFL) connected to an “AND NOT” output as follows:Output = αρI/(1 + αρIβρII).(5)Given the differential saturation properties of mechanisms I and II, this scheme readily yields the expected biphasic dose response (9) without the need for disparate values of the parameters (Fig. 3 c). Regarding the kinetics, the analysis shown in Fig. 2 makes it clear that mechanism II naturally introduces time delays in cascades or network motifs. Thus, for the incoherent FFL at high, constant s, activation of inhibition by mechanism II would tend to yield a dynamic response marked by a peak followed by adaptation (see Fig. S5). Analogous calculations were carried out for a coherent FFL as well (see Fig. S6).To summarize our conclusions and their implications for signaling downstream of Akt and other kinases, we have described a distinct, indirect signal transduction mechanism characterized by deactivation of a negative regulator. This motif shows steady-state sensitivity beyond saturation, and therefore the activity of the upstream kinase, such as Akt, can be relatively high. By comparison, the direct activation of signaling by phosphorylation requires that activity of the kinase be regulated, or specifically countered by high phosphatase activity, to maintain sensitivity and avoid saturation of the response. The mechanism described here also introduces relatively slow kinetics (for comparable parameter values). This property, together with its extended range of sensitivity, would allow the motif to be incorporated in signaling networks to yield desired steady and unsteady responses in a robust manner. Considering that key signaling processes mediated by Akt (notably activation of the mammalian target of rapamycin (mTOR) pathway) are achieved by deactivation of negative regulators, we assert that greater recognition of this mechanism and of its distinct properties is warranted.  相似文献   

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13.
The coral reef fish community of Hawaii is composed of hundreds of species, supports a multimillion dollar fishing and tourism industry, and is of great cultural importance to the local population. However, a major stock assessment of Hawaiian coral reef fish populations has not yet been conducted. Here we used the robust indicator variable “average length in the exploited phase of the population (L¯)”, estimated from size composition data from commercial fisheries trip reports and fishery-independent diver surveys, to evaluate exploitation rates for 19 Hawaiian reef fishes. By and large, the average lengths obtained from diver surveys agreed well with those from commercial data. We used the estimated exploitation rates coupled with life history parameters synthesized from the literature to parameterize a numerical population model and generate stock sustainability metrics such as spawning potential ratios (SPR). We found good agreement between predicted average lengths in an unfished population (from our population model) and those observed from diver surveys in the largely unexploited Northwestern Hawaiian Islands. Of 19 exploited reef fish species assessed in the main Hawaiian Islands, 9 had SPRs close to or below the 30% overfishing threshold. In general, longer-lived species such as surgeonfishes, the redlip parrotfish (Scarus rubroviolaceus), and the gray snapper (Aprion virescens) had the lowest SPRs, while short-lived species such as goatfishes and jacks, as well as two invasive species (Lutjanus kasmira and Cephalopholis argus), had SPRs above the 30% threshold.  相似文献   

14.
[Purpose]Aerobic exercise training (AT) reverses aging-induced deterioration of arterial stiffness via increased arterial nitric oxide (NO) production. Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of NO synthase, was decreased by AT. However, whether AT-induced changes in ADMA levels are related to changes in nitrite/nitrate (NOx) levels remains unclear. Accordingly, we aimed to clarify whether the relationship between plasma ADMA and NOx levels affected the AT-induced reduction of arterial stiffness in middle-aged and older adults.[Methods]Thirty-one healthy middle-aged and older male and female subjects (66.4 ± 1.3 years) were randomly divided into two groups: exercise intervention and sedentary controls. Subjects in the training group completed an 8-week AT (60%–70% peak oxygen uptake [V˙O2peak] for 45 min, 3 days/week).[Results]AT significantly increased V˙O2peak (p < 0.05) and decreased carotid β-stiffness (p < 0.01). Moreover, plasma ADMA levels were significantly decreased while plasma NOx levels and NOx/ADMA ratio were significantly increased by AT (p < 0.01). Additionally, no sex differences in AT-induced changes of circulating ADMA and NOx levels, NOx/ADMA ratio, and carotid β-stiffness were observed. Furthermore, the AT-induced increase in circulating ADMA levels was negatively correlated with an increase in circulating NOx levels (r = -0.414, p < 0.05), and the AT-induced increase in NOx/ADMA ratio was negatively correlated with a decrease in carotid β-stiffness (r = -0.514, p < 0.01).[Conclusion]These results suggest that the increase in circulating NOx with reduction of ADMA elicited by AT is associated with a decrease in arterial stiffness regardless of sex in middle-aged and older adults.  相似文献   

15.
BackgroundThe aim of the study was to Estimate and compare the radiobiological ratio α/β with the heuristic method for a cohort of Mexican patients with prostate cancer (PCa) who were treated with external radiotherapy (RT) techniques at three Hospital Institutions in Mexico City. With the Kaplan-Meier technique and the Cox proportional hazards model, the biochemical relapse-free survival (bRFS) is determined and characterized for cohorts of Mexican patients with PCa who received treatment with external RT. Using these clinical outcomes, the radiobiological parameter α/β is determined using the heuristic methodology of Pedicini et. al.Materials and methodsThe α/β is calculated from the survival curves for different treatment schemes implemented at three distinct hospitals. The Pedicini’s techniques allow to determine the parameters α/β, k and N0 when treatments are not radiobiologically equivalent, therefore, are built up of a set of curved pairs for the biologically effective dose (BED) versus the ratio α/β, where the ratio is given by the intersection for each pair of curves.ResultsSix different values of α/β were found: the first α/β = 2.46 Gy, the second α/β = 3.30 Gy, the third for α/β = 3.25 Gy, the fourth α/β = 3.24 Gy, the fifth α/β = 3.38 Gy and the last α/β = 4.08 Gy. These values can be explained as follows: a) The bRFS of the schemes presents a statistical variation; b) The absorbed doses given to the patient present uncertainties on the physical dosimetry that are not on the modeling; c) Finally, in the model for the bRFS of Eq. (3), there are parameters that have to be considered, such as: the number of clonogenic tumor cells N0, the overall treatment time (OTT), the kick-off time for tumor repopulation Tk and the repopulation doubling time. Therefore, the mean value to α/β for all schemes has an average value of 3.29 (± 0.52) Gy.ConclusionsThe value of α/β¯=3.29(±0.52)Gy is determined from cohorts of Mexican patients with PC a treated with external radiotherapy using the time-dependent LQ model, which is a higher value with respect to the “dogma” value of α/β 1.5 Gy obtained with the LQ model without temporal dependence. Therefore, there is a possibility of optimizing treatments radiobiologically and improving the results of bRFS in Mexican patients with PCa treated with external radiotherapy.  相似文献   

16.
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.  相似文献   

17.
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.  相似文献   

18.
19.
Conditional regulation of gene expression is a powerful and indispensable method for analyzing gene function. The “Tet-On” system is a tool widely used for that purpose. Here, the transregulator rtTA mediates expression of a gene of interest after addition of the small molecule effector doxycycline. Although very effective in rapidly turning on gene expression, the system is hampered by the long half-life of doxycycline which makes shutting down gene expression rapidly very difficult to achieve. We isolated an rtTA-binding peptide by in vivo selection that acts as a doxycycline antagonist and leads to rapid and efficient shut down of rtTA-mediated reporter gene expression in a human cell line. This peptide represents the basis for novel effector molecules which complement the “Tet-system” by enabling the investigator to rapidly turn gene expression not just on at will, but now also off.  相似文献   

20.
The voltage dependence of charges in voltage-sensitive proteins, typically displayed as charge versus voltage (Q-V) curves, is often quantified by fitting it to a simple two-state Boltzmann function. This procedure overlooks the fact that the fitted parameters, including the total charge, may be incorrect if the charge is moving in multiple steps. We present here the derivation of a general formulation for Q-V curves from multistate sequential models, including the case of infinite number of states. We demonstrate that the commonly used method to estimate the charge per molecule using a simple Boltzmann fit is not only inadequate, but in most cases, it underestimates the moving charge times the fraction of the field.Many ion channels, transporters, enzymes, receptors, and pumps are voltage dependent. This voltage dependence is the result of voltage-induced translocation of intrinsic charges that, in some way, affects the conformation of the molecule. The movement of such charges is manifested as a current that can be recorded under voltage clamp. The best-known examples of these currents are “gating” currents in voltage-gated channels and “sensing” currents in voltage-sensitive phosphatases. The time integral of the gating or sensing current as a function of voltage (V) is the displaced charge Q(V), normally called the Q-V curve.It is important to estimate how much is the total amount of net charge per molecule (Qmax) that relocates within the electric field because it determines whether a small or a large change in voltage is necessary to affect the function of the protein. Most importantly, knowing Qmax is critical if one wishes to correlate charge movement with structural changes in the protein. The charge is the time integral of the current, and it corresponds to the product of the actual moving charge times the fraction of the field it traverses. Therefore, correlating charge movement with structure requires knowledge of where the charged groups are located and the electric field profile. In recent papers by Chowdhury and Chanda (2012) and Sigg (2013), it was demonstrated that the total energy of activating the voltage sensor is equal to Qmax VM, where VM is the median voltage of charge transfer, a value that is only equal to the half-point of activation V1/2 for symmetrical Q-V curves. VM is easily estimated from the Q-V curve, but Qmax must be obtained with other methods because, as we will show here, it is not directly derived from the Q-V curve in the general case.The typical methods used to estimate charge per molecule Qmax include measurements of limiting slope (Almers, 1978) and the ratio of total charge divided by the number of molecules (Schoppa et al., 1992). The discussion on implementation, accuracy, and reliability of these methodologies has been addressed many times in the literature, and it will not be discussed here (see Sigg and Bezanilla, 1997). However, it is worth mentioning that these approaches tend to be technically demanding, thus driving researchers to seek alternative avenues toward estimating the total charge per molecule. Particularly, we will discuss here the use of a two-state Boltzmann distribution for this purpose. Our intention is to demonstrate that this commonly used method to estimate the charge per molecule is generally incorrect and likely to give a lower bound of the moving charge times the fraction of the field.The two-state Boltzmann distribution describes a charged particle that can only be in one of two positions or states that we could call S1 and S2. When the particle with charge Qmax (in units of electronic charge) moves from S1 to S2, or vice versa, it does it in a single step. The average charge found in position S2, Q(V), will depend on the energy difference between S1 and S2, and the charge of the particle. The equation that describes Q(V) is:Q(V)=Qmax1+exp[Qmax(VV1/2)kT],(1)where V1/2 is the potential at which the charge is equally distributed between S1 and S2, and k and T are the Boltzmann constant and absolute temperature, respectively. The Q(V) is typically normalized by dividing Eq. 1 by the total charge Qmax. The resulting function is frequently called a “single Boltzmann” in the literature and is used to fit normalized, experimentally obtained Q-V curves. The fit yields an apparent V1/2 (V1/2) and an apparent QMAX (Qmax), and this last value is then attributed to be the total charge moving Qmax. Indeed, this is correct but only for the case of a charge moving between two positions in a single step. However, the value of Qmax thus obtained does not represent the charge per molecule for the more general (and frequent) case when the charge moves in more than one step.To demonstrate the above statement and also estimate the possible error in using the fitted Qmax from Eq. 1, let us consider the case when the gating charge moves in a series of n steps between n + 1 states, each step with a fractional charge zi (in units of electronic charge e0) that will add up to the total charge Qmax.S1μ1S2μ2SiμiSi+1SnμnSn+1The probability of being in each of the states Si is labeled as Pi, and the equilibrium constant of each step is given byμi=exp[zi(VVi)kT],i=1n,where zi is the charge (in units of e0) of step i, and Vi is the membrane potential that makes the equilibrium constant equal 1. In steady state, the solution of Pi can be obtained by combiningPi+1Pi=μi,i=1nandi=1i=n+1Pi=1,givingPi+1=m=1iμm1+j=1nk=1jμk,i=1nandP1=11+j=1nk=1jμk.We define the reaction coordinate along the moved charged q asqi=j=1izj,i=1n.The Q-V curve is defined asQ(V)=i=1nqiPi+1.Then, replacing Pi yieldsQ(V)=i=1n[j=1izj][m=1iμm]1+j=1nk=1jμk,or written explicitly as a function of V:Q(V)=i=1n[j=1izj][m=1iexp[zm(VVm)kT]]1+j=1nk=1jexp[zk(VVk)kT].(2)Eq. 2 is a general solution of a sequential model with n + 1 states with arbitrary valences and Vi’s for each transition. We can easily see that Eq. 2 has a very different form than Eq. 1, except when there is only a single transition (n = 1). In this latter case, Eq. 2 reduces to Eq. 1 because z1 and V1 are equal to Qmax and V1/2, respectively. For the more general situation where n > 1, if one fits the Q(V) relation obeying Eq. 2 with Eq. 1, the fitted Qmax value will not correspond to the sum of the zi values (see examples below and Fig. 1). A simple way to visualize the discrepancy between the predicted value of Eqs. 1 and 2 is to compute the maximum slope of the Q-V curve. This can be done analytically assuming that Vi = Vo for all transitions and that the total charge Qmax is evenly divided among those transitions. The limit of the first derivative of the Q(V) with respect to V evaluated at V = Vo is given by this equation:dQ(V)dV|V=V0=Qmax(n+2)12nkT.(3)From Eq. 3, it can be seen that the slope of the Q-V curve decreases with the number of transitions being maximum and equal to Qmax /(4kT) when n = 1 (two states) and a minimum equal to Qmax /(12kT) when n goes to infinity, which is the continuous case (see next paragraph).Open in a separate windowFigure 1.Examples of normalized Q-V curves for a Qmax = 4 computed with Eq. 2 for the cases of one, two, three, four, and six transitions and the continuous case using Eq. 5 (squares). All the Q-V curves were fitted with Eq. 1 (lines). The insets show the fitted valence (Qmax) and half-point (V1/2).

Infinite number of steps

Eq. 2 can be generalized to the case where the charge moves continuously, corresponding to an infinite number of steps. If we makeziQmax/n, ?i = 1…n, ??ViVo, ?i = 1…n, then all µi = µ, and we can write Eq. 2 as the normalized Q(V) in the limit when n goes to infinity:Qnor(V)=limni=1n[j=1iQmaxn]m=1iexp[Qmax(VVo)nkT]Qmax[1+i=1nj=1iexp[Qmax(VVo)nkT]]=[Qmax(VVo)kT]exp[Qmax(VVo)kT]+kTQmax(VVo)[exp[Qmax(VVo)kT]1].(4)Eq. 4 can also be written asQnor(V)=12[1+coth[Qmax(VVo)2kT]2kTQmax(VV0)],(5)which is of the same form of the classical equation of paramagnetism (see Kittel, 2005).

Examples

We will illustrate now that data generated by Eq. 2 can be fitted quite well by Eq. 1, thus leading to an incorrect estimate of the total charge moved. Typically, the experimental value of the charge plotted is normalized to its maximum because there is no knowledge of the absolute amount of charge per molecule and the number of molecules. The normalized Q-V curve, Qnor, is obtained by dividing Q(V) by the sum of all the partial charges.Fig. 1 shows Qnor computed using Eq. 2 for one, two, three, four, and six transitions and for the continuous case using Eq. 5 (squares) with superimposed fits to a two-state Boltzmann distribution (Eq. 1, lines). The computations were done with equal charge in each step (for a total charge Qmax = 4e0) and also the same Vi = −25 mV value for all the steps. It is clear that fits are quite acceptable for cases up to four transitions, but the fit significantly deviates in the continuous case.Considering that experimental data normally have significant scatter, it is then quite likely that the experimenter will accept the single-transition fit even for cases where there are six or more transitions (see Fig. 1). In general, the case up to four transitions will look as a very good fit, and the fitted Qmax value may be inaccurately taken and the total charge transported might be underestimated. To illustrate how bad the estimate can be for these cases, we have included as insets the fitted value of Qmax for the cases presented in Fig. 1. It is clear that the estimated value can be as low as a fourth of the real total charge. The estimated value of V1/2 is very close to the correct value for all cases, but we have only considered cases in which all Vi’s are the same.It should be noted that if µi of the rightmost transition is heavily biased to the last state (Vi is very negative), then the Qmax estimated by fitting a two-state model is much closer to the total gating charge. In a three-state model, it can be shown that the fitted value is exact when V1→∞ and V2→−∞ because in that case, it converts into a two-state model. Although these values of V are unrealistic, the fitted value of Qmax can be very close to the total charge when V2 is much more negative than V1 (that is, V1 >> V2). On the other hand, If V1 << V2, the Q-V curve will exhibit a plateau region and, as the difference between V1 and V2 decreases, the plateau becomes less obvious and the curve looks monotonic. These cases have been discussed in detail for the two-transition model in Lacroix et al. (2012).We conclude that it is not possible to estimate unequivocally the gating charge per sensor from a “single-Boltzmann” fit to a Q-V curve of a charge moving in multiple transitions. The estimated Qmax value will be a low estimate of the gating charge Qmax, except in the case of the two-state model or the case of a heavily biased late step, which are rare occurrences. It is then safer to call “apparent gating charge” the fitted Qmax value of the single-Boltzmann fit.

Addendum

The most general case in which transitions between states include loops, branches, and steps can be derived directly from the partition function and follows the general thermodynamic treatment by Sigg and Bezanilla (1997), Chowdhury and Chanda (2012), and Sigg (2013). The reaction coordinate is the charge moving in the general case where it evolves from q = 0 to q = Qmax by means of steps, loops, or branches. In that case, the partition function is given byZ=iexp(qi(VVi)kT).(6)We can compute the mean gating charge, also called the Q-V curve, asQ(V)=q=kTZZ=kTdlnZdV=iqiexp(qi(VVi)kT)iexp(qi(VVi)kT).(7)The slope of the Q-V is obtained by taking the derivative of 〈q〉 with respect to V:dQ(V)dV=(kT)2d2lnZdV2.(8)Let us now consider the gating charge fluctuation. The charge fluctuation will depend on the number of possible conformations of the charge and is expected to be a maximum when there are only two possible charged states to dwell. As the number of intermediate states increases, the charge fluctuation decreases. Now, a measure of the charge fluctuation is given by the variance of the gating charge, which can be computed from the partition function as:Δq2=q2q2=(kT)2(ZZ(ZZ)2)=(kT)2d2lnZdV2.(9)But the variance (Eq. 9) is identical to the slope of Q(V) (Eq. 8). This implies that the slope of the Q-V is maximum when there are only two states.  相似文献   

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