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Studies of macroscopic speed modeling of bidirectional pedestrian cross-flows have relied heavily on scenario experiments, but the data itself may be deficient because large-scale scenario experiments are not easy to organize and subjects may not be walking under normal conditions. In order to explore the possibility of using microscopic pedestrian flow simulations for macroscopic speed modeling of pedestrian flows, a series of two-way pedestrian cross-flow simulation experiments were designed. Bidirectional pedestrian flows are defined as Peds1 and Peds2. The crossing angle and pedestrian flow rate are used as variables, and a bidirectional pedestrian flows simulation is designed as an orthogonal experiment. The crossing angles range from 15 to 165 degrees, and bidirectional pedestrian flow rate range from 1 ped/s to 8 ped/s. A series of simulations are built and performed on the GIS agent-based modeling architecture (GAMA) platform. By analyzing the flow data of bidirectional flows in the crossing area, it is found that when the Peds1 density falls below a threshold, Peds1 speed is determined by pedestrians themselves and mainly remains in a free flow state; otherwise, the Peds1 speed decreases with density. The clear effects such as Peds2 density on the Peds1 speed cannot be determined. A piecewise function combined with a linear function and an exponential function is constructed as the Peds1 speed model considering the influence of the crossing angle. The calibration results show that the piecewise function should be better than the non-piecewise function. Compared to the results of established studies, the results in this paper have some differences. Therefore, the simulation method cannot completely replace the scene experiments. However, this approach can provide suggestions for subsequent refinement of the experimental program, as well as a feasible direction for the construction of a speed relationship for bidirectional pedestrian flows. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the (2+1)-dimensional Kadomtsev-Petviashvili-Benjamin-Bona Mahony equation using two effective methods: the unified scheme and the advanced auxiliary equation scheme, aiming to derive precise wave solutions. These solutions are expressed as combinations of trigonometric, rational, hyperbolic, and exponential functions. Visual representations, including three-dimensional (3D) and two-dimensional (2D) combined charts, are provided for some of these solutions. The influence of the nonlinear parameter p on the wave type is thoroughly examined through diverse figures, illustrating the profound impact of nonlinearity. Additionally, we briefly investigate the Hamiltonian function and the stability of the model using a planar dynamical system approach. This involves examining trajectories, isoclines, and nullclines to illustrate stable solution paths for the wave variables. Numerical results demonstrate that these methods are reliable, straightforward, and potent tools for analyzing various nonlinear evolution equations found in physics, applied mathematics, and engineering. [ABSTRACT FROM AUTHOR]

Multimodal perception is the predominant means by which individuals experience and interact with the world. However, sensory dysfunction or loss can significantly impede this process. In such cases, cross-modality research offers valuable insight into how we can compensate for these sensory deficits through sensory substitution. Although sight and hearing are both used to estimate the distance to an object (e.g., by visual size and sound volume) and the perception of distance is an important element in navigation and guidance, it is not widely studied in cross-modal research. We investigate the relationship between audio and vibrotactile frequencies (in the ranges 47–2,764 Hz and 10–99 Hz, respectively) and distances uniformly distributed in the range 1–12 m. In our experiments participants mapped the distance (represented by an image of a model at that distance) to a frequency via adjusting a virtual tuning knob. The results revealed that the majority (more than 76%) of participants demonstrated a strong negative monotonic relationship between frequency and distance, across both vibrotactile (represented by a natural log function) and auditory domains (represented by an exponential function). However, a subgroup of participants showed the opposite positive linear relationship between frequency and distance. The strong cross-modal sensory correlation could contribute to the development of assistive robotic technologies and devices to augment human perception. This work provides the fundamental foundation for future assisted HRI applications where a mapping between distance and frequency is needed, for example for people with vision or hearing loss, drivers with loss of focus or response delay, doctors undertaking teleoperation surgery, and users in augmented reality (AR) or virtual reality (VR) environments. [ABSTRACT FROM AUTHOR]

A key metric to determine the performance of a stock in a market is its return over different investment horizons (τ). Several works have observed heavy-tailed behavior in the distributions of returns in different markets, which are observable indicators of underlying complex dynamics. Such prior works study return distributions that are marginalized across the individual stocks in the market, and do not track statistics about the joint distributions of returns conditioned on different stocks, which would be useful for optimizing inter-stock asset allocation strategies. As a step towards this goal, we study emergent phenomena in the distributions of returns as captured by their pairwise correlations. In particular, we consider the pairwise (between stocks i, j) partial correlations of returns with respect to the market mode, ci,j(τ), (thus, correcting for the baseline return behavior of the market), over different time horizons (τ), and discover two novel emergent phenomena: (i) the standardized distributions of the ci,j(τ)'s are observed to be invariant of τ ranging from from 1000min (2.5 days) to 30000min (2.5 months); (ii) the scaling of the standard deviation of ci,j(τ)'s with τ admits good fits to simple model classes such as a power-law τ−λ or stretched exponential function e-τβ (λ, β > 0). Moreover, the parameters governing these fits provide a summary view of market health: for instance, in years marked by unprecedented financial crises—for example 2008 and 2020—values of λ (scaling exponent) are substantially lower. Finally, we demonstrate that the observed emergent behavior cannot be adequately supported by existing generative frameworks such as single- and multi-factor models. We introduce a promising agent-based Vicsek model that closes this gap. [ABSTRACT FROM AUTHOR]

To investigate the evolution and stability characteristics of granite thermal damage, a series of Brazilian splitting tests is conducted on high-temperature granite samples using digital image correlation (DIC) technology. The results show that the Brazilian tensile strength and P-wave velocity exhibit a clear decline beyond a temperature threshold of 450~600°C, with a linear relationship between them. The presence of micro-cracks alters the stress transfer path, disrupting the stress balance on the Brazilian disc and leading to complex fracture patterns. At temperatures below 450°C, high strain areas and the development of micro-cracks occur at both the upper and lower loading ends of the granite Brazilian disc. However, these phenomena are only observed at the upper loading end when the temperature exceeds 450°C. Thermal cracks also cause changes in the internal structure of rock samples, and temperature variations can affect both the P-wave velocity and tensile strength. In terms of the relationship between P-wave velocity and Brazilian tensile strength (BTS) of high-temperature granite under water cooling, the negative exponential function model proposed in this study fits the experimental data very well. [ABSTRACT FROM AUTHOR]

Electrical coupling between retinal neurons contributes to the functional complexity of visual circuits. "Cut-loading" methods allow simultaneous assessment of cell-coupling between multiple retinal cell-types, but existing analysis methods impede direct comparison with gold standard direct dye injection techniques. In the current study, we both improved an existing method and developed two new approaches to address observed limitations. Each method of analysis was applied to cut-loaded dark-adapted Guinea pig retinae (n = 29) to assess coupling strength in the axonless horizontal cell type ('a-type', aHCs). Method 1 was an improved version of the standard protocol and described the distance of dye-diffusion (space constant). Method 2 adjusted for the geometric path of dye-transfer through cut-loaded cells and extracted the rate of dye-transfer across gap-junctions in terms of the coupling coefficient (kj). Method 3 measured the diffusion coefficient (De) perpendicular to the cut-axis. Dye transfer was measured after one of five diffusion times (1–20 mins), or with a coupling inhibitor, meclofenamic acid (MFA) (50–500μM after 20 mins diffusion). The standard protocol fits an exponential decay function to the fluorescence profile of a specified retina layer but includes non-specific background fluorescence. This was improved by measuring the fluorescence of individual cell soma and excluding from the fit non-horizontal cells located at the cut-edge (p<0.001) (Method 1). The space constant (Method 1) increased with diffusion time (p<0.01), whereas Methods 2 (p = 0.54) and 3 (p = 0.63) produced consistent results across all diffusion times. Adjusting distance by the mean cell-cell spacing within each tissue reduced the incidence of outliers across all three methods. Method 1 was less sensitive to detecting changes induced by MFA than Methods 2 (p<0.01) and 3 (p<0.01). Although the standard protocol was easily improved (Method 1), Methods 2 and 3 proved more sensitive and generalisable; allowing for detailed assessment of the tracer kinetics between different populations of gap-junction linked cell networks and direct comparison to dye-injection techniques. [ABSTRACT FROM AUTHOR]

Although both the erythrocyte sedimentation rate (ESR) and optically measured erythrocyte aggregation parameters are affected by the hematocrit, this interaction is not considered by the method used to estimate ESR that considers aggregation parameters. In this study, we investigated the relationship between the ESR obtained by the Westergren method and that obtained with an aggregation parameter, namely, the aggregation index (AI) of multiple hematocrit values and fibrinogen-spiked samples with an analysis time of 5–60 s, and attempted to develop a rapid and accurate ESR estimation method. The AIs obtained from 5- and 10-s optical measurements with a fixed hematocrit were highly correlated with the erythrocyte sedimentation velocity. Furthermore, the rate of the AI increase with an increasing hematocrit was not significantly affected by the fibrinogen concentration at these measurement times. On the basis of these results, we defined the hematocrit-corrected aggregation index (HAI). The exponential function of the HAI obtained from the 5-s measurement agreed well with the sedimentation velocity calculated to eliminate the effect of hindered settling, and the HAI and hematocrit could be used to calculate the time constant of the sedimentation curve with a linear regression equation. The ESR value at 1 h was calculated based on the modified Stokes' law and the HAI obtained from the 5-s measurement and showed an excellent correlation (R = 0.966) with the ESR value obtained by the Westergren method over a wide range of hematocrit and fibrinogen concentrations. [ABSTRACT FROM AUTHOR]

Introduction: This study presents an empirical method to model the high-energy photon beam percent depth dose (PDD) curve by using the home-generated buildup function and tail function (buildup-tail function) in radiation therapy. The modeling parameters n and μ of buildup-tail function can be used to characterize the Collimator Scatter Factor (Sc) either in a square field or in the different individual upper jaw and lower jaw setting separately for individual monitor unit check. Methods and materials: The PDD curves for four high-energy photon beams were modeled by the buildup and tail function in this study. The buildup function was a quadratic function in the form of dd2+n with the main parameter of d (depth in water) and n, while the tail function was in the form of e−μd and was composed by an exponential function with the main parameter of d and μ. The PDD was the product of buildup and tail function, PDD = dd2+n·e−μd. The PDD of four-photon energies was characterized by the buildup-tail function by adjusting the parameters n and μ. The Sc of 6 MV and 10 MV can then be expressed simply by the modeling parameters n and μ. Results: The main parameters n increases in buildup-tail function when photon energy increased. The physical meaning of the parameter n expresses the beam hardening of photon energy in PDD. The fitting results of parameters n in the buildup function are 0.17, 0.208, 0.495, 1.2 of four-photon energies, 4 MV, 6 MV, 10 MV, 18 MV, respectively. The parameter μ can be treated as attenuation coefficient in tail function and decreases when photon energy increased. The fitting results of parameters μ in the tail function are 0.065, 0.0515, 0.0458, 0.0422 of four-photon energies, 4 MV, 6 MV, 10 MV, 18 MV, respectively. The values of n and μ obtained from the fitted buildup-tail function were applied into an analytical formula of Sc = nE(S)0.63μE to get the collimator to scatter factor Sc for 6 and 10 MV photon beam, while nE, μE, S denotes n, μ at photon energy E of field size S, respectively. The calculated Sc were compared with the measured data and showed agreement at different field sizes to within ±1.5%. Conclusions: We proposed a model incorporating a two-parameter formula which can improve the fitting accuracy to be better than 1.5% maximum error for describing the PDD in different photon energies used in clinical setting. This model can be used to parameterize the Sc factors for some clinical requirements. The modeling parameters n and μ can be used to predict the Sc in either square field or individual jaws opening asymmetrically for treatment monitor unit double-check in dose calculation. The technique developed in this study can also be used for systematic or random errors in the QA program, thus improves the clinical dose computation accuracy for patient treatment. [ABSTRACT FROM AUTHOR]

Under inadequate chilling conditions, hydrogen cyanamide (HC) is often used to promote budbreak and improve earliness of Southern highbush blueberry (Vaccinium corymbosum L. interspecific hybrids). However, HC is strictly regulated or even banned in some countries because of its high hazardous properties. Development of safer and effective alternatives to HC is critical to sustainable subtropical blueberry production. In this study, we examined the efficacy of HC and defoliants as bud dormancy-breaking agents for 'Emerald' blueberry. First, we compared water control, 1.0% HC (9.35 L ha–1), and three defoliants [potassium thiosulfate (KTS), urea, and zinc sulfate (ZS)] applied at 6.0% (28 kg ha–1). Model fitting analysis revealed that only HC and ZS advanced both defoliation and budbreak compared with the water control. HC-induced budbreak showed an exponential plateau function with a rapid phase occurring from 0 to 22 days after treatment (DAT), whereas ZS-induced budbreak showed a sigmoidal function with a rapid phase occurring from 15 to 44 DAT. The final budbreak percentage was similar in all treatments (71.7%–83.7%). Compared with the water control, HC and ZS increased yield by up to 171% and 41%, respectively, but the yield increase was statistically significant only for HC. Phytohormone profiling was performed for water-, HC- and ZS-treated flower buds. Both chemicals did not increase gibberellin 4 and indole-3-acetic acid production, but they caused a steady increase in jasmonic acid (JA) during budbreak. Compared with ZS, HC increased JA production to a greater extent and was the only chemical that reduced abscisic acid (ABA) concentrations during budbreak. A follow-up experiment tested ZS at six different rates (0–187 kg ha–1) but detected no significant dose-response on budbreak. These results collectively suggest that defoliants are not effective alternatives to HC, and that HC and ZS have different modes of action in budbreak induction. The high efficacy of HC as a dormancy-breaking agent could be due to its ability to reduce ABA concentrations in buds. Our results also suggest that JA accumulation is involved in budbreak induction in blueberry. [ABSTRACT FROM AUTHOR]

Deuterium Magnetic Resonance Spectroscopy (DMRS) is a non-invasive technique that allows the detection of deuterated compounds in vivo. DMRS has a large potential to analyze uptake, perfusion, washout or metabolism, since deuterium is a stable isotope and therefore does not decay during biologic processing of a deuterium labelled substance. Moreover, DMRS allows the distinction between different deuterated substances. In this work, we performed DMRS of deuterated 3-O-Methylglucose (OMG). OMG is a non-metabolizable glucose analog which is transported similar to D-glucose. DMRS of OMG was performed in phantom and in vivo measurements using a preclinical 7 Tesla MRI system. The chemical shift (3.51 ± 0.1 ppm) and relaxation times were determined. OMG was injected intravenously and spectra were acquired over a period of one hour to monitor the time evolution of the deuterium signal in tumor-bearing rats. The increase and washout of OMG could be observed. Three different exponential functions were compared in terms of how well they describe the OMG washout. A mono-exponential model with offset seems to describe the observed time course best with a time constant of 1910 ± 770 s and an offset of 2.5 ± 1.2 mmol/l (mean ± std, N = 3). Chemical shift imaging could be performed with a voxel size of 7.1 mm x 7.1 mm x 7.9 mm. The feasibility of DMRS with deuterium labelled OMG could be demonstrated. These data might serve as basis for future studies that aim to characterize glucose transport using DMRS. [ABSTRACT FROM AUTHOR]

Background: Obesity is a leading risk for poor health outcomes in England. We examined best- and worst-case scenarios for the future trajectory of the obesity epidemic. Methods: Taking the last 27 years of Health Survey for England data, we determined both position and shape of the adult body mass index (BMI) distribution and projected these parameters 20 years forward in time. For the best-case scenario, we fitted linear models, allowing for a quadratic relationship between the outcome variable and time, to reflect a potential reversal in upwards trends. For the worst-case scenario, we fitted non-linear models that applied an exponential function to reflect a potential flattening of trends over time. Best-fitting models were identified using Monte Carlo cross-validation on 1991–2014 data, and predictions of population prevalence across five BMI categories were then validated using 2015–17 data. Results: Both linear and non-linear models showed a close fit to observed data (mean absolute error <2%). In the best-case scenario, the proportion of the population at increased risk (BMI≥25kg/m2) is predicted to fall from 66% in 2017 to 53% (95% confidence interval: 41% to 64%) in 2035. In the worst-case scenario, this proportion is likely to remain relatively stable overall– 64% (37% to 90%) in 2035 –but with an increasing proportion of the population at highest risk (BMI≥35kg/m2). Conclusions: While obesity prediction depends on chosen modelling methods, even under optimistic assumptions it is likely that the majority of the English population will still be at increased risk of disease due to their weight until at least 2035, without greater allocation of resources to effective interventions. [ABSTRACT FROM AUTHOR]

For the requirement in container nursery culture that growing media should be achieved the appropriate degree compaction, this paper presents an experiment on the compaction dynamics of air-dried soil under repetitive drop shocks, as a preliminary step toward the mechanization of this compaction method. The drop height used to adjust the shock intensity included 2 mm, 4 mm, 5 mm and 6 mm. And the overall packing density of soil in a vertically stratified cylinder vessel and the local packing density in each layer were taken as indicators of soil compaction states. The stretched exponential function derived from KWW law than the empirical inverse-logarithmic function has been found to be more suitable for expressing the temporal evolution of soil compaction, according to the results of curve-fitting to test values of the overall and local density. It is inherent in this experimental configuration that the drop shock intensity even at a constant drop height varies with drop times, owing to the interaction between the soil packing itself and drop shocks caused by the combination of the packing and the container. But the function t/τf(t,H) is manifested as a straight line on the drop times t with the line slope related to the drop height H, so the soil compaction dynamics caused by its drop shocks and that under the condition with actively controlled intensity actually share the common relaxation law. In addition, the soil's one-dimensional distribution of local packing density showed a slight positive gradient as similar as monodisperse particles did. [ABSTRACT FROM AUTHOR]

The deconvolution process is a key step for quantitative evaluation of fluorescence lifetime imaging microscopy (FLIM) samples. By this process, the fluorescence impulse responses (FluoIRs) of the sample are decoupled from the instrument response (InstR). In blind deconvolution estimation (BDE), the FluoIRs and InstR are jointly extracted from a dataset with minimal a priori information. In this work, two BDE algorithms are introduced based on linear combinations of multi-exponential functions to model each FluoIR in the sample. For both schemes, the InstR is assumed with a free-form and a sparse structure. The local perspective of the BDE methodology assumes that the characteristic parameters of the exponential functions (time constants and scaling coefficients) are estimated based on a single spatial point of the dataset. On the other hand, the same exponential functions are used in the whole dataset in the global perspective, and just the scaling coefficients are updated for each spatial point. A least squares formulation is considered for both BDE algorithms. To overcome the nonlinear interaction in the decision variables, an alternating least squares (ALS) methodology iteratively solves both estimation problems based on non-negative and constrained optimizations. The validation stage considered first synthetic datasets at different noise types and levels, and a comparison with the standard deconvolution techniques with a multi-exponential model for FLIM measurements, as well as, with two BDE methodologies in the state of the art: Laguerre basis, and exponentials library. For the experimental evaluation, fluorescent dyes and oral tissue samples were considered. Our results show that local and global perspectives are consistent with the standard deconvolution techniques, and they reached the fastest convergence responses among the BDE algorithms with the best compromise in FluoIRs and InstR estimation errors. [ABSTRACT FROM AUTHOR]

The Nicaraguan COVID-19 situation is exceptional for Central America. The government restricts testing and testing supplies, and the true extent of the coronavirus crisis remains unknown. Dozens of deaths have been reported among health-care workers. However, statistics on the crisis' effect on health-care workers and their risk of being infected with SARS-CoV-2 are lacking. We aimed to estimate the prevalence of SARS-CoV-2 infection in health-care workers and to examine correlations with risk factors such as age, sex and comorbidities. Study participants (N = 402, median age 38.48 years) included physicians, nurses and medical assistants, from public and private hospitals, independent of symptom presentation. SARS-CoV-2 was detected on saliva samples using the loop-mediated isothermal amplification assay. A questionnaire was employed to determine subjects' COVID-19-associated symptoms and their vulnerability to complications from risk factors such as age, sex, professional role and comorbidities. The study was performed five weeks into the exponential growth period in Nicaragua. We discovered that 30.35% of health-care workers participating in our study had been infected with SARS-CoV-2. A large percentage (54.92%) of those who tested positive were asymptomatic and were still treating patients. Nearly 50% of health-care workers who tested positive were under 40, an astonishing 30.33% reported having at least one comorbidity. In our study, sex and age are important risk factors for the probability of testing positive for SARS-CoV-2 with significance being greatest among those between 30 and 40 years of age. In general, being male resulted in higher risk. Our data are the first non-governmental data obtained in Nicaragua. They shed light on several important aspects of COVID-19 in an underdeveloped nation whose government has implemented a herd-immunity strategy, while lacking an adequate healthcare system and sufficient PPE for health-care workers. These data are important for creating policies for containing the spread of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Exponential growth bias is the phenomenon whereby humans underestimate exponential growth. In the context of infectious diseases, this bias may lead to a failure to understand the magnitude of the benefit of non-pharmaceutical interventions. Communicating the same scenario in different ways (framing) has been found to have a large impact on people's evaluations and behavior in the contexts of social behavior, risk taking and health care. We find that framing matters for people's assessment of the benefits of non-pharmaceutical interventions. In two commonly used frames, most subjects in our experiment drastically underestimate the number of cases avoided by adopting non-pharmaceutical interventions. Framing growth in terms of doubling times rather than growth rates reduces the bias. When the scenario is framed in terms of time gained rather than cases avoided, the median subject assesses the benefit of non-pharmaceutical interventions correctly. These findings suggest changes that could be adopted to better communicate the exponential spread of infectious diseases. [ABSTRACT FROM AUTHOR]

Background: HIV-1 circulating recombinant forms (CRFs) infections has been increasing in Former Soviet Union (FSU) countries in the recent decade. One is the CRF03_AB, which circulated in the region since late 1990s and probably became widespread in northwestern FSU countries. However, there is not much information provided about the dissemination of this recombinant. Here, we examine the prevalence, evolutionary dynamics and dispersion pattern of HIV-1 CRF03_AB recombinant. Methods: We analyzed 32 independent studies and 151 HIV-1 CRF03_AB pol sequences isolated from different FSU countries over a period of 22 years. Pooled prevalence was estimated using a random effects model. Bayesian coalescent-based method was used to estimate the evolutionary, phylogeographic and demographic parameters. Results: Our meta-analysis showed that the pooled prevalence of CRF03_AB infection in northwestern FSU region was 5.9% [95%CI: 4.1–7.8]. Lithuania (11.6%), Russia (5.9%) and Belarus (2.9%) were the most affected by CRF03_AB. We found that early region wide spread of HIV-1 CRF03_AB originated from one viral clade that arose in the city of Kaliningrad in 1992 [95%HPD: 1990–1995]. Fourteen migration route of this variant were found. The city of Kaliningrad is involved in most of these, confirming its leading role in CRF03_AB spread within FSU. Demographic reconstruction point to this is that CRF03_AB clade seems to have experienced an exponential growth until the mid-2000s and a decrease in recent years. Conclusion: These data provide new insights into the molecular epidemiology of CRF03_AB as well as contributing to the fundamental understanding of HIV epidemic in FSU. [ABSTRACT FROM AUTHOR]

The initial phase dynamics of an epidemic without containment measures is commonly well modelled using exponential growth models. However, in the presence of containment measures, the exponential model becomes less appropriate. Under the implementation of an isolation measure for detected infectives, we propose to model epidemic dynamics by fitting a flexible growth model curve to reported positive cases, and to infer the overall epidemic dynamics by introducing information on the detection/testing effort and recovery and death rates. The resulting modelling approach is close to the Susceptible-Infectious-Quarantined-Recovered model framework. We focused on predicting the peaks (time and size) in positive cases, active cases and new infections. We applied the approach to data from the COVID-19 outbreak in Italy. Fits on limited data before the observed peaks illustrate the ability of the flexible growth model to approach the estimates from the whole data. [ABSTRACT FROM AUTHOR]

On March 15, 2020 Puerto Rico implemented non-pharmaceutical interventions (NPIs), including a mandatory curfew, as part of a state of emergency declaration to prevent the community transmission of the SARS-CoV-2 virus. The strict enforcement of this curfew was extended through May 25, with a gradual relaxation beginning on May 1. This report summarizes an assessment of these early mitigation measures on the progression of the COVID-19 pandemic in the island. From March 15 to May 15, 2020, 70,656 results of molecular (RT-PCR) tests were reported to the Puerto Rico Department of Health. Of these, 1,704 were positive, corresponding to 1,311 individuals with COVID-19 included in the study. We derived the epidemic growth rates (r) and the corresponding reproductive numbers (R) from the epidemic curve of these 1,311 individuals with laboratory-confirmed diagnosis of COVID-19 using their date of test collection as a proxy for symptoms onset. Through May 31, 2020, there were 143 COVID-19 associated deaths in Puerto Rico, for a case fatality risk of 10.9%. We compared the observed cases and deaths with Gompertz model projections had the mitigation measures not been implemented. The number of daily RT-PCR-confirmed cases peaked on March 30 (85 cases), showing a weekly cyclical trend, with lower counts on weekends and a decreasing secular trend since March 30. The initial exponential growth rate (r) was 15.87% (95% CI: 7.59%, 24.15%), corresponding to R of 1.82 (95% CI:1.37, 2.30). After March 30, the r value reverted to an exponential decay rate (negative) of -2.95% (95% CI: -4.99%, -0.92%), corresponding to R of 0.93 (95% CI: 0.86, 0.98). We estimate that, had the initial growth rate been maintained, a total of 6,155 additional COVID-19 cases would have occurred by May 15, with 211 additional COVID-19 deaths by May 31. These findings are consistent with very effective implementation of early NPIs as mitigation measures in Puerto Rico. These results also provide a baseline to assess the impact of the transition from mitigation to subsequent containment stages in Puerto Rico. [ABSTRACT FROM AUTHOR]

Modern day investigation in fields of archaeology and palaeontology can be greatly characterised by an exponential growth of integrated new technologies, nevertheless, while these advances are of great significance to multiple lines of research, their evaluation and update over time is equally as important. Here we present an application of inter and intra-observer analysis in taphonomy based geometric morphometrics, employing robust non-parametric statistical analyses for the study of experimental carnivore tooth pit morphologies. To fully understand the influence of measurement errors in the collection of this data, our statistical assessment was performed on fully superimposed, partially superimposed and raw landmark coordinates collected from 3D surface scanning. Experimental samples used to assess these errors includes wolf and dog tooth pits used in modern day ecological livestock predation analysis. Results obtained from this study highlight the importance of landmark type in the assessment of error, emphasising the value of semi-landmark models over the use of ambiguous Type III landmarks. In addition to this, data also reveals the importance of observer experience for the collection of data alongside an interesting increase in error when working with fully superimposed landmarks due to the "Pinocchio Effect". Through this study we are able to redefine the geometric morphometric models used for tooth pit morphological analyses. This final hybrid Type II fixed landmark and semi-landmark model presents a significant reduction in human induced error, generating a more metrically reliable and replicable method that can be used for data pooling in future inter-institutional research. These results can be considered a fundamental step forward for carnivore inspired studies, having an impact on archaeological, palaeontological, modern-day ecological research as well as applications in other forensic sciences. [ABSTRACT FROM AUTHOR]

The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled Ro = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of 'super-spreaders' on the inference of Ro are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19. [ABSTRACT FROM AUTHOR]

The secondary structure prediction of proteins is a classic topic of computational structural biology with a variety of applications. During the past decade, the accuracy of prediction achieved by state-of-the-art algorithms has been >80%; meanwhile, the time cost of prediction increased rapidly because of the exponential growth of fundamental protein sequence data. Based on literature studies and preliminary observations on the relationships between the size/homology of the fundamental protein dataset and the speed/accuracy of predictions, we raised two hypotheses that might be helpful to determine the main influence factors of the efficiency of secondary structure prediction. Experimental results of size and homology reductions of the fundamental protein dataset supported those hypotheses. They revealed that shrinking the size of the dataset could substantially cut down the time cost of prediction with a slight decrease of accuracy, which could be increased on the contrary by homology reduction of the dataset. Moreover, the Shannon information entropy could be applied to explain how accuracy was influenced by the size and homology of the dataset. Based on these findings, we proposed that a proper combination of size and homology reductions of the protein dataset could speed up the secondary structure prediction while preserving the high accuracy of state-of-the-art algorithms. Testing the proposed strategy with the fundamental protein dataset of the year 2018 provided by the Universal Protein Resource, the speed of prediction was enhanced over 20 folds while all accuracy measures remained equivalently high. These findings are supposed helpful for improving the efficiency of researches and applications depending on the secondary structure prediction of proteins. To make future implementations of the proposed strategy easy, we have established a database of size and homology reduced protein datasets at http://10.life.nctu.edu.tw/UniRefNR. [ABSTRACT FROM AUTHOR]

As the Coronavirus contagion develops, it is increasingly important to understand the dynamics of the disease. Its severity is best described by two parameters: its ability to spread and its lethality. Here, we combine a mathematical model with a cohort analysis approach to determine the range of case fatality rates (CFR). We use a logistical function to describe the exponential growth and subsequent flattening of COVID-19 CFR that depends on three parameters: the final CFR (L), the CFR growth rate (k), and the onset-to-death interval (t0). Using the logistic model with specific parameters (L, k and t0), we calculate the number of deaths each day for each cohort. We build an objective function that minimizes the root mean square error between the actual and predicted values of cumulative deaths and run multiple simulations by altering the three parameters. Using all of these values, we find out which set of parameters returns the lowest error when compared to the number of actual deaths. We were able to predict the CFR much closer to reality at all stages of the viral outbreak compared to traditional methods. This model can be used far more effectively than current models to estimate the CFR during an outbreak, allowing for better planning. The model can also help us better understand the impact of individual interventions on the CFR. With much better data collection and labeling, we should be able to improve our predictive power even further. [ABSTRACT FROM AUTHOR]

In the present study we have characterized the biophysical properties of wild-type (WT) α1β2 and α3β2 GABAA receptors and probed the molecular basis for the observed differences. The activation and desensitization behavior and the residual currents of the receptors expressed in HEK293 cells were determined in whole-cell patch clamp recordings. Kinetic parameters of α1β2 and α3β2 activation differed significantly, with α1β2 and α3β2 exhibiting rise times (10–90%) of 24 ± 2 ms and 51 ± 7 ms, respectively. In contrast, the two receptors exhibited largely comparable desensitization behavior with decay currents that could be fitted to exponential functions with two or three components. Most notably, the two receptor compositions displayed different degrees of desentization, with the residual currents of α1β2 and α3β2 constituting 34 ± 2% and 21 ± 2% of the peak current, respectively. The respective contributions of the extracellular domains and the transmembrane/intracellular domains of the α-subunit to these physiological profiles were next assessed in recordings from cells expressing αβ2 receptors comprising chimeric α-subunits. The rise times displayed by α1ECD/α3TMDβ2 and α3ECD/α1TMDβ2 receptors were intermediate to those of WT α1β2 and WT α3β2, and the distribution of the different components of the current decays exhibited by the two chimeric receptors followed the same pattern as the two WT receptors. The residual current exhibited by α1ECD/α3TMDβ2 (23 ± 3%) was similar to that of α3β2 but significantly different from that of α1β2, whereas the residual current displayed by α3ECD/α1TMDβ2 (27 ± 2%) was intermediate to and did not differ significantly from either of the WT receptors. This points to molecular differences in the transmembrane/intracellular domains of the α-subunit as the main determinants of the observed differences in receptor physiology between α1β2 and α3β2 receptors. [ABSTRACT FROM AUTHOR]

Rationale: The RECIST guideline defines four categories of response to treatment for cancer patients according to post-baseline changes in tumor burden, hence ignoring disease history. However, if left untreated, tumors grow exponentially, implying that pretreatment changes in tumor size are key to thoroughly assess efficacy. We present a model-based approach to estimate the rates of changes in tumor mass, before and after treatment onset. Methods: Sixty-eight patients were eligible for the analysis of tumor size data from a Phase 1 study evaluating the effect of emactuzumab. In addition to tumor size measured at baseline and every six weeks during treatment, a pre-baseline measurement was gathered for each patient. A longitudinal regression model was used to estimate the rates of tumor size change before and after treatment onset. Results: The median pre-treatment tumor growth exponential rate was equal to 0.022 month-1, corresponding to a tumor size doubling time of 4 months, and the on-treatment median tumor shrinkage exponential rate was equal to 0.001 month-1. Among sixteen patients categorized as stable disease per RECIST, only five had similar slopes before and after treatment while nine actually improved. One patient in particular had a therapeutically induced stabilization of the disease. Conclusion: Our analysis emphasizes the importance of collecting pre-baseline scans to distinguish therapeutically induced stable disease from cases where the tumor growth is not perturbed by treatment. [ABSTRACT FROM AUTHOR]

We perform a statistical analysis for understanding the effect of the environmental temperature on the exponential growth rate of the cases infected by COVID-19 for US and Italian regions. In particular, we analyze the datasets of regional infected cases, derive the growth rates for regions characterized by a readable exponential growth phase in their evolution spread curve and plot them against the environmental temperatures averaged within the same regions, derive the relationship between temperature and growth rate, and evaluate its statistical confidence. The results clearly support the first reported statistically significant relationship of negative correlation between the average environmental temperature and exponential growth rates of the infected cases. The critical temperature, which eliminates the exponential growth, and thus the COVID-19 spread in US regions, is estimated to be TC = 86.1 ± 4.3 F0. [ABSTRACT FROM AUTHOR]

Introduction: Sputum specimen decontamination steps are essential due to the presence of other saprophytic and infectious organisms. However, they negatively affect the mycobacterial recovery. In addition, little is known about the Mycobacterium tuberculosis killing efficacy of the PANTA (polymyxin-B, amphotericin-B, nalidixic acid, trimethoprim, azilocillin) antibiotics. Moreover, M. tuberculosis can be present in more than one metabolic population, but the effect of different growth characteristics on the mycobacterial growth indicator tube (MGIT) based time-to-positive (TTP) is not well studied. Methods: We performed—(1) experiments using the solid agar and MGIT method to determine the effect of the NALC-NaOH decontamination method, (2) concentration-response studies with each individual antibiotic in the PANTA, and (3) the effect of the M. tuberculosis metabolic population on the TTP. TTP was recorded using the Epicenter software and exponential growth equation was used to calculate the doubling time of the bacteria, whereas, CFU/mL was analyzed using the Inhibitory Sigmoid Emax model for each antibiotic. Results: Decontamination resulted in 4.36+0.13 log10 CFU/mL difference in cultures treated with NALC-NaOH versus no decontamination process and the limit of detection decreased from 1.47 log10 CFU/mL to the 0.42 log10 CFU/mL following NALC-NaOH treatment. PANTA at currently used antibiotic concentrations, did not had negative effect on mycobacterial recovery. Exponential growth model estimated doubling time for the log-phase growth M. tuberculosis as 2.04 days, for the semi-dormant bacilli as 2.80 days, and 6.37 days for the anaerobic cultures. Conclusion: Specimen decontamination method negatively affect the laboratory diagnosis of M. tuberculosis, polymyxin-B and nalidixic acid have anti-tuberculosis efficacy at high concentrations, and the doubling time of different metabolic population should be considered when deciding the time-in-protocol for the MGIT system. [ABSTRACT FROM AUTHOR]

A method was developed and validated for low-level detection of glucose. The method involves quantitation of glucose though derivitization with 1-phenyl-3-methyl-5-pyrazolone (PMP) and HPLC-DAD analysis. The developed method was found to be accurate and robust achieving detection limits as low as 0.09 nM. The applicability of the method was tested against microbial samples with glucose acting as a carbon fuel source. The method was shown to be able to accurately discriminate and quantify PMP-glucose derivatives within Shewanella oneidensis MR-1 samples. The method proved capable at examining glucose usage during the early hours of microbial growth, with detectable usage occurring as early as two hours. S. oneidensis cultures were found to grow more effectively in the presence of oxygen which coincided with more efficient glucose usage. Glucose usage further increased in the presence of competing electron acceptors. The rate at which S. oneidensis reached exponential growth was affected by the presence of ferric iron under microaerobic conditions. Such samples reached exponential growth approximately two hours sooner than aerobic samples. [ABSTRACT FROM AUTHOR]

Bletilla striata is an endangered traditional Chinese medicinal plant with multiple uses and a slow regeneration rate of its germplasm resources. To evaluate the callus growth kinetics and accumulation of secondary metabolites (SMs), a callus suspension culture was proven to be a valuable approach for acquiring high yields of medicinal compounds. An effective callus suspension culture for obtaining B. striata callus growth and its SMs was achieved with the in vitro induction of calluses from B. striata seeds. The callus growth kinetics and accumulation of SMs were analyzed using a mathematical model. The resulting callus growth kinetic model revealed that the growth curves of B. striata suspension-cultured calluses were sigmoidal, indicating changes in the growth of the suspension-cultured calluses. Improved Murashige and Skoog callus growth medium was the most favorable medium for B. striata callus formation, with the highest callus growth occurring during the stationary phase of the cultivation period. Callus growth acceleration started after 7 days and thereafter gradually decreased until day 24 of the cultivation period and reached its highest at day 36 period in both the dry weight and fresh weight analyses. The coelonin concentration peaked during the exponential growth stage and decreased afterward during the stationary stage of the callus suspension culture. The maximum content of coelonin (approximately 0.3323 mg/g callus dry weight) was observed on the 18th day of the cultivation cycle, while dactylorhin A and militarine reached the highest concentrations at day 24, and p-hydroxybenzyl alcohol at day 39. This investigation also laid a foundation for a multimathematical model to better describe the accumulation variation of SMs. The production of SMs showed great specificity during callus growth and development. This research provided a well-organized way to increase the accumulation and production of SMs during the scaled-up biosynthesis of calluses in B. striata callus suspension cultures. [ABSTRACT FROM AUTHOR]

Aim: Current resuscitation guidelines recommend waveform capnography as an indirect indicator of perfusion during cardiopulmonary resuscitation (CPR). Chest compressions (CCs) and ventilations during CPR have opposing effects on the exhaled carbon dioxide (CO2) concentration, which need to be better characterized. The purpose of this study was to model the impact of ventilations in the exhaled CO2 measured from capnograms collected during out-of-hospital cardiac arrest (OHCA) resuscitation. Methods: We retrospectively analyzed OHCA monitor-defibrillator files with concurrent capnogram, compression depth, transthoracic impedance and ECG signals. Segments with CC pauses, two or more ventilations, and with no pulse-generating rhythm were selected. Thus, only ventilations should have caused the decrease in CO2 concentration. The variation in the exhaled CO2 concentration with each ventilation was modeled with an exponential decay function using non-linear-least-squares curve fitting. Results: Out of the original 1002 OHCA dataset (one per patient), 377 episodes had the required signals, and 196 segments from 96 patients met the inclusion criteria. Airway type was endotracheal tube in 64.8% of the segments, supraglottic King LT-D™ in 30.1%, and unknown in 5.1%. Median (IQR) decay factor of the exhaled CO2 concentration was 10.0% (7.8 − 12.9) with R2 = 0.98(0.95 − 0.99). Differences in decay factor with airway type were not statistically significant (p = 0.17). From these results, we propose a model for estimating the contribution of CCs to the end-tidal CO2 level between consecutive ventilations and for estimating the end-tidal CO2 variation as a function of ventilation rate. Conclusion: We have modeled the decrease in exhaled CO2 concentration with ventilations during chest compression pauses in CPR. This finding allowed us to hypothesize a mathematical model for explaining the effect of chest compressions on ETCO2 compensating for the influence of ventilation rate during CPR. However, further work is required to confirm the validity of this model during ongoing chest compressions. [ABSTRACT FROM AUTHOR]

If viruses or other pathogens infect a single host, the outcome of infection may depend on the initial basic reproduction number R0, the expected number of host cells infected by a single infected cell. This article shows that sometimes, phylogenetic models can estimate the initial R0, using only sequences sampled from the pathogenic population during its exponential growth or shortly thereafter. When evaluated by simulations mimicking the bursting viral reproduction of HIV and simultaneous sampling of HIV gp120 sequences during early viremia, the estimated R0 displayed useful accuracies in achievable experimental designs. Estimates of R0 have several potential applications to investigators interested in the progress of infection in single hosts, including: (1) timing a pathogen's movement through different microenvironments; (2) timing the change points in a pathogen's mode of spread (e.g., timing the change from cell-free spread to cell-to-cell spread, or vice versa, in an HIV infection); (3) quantifying the impact different initial microenvironments have on pathogens (e.g., in mucosal challenge with HIV, quantifying the impact that the presence or absence of mucosal infection has on R0); (4) quantifying subtle changes in infectability in therapeutic trials (either human or animal), even when therapies do not produce total sterilizing immunity; and (5) providing a variable predictive of the clinical efficacy of prophylactic therapies. [ABSTRACT FROM AUTHOR]

Previous research has consistently shown that for sounds varying in intensity over time, the beginning of the sound is of higher importance for the perception of loudness than later parts (primacy effect). However, in all previous studies, the target sounds were presented in quiet, and at a fixed average sound level. In the present study, temporal loudness weights for a time-varying narrowband noise were investigated in the presence of a continuous bandpass-filtered background noise and the average sound levels of the target stimuli were varied across a range of 60 dB. Pronounced primacy effects were observed in all conditions and there were no significant differences between the temporal weights observed in the conditions in quiet and in background noise. Within the conditions in background noise, there was a significant effect of the sound level on the pattern of weights, which was mainly caused by a slight trend for increased weights at the end of the sounds ("recency effect") in the condition with lower average level. No such effect was observed for the in-quiet conditions. Taken together, the observed primacy effect is largely independent of masking as well as of sound level. Compatible with this conclusion, the observed primacy effects in quiet and in background noise can be well described by an exponential decay function using parameters based on previous studies. Simulations using a model for the partial loudness of time-varying sounds in background noise showed that the model does not predict the observed temporal loudness weights. [ABSTRACT FROM AUTHOR]

Due to the rapid technological evolution and communications accessibility, data generated from different sources of information show an exponential growth behavior. That is, volume of data samples that need to be analyzed are getting larger, so the methods for its processing have to adapt to this condition, focusing mainly on ensuring the computation is efficient, especially when the analysis tools are based on computational intelligence techniques. As we know, if you do not have a good control of the handling of the volume of the data, some techniques that are based on learning iterative processes could represent an excessive load of computation and could take a prohibitive time in trying to find a solution that could not come close to desired. There are learning methods known as full batch, online and mini-batch, and they represent a good strategy to this problem since they are oriented to the processing of data according to the size or volume of available data samples that require analysis. In this first approach, synthetic datasets with a small and medium volume were used, since the main objective is to define its implementation and in experimentation phase through regression analysis obtain information that allows us to assess the performance and behavior of different learning methods under distinct conditions. To carry out this study, a Mamdani based neuro-fuzzy system with center-of-sets defuzzification with support of multiple inputs and outputs was designed and implemented that had the flexibility to use any of the three learning methods, which were implemented within the training process. Finally, results show that the learning method with best performances was Mini-Batch when compared to full batch and online learning methods. The results obtained by mini-batch learning method are as follows; mean correlation coefficient with 0.8268 and coefficient of determination with 0.7444, and is also the method with better control of the dispersion between the results obtained from the 30 experiments executed per each dataset processed. [ABSTRACT FROM AUTHOR]

As limited resources, soils are the largest terrestrial sinks of organic carbon. In this respect, 3D modelling of soil organic carbon (SOC) offers substantial improvements in the understanding and assessment of the spatial distribution of SOC stocks. Previous three-dimensional SOC modelling approaches usually averaged each depth increment for multi-layer two-dimensional predictions. Therefore, these models are limited in their vertical resolution and thus in the interpretability of the soil as a volume as well as in the accuracy of the SOC stock predictions. So far, only few approaches used spatially modelled depth functions for SOC predictions. This study implemented and evaluated an approach that compared polynomial, logarithmic and exponential depth functions using non-linear machine learning techniques, i.e. multivariate adaptive regression splines, random forests and support vector machines to quantify SOC stocks spatially and depth-related in the context of biodiversity and ecosystem functioning research. The legacy datasets used for modelling include profile data for SOC and bulk density (BD), sampled at five depth increments (0-5, 5-10, 10-20, 20-30, 30-50 cm). The samples were taken in an experimental forest in the Chinese subtropics as part of the biodiversity and ecosystem functioning (BEF) China experiment. Here we compared the depth functions by means of the results of the different machine learning approaches obtained based on multi-layer 2D models as well as 3D models. The main findings were (i) that 3rd degree polynomials provided the best results for SOC and BD (R2 = 0.99 and R2 = 0.98; RMSE = 0.36% and 0.07 g cm-3). However, they did not adequately describe the general asymptotic trend of SOC and BD. In this respect the exponential (SOC: R2 = 0.94; RMSE = 0.56%) and logarithmic (BD: R2 = 84; RMSE = 0.21 g cm-3) functions provided more reliable estimates. (ii) random forests with the exponential function for SOC correlated better with the corresponding 2.5D predictions (R2: 0.96 to 0.75), compared to the 3rd degree polynomials (R2: 0.89 to 0.15) which support vector machines fitted best. We recommend not to use polynomial functions with sparsely sampled profiles, as they have many turning points and tend to overfit the data on a given profile. This may limit the spatial prediction capacities. Instead, less adaptive functions with a higher degree of generalisation such as exponential and logarithmic functions should be used to spatially map sparse vertical soil profile datasets. We conclude that spatial prediction of SOC using exponential depth functions, in conjunction with random forests is well suited for 3D SOC stock modelling, and provides much finer vertical resolutions compared to 2.5D approaches. [ABSTRACT FROM AUTHOR]

Cells and organisms have developed homeostatic mechanisms which protect them against a changing environment. How growth and homeostasis interact is still not well understood, but of increasing interest to the molecular and synthetic biology community to recognize and design control circuits which can oppose the diluting effects of cell growth. In this paper we describe the performance of selected negative feedback controllers in response to different applied growth laws and time dependent outflow perturbations of a controlled variable. The approach taken here is based on deterministic mass action kinetics assuming that cell content is instantaneously mixed. All controllers behave ideal in the sense that they for step-wise perturbations in volume and a controlled compound A are able to drive A precisely back to the controllers’ theoretical set-points. The applied growth kinetics reflect experimentally observed growth laws, which range from surface to volume ratio growth to linear and exponential growth. Our results show that the kinetic implementation of integral control and the structure of the negative feedback loop are two properties which affect controller performance. Best performance is observed for controllers based on derepression kinetics and controllers with an autocatalytic implementation of integral control. Both are able to defend exponential growth and perturbations, although the autocatalytic controller shows an offset from its theoretical set-point. Controllers with activating signaling using zero-order or bimolecular (antithetic) kinetics for integral control behave very similar but less well. Their performance can be improved by implementing negative feedback structures having repression/derepression steps or by increasing controller aggressiveness. Our results provide a guide what type of feedback structures and integral control kinetics are suitable to oppose the dilution effects by different growth laws and time dependent perturbations on a deterministic level. [ABSTRACT FROM AUTHOR]

We present an analysis of queueing systems with the dropping function, infinite buffer and general distribution of the service time. Firstly, a stability condition, more general than the well-known ρ < 1, is proven. Secondly, the formulas for the queue size distribution, loss ratio and mean duration of the busy period, are derived. Thirdly, numerical examples are given, including optimizations of the shape of the dropping function with regard to the combined cost of the queue size and loss ratio. [ABSTRACT FROM AUTHOR]

Technology evolution describes a change in a technology performance over time. The modeling of technology evolution is crucial for designers, entrepreneurs, and government officials to set reasonable R&D targets, invest in promising technology, and develop effective incentive policies. Scientists and engineers have developed several mathematical functions such as logistic function and exponential function (Moore’s Law) to model technology evolution. However, these models focus on how a technology evolves in isolation and do not consider how the technology interacts with other technologies. Here, we extend the Lotka-Volterra equations from community ecology to model a technology ecosystem with system, component, and fundamental layers. We model the technology ecosystem of passenger aircraft using the Lotka-Volterra equations. The results show limited trickle-down effect in the technology ecosystem, where we refer to the impact from an upper layer technology to a lower layer technology as a trickle-down effect. The limited trickle-down effect suggests that the advance of the system technology (passenger aircraft) is not able to automatically promote the performance of the component technology (turbofan aero-engine) and the fundamental technology (engine blade superalloy) that constitute the system. Our research warns that it may not be effective to maintain the prosperity of a technology ecosystem through government incentives on system technologies only. Decision makers should consider supporting the innovations of key component or fundamental technologies. [ABSTRACT FROM AUTHOR]

The development of industrial agriculture has enabled a sharp increase in food trade at the global scale. Worldwide trade underpins food security by distributing food surpluses to food deficient countries. The study of agricultural product flows can provide insights on the complex interactions between exporting and importing countries and the resulting network structures. Commercial partnerships between countries can be modelled using a complex network approach. Based on the detailed trade matrices from FAO covering the period from 1986 to 2013, we present an analysis of the world cereal trade in terms of weighted and directed networks. The network nodes are the countries and the links are the trades of agricultural products in mass. We reveal the changing topology and degree distribution of the world network during the studied period. We distinguish three entangled subnetwork structures when considering the temporal stability of the trades. The three subnetworks display distinct properties and a differential contribution in total trade. Trades of uninterrupted activity over the 28-year study period compose the backbone network which accounts for two thirds of all traded mass and is scale-free. Inversely, two thirds of the trades only have one or two consecutive years of activity and define the transient subnetwork which displays random growth and accounts for very little traded mass. The trades of intermediate duration display an exponential growth both in numbers and in traded mass and define the intermediate subnetwork. The topology of each subnetwork is a time invariant. The identification of invariant structures is a useful basis for developing prospective agri-food network modelling to assess their resilience to perturbations and shocks. [ABSTRACT FROM AUTHOR]

Some strains of motile bacteria self-organize to form spatial patterns of high and low cell density over length scales that can be observed by eye. One such collective behavior is the formation in semisolid agar media of a high cell density swarm band. We isolated 7 wild strains of the Enterobacter cloacae complex capable of forming this band and found its propagation speed can vary 2.5 fold across strains. To connect such variability in collective motility to strain properties, each strain’s single-cell motility and exponential growth rates were measured. The band speed did not significantly correlate with any individual strain property; however, a multilinear analysis revealed that the band speed was set by a combination of the run speed and tumbling frequency. Comparison of variability in closely-related wild isolates has the potential to reveal how changes in single-cell properties influence the collective behavior of populations. [ABSTRACT FROM AUTHOR]

High Intensity Functional Training (HIFT) is a unique fitness method that promotes an active lifestyle and has seen exponential and continual growth over the last two decades. Motivation to exercise is likely to change over time as individuals' motives to initiate exercise may be different than those which motivate them to maintain an exercise program. The purpose of this study was to examine the motivational factors reported by individuals who actively engage in HIFT with varying length of participation and competition levels. 737 adults (32.4 ± 8.2 years) with more than three-months of HIFT experience completed an online version of the Exercise Motivation Inventory (EMI-2) survey. Those who had greater length of participation reported more motives associated with relatedness (i.e., affiliation, competition) and enjoyment, while those with less HIFT participation were more motivated by body-related variables (i.e., weight management). Further, motivational variables (e.g., social recognition, affiliation, challenge) varied depending on whether or not individuals had competed in an online qualifier. Understanding these differences in motivation may aid in exercise promotion, initiation, and adherence, and moreover promote long-term physical and mental health benefits. [ABSTRACT FROM AUTHOR]

Multiple studies have shown action to affect perception of motion. The speed intended in the generation of a motion by action affects the apparent speed of the motion. However, it was unclear whether action with no intention of speed affects the apparent speed of a motion. In Experiment 1, we investigated the apparent speed of a motion following a key press action. We manipulated the delay from the action to the consequent motion for shifting the timing of efference copy and found the apparent speed decreasing with increases in the delay. This could be because it is known that speed irrelevant action caused expansion of perceived duration of the consequent stimulus and it might have influenced the result in Experiment 1, we investigated the apparent duration of the action consequent static (Ex. 2-1) and motion (Ex. 2-2) stimulus. We found that the apparent duration was not changed with delay. Moreover, the apparent speed and duration had different characteristics on delay. These results were discussed in terms of the sense of agency. [ABSTRACT FROM AUTHOR]

Guide rails’ nonlinear dynamic response to the collision process between gangues and the suspended buffer is the basis for its intensity examination and fatigue prediction. This paper established a numerical model for simulating the dynamic response of buffer’s guide rails using Pro/E and Femap based on an analysis of the basic structural components of the suspended buffer. The effects of the gangues diameter and feeding rate on the guide rails’ dynamic response were investigated, which were mainly revealed by a tension in guide rails. Simulation results showed that the tension in guide rails rapidly increased to a peak value after the suspended buffer was impacted by gangues, followed by a periodic vibration. The peak tension, maximum and minimum tensions within the periodic vibration were exponential functions of the gangues diameter. And they depended linearly on the feeding rate. The vibration frequency of the tension in guide rails was an exponential function of the gangues diameter but did not depend on the feeding rate. Based on the backfilling workface parameters used in D.Ping coal miner’s 15601 working panel, the feeding rate of 500 t/h was selected. The diameter of the crushed gangues was selected to be 50 mm based on the strength of guide rails. Their expected service life is 54.2 years. Finally, an industrial test was performed in D.Ping coal miner’s 15601 working panel and the guide rails operated steadily. The measured vibration parameters of the tension in guide rails agree well with that of numerical predictions, which verified the reliability of the numerical model to some extent. [ABSTRACT FROM AUTHOR]

Saccades can either be elicited automatically by salient peripheral stimuli or can additionally depend on explicit cognitive goals. Similarly, it is thought that motor adaptation is driven by the combination of a more automatic, implicit process and a more explicit, cognitive process. However, the degree to which such implicit and explicit learning contribute to the adaptation of more reactive and voluntary saccades remains elusive. To study this question, we employed a global saccadic adaptation paradigm with both increasing and decreasing saccade amplitudes. We assessed the resulting adaptation using a dual state model of motor adaptation. This model decomposes learning into a fast and slow process, which are thought to constitute explicit and implicit learning, respectively. Our results show that adaptation of reactive saccades is equally driven by fast and slow learning, while fast learning is nearly absent when adapting voluntary (i.e. scanning) saccades. This pattern of results was present both when saccade gain was increased or decreased. Our results suggest that the increased cognitive demands associated with voluntary compared to reactive saccade planning interfere specifically with explicit learning. [ABSTRACT FROM AUTHOR]

There is a growing interest to understand financial markets as ecological systems, where the variety of trading strategies correspond to that of biological species. For this purpose, transaction data for individual traders are studied recently as empirical analyses. However, there are few empirical studies addressing how traders submit limit and market order at the level of individual traders. Since limit and market orders are key ingredients finally leading to transactions, it would be necessary to understand what kind of strategies are actually employed among traders before making transactions. Here we demonstrate the variety of limit-order and market-order strategies and show their roles in the financial markets from an ecological perspective. We find these trading strategies can be well-characterized by their response pattern to historical price changes. By applying a clustering analysis, we provide an overall picture of trading strategies as an ecological matrix, illustrating that liquidity consumers are likely to exhibit high trading performances compared with liquidity providers. Furthermore, we reveal both high-frequency traders (HFTs) and low-frequency traders (LFTs) exhibit high trading performance, despite the difference in their trading styles; HFTs attempt to maximize their trading efficiency by reducing risk, whereas LFTs make their profit by taking risk. [ABSTRACT FROM AUTHOR]

Objectives: The aims of this study were to investigate (1) whether and (2) the extent to which Taiwanese older adults’ leisure time activity (LTA) trajectories mediated the potential association between their sociodemographic factors and their functional disability trajectories. Methods: Longitudinal data from four waves of the Taiwan Longitudinal Study on Aging (TLSA), collected between 1996 and 2007, were used for analysis (N = 3,429). Parallel-process latent growth curve modeling was adopted to evaluate the process by which LTA mediated between sociodemographic factors (age, gender, education, self-rated health, comorbidities, and depression) and the outcome process of functional disabilities. Results: When mediated by baseline level of LTA, five sociodemographic factors—age, gender, education level, self-rated health, and number of comorbidities—had significant and negative mediating effects on baseline or change in functional disability, thus improving disability outcomes. However, four of the sociodemographic factors (age, education level, and number of comorbidities), when mediated through the rate of change in LTA, were found to have significant and positive mediating effects, which increased disability levels. The proportion of effects mediated by the LTA trajectory ranged from 0% to 194%. Discussion: The large proportion of effects mediated through the LTA process underlines the importance of LTA to public health policy and health programs for older adults. The study’s findings shed light on how to better target populations of older adults to promote an active lifestyle and achieve more successful aging in late life in Asian countries. [ABSTRACT FROM AUTHOR]

This paper presents a method for predicting the total nitrogen content in sandalwood using digital image processing. The goal of this study is to provide a real-time, efficient, and highly automated nutritional diagnosis system for producers by analyzing images obtained in forests. Using images acquired from field servers, which were installed in six forest farms of different cities located in northern Hainan Province, we propose a new segmentation algorithm and define a new indicator named “growth status" (GS), which includes two varieties: GSMER (the ratio of sandalwood pixels to the minimum enclosing rectangle pixels) and GSMCC (the ratio of sandalwood pixels to minimum circumscribed circle pixels). We used the error-in-variable model by considering the errors that exist in independent variables. After comparison and analysis, the obtained results show that (1) The b and L channels in the Lab color system have complementary advantages. By combining this system with the Otsu method, median filtering and a morphological operation, sandalwood can be separated from the background. (2) The fitting degree of the models improves after adding the GS indicator and shows that GSMCC performs better than GSMER. (3) After using the error-in-variable model to estimate the parameters, the accuracy and precision of the model improved compared to the results obtained using the least squares method. The optimal model for predicting the total nitrogen content is . This study demonstrates the use of Internet of Things technology in forestry and provides guidance for the nutritional diagnosis of the important sandalwood tree species. [ABSTRACT FROM AUTHOR]

Food flows underpin the complex food supply chains that are prevalent in our increasingly globalized world. Recently, much effort has been devoted to evaluating the resources (e.g. water, carbon, nutrients) embodied in food trade. Now, research is needed to understand the scientific principles of the food commodity flows that underpin these virtual resource transfers. How do food flows vary with spatial scale? To address this question, we present an empirical analysis of food commodity flow networks across the full spectrum of spatial scales: global, national, and village. We discover properties of both scale invariance and scale dependence in food flow networks. The statistical distribution of node connectivity and mass flux are consistent across scales. Node connectivity follows a generalized exponential distribution, while node mass flux follows a Gamma distribution across scales. Similarly, the relationship between node connectivity and mass flux follows a power law across scales. However, the parameters of the distributions change with spatial scale. Mean node connectivity and mass flux increase with increasing scale. A core group of nodes exists at all scales, but node centrality increases as the spatial scale decreases, indicating that some households are more critical to village food exchanges than countries are to global trade. Remarkably, the structural network properties of food flows are consistent across spatial scales, indicating that a universal mechanism may underpin food exchange systems. In future research, this understanding can be used to develop theoretical models of food flow networks and to model food flows at resolutions for which empirical information is not available. [ABSTRACT FROM AUTHOR]

Research on temporal characteristics of human dynamics has attracted much attentions for its contribution to various areas such as communication, medical treatment, finance, etc. Existing studies show that the time intervals between two consecutive events present different non-Poisson characteristics, such as power-law, Pareto, bimodal distribution of power-law, exponential distribution, piecewise power-law, et al. With the occurrences of new services, new types of distributions may arise. In this paper, we study the distributions of the time intervals between two consecutive visits to QQ and WeChat service, the top two popular instant messaging services in China, and present a new finding that when the value of statistical unit T is set to 0.001s, the inter-event time distribution follows a piecewise distribution of exponential and power-law, indicating the heterogeneous character of IM services users’ online behavior in different time scales. We infer that the heterogeneous character is related to the communication mechanism of IM and the habits of users. Then we develop a combination model of exponential model and interest model to characterize the heterogeneity. Furthermore, we find that the exponent of the inter-event time distribution of the same service is different in two cities, which is correlated with the popularity of the services. Our research is useful for the application of information diffusion, prediction of economic development of cities, and so on. [ABSTRACT FROM AUTHOR]

The membrane potential helps determine pulmonary artery smooth muscle cell (PASMC) contraction. The Kv7 channel activators, retigabine and flupirtine, are thought to dilate pulmonary arteries by hyperpolarising PASMC. Zinc pyrithione activates Kv7 channels by a mechanism distinct from retigabine and with different Kv7 subunit selectivity. This study aimed to determine if zinc pyrithione selectively activates Kv7 channels in rat PASMC to evoke pulmonary artery dilation. Zinc pyrithione relaxed pulmonary arteries with half-maximal effect at 4.3μM. At 10μM it activated pronounced voltage-dependent K+ current and hyperpolarized PASMCs by around 10mV. Tetraethylammonium ions (TEA, 10mM) and paxilline (1μM) abolished both the current and hyperpolarisation. XE991 (10μM) blocked the hyperpolarization and reduced the current by 30%. Iberiotoxin (50nM) had no effect on the hyperpolarisation, but reduced the current by 40%. The XE991-sensitive current activated with an exponential time course (time constant 17ms), whereas the iberiotoxin-sensitive current followed a bi-exponential time course (time constants 6 and 57ms), suggesting that the drugs blocked different components of the zinc pyrithione-induced current. Zinc pyrithione therefore appears to activate at least two types of K+ channel in PASMC; an XE991, TEA and paxilline-sensitive Kv7 channel and a TEA, paxilline and iberiotoxin-sensitive BKCa channel. Both could contribute to the relaxing effect of zinc pyrithione on pulmonary artery. [ABSTRACT FROM AUTHOR]

Conducting a Reliability Demonstration Test (RDT) is a crucial step in production. Products are tested under certain schemes to demonstrate whether their reliability indices reach pre-specified thresholds. Test schemes for RDT have been studied in different situations, e.g., lifetime testing, degradation testing and accelerated testing. Systems designed with several structures are also investigated in many RDT plans. Despite the availability of a range of test plans for different systems, RDT planning for load-sharing systems hasn’t yet received the attention it deserves. In this paper, we propose a demonstration method for two specific types of load-sharing systems with components subject to two distributions: exponential and Weibull. Based on the assumptions and interpretations made in several previous works on such load-sharing systems, we set the mean time to failure (MTTF) of the total system as the demonstration target. We represent the MTTF as a summation of mean time between successive component failures. Next, we introduce generalized test statistics for both the underlying distributions. Finally, RDT plans for the two types of systems are established on the basis of these test statistics. [ABSTRACT FROM AUTHOR]

In the context of limited marine resources, the exponential growth of aquaculture requires the substitution of fish oil and fishmeal, the traditional components of fish feeds by terrestrial plant ingredients. High levels of such substitution are known to negatively impact fish performance such as growth and survival in rainbow trout (Oncorhynchus mykiss) as in other salmonids. In this respect, genetic selection is a key enabler for improving those performances and hence for the further sustainable development of aquaculture. We selected a rainbow trout line over three generations for its ability to survive and grow on a 100% plant-based diet devoid of both fish oil and fishmeal (V diet) from the very first meal. In the present study, we compared the control line and the selected line after 3 generations of selection, both fed either the V diet or a marine resources-based diet (M diet). The objective of the study was to assess the efficiency of selection and the consequences on various correlated nutritional traits: feed intake, feed efficiency, digestibility, composition of whole fish, nutrient retention and fatty acid (FA) profile. We demonstrated that the genetic variability present in our rainbow trout population can be selected to improve survival and growth. The major result of the study is that after only three generations of selection, selected fish fed the V diet grew at the same rate as the control line fed the M diet, whilst the relative reduction of body weight was 36.8% before the selection. This enhanced performance on the V diet seems to be mostly linked to a higher feed intake for the selected fish. [ABSTRACT FROM AUTHOR]