Ancestral sequence reconstructions evaluated by extant sequence cross-validation

Ancestral sequence reconstruction (ASR) has become widely used to analyze the properties of ancient biomolecules and to elucidate the mechanisms of molecular evolution. By recapitulating the structural, mechanistic, and functional changes of proteins during their evolution, ASR has been able to address many fundamental and challenging evolutionary questions where more traditional methods have failed. Despite the tangible successes of ASR, the accuracy of its reconstructions is currently unknown, because it is generally impossible to compare resurrected proteins to the true ancient ancestors that are now extinct. Which evolutionary models are the best for ASR? How accurate are the resulting inferences? Here we answer these questions by applying cross-validation (CV) to sets of aligned extant sequences. To assess the adequacy of a chosen evolutionary model for predicting extant sequence data, our column-wise CV method iteratively cross-validates each column in an alignment. Unlike other phylogenetic model selection criteria, this method does not require bias correction and does not make restrictive assumptions commonly violated by phylogenetic data. We find that column-wise CV generally provides a more conservative criterion than the AIC by preferring less complex models. To validate ASR methods, we also apply cross-validation to each sequence in an alignment by reconstructing the extant sequences using ASR methodology, a method we term extant sequence reconstruction (ESR). We can thus quantify the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability of the sequence, is indeed a good estimate of the fraction of the sequence that is correct when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions, because more accurate phylogenetic models typically result in reconstructions with lower average probabilities. In contrast, the entropy of the reconstructed distribution is a reliable indicator of the quality of a reconstruction, as the entropy provides an accurate estimate of the log-probability of the true sequence. Both column-wise CV and ESR are useful methods to validate evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences.

The Assessment, Management and Prevention of Calf Muscle Strain Injuries: A Qualitative Study of the Practices and Perspectives of 20 Expert Sports Clinicians

Background Despite calf muscle strain injuries (CMSI) being problematic in many sports, there is a dearth of research to guide clinicians dealing with these injuries. The aim of this study was to evaluate the current practices and perspectives of a select group of international experts regarding the assessment, management and prevention of CMSI using in-depth semi-structured interviews. Results Twenty expert clinicians working in elite sport and/or clinician-researchers specialising in the field completed interviews. A number of key points emerged from the interviews. Characteristics of CMSI were considered unique compared to other muscle strains. Rigor in the clinical approach clarifies the diagnosis, whereas ongoing monitoring of calf capacity and responses to loading exposure provides the most accurate estimate of prognosis. Athlete intrinsic characteristics, injury factors and sport demands shaped rehabilitation across six management phases, which were guided by key principles to optimise performance at return to play (RTP) while avoiding subsequent injury or recurrence. To prevent CMSI, periodic monitoring is common, but practices vary and data are collected to inform load-management and exercise selection rather than predict future CMSI. A universal injury prevention program for CMSI may not exist. Instead, individualised strategies should reflect athlete intrinsic characteristics and sport demands. Conclusions Information provided by experts enabled a recommended approach to clinically evaluate CMSI to be outlined, highlighting the injury characteristics considered most important for diagnosis and prognosis. Principles for optimal management after CMSI were also identified, which involved a systematic approach to rehabilitation and the RTP decision. Although CMSI were reportedly difficult to prevent, on- and off-field strategies were implemented by experts to mitigate risk, particularly in susceptible athletes.

Gas-Solid Heat Transfer Computation from Particle-Resolved Direct Numerical Simulations

Particle-Resolved simulations (PR-DNS) have been conducted using a second order implicit Viscous Penalty Method (VPM) to study the heat transfer between a set of particles and an incompressible carrier fluid. A Lagrange extrapolation coupled to a Taylor interpolation of a high order is utilized to the accurate estimate of heat transfer coefficients on an isolated sphere, a fixed Faced-Centered Cubic array of spheres, and a random pack of spheres. The simulated heat transfer coefficients are compared with success to various existing Nusselt laws of the literature.

Restaurant Sales Prediction Using Machine Learning

In general, the revenue forecast, offer information, and the weather gauge setting will record an accurate estimate of any restaurant's future revenue. The turnover is significantly focused on the need of the customers. Either way, the performance has transformed over the past couple of years with the presentation of huge amounts of information and calculations during the time taken to gain the upper hand. It is fundamental to learn and understand the importance of the information that will be used in any business process. Again, climate forecasting can be done alongside business expectations with the organization.

Development of a Hydrodynamic-Based Flood-Risk Management Tool for Assessing Redistribution of Expected Annual Damages in a Floodplain

Despite spending ample resources and procedural development in flood management, flood losses are still increasing worldwide. The losses caused by floods and costs incurred on management are two components of expected annual damages (EAD) due to floods. This study introduces a generalized approach for risk-based design where a range of probable floods are considered before and after a flood mitigation measure is implemented. The proposed approach is customized from the ISO Guide 31000 along with additional advantages of flood risk visualization. A Geographic Information System (GIS)-based design of a flood-protection dike is performed to exhibit the risk redistribution. The Chenab River is selected for the existing dike system. Detailed hazard behaviour and societal vulnerability are modelled and visualized for a range of all probable floods before and after the implementation of flood-protection dikes. EAD maps demonstrate the redistribution of induced and residual risks. It can be concluded that GIS-based EAD maps not only facilitate cost-effective solutions but also provide an accurate estimate of residual risks after the mitigation measures are applied. EAD maps also indicate the high-risk areas to facilitate designing secondary measures.

Serial measurement of cytokines strongly predict COVID-19 outcome

Purpose Cytokines are major mediators of COVID-19 pathogenesis and several of them are already being regarded as predictive markers for the clinical course and outcome of COVID-19 cases. A major pitfall of many COVID-19 cytokine studies is the lack of a benchmark sampling timing. Since cytokines and their relative change during an infectious disease course is quite dynamic, we evaluated the predictive value of serially measured cytokines for COVID-19 cases. Methods In this single-center, prospective study, a broad spectrum of cytokines were determined by multiplex ELISA assay in samples collected at admission and at the third day of hospitalization. Appropriateness of cytokine levels in predicting mortality were assessed by receiver-operating characteristic (ROC) analyses for both sampling times in paralel to conventional biomarkers. Results At both sampling points, higher levels of IL-6, IL-7, IL-10, IL-15, IL-27 IP-10, MCP-1, and GCSF were found to be more predictive for mortality (p<0.05). Some of these cytokines, such as IL-6, IL-10, IL-7 and GCSF, had higher sensitivity and specificity in predicting mortality. AUC values of IL-6, IL-10, IL-7 and GCSF were 0.85 (0.65 to 0.92), 0.88 (0.73 to 0.96), 0.80 (0.63 to 0.91) and 0.86 (0.70 to 0.95), respectively at hospital admission. Compared to hospital admission, on the 3rd day of hospitalization serum levels of IL-6 and, IL-10 decreased significantly in the survivor group, unlike the non-survivor group (IL-6, p = 0.015, and IL-10, p = 0.016). Conclusion Our study results suggest that single-sample-based cytokine analyzes can be misleading and that cytokine levels measured serially at different sampling times provide a more precise and accurate estimate for the outcome of COVID-19 patients.

The Panchromatic Afterglow of GW170817: The Full Uniform Data Set, Modeling, Comparison with Previous Results, and Implications

We present the full panchromatic afterglow light-curve data of GW170817, including new radio data as well as archival optical and X-ray data, between 0.5 and 940 days post-merger. By compiling all archival data and reprocessing a subset of it, we have evaluated the impact of differences in data processing or flux determination methods used by different groups and attempted to mitigate these differences to provide a more uniform data set. Simple power-law fits to the uniform afterglow light curve indicate a t 0.86±0.04 rise, a t −1.92±0.12 decline, and a peak occurring at 155 ± 4 days. The afterglow is optically thin throughout its evolution, consistent with a single spectral index (−0.584 ± 0.002) across all epochs. This gives a precise and updated estimate of the electron power-law index, p = 2.168 ± 0.004. By studying the diffuse X-ray emission from the host galaxy, we place a conservative upper limit on the hot ionized interstellar medium density, <0.01 cm−3, consistent with previous afterglow studies. Using the late-time afterglow data we rule out any long-lived neutron star remnant having a magnetic field strength between 1010.4 and 1016 G. Our fits to the afterglow data using an analytical model that includes Very Long Baseline Interferometry proper motion from Mooley et al., and a structured jet model that ignores the proper motion, indicates that the proper-motion measurement needs to be considered when seeking an accurate estimate of the viewing angle.

A Forward-Backward Iterative Procedure for Improving the Resolution of Resonant Microwave Sensors

This paper sets out a method for improving the resolution of resonant microwave sensors. Usually, the frequency response of these devices is associated with a low quality factor, and consequently with a low resolution in terms of tracking capacity of the resonance frequency shift. Furthermore, since only a finite number of samples can be acquired during the measurement process, the “true” resonance frequency may not be included in the set of acquired data. In order to have an accurate estimate of the resonance frequency, high performance systems with very fine frequency sampling are thus required. To limit these drawbacks, an iterative algorithm is presented which aims to refine the response of resonant microwave sensors by means of a suitable post-processing. The algorithm evaluation is first carried out on synthetic data, and then applied on experimental data referring to a practical scenario, which is inherent to return loss measurements performed by a microwave patch antenna immersed in a water-glucose solution with different concentrations.

Hydrothermal plumes as hotspots for deep-ocean heterotrophic microbial biomass production

Carbon budgets of hydrothermal plumes result from the balance between carbon sinks through plume chemoautotrophic processes and carbon release via microbial respiration. However, the lack of comprehensive analysis of the metabolic processes and biomass production rates hinders an accurate estimate of their contribution to the deep ocean carbon cycle. Here, we use a biogeochemical model to estimate the autotrophic and heterotrophic production rates of microbial communities in hydrothermal plumes and validate it with in situ data. We show how substrate limitation might prevent net chemolithoautotrophic production in hydrothermal plumes. Elevated prokaryotic heterotrophic production rates (up to 0.9 gCm−2y−1) compared to the surrounding seawater could lead to 0.05 GtCy−1 of C-biomass produced through chemoorganotrophy within hydrothermal plumes, similar to the Particulate Organic Carbon (POC) export fluxes reported in the deep ocean. We conclude that hydrothermal plumes must be accounted for as significant deep sources of POC in ocean carbon budgets.

Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields

<p>In order to carry out assessment of marine stock levels, an accurate estimate of the current year's population abundance must be formulated. Standardized catch per unit of effort (CPUE) values are, in theory, proportional to population abundance. However, this only holds if the species catchability is constant over time. In almost all cases it is not, due to the existence of spatial and temporal variation. In this thesis, we fit various models to test different combinations and structures of spatial and temporal autocorrelation within hoki (Macruronus novaezelandiae) CPUE. A Bayesian approach was taken, and the spatial and temporal components were modelled using Gaussian Markov random fields. The data was collected from summer research trawl surveys carried out by the National Institute of Water and Atmospheric Research (NIWA) and the Ministry for Primary Industries (MPI). It allowed us to model spatial distribution using both areal and point reference approaches. To fit the models, we used the software Stan (Gelman et al., 2015) which implements Hamiltonian Monte Carlo. Model comparison was carried out using the Watanabe-Akaike information criterion (WAIC, (Watanabe, 2010)). We found that trawl year was the most important factor to explain variation in research survey hoki CPUE. Furthermore, the areal approach provided better indices of abundance than the point reference approach.</p>