Forecast accuracy hardly improves with method complexity when completing cohort fertility.

Forecasts of completed fertility predict how many children will be born on average by women over their entire reproductive lifetime. These forecasts are important in informing public policy and influencing additional research in the social sciences. However, nothing is known about how to choose a forecasting method from a large basket of variants. We identified 20 major methods, with 162 variants altogether. The approaches range from naive freezing of current age-specific fertility rates to methods that use statistically sophisticated techniques or are grounded in demographic theory. We assess each method by evaluating the overall accuracy and if provided, uncertainty estimates using fertility data of all available birth cohorts and countries of the Human Fertility Database, which covers 1,096 birth cohorts from 29 countries. Across multiple measures of forecast accuracy, we find only four methods that consistently outperform the naive freeze rates method, and only two methods produce uncertainty estimates that are not severely downward biased. Among the top four, there are two simple extrapolation methods and two Bayesian methods. The latter are demanding in terms of input data, statistical techniques, and computational power but do not consistently complete cohort fertility more accurately at all truncation ages than simple extrapolation. This broad picture is unchanged if we base the validation on 201 United Nations countries and six world regions, including Africa, Asia, Europe, Latin America and the Caribbean, northern America, and Oceania.

Anesthesia and Circulating Tumor Cells in Primary Breast Cancer Patients: A Randomized Controlled Trial.

BACKGROUND: The effect of anesthetic drugs on cancer outcomes remains unclear. This trial aimed to assess postoperative circulating tumor cell counts-an independent prognostic factor for breast cancer-to determine how anesthesia may indirectly affect prognosis. It was hypothesized that patients receiving sevoflurane would have higher postoperative tumor cell counts. METHODS: The parallel, randomized controlled trial was conducted in two centers in Switzerland. Patients aged 18 to 85 yr without metastases and scheduled for primary breast cancer surgery were eligible. The patients were randomly assigned to either sevoflurane or propofol anesthesia. The patients and outcome assessors were blinded. The primary outcome was circulating tumor cell counts over time, assessed at three time points postoperatively (0, 48, and 72 h) by the CellSearch assay. Secondary outcomes included maximal circulating tumor cells value, positivity (cutoff: at least 1 and at least 5 tumor cells/7.5 ml blood), and the association between natural killer cell activity and tumor cell counts. This trial was registered with ClinicalTrials.gov (NCT02005770). RESULTS: Between March 2014 and April 2018, 210 participants were enrolled, assigned to sevoflurane (n = 107) or propofol (n = 103) anesthesia, and eventually included in the analysis. Anesthesia type did not affect circulating tumor cell counts over time (median circulating tumor cell count [interquartile range]; for propofol: 1 [0 to 4] at 0 h, 1 [0 to 2] at 48 h, and 0 [0 to 1] at 72 h; and for sevoflurane: 1 [0 to 4] at 0 h, 0 [0 to 2] at 48 h, and 1 [0 to 2] at 72 h; rate ratio, 1.27 [95% CI, 0.95 to 1.71]; P = 0.103) or positivity. In one secondary analysis, administrating sevoflurane led to a significant increase in maximal tumor cell counts postoperatively. There was no association between natural killer cell activity and circulating tumor cell counts. CONCLUSIONS: In this randomized controlled trial investigating the effect of anesthesia on an independent prognostic factor for breast cancer, there was no difference between sevoflurane and propofol with respect to circulating tumor cell counts over time.

Curvature of designed armadillo repeat proteins allows modular peptide binding.

Designed armadillo repeat proteins (dArmRPs) were developed to create a modular peptide binding technology where each of the structural repeats binds two residues of the target peptide. An essential prerequisite for such a technology is a dArmRP geometry that matches the peptide bond length. To this end, we determined a large set (n=27) of dArmRP X-ray structures, of which 12 were previously unpublished, to calculate curvature parameters that define their geometry. Our analysis shows that consensus dArmRPs exhibit curvatures close to the optimal range for modular peptide recognition. Binding of peptide ligands can induce a curvature within the desired range, as confirmed by single-molecule FRET experiments in solution. On the other hand, computationally designed ArmRPs, where side chains have been chosen with the intention to optimally fit into a geometrically optimized backbone, turned out to be more divergent in reality, and thus not suitable for continuous peptide binding. Furthermore, we show that the formation of a crystal lattice can induce small but significant deviations from the curvature adopted in solution, which can interfere with the evaluation of repeat protein scaffolds when high accuracy is required. This study corroborates the suitability of consensus dArmRPs as a scaffold for the development of modular peptide binders.

Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts.

Evaluating the predictive ability of mortality forecasts is important yet difficult. Death rates and mean lifespan are basic life table functions typically used to analyze to what extent the forecasts deviate from their realized values. Although these parameters are useful for specifying precisely how mortality has been forecasted, they cannot be used to assess whether the underlying mortality developments are plausible. We therefore propose that in addition to looking at average lifespan, we should examine whether the forecasted variability of the age at death is a plausible continuation of past trends. The validation of mortality forecasts for Italy, Japan, and Denmark demonstrates that their predictive performance can be evaluated more comprehensively by analyzing both the average lifespan and lifespan disparity-that is, by jointly analyzing the mean and the dispersion of mortality. Approaches that account for dynamic age shifts in survival improvements appear to perform better than others that enforce relatively invariant patterns. However, because forecasting approaches are designed to capture trends in average mortality, we argue that studying lifespan disparity may also help to improve the methodology and thus the predictive ability of mortality forecasts.

Surface Acoustic Wave (SAW) Resonators for Monitoring Conditioning Film Formation.

We propose surface acoustic wave (SAW) resonators as a complementary tool for conditioning film monitoring. Conditioning films are formed by adsorption of inorganic and organic substances on a substrate the moment this substrate comes into contact with a liquid phase. In the case of implant insertion, for instance, initial protein adsorption is required to start wound healing, but it will also trigger immune reactions leading to inflammatory responses. The control of the initial protein adsorption would allow to promote the healing process and to suppress adverse immune reactions. Methods to investigate these adsorption processes are available, but it remains difficult to translate measurement results into actual protein binding events. Biosensor transducers allow user-friendly investigation of protein adsorption on different surfaces. The combination of several transduction principles leads to complementary results, allowing a more comprehensive characterization of the adsorbing layer. We introduce SAW resonators as a novel complementary tool for time-resolved conditioning film monitoring. SAW resonators were coated with polymers. The adsorption of the plasma proteins human serum albumin (HSA) and fibrinogen onto the polymer-coated surfaces were monitored. Frequency results were compared with quartz crystal microbalance (QCM) sensor measurements, which confirmed the suitability of the SAW resonators for this application.

A demographic scaling model for estimating the total number of COVID-19 infections.

BACKGROUND: Understanding how widely COVID-19 has spread is critical information for monitoring the pandemic. The actual number of infections potentially exceeds the number of confirmed cases. DEVELOPMENT: We develop a demographic scaling model to estimate COVID-19 infections, based on minimal data requirements: COVID-19-related deaths, infection fatality rates (IFRs), and life tables. As many countries lack IFR estimates, we scale them from a reference country based on remaining lifetime to better match the context in a target population with respect to age structure, health conditions and medical services. We introduce formulas to account for bias in input data and provide a heuristic to assess whether local seroprevalence estimates are representative for the total population. APPLICATION: Across 10 countries with most reported COVID-19 deaths as of 23 July 2020, the number of infections is estimated to be three [95% prediction interval: 2-8] times the number of confirmed cases. Cross-country variation is high. The estimated number of infections is 5.3 million for the USA, 1.8 million for the UK, 1.4 million for France, and 0.4 million for Peru, or more than one, six, seven and more than one times the number of confirmed cases, respectively. Our central prevalence estimates for entire countries are markedly lower than most others based on local seroprevalence studies. CONCLUSIONS: The national infection estimates indicate that the pandemic is far more widespread than the numbers of confirmed cases suggest. Some local seroprevalence estimates largely deviate from their corresponding national mean and are unlikely to be representative for the total population.

Rigid fusions of designed helical repeat binding proteins efficiently protect a binding surface from crystal contacts.

Designed armadillo repeat proteins (dArmRPs) bind extended peptides in a modular way. The consensus version recognises alternating arginines and lysines, with one dipeptide per repeat. For generating new binding specificities, the rapid and robust analysis by crystallography is key. Yet, we have previously found that crystal contacts can strongly influence this analysis, by displacing the peptide and potentially distorting the overall geometry of the scaffold. Therefore, we now used protein design to minimise these effects and expand the previously described concept of shared helices to rigidly connect dArmRPs and designed ankyrin repeat proteins (DARPins), which serve as a crystallisation chaperone. To shield the peptide-binding surface from crystal contacts, we rigidly fused two DARPins to the N- and C-terminal repeat of the dArmRP and linked the two DARPins by a disulfide bond. In this ring-like structure, peptide binding, on the inside of the ring, is very regular and undistorted, highlighting the truly modular binding mode. Thus, protein design was utilised to construct a well crystallising scaffold that prevents interference from crystal contacts with peptide binding and maintains the equilibrium structure of the dArmRP. Rigid DARPin-dArmRPs fusions will also be useful when chimeric binding proteins with predefined geometries are required.

Probabilistic mortality forecasting with varying age-specific survival improvements.

Many mortality forecasting approaches extrapolate past trends. Their predictions of the future development can be quite precise as long as turning points and/or age-shifts of mortality decline are not present. To account even for such mortality dynamics, we propose a model that combines recently developed ideas in a single framework. It (1) uses rates of mortality improvement to model the aging of mortality decline, and it (2) optionally combines the mortality trends of multiple countries to catch anticipated turning points. We use simulation-based Bayesian inference to estimate and run this model that also provides prediction intervals to quantify forecast uncertainty. Validating mortality forecasts for British and Danish women from 1991 to 2011 suggest that our model can forecast regular and irregular mortality developments and that it can perform at least as well as other widely accepted approaches like, for instance, the Lee-Carter model or the UN Bayesian approach. Moreover, prospective mortality forecasts from 2012 to 2050 suggest gradual increases for British and Danish life expectancy at birth.

Computationally Designed Armadillo Repeat Proteins for Modular Peptide Recognition.

Armadillo repeat proteins (ArmRPs) recognize their target peptide in extended conformation and bind, in a first approximation, two residues per repeat. Thus, they may form the basis for building a modular system, in which each repeat is complementary to a piece of the target peptide. Accordingly, preselected repeats could be assembled into specific binding proteins on demand and thereby avoid the traditional generation of every new binding molecule by an independent selection from a library. Stacked armadillo repeats, each consisting of 42 aa arranged in three α-helices, build an elongated superhelical structure. Here, we analyzed the curvature variations in natural ArmRPs and identified a repeat pair from yeast importin-α as having the optimal curvature geometry that is complementary to a peptide over its whole length. We employed a symmetric in silico design to obtain a uniform sequence for a stackable repeat while maintaining the desired curvature geometry. Computationally designed ArmRPs (dArmRPs) had to be stabilized by mutations to remove regions of higher flexibility, which were identified by molecular dynamics simulations in explicit solvent. Using an N-capping repeat from the consensus-design approach, two different crystal structures of dArmRP were determined. Although the experimental structures of dArmRP deviated from the designed curvature, the insertion of the most conserved binding pockets of natural ArmRPs onto the surface of dArmRPs resulted in binders against the expected peptide with low nanomolar affinities, similar to the binders from the consensus-design series.

A combined NMR and computational approach to investigate peptide binding to a designed Armadillo repeat protein.

The specific recognition of peptide sequences by proteins plays an important role both in biology and in diagnostic applications. Here we characterize the relatively weak binding of the peptide neurotensin (NT) to the previously developed Armadillo repeat protein VG_328 by a multidisciplinary approach based on solution NMR spectroscopy, mutational studies, and molecular dynamics (MD) simulations, totaling 20µs for all MD runs. We describe assignment challenges arising from the repetitive nature of the protein sequence, and we present novel approaches to address them. Partial assignments obtained for VG_328 in combination with chemical shift perturbations allowed us to identify the repeats not involved in binding. Their subsequent elimination resulted in a reduced-size binder with very similar affinity for NT, for which near-complete backbone assignments were achieved. A binding mode suggested by automatic docking and further validated by explicit solvent MD simulations is consistent with paramagnetic relaxation enhancement data collected using spin-labeled NT. Favorable intermolecular interactions are observed in the MD simulations for the residues that were previously shown to contribute to binding in an Ala scan of NT. We further characterized the role of residues within the N-cap for protein stability and peptide binding. Our multidisciplinary approach demonstrates that an initial low-resolution picture for a low-micromolar-peptide binder can be refined through the combination of NMR, protein design, docking, and MD simulations to establish its binding mode, even in the absence of crystallographic data, thereby providing valuable information for further design.

Variants of a Thermus aquaticus DNA polymerase with increased selectivity for applications in allele- and methylation-specific amplification.

The selectivity of DNA polymerases is crucial for many applications. For example, high discrimination between the extension of matched versus mismatched primer termini is desired for the detection of a single nucleotide variation at a particular locus within the genome. Here we describe the generation of thermostable mutants of the large fragment of Thermus aquaticus DNA polymerase (KlenTaq) with increased mismatch extension selectivity. In contrast to previously reported much less active KlenTaq mutants with mismatch discrimination abilities, many of the herein discovered mutants show conserved wild-type-like high activities. We demonstrate for one mutant containing the single amino acid exchange R660V the suitability for application in allele-specific amplifications directly from whole blood without prior sample purification. Also the suitability of the mutant for methylation specific amplification in the diagnostics of 5-methyl cytosines is demonstrated. Furthermore, the identified mutant supersedes other commercially available enzymes in human leukocyte antigen (HLA) analysis by sequence-specific primed polymerase chain reactions (PCRs).

Spontaneous self-assembly of engineered armadillo repeat protein fragments into a folded structure.

Repeat proteins are built of modules, each of which constitutes a structural motif. We have investigated whether fragments of a designed consensus armadillo repeat protein (ArmRP) recognize each other. We examined a split ArmRP consisting of an N-capping repeat (denoted Y), three internal repeats (M), and a C-capping repeat (A). We demonstrate that the C-terminal MA fragment adopts a fold similar to the corresponding part of the entire protein. In contrast, the N-terminal YM2 fragment constitutes a molten globule. The two fragments form a 1:1 YM2:MA complex with a nanomolar dissociation constant essentially identical to the crystal structure of the continuous YM3A protein. Molecular dynamics simulations show that the complex is structurally stable over a 1 µs timescale and reveal the importance of hydrophobic contacts across the interface. We propose that the existence of a stable complex recapitulates possible intermediates in the early evolution of these repeat proteins.

Optimization of designed armadillo repeat proteins by molecular dynamics simulations and NMR spectroscopy.

A multidisciplinary approach based on molecular dynamics (MD) simulations using homology models, NMR spectroscopy, and a variety of biophysical techniques was used to efficiently improve the thermodynamic stability of armadillo repeat proteins (ArmRPs). ArmRPs can form the basis of modular peptide recognition and the ArmRP version on which synthetic libraries are based must be as stable as possible. The 42-residue internal Arm repeats had been designed previously using a sequence-consensus method. Heteronuclear NMR revealed unfavorable interactions present at neutral but absent at high pH. Two lysines per repeat were involved in repulsive interactions, and stability was increased by mutating both to glutamine. Five point mutations in the capping repeats were suggested by the analysis of positional fluctuations and configurational entropy along multiple MD simulations. The most stabilizing single C-cap mutation Q240L was inferred from explicit solvent MD simulations, in which water penetrated the ArmRP. All mutants were characterized by temperature- and denaturant-unfolding studies and the improved mutants were established as monomeric species with cooperative folding and increased stability against heat and denaturant. Importantly, the mutations tested resulted in a cumulative decrease of flexibility of the folded state in silico and a cumulative increase of thermodynamic stability in vitro. The final construct has a melting temperature of about 85°C, 14.5° higher than the starting sequence. This work indicates that in silico studies in combination with heteronuclear NMR and other biophysical tools may provide a basis for successfully selecting mutations that rapidly improve biophysical properties of the target proteins.

Residue-resolved stability of full-consensus ankyrin repeat proteins probed by NMR.

We investigated the stability determinants and the unfolding characteristics of full-consensus designed ankyrin repeat proteins (DARPins) by NMR. Despite the repeating sequence motifs, the resonances could be fully assigned using (2)H,(15)N,(13)C triple-labeled proteins. To remove further ambiguities, we attached paramagnetic spin labels to either end of these elongated proteins, which attenuate the resonances of the spatially closest residues. Deuterium exchange experiments of DARPins with two and three internal repeats between N- and C-terminal capping repeats (NI(2)C, NI(3)C) and NI(3)C_Mut5, where the C-cap had been reengineered, indicate that the stability of the full-consensus ankyrin repeat proteins is strongly dependent on the coupling between repeats, as the stabilized cap decreases the exchange rate throughout the whole protein. Some amide protons require more than a year to exchange at 37 degrees C, highlighting the extraordinary stability of the proteins. Denaturant-induced unfolding, followed by deuterium exchange, chemical shift change, and heteronuclear nuclear Overhauser effects, is consistent with an Ising-type description of equilibrium folding for NI(3)C_Mut5, while for native-state deuterium exchange, we postulate local fluctuations to dominate exchange as unfolding events are too slow in these very stable proteins. The location of extraordinarily slowly exchanging protons indicates a very stable core structure in the DARPins that combines hydrophobic shielding with favorable electrostatic interactions. These investigations help the understanding of repeat protein architecture and the further design of DARPins for biomedical applications where high stability is required.