Frequency Trajectories Research Articles

Estimating Temporally Variable Selection Intensity from Ancient DNA Data.

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage, and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat coloration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

Estimating linkage disequilibrium and selection from allele frequency trajectories.

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.

Input-output scaling factors tuning of type-2 fuzzy PID controller using multi-objective optimization technique

<abstract><p>The PID controller is a popular controller that is widely used in various industrial applications. On the other hand, the control problems in microgrids (MGs) are so challenging, because of natural disturbances such as wind speed changes, load variation, and changes in other sources. This paper proposes an input-output scaling factor tuning of interval type-2 fuzzy (IT2F) PID controller using a multi-objective optimization technique. The suggested controller is applied to an MG frequency regulation problem. In the introduced controller the effect of variations of renewable energies (REs) and other disturbances are taken into account, and the robustness is investigated. In the multi-objective scheme, some factors such as least overshoot, and minimum settling/rising time are considered. The simulations show that by considering the suitable adjustment the desired regulation accuracy is achieved, such that the frequency trajectory shows the desired overshoot, and settling/rising time.</p></abstract>

Robust trajectory-constrained frequency control for microgrids considering model linearization error

Grid supportive modes integrated within inverter-based resources can improve the frequency response of renewable-rich microgrids. The synthesis of grid supportive modes to guarantee frequency trajectory constraints under a predefined disturbance set is challenging but essential. To tackle this challenge, a numerical optimal control (NOC)-based control synthesis methodology is proposed. Without loss of generality, a wind-diesel fed microgrid is studied, where we aim to design grid supportive functions in the wind turbine. In the control design, linearized models are used, and the linearization-induced errors are quantitatively analyzed by reachability and interval arithmetics and represented in the form of interval uncertainties. Then, the NOC problem can be formulated into a robust mixed-integer linear program. The control structure is strategically configured into two levels to realize online deployment. The proposed control is verified on the modified 33-node microgrid with a full-order three-phase nonlinear model in Simulink. The simulation results show the effectiveness of the proposed control paradigm and the necessity of considering linearization-induced uncertainty.

Computer-assisted Analysis of Field Recordings: A Case Study of Georgian Funeral Songs

Three-voiced funeral songs from Svaneti in North-West Georgia (also referred to as Zär) are believed to represent one of Georgia’s oldest preserved forms of collective music-making. Throughout a Zär performance, the singers often jointly and intentionally drift upwards in pitch. Furthermore, the singers tend to use pitch slides at the beginning and end of sung notes. Musicological studies on tonal analysis or transcription have to account for such musical peculiarities, e.g., by compensating for pitch drifts or identifying stable note events (located between pitch slides). These tasks typically require labor-intensive annotation processes with manual corrections executed by experts with domain knowledge. For instance, in the context of a previous musicological study on pitch inventories (or pitch-class histograms) of Zär performances, ethnomusicologists tediously annotated fundamental frequency (F0) trajectories, stable note events, and pitch drifts for a set of 11 multitrack field recordings. In this article, we study how musicological studies on field recordings can benefit from interactive computational tools that support such annotation processes. As one contribution of this article, we compile a dataset from the previously annotated audio material, which we release under an open-source license for research purposes. As a second contribution, we introduce two computational tools for removing pitch slides and compensating pitch drifts in performances. Our tools were developed in close collaboration with ethnomusicologists and allow for incorporating domain knowledge (e.g., on singing styles or musically relevant harmonic intervals) in the different processing steps. In a case study using our Zär dataset, we evaluate our tools by reproducing the pitch inventories from the original musicological study and subsequently discuss the potential of computer-assisted approaches for interdisciplinary research.

Rapid weed adaptation and range expansion in response to agriculture over the past two centuries

North America has experienced a massive increase in cropland use since 1800, accompanied more recently by the intensification of agricultural practices. Through genome analysis of present-day and historical samples spanning environments over the past two centuries, we studied the effect of these changes in farming on the extent and tempo of evolution across the native range of the common waterhemp (Amaranthus tuberculatus), a now pervasive agricultural weed. Modern agriculture has imposed strengths of selection rarely observed in the wild, with notable shifts in allele frequency trajectories since agricultural intensification in the 1960s. An evolutionary response to this extreme selection was facilitated by a concurrent human-mediated range shift. By reshaping genome-wide diversity across the landscape, agriculture has driven the success of this weed in the 21st century.

Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. Case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.

Local maximum synchrosqueezes form scaling-basis chirplet transform.

In recent years, time-frequency analysis (TFA) methods have received widespread attention and undergone rapid development. However, traditional TFA methods cannot achieve the desired effect when dealing with nonstationary signals. Therefore, this study proposes a new TFA method called the local maximum synchrosqueezing scaling-basis chirplet transform (LMSBCT), which is a further improvement of the scaling-basis chirplet transform (SBCT) with energy rearrangement in frequency and can be viewed as a good combination of SBCT and local maximum synchrosqueezing transform. A better concentration in terms of the time-frequency energy and a more accurate instantaneous frequency trajectory can be achieved using LMSBCT. The time-frequency distribution of strong frequency-modulated signals and multicomponent signals can be handled well, even for signals with close signal frequencies and low signal-to-noise ratios. Numerical simulations and real experiments were conducted to prove the superiority of the proposed method over traditional methods.

Alcohol use among Australian parents during the COVID-19 pandemic – April-2020 to May 2021

AimsThis study examined the trajectory of alcohol use frequency among parents from April-2020 to May-2021 during the COVID-19 pandemic in the state of Victoria, Australia (who experienced one of the longest lockdowns in the world), compared to parents from the other states of Australia (who experienced relatively fewer restrictions). We further examined the extent to which baseline demographic factors were associated with changes in alcohol use trajectories among parents. MethodData were from the COVID-19 Pandemic Adjustment Survey (2,261 parents of children 0–18 years). Alcohol use frequency was assessed over 13 waves. Baseline demographic predictors included parent gender, age, speaking a language other than English, number of children, partnership status, education, employment, and income. ResultsOverall, alcohol trajectories declined over time. Victorian parents, in comparison to parents from other states, reported a smaller reduction in alcohol use frequency across 2020, with a more notable decline during 2021. Female/other gender, speaking a language other than English at home, unemployment, and lower income (Victoria only) were associated with alcohol trajectories of less frequent use, and older age was associated with a trajectory of more frequent use. ConclusionsResults suggest subtle difference in alcohol trajectories reflecting COVID-19 restrictions, when comparing Victoria and other states in Australia. Socioeconomically advantaged groups were most at risk for elevated trajectories of alcohol use frequency. Population level support may beneficial to reduce drinking behaviours.

Reactive power assisted frequency regulation scheme for large-scale solar-PV plants

Due to reduction in power system inertia and frequency regulation reserve with high penetration of power-electronic converter (PEC) interfaced renewable sources, advanced control strategies must be developed to exploit the full potential of solar-photovoltaic (PV) systems to improve the frequency stability. In this paper, a new frequency regulation approach is proposed based on reactive-power control (i.e., frequency regulation via reactive-power control (FRQC) scheme) for solar-PV systems, which manipulates the active power demand as a function of the system frequency deviation by varying network voltages via reactive power control. The feasibility of the proposed control scheme is investigated using both theoretical analysis and simulation studies following a loss of a major generation source. Frequency and voltage deadbands, and time delays are added to the FRQC scheme to activate and deactivate the control scheme, and hence it maximises the frequency support without affecting the voltage stability. The reliability and voltage stability (RVS) test system is used to demonstrate the effectiveness of the proposed FRQC scheme under different gains of the controller, PV loading levels, and different PV penetration levels. The comparative results show that the proposed FRQC scheme improves the post-fault frequency trajectory, and hence the system frequency stability.

Leisure activity engagement across adulthood predicts cognitive change after five years: Do gender and age matter?

To examine the association between leisure activity (LA) frequency and cognitive trajectories over 5 years across adulthood, and whether gender and age moderate these associations. A total of 234 cognitively healthy adults (21-80 years) completed a LA questionnaire at baseline and neuropsychological measures at baseline and after 5 years. Latent change score analysis was applied to generate latent variables estimating changes in different cognitive domains. For a secondary analysis, LA components' scores were calculated, reflecting cognitive-intellectual, social, and physical activities. Regression analysis examined the association between baseline LA and cognitive change, and potential moderation of gender and age. In addition, we tested the influence of cortical gray matter thickness on the results. We found that higher LA engagement was associated with slower cognitive decline for reasoning, speed, and memory, as well as better vocabulary across two time points. Regarding LA components, higher Social-LA and Intellectual-LA predicted slower rates of cognitive decline across different domains, while Physical-LA was not associated with cognitive change. Gender, but not age, moderated some of the associations observed. Our results remained the same after controlling for cortical gray matter thickness. We demonstrated a protective effect of LA engagement on cognitive trajectories over 5 years, independent from demographics and a measure of brain health. The effects were in part moderated by gender, but not age. Results should be replicated in larger and more diverse samples. Our findings support cognitive reserve hypothesis and have implications for future reserve-enhancing interventions.

Selective sweeps on different pigmentation genes mediate convergent evolution of island melanism in two incipient bird species.

Insular organisms often evolve predictable phenotypes, like flightlessness, extreme body sizes, or increased melanin deposition. The evolutionary forces and molecular targets mediating these patterns remain mostly unknown. Here we study the Chestnut-bellied Monarch (Monarcha castaneiventris) from the Solomon Islands, a complex of closely related subspecies in the early stages of speciation. On the large island of Makira M. c. megarhynchus has a chestnut belly, whereas on the small satellite islands of Ugi, and Santa Ana and Santa Catalina (SA/SC) M. c. ugiensis is entirely iridescent blue-black (i.e., melanic). Melanism has likely evolved twice, as the Ugi and SA/SC populations were established independently. To investigate the genetic basis of melanism on each island we generated whole genome sequence data from all three populations. Non-synonymous mutations at the MC1R pigmentation gene are associated with melanism on SA/SC, while ASIP, an antagonistic ligand of MC1R, is associated with melanism on Ugi. Both genes show evidence of selective sweeps in traditional summary statistics and statistics derived from the ancestral recombination graph (ARG). Using the ARG in combination with machine learning, we inferred selection strength, timing of onset and allele frequency trajectories. MC1R shows evidence of a recent, strong, soft selective sweep. The region including ASIP shows more complex signatures; however, we find evidence for sweeps in mutations near ASIP, which are comparatively older than those on MC1R and have been under relatively strong selection. Overall, our study shows convergent melanism results from selective sweeps at independent molecular targets, evolving in taxa where coloration likely mediates reproductive isolation with the neighboring chestnut-bellied subspecies.

A fast sparse least squares support vector machine hysteresis model for piezoelectric actuator

The inherent nonlinearities of piezoelectric actuator (PEA), especially hysteresis, greatly reduce the tracking performance of PEA. With a lot of computing resources consumed in the predicting process, the hysteresis modeling method of PEA based on the least-squares support vector machine (LSSVM) cannot be used for hysteresis compensation at high frequency. To solve this problem, a sequential selection approximate algorithm is proposed to obtain a fast sparse LSSVM (SLSSVM) hysteresis model. The SLSSVM model’s support vectors are only 6.8% of the original LSSVM model, by which the modeling speed and calculation speed are greatly improved. The experimental results show that the SLSSVM model improves the tracking accuracy when used in hybrid control system, especially for high frequency trajectories.

Scaling demodulation-based mode decomposition for analyzing nonstationary signal with close-spaced and intersecting frequency trajectories

Mode decomposition is an important tool for processing vibration signals in engineering fields. However, classic methods cannot work for decomposing a signal with close-spaced and intersecting frequency trajectories. For addressing the aforementioned problem, a novel technique, termed scaling demodulation-based mode decomposition (SDMD), is proposed in this paper. The main novelties of this paper are summarized as follows: (a) a scaling demodulation transform (SDT) is developed to estimate instantaneous frequency (IF) curves by selecting optimal demodulation parameters from a series of parameter sets with a criterion for searching maximal spectrum peaks of demodulated signals; and (b) a time–frequency representation (TFR) with high energy concentration is constructed using the Dirac delta function, estimated IFs and instantaneous amplitudes (IAs). The performance of the SDMD is evaluated through simulated and measured signals. Furthermore, comparison results with classic methods show that the developed technique has a much better ability to decompose and characterize nonstationary signals with close-spaced and intersecting frequency trajectories.

High-resolution time-resolved spectroscopy based on hybrid asynchronous optical sampling

The capability of characterizing arbitrary and non-repetitive emission spectra with a high resolution in real-time is of great merit in various research fields. Optical frequency combs provide precise and stable frequency grids for the measurement of a single spectral line with high accuracy. Particularly, dual-comb spectroscopy enables spectral measurement with a large bandwidth spanning tens of nanometers, but it is limited to measuring absorption spectra and has to trade-off spectral resolution vs the acquisition frame rate set by the repetition rate. Here, to alleviate these restrictions, we propose and demonstrate time-resolved spectroscopy for an emission spectrum based on hybrid asynchronous optical sampling, which features a spectral resolution of 0.63 pm, a frame rate of 1 MHz, and a measurement bandwidth of 13.6 nm, simultaneously. A mode-locked fiber comb with a repetition frequency of f1 is harnessed to interrogate emission spectral features with high resolution via optical Fourier transform achieved using a time-lens. Subsequently, a soliton microcomb of repetition frequency f2s ≈ 1000f1 serving as a probe pulse implements hybrid asynchronous optical sampling, thus significantly increasing the acquisition rate by nearly 3 orders of magnitude. As a proof-of-concept demonstration, the frequency trajectory of a rapidly scanning laser with a linear chirp of 6.2 THz/s is tracked. We believe that chip-scale microcombs will make the fast and high-resolution emission spectroscopy presented here a powerful tool for widespread applications.

Geomorphic heterogeneity as a framework for assessing river corridor processes and characteristics

AbstractSpatial and temporal heterogeneity, or messiness, is a broadly desirable characteristic of river corridors and an indicator of many of the geomorphic processes that sustain fluvial ecosystems. However, quantifying geomorphic heterogeneity is complicated by a lack of consistent metrics, classification schemas for dividing the river corridor into the patches that form the basis for those metrics, and guidance on interpreting metrics. Drawing from both geomorphic and landscape ecology concepts, we offer ideas and guidance intended to help investigators, from researchers to restoration practitioners, more effectively and reliably use heterogeneity to describe river corridor processes and characteristics. We define geomorphic heterogeneity both spatially and temporally. Spatially, heterogeneity can be described by diversity, or the evenness and richness of geomorphic units, and spatial configuration, or the arrangement and shape of geomorphic units. Temporally, heterogeneity can be described by turnover rate, or the rate of change of geomorphic units. Interpretation of heterogeneity metrics depends integrally on the definition of the geomorphic unit schema on which metrics are based. Contextual information, such as measurements of process space (i.e., how much room a river has to move), disturbance frequency, and geomorphic trajectory, can also be key to interpreting measurements of heterogeneity. Geomorphic applications of heterogeneity require carefully defined geomorphic unit schemas that reflect processes and characteristics of interest, robust metrics of heterogeneity whose meaning is appropriate to the question at hand, and interpretation of those metrics based on the context of expected geomorphic processes and the disturbance regime.

Frequency Trajectory Planning-Based Transient Frequency Regulation Strategy for Wind Turbine Systems

A high proportion of wind power generation weakens the power system inertia, causing concerns over frequency stability. The direct-drive permanent magnet synchronous generators (PMSG)-based wind turbine system (WTS) usually uses virtual inertia control (VIC) to support frequency. However, the related controller parameters are difficult to design, and the deep participation in frequency regulation (FR) will reduce the economic benefits of WTS. To this end, this article breaks out of the VIC framework and proposes a transient FR strategy based on frequency trajectory planning (FTP). The basic idea is to actively plan a frequency trajectory with a safety margin according to the grid frequency threshold when the system is disturbed so that the FR power provided by the WTS can be controlled according to the planned frequency trajectory and achieve rapid support for the system transient frequency, ensuring the safe and stable operation of the grid while achieving the economy of wind power generation. Simulation and hardware-in-loop experimental results demonstrate that the proposed FTP-based transient FR strategy for WTS is effective and economical.

Adaptive Linear Chirplet Transform for Analyzing Signals With Crossing Frequency Trajectories

Signal time-frequency analysis is widely used in industrial fields, for example, machinery fault diagnosis. Current methods cannot well address the multicomponents’ signals with crossing frequency trajectories. For example, the adaptive decomposition methods have a mode-mixing problem. The linear transforms have smear problems due to the mutual interference caused by crossing frequencies. To address these issues, in this article, we propose an iterative approach named adaptive linear chirplet transform (ALCT). It works by calculating the signal linear chirplet transform (LCT) with a series of chirp rates as parameters, iteratively estimating the characteristics of the signal mode with the highest amplitude in the residual LCT and updating the residual LCT by removing the LCT of the detected mode, and constructing the signal time-frequency representation (TFR) based on the estimated characteristics of the detected modes. The ALCT can clearly reveal the TFRs of signals with crossing frequency trajectories. It does not have the frequency estimation errors caused by mode mixing and avoids the mutual interference of the crossing frequencies. It is efficient, as it can obtain the LCT of the detected mode by directly weighting and shifting the LCT reference. The effectiveness of the ALCT is validated using simulations and real-world signals.

Dairying, diseases and the evolution of lactase persistence in Europe.

In European and many African, Middle Eastern and southern Asian populations, lactase persistence (LP) is the most strongly selected monogenic trait to have evolved over the past 10,000 years1. Although the selection ofLP and the consumption ofprehistoric milk must be linked, considerable uncertainty remains concerning their spatiotemporal configuration and specific interactions2,3. Here we provide detailed distributions of milk exploitation across Europe over the past 9,000 years using around7,000 pottery fat residues from more than 550 archaeological sites. European milk use was widespread from the Neolithic period onwards but varied spatially and temporally in intensity. Notably,LP selection varying with levels of prehistoric milk exploitation is no better at explaining LP allele frequency trajectories than uniform selection since the Neolithic period. In the UK Biobank4,5 cohort of 500,000 contemporary Europeans, LP genotype was only weakly associated with milk consumption and did not show consistent associations with improved fitness or health indicators. This suggests that other reasons for the beneficial effects of LP should be considered for its rapid frequency increase. We propose that lactasenon-persistent individuals consumed milk when it became available but, under conditions of famine and/or increased pathogen exposure, this was disadvantageous, driving LP selection in prehistoric Europe. Comparison of model likelihoods indicates that population fluctuations, settlement density and wild animal exploitation-proxies for these drivers-provide better explanations of LP selection than the extent of milk exploitation. These findings offer new perspectives on prehistoric milk exploitation and LP evolution.

Declines in prevalence alter the optimal level of sexual investment for the malaria parasite Plasmodium falciparum

Successful infectious disease interventions can result in large reductions in parasite prevalence. Such demographic change has fitness implications for individual parasites and may shift the parasite's optimal life history strategy. Here, we explore whether declining infection rates can alter Plasmodium falciparum's investment in sexual versus asexual growth. Using a multiscale mathematical model, we demonstrate how the proportion of polyclonal infections, which decreases as parasite prevalence declines, affects the optimal sexual development strategy: Within-host competition in multiclone infections favors a greater investment in asexual growth whereas single-clone infections benefit from higher conversion to sexual forms. At the same time, drug treatment also imposes selection pressure on sexual development by shortening infection length and reducing within-host competition. We assess these models using 148 P. falciparum parasite genomes sampled in French Guiana over an 18-y period of intensive intervention (1998 to 2015). During this time frame, multiple public health measures, including the introduction of new drugs and expanded rapid diagnostic testing, were implemented, reducing P. falciparum malaria cases by an order of magnitude. Consistent with this prevalence decline, we see an increase in the relatedness among parasites, but no single clonal background grew to dominate the population. Analyzing individual allele frequency trajectories, we identify genes that likely experienced selective sweeps. Supporting our model predictions, genes showing the strongest signatures of selection include transcription factors involved in the development of P. falciparum's sexual gametocyte form. These results highlight how public health interventions impose wide-ranging selection pressures that affect basic parasite life history traits.