Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Prairie strips improve biodiversity and the delivery of multiple ecosystem services from corn-soybean croplands.

Loss of biodiversity and degradation of ecosystem services from agricultural lands remain important challenges in the United States despite decades of spending on natural resource management. To date, conservation investment has emphasized engineering practices or vegetative strategies centered on monocultural plantings of nonnative plants, largely excluding native species from cropland. In a catchment-scale experiment, we quantified the multiple effects of integrating strips of native prairie species amid corn and soybean crops, with prairie strips arranged to arrest run-off on slopes. Replacing 10% of cropland with prairie strips increased biodiversity and ecosystem services with minimal impacts on crop production. Compared with catchments containing only crops, integrating prairie strips into cropland led to greater catchment-level insect taxa richness (2.6-fold), pollinator abundance (3.5-fold), native bird species richness (2.1-fold), and abundance of bird species of greatest conservation need (2.1-fold). Use of prairie strips also reduced total water runoff from catchments by 37%, resulting in retention of 20 times more soil and 4.3 times more phosphorus. Corn and soybean yields for catchments with prairie strips decreased only by the amount of the area taken out of crop production. Social survey results indicated demand among both farming and nonfarming populations for the environmental outcomes produced by prairie strips. If federal and state policies were aligned to promote prairie strips, the practice would be applicable to 3.9 million ha of cropland in Iowa alone.

Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems.

BACKGROUND: A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs). MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. RESULTS: We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM(2)): an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM) algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM(2) substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods. CONCLUSIONS: This work provides a novel, accelerated version of a likelihood-based parameter estimation method that can be readily applied to stochastic biochemical systems. In addition, our results suggest opportunities for added efficiency improvements that will further enhance our ability to mechanistically simulate biological processes.

The United States COVID-19 Forecast Hub dataset.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Developing a decision-making framework for insect pest management: a case study using Aphis glycines (Hemiptera: Aphididae).

BACKGROUND: The profitability of farming varies based on factors such as a crop's market value, input costs and occurrence of resistant pests, all capable of altering the value of pest management tactics in an integrated pest management program. We provide a framework for calculating expected yield and expected net revenue of pest management scenarios, using the soybean aphid (Aphis glycines) as a case study. Foliar insecticide and host-plant resistance are effective management tactics for preventing yield loss from soybean aphid outbreaks; however, pyrethroid-resistant aphid populations pose a management challenge for farmers. We evaluated eight scenarios relevant to soybean aphid management in Iowa with varying probabilities of aphid outbreaks and insecticide-resistant aphids occurring. RESULTS: Our equation suggests that insecticide use is profitable when the probability of an aphid outbreak is ≥29%, and soybean production will become more costly with increasing probability of pyrethroid-resistant aphids. If farmers continue to use pyrethroids, they will not experience financial consequences from pyrethroid-resistant aphids until the chance of insecticide resistance is 48%. Aphid-resistant varieties provided consistent yield and offered the highest net revenue under all conditions. CONCLUSION: This framework can be used for other crop-pest systems to evaluate the profitability of management tactics and investigate how resistance impacts revenue for farmers. Including the cost of resistance in crop budgets can help farmers and agronomic consultants comprehend these impacts and enhance decision-making to increase revenue and curb resistance development.

Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.

Knot selection in sparse Gaussian processes with a variational objective function.

Sparse, knot-based Gaussian processes have enjoyed considerable success as scalable approximations of full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots can be selected to minimize the Kullback-Leibler divergence between the approximate and true posterior. While this has been a successful approach, simultaneous optimization of knots can be slow due to the number of parameters being optimized. Furthermore, there have been few proposed methods for selecting the number of knots, and no experimental results exist in the literature. We propose using a one-at-a-time knot selection algorithm based on Bayesian optimization to select the number and locations of knots. We showcase the competitive performance of this method relative to optimization of knots simultaneously on three benchmark datasets, but at a fraction of the computational cost.

Multivariate temporal modeling of crime with dynamic linear models.

Interest in modeling contemporary crime trends, a task that has historically been considered valuable to the public, researchers, and policymakers, is resurging. Advancements in criminology have made it clear that understanding crime trends necessarily involves understanding trends in how likely individuals are to report crimes to the police, as well as how likely the police are to accurately record those crimes. In this paper, we use dynamic linear models to simultaneously model the time series for several crime types in order to gain insight into trends in crime and crime reporting. We analyze crime data from Chicago spanning 2007 through 2016 and show how correlations in the way crime trends evolve may contain information about drivers of crime and crime reporting. We provide evidence of substantial differences in the relationships between the trends of crimes of different types depending on whether crimes are violent or nonviolent and whether or not crimes are tracked in the FBI's Uniform Crime Report.

Fully Bayesian analysis of allele-specific RNA-seq data.

Diploid organisms have two copies of each gene, called alleles, that can be separately transcribed. The RNA abundance associated to any particular allele is known as allele-specific expression (ASE). When two alleles have polymorphisms in transcribed regions, ASE can be studied using RNA-seq read count data. ASE has characteristics different from the regular RNA-seq expression: ASE cannot be assessed for every gene, measures of ASE can be biased towards one of the alleles (reference allele), and ASE provides two measures of expression for a single gene for each biological samples with leads to additional complications for single-gene models. We present statistical methods for modeling ASE and detecting genes with differential allelic expression. We propose a hierarchical, overdispersed, count regression model to deal with ASE counts. The model accommodates gene-specific overdispersion, has an internal measure of the reference allele bias, and uses random effects to model the gene-specific regression parameters. Fully Bayesian inference is obtained using the fbseq package that implements a parallel strategy to make the computational times reasonable. Simulation and real data analysis suggest the proposed model is a practical and powerful tool for the study of differential ASE.

Fully Bayesian analysis of RNA-seq counts for the detection of gene expression heterosis.

Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. Heterosis is extensively used in agriculture, and the underlying mechanisms are unclear. To investigate the molecular basis of phenotypic heterosis, researchers search tens of thousands of genes for heterosis with respect to expression in the transcriptome. Difficulty arises in the assessment of heterosis due to composite null hypotheses and non-uniform distributions for p-values under these null hypotheses. Thus, we develop a general hierarchical model for count data and a fully Bayesian analysis in which an efficient parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression heterosis in a two-hybrid plant-breeding scenario, both in a real RNA-seq maize dataset and in simulation studies. In the simulation studies, we show our method has well-calibrated posterior probabilities and credible intervals when the model assumed in analysis matches the model used to simulate the data. Although model misspecification can adversely affect calibration, the methodology is still able to accurately rank genes. Finally, we show that hyperparameter posteriors are extremely narrow and an empirical Bayes (eBayes) approach based on posterior means from the fully Bayesian analysis provides virtually equivalent posterior probabilities, credible intervals, and gene rankings relative to the fully Bayesian solution. This evidence of equivalence provides support for the use of eBayes procedures in RNA-seq data analysis if accurate hyperparameter estimates can be obtained.

The impact of dietary supplementation of arginine during gestation in a commercial swine herd: II. Offspring performance.

Arginine (Arg) is an important amino acid of pig fetal development; however, whether Arg improves postnatal performance is ill-defined. Therefore, the influence of Arg supplementation at different gestational stages on offspring performance was evaluated in a commercial swine herd. Sows (n = 548) were allocated into 4, diet by stage of gestation treatments: Control (n = 143; 0% suppl. Arg), or dietary treatments supplemented with 1% L-Arg (free-base; Ajinomoto Animal Nutrition North America, Inc., Chicago, IL): from 15 to 45 d of gestation (n = 138; Early-Arg); 15 d of gestation to farrowing (n = 139; Full-Arg); and from day 85 of gestation to farrowing (n = 128; Late-Arg). All offspring were individually identified and weighed at birth; at weaning, a subset was selected for evaluation of carcass performance at market. All data were analyzed using birth weight (BiWt) and age as covariates. Wean weights (WW) and prewean (PW) ADG tended to increase (P = 0.06) in progeny from sows supplemented with Arg, as compared to progeny from Control sows. Preplanned contrast comparisons revealed an increased (P = 0.03) BiWt for pigs from sows receiving 1% L-Arg prior to day 45 of gestation (Early-Arg and Full-Arg; 1.38 kg/pig), as compared to pigs from sows not supplemented prior to day 45 of gestation (Control and Late-Arg; 1.34 kg/pig). No difference in BiWt was observed (1.36 kg/pig; P = 0.68) for Arg supplementation after day 85 of gestation (Full-Arg and Late-Arg), as compared to those not receiving Arg supplementation after day 85 (Control and Early-Arg); although WW and PW ADG were greater (P = 0.02), respectively. A 3.6% decrease (P = 0.05) in peak lean accretion ADG occurred when dams received 1% L-Arg prior to day 45 of gestation (Early-Arg and Full-Arg), however, no other significant differences were detected in finishing growth parameters or carcass characteristics (P ≥ 0.1). Pig mortality rates tended (P = 0.07) to decrease in progeny of dams supplemented Arg after day 85 (3.6%) compared to dams not provided additional Arg during late gestation (4.9%). Collectively, these data suggest that Arg provided during late gestation may improve WW and PW ADG, however, finishing performance was not affected. While Arg supplementation provided some moderate production benefits, further investigation is warranted to comprehensively understand the gestational timing and biological role of Arg supplementation during fetal and postnatal development in commercial production systems.

The impact of dietary supplementation of arginine during gestation in a commercial swine herd: I. Gilt reproductive performance.

Supplemental arginine (Arg) during gestation purportedly benefits fetal development. However, the benefits of a gestational Arg dietary strategy in commercial production are unclear. Therefore, the objectives of this study examined Arg supplementation during different gestational stages and the effects on gilt reproductive performance. Pubertal gilts (n = 548) were allocated into 4 treatment groups: Control (n = 143; 0% supplemental Arg) or 1 of 3 supplemental Arg (1% as fed) treatments: from 15 to 45 d of gestation (n = 138; Early-Arg); from 15 d of gestation until farrowing (n = 139; Full-Arg); or from 85 d of gestation until farrowing (n = 128; Late-Arg). At farrowing, the number of total born (TB), born alive (BA), stillborn piglets (SB), mummified fetuses (MM), and individual piglet birth weights (BiWt) were recorded. The wean-to-estrus interval (WEI) and subsequent sow reproductive performance (to third parity) were also monitored. No significant effect of supplemental Arg during any part of P0 gestation was observed for TB, BA, SB, or MM (P ≥ 0.29). Offspring BiWt and variation among individual piglet birth weights did not differ (P = 0.42 and 0.89, respectively) among treatment groups. Following weaning, the WEI was similar among treatments (average of 8.0 ± 0.8 d; P = 0.88). Litter performance over 3 parities revealed a decrease (P = 0.02) in BA for Early-Arg fed gilts compared with all other treatments, whereas TB and WEI were similar among treatments over 3 parities (P > 0.05). There was an increased proportion of sows with average size litters (12 to 16 TB) from the Full-Arg treatment sows (76.8% ± 3.7%) when compared with Control (58.7% ± 4.2%; P = 0.01); however, the proportion of sows with high (>16 TB) and low (<12 TB) litters was not different among treatments (P = 0.20). These results suggest that gestational Arg supplementation had a minimal impact on reproductive performance in first parity sows. These data underscore the complexity of AA supplementation and the need for continued research into understanding how and when utilizing a gestational dietary Arg strategy can optimize fetal development and sow performance.

Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015-2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Cytotype regulation by telomeric P elements in Drosophila melanogaster: interactions with P elements from M' strains.

P strains of Drosophila are distinguished from M strains by having P elements in their genomes and also by having the P cytotype, a maternally inherited condition that strongly represses P-element-induced hybrid dysgenesis. The P cytotype is associated with P elements inserted near the left telomere of the X chromosome. Repression by the telomeric P elements TP5 and TP6 is significantly enhanced when these elements are crossed into M' strains, which, like P strains, carry P elements, but have little or no ability to repress dysgenesis. The telomeric and M' P elements must coexist in females for this enhanced repression ability to develop. However, once established, it is transmitted maternally to the immediate offspring independently of the telomeric P elements themselves. Females that carry a telomeric P element but that do not carry M' P elements may also transmit an ability to repress dysgenesis to their offspring independently of the telomeric P element. Cytotype regulation therefore involves a maternally transmissible product of telomeric P elements that can interact synergistically with products from paternally inherited M' P elements. This synergism between TP and M' P elements also appears to persist for at least one generation after the TP has been removed from the genotype.

A synthetic biology challenge: making cells compute.

Advances in biology and engineering have enabled the reprogramming of cells with well-defined functions, leading to the emergence of synthetic biology. Early successes in this nascent field suggest its potential to impact diverse areas. Here, we examine the feasibility of engineering circuits for cell-based computation. We illustrate the basic concepts by describing the mapping of several computational problems to engineered gene circuits. Revolving around these examples and past studies, we discuss technologies and computational methods available to design, test, and optimize gene circuits. We conclude with discussion of challenges involved in a typical design cycle, as well as those specific to cellular computation.

Impairment of cytotype regulation of P-element activity in Drosophila melanogaster by mutations in the Su(var)205 gene.

Cytotype regulation of transposable P elements in the germ line of Drosophila melanogaster is associated with maternal transmission of P elements inserted at the left telomere of the X chromosome. This regulation is impaired in long-term stocks heterozygous for mutations in Suppressor of variegation 205 [Su(var)205], a gene implicated in the control of telomere length. Regulation by TP5, a structurally incomplete P element at the X telomere, is more profoundly impaired than regulation by TP6, a different incomplete P element inserted at the same site in a TAS repeat at the X telomere. Genetic analysis with the TP5 element indicates that its regulatory ability is not impaired in flies whose fathers came directly from a stock heterozygous for a Su(var)205 mutation, even when the flies themselves carry this mutation. However, it is impaired in flies whose grandfathers came from such a stock. Furthermore, this impairment occurs even when the Su(var)205 mutation is not present in the flies themselves or in their mothers. The impaired regulatory ability of TP5 persists for at least several generations after TP5 X chromosomes extracted from a long-term mutant Su(var)205 stock are made homozygous in the absence of the Su(var)205 mutation. Impairment of TP5-mediated regulation is therefore not directly dependent on the Su(var)205 mutation. However, it is characteristic of the six mutant Su(var)205 stocks that were tested and may be related to the elongated telomeres that develop in these stocks. Impairment of regulation by TP5 is also seen in a stock derived from Gaiano, a wild-type strain that has elongated telomeres due to a dominant mutation in the Telomere elongation (Tel) gene. Regulation by TP6 is not impaired in the Gaiano genetic background. The regulatory abilities of the TP5 and TP6 elements are therefore not equally susceptible to the effects of elongated telomeres in the mutant Su(var)205 and Gaiano stocks.

Establishment and maintenance of the P cytotype associated with telomeric P elements in Drosophila melanogaster.

P elements inserted near the left telomere of the X chromosome are associated with the P cytotype, a maternally transmitted condition that strongly regulates the activity of the P transposon family in some strains of Drosophila. The regulatory abilities of two such elements, TP5 and TP6, are stable in homozygous stocks over many generations. However, these regulatory abilities are attenuated when the telomeric P elements are transmitted through heterozygous females, and they are utterly lost when the elements are transmitted through males. Paternally transmitted telomeric P elements reacquire regulatory ability when they pass through a female germ line. This reacquisition is enhanced if the females in which it occurs came from mothers who carried a telomeric P element. The enhancement has two components: (1). a strictly maternal effect that is transmitted to the females independently of the mother's telomeric P element ("presetting" or the "pre-P cytotype") and (2). a zygotic effect associated with inheritance of the mother's telomeric P element. One telomeric P element can enhance the reacquisition of another's regulatory ability. When X chromosomes that carry telomeric P elements are extracted through males and made homozygous by using a balancer chromosome, most of the resulting stocks develop strong regulatory abilities in a few generations. However, some of the stocks do not attain the regulatory ability of the original population.