Primary cosmic-ray positrons and negatrons in 1968 at energies between 11 and 204 MeV

The cosmic-ray positron and negatron spectra between 11 and 204 MeV have been measured in a series of 3 high-altitude balloon flights launched from Fort Churchill, Manitoba, on July 16, July 21, and July 29, 1968. The detector system consisted of a magnetic spectrometer utilizing a 1000-gauss permanent magnet, scintillation counters, and a lucite Čerenkov counter.

Launches were timed so that the ascent through the 100 g/cm² level of residual atmosphere occurred after the evening geomagnetic cutoff transition. Data gathered during ascent are used to correct for the contribution of atmospheric secondary electrons to the flux measured at float altitude. All flights floated near 2.4 g/cm².

A pronounced morning intensity increase was observed in each flight. We present daytime positron and negatron data which support the interpretation of the diurnal flux variation as a change in the local geomagnetic cutoff. A large diurnal variation was observed in the count rate of positrons and negatrons with magnetic rigidities less than 11 MV and is evidence that the nighttime cutoff was well below this value.

Using nighttime data we derive extraterrestrial positron and negatron spectra. The positron-to-total-electron ratio which we measure indicates that the interstellar secondary, or collision, source contributes ≾50 percent of the electron flux within this energy interval. By comparing our measured positron spectrum with the positron spectrum calculated for the collision source we derive the absolute solar modulation for positrons in 1968. Assuming negligible energy loss during modulation, we derive the total interstellar electron spectrum as well as the spectrum of directly accelerated, or primary, electrons. We examine the effect of adiabatic deceleration and find that many of the conclusions regarding the interstellar electron spectrum are not significantly altered for an assumed energy loss of up to 50 percent of the original energy.

Metastability and dynamics of stem cells: from direct observations to inference at the single cell level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions.

While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages.

Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states.

To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency.

Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark.

Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states.

Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.