Traveling-wave Thomson scattering for electron-beam spectroscopy

We propose a method to use traveling-wave Thomson scattering for spatiotemporally-resolved electron spectroscopy. This can enable ultrafast time-resolved measurements of the dynamics of relativistic electrons in the presence of extremely intense light fields, either in vacuum or in plasma, such as in laser wakefield accelerators. We demonstrate, with test-particle simulation and analysis, the capability of this technique for measurements of various high field phenomena: radiation reaction of electrons due to scattering, dephasing of a laser wakefield accelerator, and acceleration of electrons in multiple buckets by a laser wakefield. We propose a method to use traveling-wave Thomson scattering for spatiotemporally-resolved electron spectroscopy. This can enable ultrafast time-resolved measurements of the dynamics of relativistic electrons in the presence of extremely intense light fields, either in vacuum or in plasma, such as in laser wakefield accelerators. We demonstrate, with test-particle simulation and analysis, the capability of this technique for measurements of various high field phenomena: radiation reaction of electrons due to scattering, dephasing of a laser wakefield accelerator, and acceleration of electrons in multiple buckets by a laser wakefield

Supervised learning with quantum enhanced feature spaces

Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous for pattern recognition, with support vector machines (SVMs) being the most well-known method for classification problems. However, there are limitations to the successful solution to such problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element to computational speed-ups afforded by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here, we propose and experimentally implement two novel methods on a superconducting processor. Both methods represent the feature space of a classification problem by a quantum state, taking advantage of the large dimensionality of quantum Hilbert space to obtain an enhanced solution. One method, the quantum variational classifier builds on [1,2] and operates through using a variational quantum circuit to classify a training set in direct analogy to conventional SVMs. In the second, a quantum kernel estimator, we estimate the kernel function and optimize the classifier directly. The two methods present a new class of tools for exploring the applications of noisy intermediate scale quantum computers [3] to machine learning.Comment: Fixed typos, added figures and discussion about quantum error mitigatio

Comparison of rainforest butterfly assemblages across three biogeographical regions using standardized protocols

Insects, like most other organisms, are more diverse in tropical than in temperate regions, but standardized comparisons of diversity among tropical regions are rare. Disentangling the effects of ecological, evolutionary, and biogeographic factors on community diversity requires standardized protocols and long-term studies. We compared the abundance and diversity of butterflies using standardised ‘Pollard walk’ transect counts in the understory of closed-canopy lowland rainforests in Panama (Barro Colorado Island, BCI), Thailand (Khao Chong, KHC) and Papua New Guinea (Wanang, WAN). We observed 1792, 1797 and 3331 butterflies representing 128, 131 and 134 species during 230, 231 and 120 transects at BCI, KHC and WAN, respectively. When corrected for length and duration of transects, butterfly abundance and species richness were highest at WAN and KHC, respectively. Although high butterfly abundance at WAN did not appear to result from methodological artefacts, the biological meaning of this observation remains obscure. The WAN site appeared as floristically diverse as KHC, but supported lower butterfly diversity. This emphasizes that factors other than plant diversity, such as biogeographic history, may be crucial for explaining butterfly diversity. The KHC butterfly fauna may be unusually species rich because the site is at a biogeographic crossroads between the Indochinese and Sundaland regions. In contrast, WAN is firmly within the Australian biogeographic region and relatively low species numbers may result from island biogeographic processes. The common species at each of the three sites shared several traits: fruit and nectar feeders were equally represented, more than half of common species fed on either epiphytes or lianas as larvae, and their range in wing sizes was similar. These observations suggest that Pollard walks in different tropical rainforests target similar assemblages of common species, and, hence, represent a useful tool for long-term monitoring of rainforest butterfly assemblages.Organismic and Evolutionary Biolog

Microguards and micromessengers of the genome

The regulation of gene expression is of fundamental importance to maintain organismal function and integrity and requires a multifaceted and highly ordered sequence of events. The cyclic nature of gene expression is known as ‘transcription dynamics’. Disruption or perturbation of these dynamics can result in significant fitness costs arising from genome instability, accelerated ageing and disease. We review recent research that supports the idea that an important new role for small RNAs, particularly microRNAs (miRNAs), is in protecting the genome against short-term transcriptional fluctuations, in a process we term ‘microguarding’. An additional emerging role for miRNAs is as ‘micromessengers’—through alteration of gene expression in target cells to which they are trafficked within microvesicles. We describe the scant but emerging evidence that miRNAs can be moved between different cells, individuals and even species, to exert biologically significant responses. With these two new roles, miRNAs have the potential to protect against deleterious gene expression variation from perturbation and to themselves perturb the expression of genes in target cells. These interactions between cells will frequently be subject to conflicts of interest when they occur between unrelated cells that lack a coincidence of fitness interests. Hence, there is the potential for miRNAs to represent both a means to resolve conflicts of interest, as well as instigate them. We conclude by exploring this conflict hypothesis, by describing some of the initial evidence consistent with it and proposing new ideas for future research into this exciting topic

Language and ethnobiological skills decline precipitously in Papua New Guinea, the world's most linguistically diverse nation

Papua New Guinea is home to >10% of the world’s languages and rich and varied biocultural knowledge, but the future of this diversity remains unclear. We measured language skills of 6,190 students speaking 392 languages (5.5% of the global total) and modeled their future trends using individual-level variables characterizing family language use, socioeconomic conditions, students’ skills, and language traits. This approach showed that only 58% of the students, compared to 91% of their parents, were fluent in indigenous languages, while the trends in key drivers of language skills (language use at home, proportion of mixed-language families, urbanization, students’ traditional skills) predicted accelerating decline of fluency to an estimated 26% in the next generation of students. Ethnobiological knowledge declined in close parallel with language skills. Varied medicinal plant uses known to the students speaking indigenous languages are replaced by a few, mostly nonnative species for the students speaking English or Tok Pisin, the national lingua franca. Most (88%) students want to teach indigenous language to their children. While crucial for keeping languages alive, this intention faces powerful external pressures as key factors (education, cash economy, road networks, and urbanization) associated with language attrition are valued in contemporary society