The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation, and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and the naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. In terms of accuracy and number of measurements, the methods described here dramatically outperform conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as light detection and ranging, and microscopy.

A Schwarzschild reflective objective with a numerical aperture of 0.3 and working distance of 10 cm was used for laser ablation sampling of tissue for off-line mass spectrometry. The objective focused the laser to a diameter of 5 μm and produced 10 μm ablation spots on thin ink films and tissue sections. Rat brain tissue sections 50 μm thick were ablated in transmission geometry, and the ablated material was captured in a microcentrifuge tube containing solvent. Proteins from ablated tissue sections were quantified with a Bradford assay, which indicated that approximately 300 ng of protein was captured from a 1 mm 2 area of ablated tissue. Areas of tissue ranging from 0.01 to 1 mm 2 were ablated and captured for bottom-up proteomics. Proteins were extracted from the captured tissue and digested for liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis for peptide and protein identification.

Malnutrition is an increasing threat to honey bees that can be mitigated by feeding artificial pollen substitute diets; however, little is known about the molecular mechanisms underlying their impact on bee health. Here, we examined proteomic responses to natural and artificial diets in the honey bee fat body, a tissue with central nutrient storage and metabolic functions. Bees were fed protein diets of natural pollen, a commercial plant-based diet used by beekeepers that does not contain pollen (Ultra Bee), and two novel cyanobacteria diets comprising dried or fresh laboratory-grown Arthrospira platensis (commonly, spirulina). Relative to a protein-free control group, diet consumption elicited broad upregulation of metabolic processes associated with amino acids, carbohydrates, and lipids. Plant and cyanobacteria diets led to equivalent dietary protein assimilation and a marked overlap in proteome expression patterns, indicative of comparable nutritive and metabolic impacts. This was corroborated by equivalent titers of the storage lipoprotein vitellogenin and nutritionally-regulated stress response proteins (superoxide dismutase, glutathione Stransferase 1, catalase, and heat shock protein 90). The tested diets recapitulated the proteomic effects of a natural pollen diet and support stress resistance via improved nutritional status. Our results provide new insights into the impact of artificial feed on honey bees and highlight the potential of cyanobacterial biomass as a sustainable nutrition source for improving bee health.

Background Understanding and identifying the factors responsible for genetic differentiation is of fundamental importance for efficient utilization and conservation of traditional rice landraces. In this study, we examined the spatial genetic differentiation of 594 individuals sampled from 28 locations in Yunnan Province, China, covering a wide geographic distribution and diverse growing conditions. All 594 accessions were studied using ten unlinked target genes and 48 microsatellite loci, and the representative 108 accessions from the whole collection were sampled for resequencing. Results The genetic diversity of rice landraces was quite different geographically and exhibited a geographical decline from south to north in Yunnan, China. Population structure revealed that the rice landraces could be clearly differentiated into japonica and indica groups, respectively. In each group, the rice accessions could be further differentiated corresponded to their geographic locations, including three subgroups from northern, southern and middle locations. We found more obvious internal geographic structure in the japonica group than in the indica group. In the japonica group, we found that genetic and phenotypic differentiation were strongly related to geographical distance, suggesting a pattern of isolation by distance (IBD); this relationship remained highly significant when we controlled for environmental effects, where the likelihood of gene flow is inversely proportional to the distance between locations. Moreover, the gene flow also followed patterns of isolation by environment (IBE) whereby gene flow rates are higher in similar environments. We detected 314 and 216 regions had been differentially selected between Jap-N and Jap-S, Ind-N and Ind-S, respectively, and thus referred to as selection signatures for different geographic subgroups. We also observed a number of significant and interesting associations between loci and environmental factors, which implies adaptation to local environment. Conclusions Our findings highlight the influence of geographical isolation and environmental heterogeneity on the pattern of the gene flow, and demonstrate that both geographical isolation and environment drives adaptive divergence play dominant roles in the genetic differentiation of the rice landraces in Yunnan, China as a result of limited dispersal.

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