High-resolution optical imaging is critical to understanding brain function. We demonstrate that three-photon microscopy at 1,300-nm excitation enables functional imaging of GCaMP6s-labeled neurons beyond the depth limit of two-photon microscopy. We record spontaneous activity from up to 50 neurons in the hippocampal stratum pyramidale at ~1-mm depth within an intact mouse brain. our method creates opportunities for noninvasive recording of neuronal activity with high spatial and temporal resolution deep within scattering brain tissues.

BackgroundThe analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.ResultsIn this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.ConclusionsIn general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2599-6) contains supplementary material, which is available to authorized users.

In addition to single-nucleotide polymorphisms, structural variation is abundant in many plant genomes. The structural variation across a species can be represented by a ‘pan-genome', which is essential to fully understand the genetic control of phenotypes. However, the pan-genome's complexity hinders its accurate assembly via sequence alignment. Here we demonstrate an approach to facilitate pan-genome construction in maize. By performing 18 trillion association tests we map 26 million tags generated by reduced representation sequencing of 14,129 maize inbred lines. Using machine-learning models we select 4.4 million accurately mapped tags as sequence anchors, 1.1 million of which are presence/absence variations. Structural variations exhibit enriched association with phenotypic traits, indicating that it is a significant source of adaptive variation in maize. The ability to efficiently map ultrahigh-density pan-genome sequence anchors enables fine characterization of structural variation and will advance both genetic research and breeding in many crops.

infection is the most common cause of antibiotic-associated diarrhea in developed countries. The major virulence factor, toxin B (TcdB), targets colonic epithelia by binding to the frizzled (FZD) family of Wnt receptors, but how TcdB recognizes FZDs is unclear. Here, we present the crystal structure of a TcdB fragment in complex with the cysteine-rich domain of human FZD2 at 2.5-angstrom resolution, which reveals an endogenous FZD-bound fatty acid acting as a co-receptor for TcdB binding. This lipid occupies the binding site for Wnt-adducted palmitoleic acid in FZDs. TcdB binding locks the lipid in place, preventing Wnt from engaging FZDs and signaling. Our findings establish a central role of fatty acids in FZD-mediated TcdB pathogenesis and suggest strategies to modulate Wnt signaling.

SUMMARYFlowering time is one of the major adaptive traits in domestication of maize and an important selection criterion in breeding. To detect more maize flowering time variants we evaluated flowering time traits using an extremely large multi-genetic background population that contained more than 8000 lines under multiple Sino-United States environments. The population included two nested association mapping (NAM) panels and a natural association panel. Nearly 1 million single-nucleotide polymorphisms (SNPs) were used in the analyses. Through the parallel linkage analysis of the two NAM panels, both common and unique flowering time regions were detected. Genome wide, a total of 90 flowering time regions were identified. One-third of these regions were connected to traits associated with the environmental sensitivity of maize flowering time. The genome-wide association study of the three panels identified nearly 1000 flowering time-associated SNPs, mainly distributed around 220 candidate genes (within a distance of 1 Mb). Interestingly, two types of regions were significantly enriched for these associated SNPs -one was the candidate gene regions and the other was the approximately 5 kb regions away from the candidate genes. Moreover, the associated SNPs exhibited high accuracy for predicting flowering time.

Semiconducting silicon nanowires (SiNWs) represent one of the most interesting research directions in nanoscience and nanotechnology, with capabilities of realizing structural and functional complexity through rational design and synthesis. The exquisite control of chemical composition, structure, morphology, doping, and assembly of SiNWs, in both individual and array format, as well as incorporation with other materials, offers a nanoscale building block with unique electronic, optoelectronic, and catalytic properties, thus allowing for a variety of exciting opportunities in the fields of life sciences and renewable energy. This review provides a brief summary of SiNW research in the past decade, from the SiNW synthesis by both the top-down approaches and the bottom-up approaches, to several important biological and energy applications including biomolecule sensing, interfacing with cells and tissues, lithium-ion batteries, solar cells, and photoelectrochemical conversion.

Brain-inspired neuromorphic computing has shown great promise beyond the conventional Boolean logic. Nanoscale electronic synapses, which have stringent demands for integration density, dynamic range, energy consumption, etc., are key computational elements of the brain-inspired neuromorphic system. Ferroelectric tunneling junctions have been shown to be ideal candidates to realize the functions of electronic synapses due to their ultra-low energy consumption and the nature of ferroelectric tunneling. Here, we report a new electronic synapse based on a three-dimensional vertical Hf0.5Zr0.5O2-based ferroelectric tunneling junction that meets the full functions of biological synapses. The fabricated three-dimensional vertical ferroelectric tunneling junction synapse (FTJS) exhibits high integration density and excellent performances, such as analog-like conductance transition under a training scheme, low energy consumption of synaptic weight update (1.8 pJ per spike) and good repeatability (>103 cycles). In addition, the implementation of pattern training in hardware with strong tolerance to input faults and variations is also illustrated in the 3D vertical FTJS array. Furthermore, pattern classification and recognition are achieved, and these results demonstrate that the Hf0.5Zr0.5O2-based FTJS has high potential to be an ideal electronic component for neuromorphic system applications.

The objectives of this study were to (i) measure genetic gain using a set of maize (Zea mays L.) single‐cross hybrids that were widely used in Chinese maize production from 1964 to 2001, (ii) determine if there were changes in morphological characteristics, and (iii) examine the germplasm backgrounds of these hybrids. Yield trials were conducted for 3 yr, using a split‐plot design. Each hybrid was planted at three different densities in four locations, two locations each representing summer and spring corn areas. Mean rates of genetic gain were 52 kg ha−1 yr−1 when measured at the spring locations, 69 kg ha−1 yr−1 when measured at the summer locations, and 60 kg ha−1 yr−1 when measured across all locations. There was no significant effect of planting density on genetic gain. Genetic gain has been largely contributed by increased yield per plant and this strategy was reflected in changes in ear and plant morphology. Analyses of pedigree backgrounds showed continuing dependence on U.S. germplasm backgrounds, notably C103, Oh43, Mo17, and Iowa Stiff Stalk Synthetic (BSSS).