Fine-scale genetic structure in the orchid Gymnadenia conopsea is not associated with local density of flowering plants.

PREMISE: Density-dependent pollinator visitation can lead to density-dependent mating patterns and within-population genetic structure. In Gymnadenia conopsea, individuals in low-density patches receive more self pollen than individuals in high-density patches, suggesting higher relatedness at low density. Ongoing fragmentation is also expected to cause more local matings, potentially leading to biparental inbreeding depression. METHODS: To evaluate whether relatedness decreases with local density, we analyzed 1315 SNP loci in 113 individuals within two large populations. We quantified within-population genetic structure in one of the populations, recorded potential habitat barriers, and visualized gene flow using estimated effective migration surfaces (EEMS). We further estimated the magnitude of biparental inbreeding depression that would result from matings restricted to within 5 m. RESULTS: There was no significant relationship between local density and relatedness in any population. We detected significant fine-scale genetic structure consistent with isolation by distance, with positive kinship coefficients at distances below 10 m. Kinship coefficients were low, and predicted biparental inbreeding depression resulting from matings within the closest 5 m was a modest 1-3%. The EEMS suggested that rocks and bushes may act as barriers to gene flow within a population. CONCLUSIONS: The results suggest that increased self-pollen deposition in sparse patches does not necessarily cause higher selfing rates or that inbreeding depression results in low establishment success of inbred individuals. The modest relatedness suggests that biparental inbreeding depression is unlikely to be an immediate problem following fragmentation of large populations. The results further indicate that habitat structure may contribute to governing fine-scale genetic structure in G. conopsea.

Application of Scanning Tunneling Microscopy and Spectroscopy in the Studies of Colloidal Quantum Qots.

Colloidal quantum dots display remarkable optical and electrical characteristics with the potential for extensive applications in contemporary nanotechnology. As an ideal instrument for examining surface topography and local density of states (LDOS) at an atomic scale, scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS) has become indispensable approaches to gain better understanding of their physical properties. This article presents a comprehensive review of the research advancements in measuring the electronic orbits and corresponding energy levels of colloidal quantum dots in various systems using STM and STS. The first three sections introduce the basic principles of colloidal quantum dots synthesis and the fundamental methodology of STM research on quantum dots. The fourth section explores the latest progress in the application of STM for colloidal quantum dot studies. Finally, a summary and prospective is presented.

Scaling of biological rates with body size as a backbone in the assembly of metacommunity biodiversity.

The dispersal-body mass association has been highlighted as a main determinant of biodiversity patterns in metacommunities. However, less attention has been devoted to other well-recognized determinants of metacommunity diversity: the scaling in density and regional richness with body size. Among active dispersers, the increase in movement with body size may enhance local richness and decrease ß-diversity. Nevertheless, the reduction of population size and regional richness with body mass may determine a negative diversity-body size association. Consequently, metacommunity assembly probably emerges from a balance between the effect of these scalings. We formalize this hypothesis by relating the exponents of size-scaling rules with simulated trends in α-, ß- and γ-diversity with body size. Our results highlight that the diversity-body size relationship in metacommunities may be driven by the combined effect of different scaling rules. Given their ubiquity in most terrestrial and aquatic biotas, these scaling rules may represent the basic determinants-backbone-of biodiversity, over which other mechanisms operate determining metacommunity assembly. Further studies are needed, aimed at explaining biodiversity patterns from functional relationships between biological rates and body size, as well as their association with environmental conditions and species interactions.

Tri-Training Algorithm for Adaptive Nearest Neighbor Density Editing and Cross Entropy Evaluation.

Tri-training expands the training set by adding pseudo-labels to unlabeled data, which effectively improves the generalization ability of the classifier, but it is easy to mislabel unlabeled data into training noise, which damages the learning efficiency of the classifier, and the explicit decision mechanism tends to make the training noise degrade the accuracy of the classification model in the prediction stage. This study proposes the Tri-training algorithm for adaptive nearest neighbor density editing and cross-entropy evaluation (TTADEC), which is used to reduce the training noise formed during the classifier iteration and to solve the problem of inaccurate prediction by explicit decision mechanism. First, the TTADEC algorithm uses the nearest neighbor editing to label high-confidence samples. Then, combined with the relative nearest neighbor to define the local density of samples to screen the pre-training samples, and then dynamically expand the training set by adaptive technique. Finally, the decision process uses cross-entropy to evaluate the completed base classifier of training and assign appropriate weights to it to construct a decision function. The effectiveness of the TTADEC algorithm is verified on the UCI dataset, and the experimental results show that compared with the standard Tri-training algorithm and its improvement algorithm, the TTADEC algorithm has better classification performance and can effectively deal with the semi-supervised classification problem where the training set is insufficient.

An Improved Density Peak Clustering Algorithm for Multi-Density Data.

Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when one parameter cannot satisfy all data, clustering often cannot achieve good results. Moreover, the subjective selection of cluster centers through decision diagrams is often not very convincing, and there are also certain errors. In view of the above problems, in order to achieve better clustering of multi-density data, this paper improves the density peak clustering algorithm. Aiming at the selection of parameter dc, the K-nearest neighbor idea is used to sort the neighbor distance of each data, draw a line graph of the K-nearest neighbor distance, and find the global bifurcation point to divide the data with different densities. Aiming at the selection of cluster centers, the local density and distance of each data point in each data division is found, a γ map is drawn, the average value of the γ height difference is calculated, and through two screenings the largest discontinuity point is found to automatically determine the cluster center and the number of cluster centers. The divided datasets are clustered by the DPC algorithm, and then the clustering results are perfected and integrated by using the cluster fusion rules. Finally, a variety of experiments are designed from various perspectives on various artificial simulated datasets and UCI real datasets, which demonstrate the superiority of the F-DPC algorithm in terms of clustering effect, clustering quality, and number of samples.

Co-Density Distribution Maps for Advanced Molecule Colocalization and Co-Distribution Analysis.

Cellular and subcellular spatial colocalization of structures and molecules in biological specimens is an important indicator of their co-compartmentalization and interaction. Presently, colocalization in biomedical images is addressed with visual inspection and quantified by co-occurrence and correlation coefficients. However, such measures alone cannot capture the complexity of the interactions, which does not limit itself to signal intensity. On top of the previously developed density distribution maps (DDMs), here, we present a method for advancing current colocalization analysis by introducing co-density distribution maps (cDDMs), which, uniquely, provide information about molecules absolute and relative position and local abundance. We exemplify the benefits of our method by developing cDDMs-integrated pipelines for the analysis of molecules pairs co-distribution in three different real-case image datasets. First, cDDMs are shown to be indicators of colocalization and degree, able to increase the reliability of correlation coefficients currently used to detect the presence of colocalization. In addition, they provide a simultaneously visual and quantitative support, which opens for new investigation paths and biomedical considerations. Finally, thanks to the coDDMaker software we developed, cDDMs become an enabling tool for the quasi real time monitoring of experiments and a potential improvement for a large number of biomedical studies.

Control of Vibronic Transition Rates by Resonant Single-Molecule-Nanoantenna Coupling.

Plasmonic nanostructures dramatically alter the radiative and nonradiative properties of single molecules in their vicinity. This coupling-induced change in decay channels selectively enhances specific vibronic transitions, which can enable plasmonic control of molecular reactivity. Here, we report coupling-dependent spectral emission shaping of single Rhodamine 800 molecules in the vicinity of plasmonic gold nanorods. We show that the relative vibronic transition rates of the first two vibronic transitions of the spontaneous emission spectrum can be tuned in the weak coupling regime, by approximately 25-fold, on the single molecule level.

Cold and Hot Spots: From Inhibition to Enhancement by Nanoscale Phase Tuning of Optical Nanoantennas.

Optical nanoantennas are well-known for the confinement of light into nanoscale hot spots, suitable for emission enhancement and sensing applications. Here, we show how control of the antenna dimensions allows tuning the local optical phase, hence turning a hot spot into a cold spot. We manipulate the local intensity exploiting the interference between driving and scattered field. Using single molecules as local detectors, we experimentally show the creation of subwavelength pockets with full suppression of the driving field. Remarkably, together with the cold excitation spots, we observe inhibition of emission by the phase-tuned nanoantenna. The fluorescence lifetime of a molecule scanned in such volumes becomes longer, showing slow down of spontaneous decay. In conclusion, the spatial phase of a nanoantenna is a powerful knob to tune between enhancement and inhibition in a 3-dimensional subwavelength volume.

Nanoscale Design of the Local Density of Optical States.

We propose a design concept for tailoring the local density of optical states (LDOS) in dielectric nanostructures, based on the phase distribution of the scattered optical fields induced by point-like emitters. First we demonstrate that the LDOS can be expressed in terms of a coherent summation of constructive and destructive contributions. By using an iterative approach, dielectric nanostructures can be designed to effectively remove the destructive terms. In this way, dielectric Mie resonators, featuring low LDOS for electric dipoles, can be reshaped to enable enhancements of 3 orders of magnitude. To demonstrate the generality of the method, we also design nanocavities that enhance the radiated power of a circular dipole, a quadrupole, and an arbitrary collection of coherent dipoles. Our concept provides a powerful tool for high-performance dielectric resonators and affords fundamental insights into light-matter coupling at the nanoscale.

Imaging of Plasmonic Chiral Radiative Local Density of States with Cathodoluminescence Nanoscopy.

Chiral light-matter interactions as an emerging aspect of quantum optics enable exceptional physical phenomena and advanced applications in nanophotonics through the nanoscale exploitation of photon-emitter interactions. The chiral radiative properties of quantum emitters strongly depend on the photonic environment, which can be drastically altered by plasmonic nanostructures with a high local density of states (LDOS). Hence, precise knowledge of the chiral photonic environment is essential for manipulating the chirality of light-matter interactions, which requires high resolution chiral characterization techniques. In this work, chiral radiative LDOS distributions of single plasmonic nanostructures that directly govern the chiral radiative spontaneous decay of quantum emitters are imaged at the nanoscale by using cathodoluminescence nanoscopy, enabling precise and highly efficient control of chiral photon emission for chiroptical technologies. Radiative LDOS hot-spots with the chirality larger than 93% are obtained by properly designing chiral plasmonic modes of Au nanoantennas. After fabricating monolayered WSe2 nanodisks (NDs) at chiral radiative LDOS hot-spots and forming ND/Au hybrid nanostructures, the chiral radiative properties of WSe2 NDs are significantly modified, leading to chiral photoluminescence. Our experimental concept and method provide an effective way to characterize and manipulate chiral light-matter interactions at the nanoscale, facilitating future applications in chiral quantum nanophotonics such as single-photon sources and light emission devices.

UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm.

Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.

Nanoscale Imaging of Light-Matter Coupling Inside Metal-Coated Cavities with a Pulsed Electron Beam.

Many applications in (quantum) nanophotonics rely on controlling light-matter interaction through strong, nanoscale modification of the local density of states (LDOS). All-optical techniques probing emission dynamics in active media are commonly used to measure the LDOS and benchmark experimental performance against theoretical predictions. However, metal coatings needed to obtain strong LDOS modifications in, for instance, nanocavities, are incompatible with all-optical characterization. So far, no reliable method exists to validate theoretical predictions. Here, we use subnanosecond pulses of focused electrons to penetrate the metal and excite a buried active medium at precisely defined locations inside subwavelength resonant nanocavities. We reveal the spatial layout of the spontaneous-emission decay dynamics inside the cavities with deep-subwavelength detail, directly mapping the LDOS. We show that emission enhancement converts to inhibition despite an increased number of modes, emphasizing the critical role of optimal emitter location. Our approach yields fundamental insight in dynamics at deep-subwavelength scales for a wide range of nano-optical systems.

Europium-Doped NaYF4 Nanocrystals as Probes for the Electric and Magnetic Local Density of Optical States throughout the Visible Spectral Range.

Absorption and emission in the ultraviolet, visible, and infrared spectral range are usually mediated by the electric-field component of light. Only some electronic transitions have significant "magnetic-dipole" character, meaning that they couple to the magnetic field of light. Nanophotonic control over magnetic-dipole emission has recently been demonstrated, and magnetic-dipole transitions have been used to probe the magnetic-field profiles of photonic structures. However, the library of available magnetic-dipole emitters is currently limited to red or infrared emitters and mostly doped solids. Here, we show that NaYF4 nanocrystals doped with Eu3+ have various electric- and magnetic-dipole emission lines throughout the visible spectral range from multiple excited states. At the same time, the colloidal nature of the nanocrystals allows easy handling. We demonstrate the use of these nanocrystals as probes for the radiative electric and magnetic local density of optical states in a planar mirror geometry. A single emission spectrum can reveal enhancement or suppression of the density of optical states at multiple frequencies simultaneously. Such nanocrystals may find application in the characterization of nanophotonic structures or as model emitters for studies into magnetic light-matter interaction at optical frequencies.

Local densities connect spatial ecology to game, multilevel selection and inclusive fitness theories of cooperation.

Cooperation plays a crucial role in many aspects of biology. We use the spatial ecological metrics of local densities to measure and model cooperative interactions. While local densities can be found as technical details in current theories, we aim to establish them as central to an approach that describes spatial effects in the evolution of cooperation. A resulting local interaction model neatly partitions various spatial and non-spatial selection mechanisms. Furthermore, local densities are shown to be fundamental for important metrics of game theory, multilevel selection theory and inclusive fitness theory. The corresponding metrics include structure coefficients, spatial variance, contextual covariance, relatedness, and inbreeding coefficient or F-statistics. Local densities serve as the basis of an emergent spatial theory that draws from and brings unity to multiple theories of cooperation.

High-Fugacity Expansion and Crystallization in Non-sliding Hard-Core Lattice Particle Models Without a Tiling Constraint.

In this paper, we prove the existence of a crystallization transition for a family of hard-core particle models on periodic graphs in dimension d ≥ 2 . We consider only models featuring a single species of particles, which in particular forbids the particles from rotation and reflection, and establish a criterion under which crystallization occurs at sufficiently high densities. The criterion is more general than that in Jauslin and Lebowitz (Commun Math Phys 364:655-682, 2018), as it allows models in which particles do not tile the space in the close-packing configurations, such as discrete hard-disk models. To prove crystallization, we prove that the pressure is analytic in the inverse of the fugacity for large enough complex fugacities, using Pirogov-Sinai theory. One of the main new tools used for this result is the definition of a local density, based on a discrete generalization of Voronoi cells. We illustrate the criterion by proving that it applies to three examples: staircase models and the radius 2.5 hard-disk model on Z 2 , and a heptacube model on Z 3 .

Identification and Manipulation of Atomic Defects in Monolayer SnSe.

SnSe, an environmental-friendly group-IV monochalcogenide semiconductor, demonstrates outstanding performance in various applications ranging from thermoelectric devices to solar energy harvesting. Its ultrathin films show promise in the fabrication of ferroelectric nonvolatile devices. However, the microscopic identification and manipulation of point defects in ultrathin SnSe single crystalline films, which significantly impact their electronic structure, have been inadequately studied. This study presents a comprehensive investigation of point defects in monolayer SnSe films grown via molecular beam epitaxy. By combining scanning tunneling microscopy (STM) characterization with first-principles calculations, we identified four types of atomic/molecular vacancies, four types of atomic substitutions, and three types of extrinsic defects. Notably, we have demonstrated the ability to convert a substitutional defect into a vacancy and to reposition an adsorbate by manipulating a single atom or molecule using an STM tip. We have also analyzed the local atomic displacement induced by the vacancies. This work provides a solid foundation for engineering the electronic structure of future SnSe-based nanodevices.

Rise and Decay of Photoluminescence in Upconverting Lanthanide-Doped Nanocrystals.

Nanocrystals (NCs) doped with lanthanides are capable of efficient photon upconversion, i.e., absorbing long-wavelength light and emitting shorter-wavelength light. The internal processes that enable upconversion are a complex network of electronic transitions within and energy transfer between dopant centers. In this work, we study the rise and decay dynamics of upconversion emission from ß-NaYF4 NCs codoped with Er3+ and Yb3+. The rise dynamics of the red and green upconverted emissions are nonlinear, reflecting the nonlinear nature of upconversion and revealing the mechanisms that populate the emitting states. The excited-state decay dynamics are nonexponential. We unravel the underlying decay pathways using photonic experiments. These reveal the contributions of different upconversion pathways visually, as each pathway exhibits a distinct response to systematic variation of the local density of optical states. Moreover, the effect of the local density of optical states on core-only NCs is qualitatively different from core-shell NCs. This is due to the different balance between feeding and decay of the electronic levels that produce upconverted emission. The understanding of the upconversion dynamics provided here could lead to better imaging and sensing methods relying on upconversion lifetimes or guide the rational optimization of the dopant concentrations for brighter upconversion.

Density estimations and comparisons of a fragmented single fiber using X-ray computed tomography.

A commercial X-ray computed tomography (CT) apparatus using a quasi-monochromatic beam was utilized for density estimations and comparisons of a fragmented single fiber. The validation of quasi-monochromaticity of the X-ray source was investigated by radiograph measurements. For the case of a transmittance higher than 50%, the contribution of Cu Kα characteristic X-rays was dominant. To realize a sufficient statistical quality, an attempt to increase the number of averaged voxels was demonstrated using the neighboring slices of the 3D-CT image. A minimum value of the coefficient of variation (CV) was achieved using multiple images rather than using a single image. The observed values of the inverse of the transmitted X-ray intensity (CT value) of the polymers showed a fairly good relationship with their density. An analytical curve derived from measurements of reference samples of known densities could provide the relative density of an unknown fragmented fiber down to the size of 30 µm in diameter and 35 µm in length. The CV of the estimated density was from 1.5 to 2%, which was estimated from the CV of CT values. Moreover, the correlation of CT values was improved with the linear absorption coefficient than the density. A better performance of discrimination of polymers including fibers might be realized with the difference of linear absorption coefficients for X-rays.

The influence of explicit local dynamics on range expansions driven by long-range dispersal.

Range expansions are common in natural populations. They can take such forms as an invasive species spreading into a new habitat or a virus spreading from host to host during a pandemic. When the expanding species is capable of dispersing offspring over long distances, population growth is driven by rare but consequential long-range dispersal events that seed satellite colonies far from the densely occupied core of the population. These satellites accelerate growth by accessing unoccupied territory, and also act as reservoirs for maintaining neutral genetic variation present in the originating population, which would ordinarily be lost to drift. Prior theoretical studies of dispersal-driven expansions have shown that the sequential establishment of satellites causes initial genetic diversity to be either lost or maintained to a level determined by the breadth of the distribution of dispersal distances. If the tail of the distribution falls off faster than a critical threshold, diversity is steadily eroded over time; by contrast, broader distributions with a slower falloff allow some initial diversity to be maintained for arbitrarily long times. However, these studies used lattice-based models and assumed an instantaneous saturation of the local carrying capacity after the arrival of a founder. Real-world populations expand in continuous space with complex local dynamics, which potentially allow multiple pioneers to arrive and establish within the same local region. Here, we evaluate the impact of local dynamics on the population growth and the evolution of neutral diversity using a computational model of range expansions with long-range dispersal in continuous space, with explicit local dynamics that can be controlled by altering the mix of local and long-range dispersal events. We found that many qualitative features of population growth and neutral genetic diversity observed in lattice-based models are preserved under more complex local dynamics, but quantitative aspects such as the rate of population growth, the level of maintained diversity, and the rate of decay of diversity all depend strongly on the local dynamics. Besides identifying situations in which modeling the explicit local population dynamics becomes necessary to understand the population structure of jump-driven range expansions, our results show that local dynamics affects different features of the population in distinct ways, and can be more or less consequential depending on the degree and form of long-range dispersal as well as the scale at which the population structure is measured.

Emergence of metallic surface states and negative differential conductance in thinß-FeSi2films on Si(001).

The electronic properties of the surface ofß-FeSi2have been debated for a long. We studied the surface states ofß-FeSi2films grown on Si(001) substrates using scanning tunnelling microscopy (STM) and spectroscopy (STS), with the aid of density functional theory calculations. STM simulations using the surface model proposed by Romanyuket al(2014Phys. Rev.B90155305) reproduce the detailed features of experimental STM images. The result of STS showed metallic surface states in accordance with theoretical predictions. The Fermi level was pinned by a surface state that appeared in the bulk band gap of theß-FeSi2film, irrespective of the polarity of the substrate. We also observed negative differential conductance at ∼0.45 eV above the Fermi level in STS measurements performed at 4.5 K, reflecting the presence of an energy gap in the unoccupied surface states ofß-FeSi2.