Single Locality Research Articles

Catasetum × vilhenense (Orchidaceae: Catasetinae), a new natural hybrid from the Amazon forest

This study describes a new natural hybrid of Catasetum, C. × vilhenense, from the State of Rondônia (Brazil) found in ombrophilous forests near the Roosevelt River. The hybrid shows intermediate morphological features between its parental species, C. ariquemense and C. multifidum, with specific differences in floral morphology and inflorescence structure. We provide detailed data on its habitat, ecology, and geographic distribution, and a photographic plate of the hybrid. Given its restricted range, limited to a single locality with an area of occupancy (AOO) of less than 10 km², C. × vilhenense may be Critically Endangered (CR) due to habitat loss and fragmentation. An identification key is also proposed for the species and natural hybrids of Catasetum with symmetrical and parallel antennae in the state of Rondônia, contributing to the understanding and classification of this genus in the Amazon forest.

Artificial Intelligence-Empowered Spectroscopic Single Molecule Localization Microscopy.

Spectroscopic single-molecule localization microscopy (SMLM) has revolutionized the visualization and analysis of molecular structures and dynamics at the nanoscale level. The technique of combining high spatial resolution of SMLM with spectral information, enables multicolor super-resolution imaging and provides insights into the local chemical environment of individual molecules. However, spectroscopic SMLM faces significant challenges, including limited spectral resolution and compromised localization precision because of signal splitting and the difficulties in analyzing complex, multidimensional datasets, that limit its application in studying intricate biological systems and materials. The recent integration of artificial intelligence (AI) with spectroscopic SMLM has emerged as a powerful approach for addressing these challenges. Here, it is reviewed how AI-based methods applied to spectroscopic SMLM enhance and expand the capabilities of these applications. Recent advancements in AI-driven data analysis for spectroscopic SMLM, including improved spectral classification, localization precision, and extraction of rich spectral information from unmodified point-spread functions are discussed, further examining their applications in biological studies, materials science, and single-molecule reaction analysis, which highlight how AI provides new insights into molecular behavior and interactions. The AI-empowered approach adds new dimensions of information and provides new opportunities and insights into the nanoscale world of rapidly evolving field of spectroscopic SMLM.

Mapping membrane biophysical nano-environments

The mammalian plasma membrane is known to contain domains with varying lipid composition and biophysical properties. However, studying these membrane lipid domains presents challenges due to their predicted morphological similarity to the bulk membrane and their scale being below the classical resolution limit of optical microscopy. To address this, we combine the solvatochromic probe di-4-ANEPPDHQ, which reports on its biophysical environment through changes in its fluorescence emission, with spectrally resolved single-molecule localisation microscopy. The resulting data comprises nanometre-precision localisation coordinates and a generalised polarisation value related to the probe’s environment – a marked point pattern. We introduce quantification algorithms based on topological data analysis (PLASMA) to detect and map nano-domains in this marked data, demonstrating their effectiveness in both artificial membranes and live cells. By leveraging environmentally sensitive fluorophores, multi-modal single molecule localisation microscopy, and advanced analysis methods, we achieve nanometre scale mapping of membrane properties and assess changes in response to external perturbation with methyl-β-cyclodextrin. This integrated methodology represents an integrated toolset for investigating marked point pattern data at nanometre spatial scales.

Integrating synthetic datasets with CLIP semantic insights for single image localization advancements

Accurate localization of pedestrians and mobile robots is critical for navigation, emergency response, and autonomous driving. Traditional localization methods, such as satellite signals, often prove ineffective in certain environments, and acquiring sufficient positional data can be challenging. Single image localization techniques have been developed to address these issues. However, current deep learning frameworks for single image localization that rely on domain adaptation fail to effectively utilize semantically rich high-level features obtained from large-scale pretraining. This paper introduces a novel framework that leverages the Contrastive Language-Image Pre-training model and prompts to enhance feature extraction and domain adaptation through semantic information. The proposed framework generates an integrated score map from scene-specific prompts to guide feature extraction and employs adversarial components to facilitate domain adaptation. Furthermore, a reslink component is incorporated to mitigate the precision loss in high-level features compared to the original data. Experimental results demonstrate that the use of prompts reduces localization errors by 26.4 % in indoor environments and 24.3 % in outdoor settings. The model achieves localization errors as low as 0.75 m and 8.09 degrees indoors, and 4.56 m and 7.68 degrees outdoors. Analysis of prompts from labeled datasets confirms the model’s capability to effectively interpret scene information. The weights of the integrated score map enhance the model’s transparency, thereby improving interpretability. This study underscores the efficacy of integrating semantic information into image localization tasks.

Automated cell tracking using 3D nnUnet and Light Sheet Microscopy to quantify regional deformation in zebrafish.

Light Sheet Microscopy (LSM) in conjunction with embryonic zebrafish, is rapidly advancing three-dimensional, in vivo characterization of myocardial contractility. Preclinical cardiac deformation imaging is predominantly restricted to a low-order dimensionality image space (2i) or suffers from poor reproducibility. In this regard, LSM has enabled high throughput, non-invasive 4i (3d+time) characterization of dynamic organogenesis within the transparent zebrafish model. More importantly, LSM offers cellular resolution across large imaging Field-of-Views at millisecond camera frame rates, enabling single cell localization for global cardiac deformation analysis. However, manual labeling of cells within multilayered tissue is a time-consuming task and requires substantial expertise. In this study, we applied the 3i nnU-Net with Linear Assignment Problem (LAP) framework for automated segmentation and tracking of myocardial cells. Using binarized labels from the neural network, we quantified myocardial deformation of the zebrafish ventricle across 4-6 days post fertilization (dpf). Our study offers tremendous promise for developing highly scalable and disease-specific biomechanical quantification of myocardial microstructures.

TC10 differently controls the dynamics of Exo70 in growth cones of cortical and hippocampal neurons

The exocyst is an octameric protein complex that acts as a tether for GOLGI-derived vesicles at the plasma membrane during exocytosis. It is involved in membrane expansion during axonal outgrowth. Exo70 is a major subunit of the exocyst complex and is controlled by TC10, a Rho family GTPase. How TC10 affects the dynamics of Exo70 at the plasma membrane is not well understood. There is also evidence that TC10 controls Exo70 dynamics differently in nonpolar cells and axons. To address this, we used super-resolution microscopy to study the spatially resolved effects of TC10 on Exo70 dynamics in HeLa cells and the growth cone of cortical and hippocampal neurons. We generated single particle localization and trajectory maps and extracted mean square displacements, diffusion coefficients, and alpha coefficients to characterize Exo70 diffusion. We found that the diffusivity of Exo70 was different in nonpolar cells and in the growth cone of neurons. TC10 stimulated the mobility of Exo70 in HeLa cells, but decreased the diffusion of Exo70 in the growth cone of cortical neurons. In contrast to cortical neurons, TC10 overexpression did not affect the mobility of Exo70 in the axonal growth cone of hippocampal neurons. These data suggest that mainly exocyst tethering in cortical neurons was under the control of TC10.

×Gymnigritella suaveolens (Orchidaceae), a New Natural Hybrid to the Flora of Serbia

×Gymnigritella suaveolens (Orchidaceae), a natural hybrid between Gymnadenia conopsea and Nigritella rhellicani, was found at Mt Kopaonik for the first time in Serbia, representing its single known locality in the Central Balkans. Morphological characteristics, distribution data, habitat and ecological preferences are indicated. The hybrid was found at 1552 m a.s.l., on sericite schists, as a member of the Festuco-Deschampsietum flexuosae grassland community. Two hybrid specimens were recorded at the site where the two parental species grow in sympatry, and where the population size of G. conopsea was larger than the population size of N. rhellicani.

Research on the fusion positioning system of UWB/LiDAR based on the algorithm of SPF and KF

In order to overcome the disadvantages of single sensor localization in complex indoor environments, this paper proposes a design scheme for the ultra-wide band (UWB) and LiDAR co-location fusion system. To attain the ideal positioning effect of this system, a segmented point fusion approach based on Euclid's theorem is put forward to optimize the conventional extended Kalman filter and unscented Kalman filter algorithms. It can effectively reduce the positioning error of UWB in non-line-of-sight environments and under the multipath effect, and also assist LiDAR in correcting trajectory drift in sparsely textured scenes. Through both simulations and experiments, the feasibility of the proposed algorithm in the UWB/LiDAR positioning system is verified. The results reveal that the positioning error of the enhanced multi-sensor fusion algorithm is reduced by 22% compared with the original algorithm and 25.7% compared with the single sensor positioning method.

Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming

Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of [Formula: see text] pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.

An overview of the Brazilian Atlantic Forest species of Pachythone Bates (Lepidoptera: Riodinidae), with the description of a new species and an updated checklist of Pachythonina.

The taxonomy of the Brazilian Atlantic Forest endemic species belonging to the Neotropical metalmark genus Pachythone Bates, 1868 is revised. A new endemic species is described, Pachythone gagarini Dolibaina & Dias, sp. nov., based on historical specimens from a single locality in Rio de Janeiro state. Adults and genitalia of the new species are illustrated and described in detail. Pachythone palades Hewitson, (1873) and P. pasicles Hewitson, (1873), described from females and males, respectively, and regarded as two distinct species since their descriptions, are here recognized as a single species, the former being considered a junior subjective synonym of the latter (syn. nov.). Lectotypes are designated for both P. palades and P. pasicles. Pachythone sumare Callaghan, 1999, so far known from its type locality in Rio de Janeiro state, is here reported from Joinville, Santa Catarina, which extends its occurrence 700 km southwards. Lastly, an updated checklist for the recently proposed subtribe Pachythonina is given.

Archaeopteris trees at high southern latitudes in the late Devonian

During the Devonian Period plants first reached forest stature, impacting chemical weathering of rocks, fluvial systems, atmospheric composition and possibly aquatic eutrophication. Hypothetically these factors contributed to increasing climatic instability culminating in the End Devonian Mass Extinction Event. Understanding the timing of the spread of forests is however a prerequisite to correlation with its proposed consequences. Though evidence for forests at low palaeolatitudes demonstrates their emergence by the mid Devonian, sparse high-palaeolatitude records almost entirely comprise herbaceous lycopods. By the Famennian forest ecosystems are widely evidenced at low palaeolatitudes, however high latitude palaeofloras are almost exclusively represented by a single locality, the Waterloo Farm lagerstätte from South Africa (approximate palaeolatitude, 70°S). Understanding climatic and ecological conditions at this locality is doubly important as it also hosts diverse vertebrate taxa, including the only high latitude Devonian tetrapods. Archaeopteris, the quintessential Late Devonian woody tree, has previously been identified at this locality on the basis of leafy branch system fragments, though some uncertainty has remained as to whether these represent tree sized organisms. Here we present co-occurring large axes, including a trunk base, attributable to Archaeopteris trees inferred to be in excess of 20 m height, the first demonstration of forest stature at high latitudes in the Devonian. This possibly reflects high latitude climatic amelioration, resultant from warm ocean currents circulating southwards in response to progressive closure of the Iapetus Sea. As such, changing continental configurations may have indirectly facilitated the spread of forest ecosystems and helped to drive climatic instability and ultimately extinctions towards the end of the Devonian.

Targeted Photoactivatable Green-Emitting BODIPY Based on Directed Photooxidation-Induced Activation and its Application to Live Dynamic Super-Resolution Microscopy.

Photoactivatable fluorescent probes are valuable tools in bioimaging for tracking cells down to single molecules and for single molecule localization microscopy. For the latter application, green emitting dyes are in demand. We herein developed an efficient green-emitting photoactivatable furanyl-BODIPY (PFB) and we established a new mechanism of photoactivation called Directed Photooxidation Induced Activation (DPIA) where the furan is photo-oxidized in a directed manner by the singlet oxygen produced by the probe. The efficient photoconverter (93-fold fluorescence enhancement at 510 nm, 49 % yield conversion) is functionalizable and allowed targeting of several subcellular structures and organelles, which were photoactivated in live cells. Finally, we demonstrated the potential of PFB in super-resolution imaging by performing PhotoActivated Localization Microscopy (PALM) in live cells.

Petrogenesis of Montana, USA Sapphires Inferred from Oxygen Isotopes and Zircon Inclusions

Abstract Montana hosts the largest sapphire deposits in the US, but the genesis of and connection among the various secondary and primary sapphire occurrences remains cryptic. In situ SIMS measurements of oxygen isotopes in sapphires and zircon inclusions in sapphires provide an opportunity to study the isotope and trace element geochemistry in order to understand sapphire-forming protoliths (i.e. crustal setting and alteration). Sapphire from Montana was transported as xenocrysts in carrier (host) magmas that resorbed sapphire exteriors during transport. The timing and nature of sapphire genesis is elucidated by SIMS measurements of trace elements and U–Pb from discrete zones in zircon inclusions with rims that are interpreted to be syngenetic with host sapphire. Montana sapphires exhibit a large range of δ18O values, from −3‰ to +12‰ VSMOW. However, all but two anomalous crystals fall in the range of 0‰ to 8‰. There is significant crystal-to-crystal variability yet averages at most deposits are consistent with high-temperature equilibration with the mantle (δ18O(Crn) = 4.4‰ to 5.7‰), with the exception of the commercial sapphire deposits at Rock Creek that average 2.7‰. Ruby analyses are limited, but typically have lower δ18O values compared to sapphires from the same detrital localities. Homogeneity within individual crystals (avg. 2 s = ±0.2‰) indicates the absence of isotopically distinct fluid or melt during crystallization. But intercrystalline δ18O ranges by up to 7‰ at a single locality, suggesting sapphire variability at a deposit reflects heterogeneity in the original protolith. Oxygen isotope fractionations between zircon rims and surrounding sapphire suggest comagmatic zircon inclusions and corundum equilibrated at high temperature. No correlation is seen for the degree of radiation damage and alteration of δ18O(Zrc) when zircon inclusions are surrounded and armored by sapphire. U–Pb ages and trace elements were measured in a small subset of syngenetic zircon inclusions in Dry Cottonwood Creek sapphires, revealing a Proterozoic (1778 ± 9 Ma) age for the protolith of sapphires at this locality and a likely polygenetic history. Previous work has suggested formation of these sapphires through partial melting of anorthosites and several anorthosites occur locally and match the age of zircon inclusion cores—the Boehls Butte anorthosite (~180 km NW of Rock Creek) and the Bitterroot anorthosite (~55 km W of Rock Creek) could correlate with Al-rich protoliths at depth. Proterozoic U–Pb ages of zircon from the Boehls Butte anorthosite (1787 ± 2 Ma) match well with the age of zircon inclusion cores in Dry Cottonwood Creek sapphires and suggest genesis in these or similar protoliths. Zircon rims with Tera-Wasserburg lower intercept ages of 110 ± 9 Ma are consistent with previous observations of a xenocrystic relationship to the ~50 Ma Eocene volcanic rocks. Corundum that formed over 50 Ma prior to being scavenged by Eocene magmas likely originated by the anatexis of Precambrian anorthosites and possibly other aluminum-rich rocks at depth.

Contribution to the knowledge of the Amphipoda (stygobionts) collected from the Meknes aquifer, Morocco

Abstract Groundwater in the Saïss Basin is an important resource economic and social for the region. They are also the reservoir of an original biological diversity. The subterranean aquatic fauna is to date largely unknown in the Meknes region. It, therefore, represents both a significant part of the national natural heritage and an essential part of the regional natural heritage to be taken into account when conservation measures are considered. Eight wells and two springs were investigated between February 2013 and February 2014. The study was conducted in three localities in the surroundings of Meknes (Meknes, El Hajeb, and Agouray). In fact, Amphipod distribution data is based on unpublished data from my thesis work. Most hypogean taxa are crustaceans, principally the Amphipoda. This research work is a contribution to the knowledge of Amphipoda (stygobionts) collected from the Meknes aquifer. Among the subterranean Amphipoda, species belonging to the genera Metacrangonyx and Pseudoniphargus, which have never been described previously, are limited to a single locality (Agouray), typically found in Wells W4 and W5. In addition, the first record of (Metacrangonyx sp, and Pseudoniphargus sp1 and sp2.) for the stygofauna of the Meknes aquifer was also recorded. These results represent an important contribution to regional biodiversity and finally to complete the inventory of the stygofauna of Morocco.

Achieving Single Cell Acoustic Localisation with Deactivation Super Resolution.

Super-resolution optical microscopy enables optical imaging of cells, molecules and other biological structures beyond the diffraction limit. However, no similar method exists to super-resolve specific cells with ultrasound. Here we introduce Deactivation Super Resolution (DSR), an ultrasound imaging method that uses the acoustic deactivation of genetically encodable contrast agents to super-resolve individual cells with ultrasound as they navigate through structures that cannot be resolved by conventional imaging methods. DSR takes advantage of gas vesicles, which are air-filled sub-micron protein particles that can be expressed in genetically engineered cells to produce ultrasound contrast. Our experimental results show that DSR can distinguish sub-wavelength microstructures that standard B-mode ultrasound images fail to resolve by super- localizing individual mammalian cells. This study provides a proof of concept for the potential of DSR to serve as a super- resolution ultrasound technique for individual cell localization, opening new horizons in the field.

Three-color single-molecule localization microscopy in chromatin.

Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin. Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales, from nanometers to micrometers. Single molecule localization microscopy (SMLM) methods, such as STORM, are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization. Multi-label imaging of chromatin is necessary to unpack its structural complexity. However, these efforts are challenged by the high-density nuclear environment, which can affect antibody binding affinities, diffusivity and non-specific interactions. Optimizing buffer conditions, fluorophore stability, and antibody specificity is crucial for achieving effective antibody conjugates. Here, we demonstrate a sequential immunolabeling protocol that reliably enables three-label studies within the dense nuclear environment. This protocol couples multiplexed localization datasets with a robust analysis algorithm, which utilizes localizations from one target as seed points for distance, density and multi-label joint affinity measurements to explore complex organization of all three targets. Applying this multi-plexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories, but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters. This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.

The Limits of the Metapopulation: Lineage Fragmentation in a Widespread Terrestrial Salamander (Plethodon cinereus)

Abstract In vicariant species formation, divergence results primarily from periods of allopatry and restricted gene flow. Widespread species harboring differentiated, geographically distinct sublineages offer a window into what may be a common mode of species formation, whereby a species originates, spreads across the landscape, then fragments into multiple units. However, incipient lineages usually lack reproductive barriers that prevent their fusion upon secondary contact, blurring the boundaries between a single, large metapopulation-level lineage and multiple independent species. Here, we explore this model of species formation in the Eastern Red-backed Salamander (Plethodon cinereus), a widespread terrestrial vertebrate with at least 6 divergent mitochondrial clades throughout its range. Using anchored hybrid enrichment data, we applied phylogenomic and population genomic approaches to investigate patterns of divergence, gene flow, and secondary contact. Genomic data broadly match most mitochondrial groups but reveal mitochondrial introgression and extensive admixture at several contact zones. While species delimitation analyses in Bayesian Phylogenetics and Phylogeography supported 5 lineages of P. cinereus, genealogical divergence indices (gdi) were highly sensitive to the inclusion of admixed samples and the geographic representation of candidate species, with increasing support for multiple species when removing admixed samples or limiting sampling to a single locality per group. An analysis of morphometric data revealed differences in body size and limb proportions among groups, with a reduction of forelimb length among warmer and drier localities consistent with increased fossoriality. We conclude that P. cinereus is a single species, but one with highly structured component lineages of various degrees of independence.

A phylogeographic analysis of the North American medicinal leech, Macrobdella decora (Say, 1824)

AbstractIn spite of their important roles in many ecosystems, data regarding population structure and biogeographic patterns of leeches are scarce. To begin to address this knowledge gap, we herein perform a phylogeographic analysis of the North American medicinal leech, Macrobdella decora (Say, 1824). A total of 224 M. decora specimens were collected from 35 localities across large swaths of USA and Canada and covering most of the known range of the species. Using four loci (mitochondrial cytochrome c oxidase subunit I [COI] and NADH dehydrogenase I [ND1], as well as nuclear 18S rRNA [18S] and 28S rRNA [28S]), we construct phylogenetic trees using several optimality criteria and superimpose geographic patterns onto the trees in order to tease out any potential structure among the populations. Rather surprisingly, given the large geographic range of the species and abundance of potential geographic barriers to gene flow, the analyses showed a conspicuous lack of structure among the different populations of M. decora. However, an AMOVA did show statistically significant differences between the genetic variation within populations and between populations (COI: FST = 0.65412, p < .00001; ND1: FST = 0.69245, p < .00001), which was largely driven by only 6 out of the 35 populations, and indicated a potential barrier for dispersal across the Appalachian Mountains. Finally, a Mantel test showed a weak, but significant, correlation between geographic distance and genetic distance (COI: r = 0.209, p = .027; ND1: r = 0.1289, p = .030); however, this correlation was primarily driven by a single locality. The overall weak structure suggests that M. decora is panmictic throughout its range, and we discuss this in light of previous population level studies in both bloodfeeding and non‐bloodfeeding species, concluding that the lack of structure in M. decora might be due to its high capacity for dispersal via hosts.

DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships.

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

Markov chain models of emitter activations in single molecule localization microscopy.

A well-reasoned model of data movie in single molecule localization microscopy (SMLM) is desired. A model of data movie can be decomposed into a model of emitter activation process and a model of data frame. In this paper, we focus on Markov chain modeling and analyzing of emitter activation process for both cycled and continuous illuminations. First, a two-phase Markov chain is proposed to model the activation process for a pair of conjugated activator and emitter with cycled illumination. By converting the frame-based Markov chain into several cycle-based Markov chains, the stationary state distribution in the photoactivatable period is derived. Further obtained are several formulas that capture the characterization of the two-phase Markov chain. Second, the Markov chain and analytical result are extended to the continuous illumination where an emitter is excited continuously in all frames. Finally, incorporating the model of emitter activation process with our previous model of data frame, the model of data movie for both cycled and continuous illuminations in 3D and 2D imaging are simulated by custom codes. It is shown that the model can synthesize data movies well and the analytical formulas predict the simulation results accurately. The models provide a means to be broadly utilized in generating well-reasoned data movies for training of neural networks and evaluation of localization algorithms.