Spatiotemporal Selection Research Articles

Potential fitness consequences of roosting spatiotemporal selection in an endangered endemic damselfly: conservation implications

Understanding habitat requirements of species of conservation concern is central for their conservation and management. Although much of the research attention has been focused on reproductive sites, the understanding of roosting behavior and microhabitat selection, and their potential fitness consequences is also crucial. Here, we assess the roosting behavior of an endangered endemic damselflyCalopteryx exulSelys in a lotic habitat of Northeast Algeria. Based on marked individuals, we specifically investigated their vertical and horizontal distribution at roosting sites, as well as the timing of roosting and its correlation with lifespan (as a measure of fitness). We found that individuals were philopatric to roosting sites and less so to vertical stratification. Roosting sites were used for both foraging and roosting. Individuals that occupied lower strata in roosting sites had longer lifespans and ceased roosting earlier. Average temperature of the day affected the timing of roosting such that on warm days roosting started later and ended earlier. Individuals with longer lifespans roosted earlier, suggesting potential scramble competition for roosting sites. Our results suggest thatC. exulindividuals show variability in the vertical and horizontal location as well as the timing of roosting, and these choices potentially have fitness consequences. This study highlights the importance of bank vegetation as roosting sites for lotic insects, and emphasizes the benefits of protecting these sites and including them as integral parts of the conservation plans of species.

Accounting for behaviour in fine-scale habitat selection: A case study highlighting methodological intricacies.

Animal habitat selection-central in both theoretical and applied ecology-may depend on behavioural motivations such as foraging, predator avoidance, and thermoregulation. Step-selection functions (SSFs) enable assessment of fine-scale habitat selection as a function of an animal's movement capacities and spatiotemporal variation in extrinsic conditions. If animal location data can be associated with behaviour, SSFs are an intuitive approach to quantify behaviour-specific habitat selection. Fitting SSFs separately for distinct behavioural states helped to uncover state-specific selection patterns. However, while the definition of the availability domain has been highlighted as the most critical aspect of SSFs, the influence of accounting for behaviour in the use-availability design has not been quantified yet. Using a predator-free population of high-arctic muskoxen Ovibos moschatus as a case study, we aimed to evaluate how (1) defining behaviour-specific availability domains, and/or (2) fitting separate behaviour-specific models impacts (a) model structure, (b) estimated selection coefficients and (c) model predictive performance as opposed to behaviour-unspecific approaches. To do so, we first applied hidden Markov models to infer different behavioural modes (resting, foraging, relocating) from hourly GPS positions (19 individuals, 153-1062 observation days/animal). Using SSFs, we then compared behaviour-specific versus behaviour-unspecific habitat selection in relation to terrain features, vegetation and snow conditions. Our results show that incorporating behaviour into the definition of the availability domain primarily impacts model structure (i.e. variable selection), whereas fitting separate behaviour-specific models mainly influences selection strength. Behaviour-specific availability domains improved predictive performance for foraging and relocating models (i.e. behaviours with medium to large spatial displacement), but decreased performance for resting models. Thus, even for a predator-free population subject to only negligible interspecific competition and human disturbance we found that accounting for behaviour in SSFs impacted model structure, selection coefficients and predictive performance. Our results indicate that for robust inference, both a behaviour-specific availability domain and behaviour-specific model fitting should be explored, especially for populations where strong spatiotemporal selection trade-offs are expected. This is particularly critical if wildlife habitat preferences are estimated to inform management and conservation initiatives.

In situ visualization of apoptosis process and modulation of cancer cell ablation based on a monocomponent multi-functional smart nanozyme

Nanozymes have exhibited an excellent prospect in cancer ablation. However, the real-time sensing of anticancer efficacy in catalytic process is often neglected. Designing the smart nanozymes for in situ visualizing treatment efficacy at subcellular level during catalytic therapy is crucial for precise cancer treatment, but hampered owing to the lack of a robust design strategy and efficient synthesis method. Herein, a monocomponent multi-functional photo-responsive carbon dots-based (CDs) nanozyme for instant self-predicting therapy efficacy and synchronous cancer cell ablation is developed through a direct microwave-assisted method. The CDs nanozyme can significantly induce cancer cell ablation due to its unique dual enzyme-mimic activities (oxidase-mimic and peroxidase-mimic), mitochondria-targeting ability, light-response feature improved synergistic therapy and high spatiotemporal selection. More importantly, the excessive reactive oxygen species (ROS) generated from catalytic treatment induces mitochondrial oxidative stress, accompanied by decreased mitochondrial membrane potential (MMP), which results in the transfer of CDs nanozyme from the mitochondrion to the nucleolus and thus enables the in situ real-time detection of therapy efficiency via fluorescent signal migration. This work not only represents an emerging paradigm with the self-predicting function for precise cancer treatment but also inspires the future design of the new generation of nanozyme.

Clustering-Based Decision Tree for Vehicle Routing Spatio-Temporal Selection

The algorithm of the clustering-based decision tree, which is a methodology of multimodal fusion, has made many achievements in many fields. However, it is not common in the field of transportation, especially in the application of automobile navigation. Meanwhile, the concept of Spatio-temporal data is now widely used. Therefore, we proposed a vehicle routing Spatio-temporal selection system based on a clustering-based decision tree. By screening and clustering Spatio-temporal data, which is a collection of individual point data based on historical driving data, we can identify the routes and many other features. Through the decision tree modeling of the state information of Spatio-temporal data, which includes the features of the historical data and route selection, we can obtain an optimal result, that is, the route selection made by the system. Moreover, all the above calculations and operations are done on the edge, which is different from the vast majority of current cloud computing vehicle navigation. We have also experimented with our system using real vehicle data. The experiments show that it can output path decision results for a given situation, which takes little time and is the same as the approximated case of networked navigation. The experiments yielded satisfactory results. Our system could save a lot of cloud computing power, which might change the current navigation systems.

Spatio-temporal selection of reference genes in the two congeneric species of Glycyrrhiza

Glycyrrhiza, a genus of perennial medicinal herbs, has been traditionally used to treat human diseases, including respiratory disorders. Functional analysis of genes involved in the synthesis, accumulation, and degradation of bioactive compounds in these medicinal plants requires accurate measurement of their expression profiles. Reverse transcription quantitative real-time PCR (RT-qPCR) is a primary tool, which requires stably expressed reference genes to serve as the internal references to normalize the target gene expression. In this study, the stability of 14 candidate reference genes from the two congeneric species G. uralensis and G. inflata, including ACT, CAC, CYP, DNAJ, DREB, EF1, RAN, TIF1, TUB, UBC2, ABCC2, COPS3, CS, R3HDM2, were evaluated across different tissues and throughout various developmental stages. More importantly, we investigated the impact of interactions between tissue and developmental stage on the performance of candidate reference genes. Four algorithms, including geNorm, NormFinder, BestKeeper, and Delta Ct, were used to analyze the expression stability and RefFinder, a comprehensive software, provided the final recommendation. Based on previous research and our preliminary data, we hypothesized that internal references for spatio-temporal gene expression are different from the reference genes suited for individual factors. In G. uralensis, the top three most stable reference genes across different tissues were R3HDM2, CAC and TUB, while CAC, CYP and ABCC2 were most suited for different developmental stages. CAC is the only candidate recommended for both biotic factors, which is reflected in the stability ranking for the spatio (tissue)-temporal (developmental stage) interactions (CAC, R3HDM2 and DNAJ). Similarly, in G. inflata, COPS3, R3HDM2 and DREB were selected for tissues, while RAN, COPS3 and CS were recommended for developmental stages. For the tissue-developmental stage interactions, COPS3, DREB and ABCC2 were the most suited reference genes. In both species, only one of the top three candidates was shared between the individual factors and their interactions, specifically, CAC in G. uralensis and COPS3 in G. inflata, which supports our overarching hypothesis. In summary, spatio-temporal selection of reference genes not only lays the foundation for functional genomics research in Glycyrrhiza, but also facilitates these traditional medicinal herbs to reach/maximize their pharmaceutical potential.

Spatially explicit models of seasonal habitat for greater sage-grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California.

Defining boundaries of species' habitat across broad spatial scales is often necessary for management decisions, and yet challenging for species that demonstrate differential variation in seasonal habitat use. Spatially explicit indices that incorporate temporal shifts in selection can help overcome such challenges, especially for species of high conservation concern. Greater sage‐grouse Centrocercus urophasianus (hereafter, sage‐grouse), a sagebrush obligate species inhabiting the American West, represents an important case study because sage‐grouse exhibit seasonal habitat patterns, populations are declining in most portions of their range and are central to contemporary national land use policies. Here, we modeled spatiotemporal selection patterns for telemetered sage‐grouse across multiple study sites (1,084 sage‐grouse; 30,690 locations) in the Great Basin. We developed broad‐scale spatially explicit habitat indices that elucidated space use patterns (spring, summer/fall, and winter) and accounted for regional climatic variation using previously published hydrographic boundaries. We then evaluated differences in selection/avoidance of each habitat characteristic between seasons and hydrographic regions. Most notably, sage‐grouse consistently selected areas dominated by sagebrush with few or no conifers but varied in type of sagebrush selected by season and region. Spatiotemporal variation was most apparent based on availability of water resources and herbaceous cover, where sage‐grouse strongly selected upland natural springs in xeric regions but selected larger wet meadows in mesic regions. Additionally, during the breeding period in spring, herbaceous cover was selected strongly in the mesic regions. Lastly, we expanded upon an existing joint–index framework by combining seasonal habitat indices with a probabilistic index of sage‐grouse abundance and space use to produce habitat maps useful for sage‐grouse management. These products can serve as conservation planning tools that help predict expected benefits of restoration activities, while highlighting areas most critical to sustaining sage‐grouse populations. Our joint–index framework can be applied to other species that exhibit seasonal shifts in habitat requirements to help better guide conservation actions.

Spatio-temporal additive regression model selection for urban water demand

Understanding the factors influencing urban water use is critical for meeting demand and conserving resources. To analyze the relationships between urban household-level water demand and potential drivers, we develop a method for Bayesian variable selection in partially linear additive regression models, particularly suited for high-dimensional spatio-temporally dependent data. Our approach combines a spike-and-slab prior distribution with a modified version of the Bayesian group lasso to simultaneously perform selection of null, linear, and nonlinear models and to penalize regression splines to prevent overfitting. We investigate the effectiveness of the proposed method through a simulation study and provide comparisons with existing methods. We illustrate the methodology on a case study to estimate and quantify uncertainty of the associations between several environmental and demographic predictors and spatio-temporally varying household-level urban water demand in Tampa, FL.

High spatiotemporal heterogeneity, clonal selection and neoantigen evolution in acquired sorafenib-resistant patient-derived xenograft models of hepatocellular carcinoma.

e15641 Background: Patient-derived xenograft (PDX) models have been regarded as valuable preclinical models for oncology drug development and exploring underlying mechanism of drug resistance. As the standard treatment option for advanced hepatocellular carcinoma (HCC), the survival benefit of sorafenib is modest. Even among those who initially responded well to sorafenib treatment, most patients ultimately develop progressive disease owing to acquired drug resistance. Understanding how selective pressure from sorafenib directs the evolution of HCC and shapes its clonal architecture is a central biological question with important clinical implications to combat acquired drug resistance. Methods: Three sorafenib-resistant PDX models were generated by continuous sorafenib treament for more than six months. Whole exome sequencing (WES) and RNA sequencing were performed. The subclone of mutation, fusion genes, and the evolving landscape of tumor neoantigens were further explored. Finally, the antitumor efficacy of recombinant mouse PD-1 antibody was evaluated in sorafenib resistance syngeneic tumors model by immune-competent C57BL/6J mice with Hepa 1-6. Results: High spatiotemporal heterogeneity was observed in engraftment among pre- and post- sorafenib resistance in established acquired-resistance PDX models. Clonal selection on engraftment was observed in established acquired-resistance PDX models. There are many high expressed genes, which showed subclone expansion with the emergence of sorafenib resistance. There is vary difference of neoantigens between pre- and post-resistance engraftment as well as sampling points in same tissues. It was presented that a high burden of clonal neoantigens in sorafenib resistant tissues (P < 0.05), while there has no difference in tumor mutation burden. Moreover, the intra-tumor heterogeneity upon the neoantigen landscape was much bigger than that upon tumor mutation burden. We found that fusion subclone sizes were increased significantly after sorafenib resistance (P < 0.05). Finally, the antitumor efficacy of recombinant mouse PD-1 antibody was confirmed in sorafenib resistance syngeneic tumors mice model (P < 0.001). Conclusions: High spatiotemporal heterogeneity, clonal selection and neoantigen evolution was observed in acquired sorafenib-resistant PDX model of HCC. Immunotherapy such as immune checkpoint inhibitors might be a promising strategy for HCC patients with acquired sorafenib resistance. .

Candidate gene variation in gilthead sea bream reveals complex spatiotemporal selection patterns between marine and lagoon habitats

In marine fishes, the extent to which spatial patterns induced by selection remain stable across generations remains largely unknown. In the gilthead sea bream (Sparus aurata), polymorphisms in the growth hormone (GH) and prolactin (Prl) genes can display high levels of differentiation between marine and lagoon habitats. These genotype-environment associations have been attributed to differential selection following larval settlement, but it remains unclear whether selective mortality during later juvenile stages further shapes genetic differences among habitats. We addressed this question by analysing differentiation patterns at GH and Prl markers together with a set of twenty-one putatively neutral microsatellite loci. We compared genetic variation of spring juveniles that had just settled in three ecologically different lagoons, against older juveniles sampled from the same sites in autumn, at the onset of winter outmigration. In spring, genetic differentiation among lagoons was greater than expected from neutrality, for both candidate gene markers. Surprisingly, this signal disappeared completely in the older juveniles, with no significant differentiation for either locus a few months later in autumn. We searched for signals of haplotype structure within GH and Prl genes using next-generation amplicon deep-sequencing. Both genes contained two groups of haplotypes, but high similarities among groups indicated that signatures of selection, if any, had largely been erased by recombination. Our results are consistent with the view that differential selection operates during early juvenile life in sea bream, and highlight the importance of temporal replication in studies of post-settlement selection in marine fish.

Average Transmit Power Gain of MIMO Fading Channels Over SISO AWGN Channels

This paper is concerned with the average transmit power under adaptive transmission for multiple-input multiple-output (MIMO) antenna systems with multi-beams in fading environments when the channel-state information at both the transmitter and the receiver is available. The transmitter is supposed to be capable of spatiotemporal subchannel selection and power control as eigenvalues of channel matrix changes. Our focus is on an eigenvalue-inversion based optimization transmission scheme. With the scheme, we first introduce a concept of average transmit power gain (ATPG) of MIMO fading channels over single-input single-output additive white Gaussian noise channels in order to analyze deeply relationship between the average transmit power, the number of transmit antennas, the number of receive antennas, and fading channel characterizations. Then in Rayleigh fading we derive several insightful ATPG expressions for single-beam and multi-beam transmissions, respectively. Based on these expressions, the potential amounts of transmit power savings are further estimated in several cases when the underlying MIMO system is very large. Moreover, consideration of extending ATPG is also given in the presence of spatial correlation.

Spatially distributed dendritic resonance selectively filters synaptic input.

An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs.

Continuous Transition between Two Limits of Spiral Wave Dynamics in an Excitable Medium

By application of a free-boundary approach, we prove the existence of a continuous transition and a full spectrum of solutions between the two known limits of spiral wave dynamics. We identify a control parameter whose essential importance was not realized in earlier studies of spatiotemporal pattern selection in excitable media. The predictions of the free-boundary approach are in good quantitative agreement with results from numerical reaction-diffusion simulations performed on the modified Barkley model.

Variation in Call Structure and Calling Behavior in Treefrogs of the Genus Scinax

Abstract We investigated calling behavior and call structure for eight species of treefrogs in the genus Scinax, and document noteworthy patterns of interspecific variation that largely reflect habitat and social environment in this report. The five species in the S. ruber clade call in open areas or forest edges near standing water and produce simple calls at relatively high rates and low to moderate frequencies. Species from the S. catharinae clade call from more forested habitats and produce complex, higher frequency calls at relatively low rates. Differences in the range of activity temperatures and habitats among species reflect differences in geographical distribution and spatio-temporal selection of microhabitat during calling. In general, species with high calling rates also had high rates of locomotion during calling activity and were active at warmer temperatures. One exception was S. crospedospilus, which has an extremely high calling rate but exhibits among the lowest rates of locomotion and a...

Distributed Bayesian fault diagnosis of jump Markov systems in wireless sensor networks

A Bayesian distributed online change detection algorithm is proposed for monitoring a dynamical system by a wireless sensor network. The proposed solution relies on modelling the system dynamics by a jump Markov system with a finite set of states, including the abrupt change behaviour. For each discrete state, an observed system is assumed to evolve according to a state-space model. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communications bandwith. An efficient Rao-Blackwellised Collaborative Particle Filter (RB-CPF) is proposed to estimate the a posteriori probability of the discrete states of the observed systems. The Rao-Blackwellisation procedure combines a Sequential Monte-Carlo (SMC) filter with a bank of distributed Kalman filters. In order to prolong the sensor network lifetime, only few active (leader) nodes are selected according to a spatio-temporal selection protocol. This protocol is based on a trade-off between error propagation, communications constraints and information content complementarity of distributed data. Only sufficient statistics are communicated between leader nodes and their collaborators.