Nanopore adaptive sampling effectively enriches bacterial plasmids

Bacterial plasmids play a major role in the spread of antibiotic resistance genes. However, their characterization via DNA sequencing suffers from the low abundance of plasmid DNA in those samples. Although sample preparation methods can enrich the proportion of plasmid DNA before sequencing, these methods are expensive and laborious, and they might introduce a bias by enriching only for specific plasmid DNA sequences. Nanopore adaptive sampling could overcome these issues by rejecting uninteresting DNA molecules during the sequencing process. In this study, we assess the application of adaptive sampling for the enrichment of low-abundant plasmids in known bacterial isolates using two different adaptive sampling tools. We show that a significant enrichment can be achieved even on expired flow cells. By applying adaptive sampling, we also improve the quality of de novo plasmid assemblies and reduce the sequencing time. However, our experiments also highlight issues with adaptive sampling if target and non-target sequences span similar regions.

IMPORTANCE

Antimicrobial resistance causes millions of deaths every year. Mobile genetic elements like bacterial plasmids are key drivers for the dissemination of antimicrobial resistance genes. This makes the characterization of plasmids via DNA sequencing an important tool for clinical microbiologists. Since plasmids are often underrepresented in bacterial samples, plasmid sequencing can be challenging and laborious. To accelerate the sequencing process, we evaluate nanopore adaptive sampling as an in silico method for the enrichment of low-abundant plasmids. Our results show the potential of this cost-efficient method for future plasmid research but also indicate issues that arise from using reference sequences.

Single-Molecule DNA Methylation Reveals Unique Epigenetic Identity Profiles of T Helper Cells

Both identity and plasticity of CD4 T helper (Th) cells are regulated in part by epigenetic mechanisms. However, a method that reliably and readily profiles DNA base modifications is still needed to finely study Th cell differentiation. Cytosine methylation in CpG context (5mCpG) and cytosine hydroxymethylation (5hmCpG) are DNA modifications that identify stable cell phenotypes, but their potential to characterize intermediate cell transitions has not yet been evaluated. To assess transition states in Th cells, we developed a method to profile Th cell identity using Cas9-targeted single-molecule nanopore sequencing. Targeting as few as 10 selected genomic loci, we were able to distinguish major in vitro polarized murine T cell subtypes, as well as intermediate phenotypes, by their native DNA 5mCpG patterns. Moreover, by using off-target sequences, we were able to infer transcription factor activities relevant to each cell subtype. Detection of 5mCpG and 5hmCpG was validated on intestinal Th17 cells escaping transforming growth factor β control, using single-molecule adaptive sampling. A total of 21 differentially methylated regions mapping to the 10-gene panel were identified in pathogenic Th17 cells relative to their nonpathogenic counterpart. Hence, our data highlight the potential to exploit native DNA methylation profiling to study physiological and pathological transition states of Th cells.

Strain-level characterization of foodborne pathogens without culture enrichment for outbreak investigation using shotgun metagenomics facilitated with nanopore adaptive sampling

Introduction

Shotgun metagenomics has previously proven effective in the investigation of foodborne outbreaks by providing rapid and comprehensive insights into the microbial contaminant. However, culture enrichment of the sample has remained a prerequisite, despite the potential impact on pathogen detection resulting from the growth competition. To circumvent the need for culture enrichment, we explored the use of adaptive sampling using various databases for a targeted nanopore sequencing, compared to shotgun metagenomics alone.

Methods

The adaptive sampling method was first tested on DNA of mashed potatoes mixed with DNA of a Staphylococcus aureus strain previously associated with a foodborne outbreak. The selective sequencing was used to either deplete the potato sequencing reads or enrich for the pathogen sequencing reads, and compared to a shotgun sequencing. Then, living S. aureus were spiked at 105 CFU into 25 g of mashed potatoes. Three DNA extraction kits were tested, in combination with enrichment using adaptive sampling, following whole genome amplification. After data analysis, the possibility to characterize the contaminant with the different sequencing and extraction methods, without culture enrichment, was assessed.

Results

Overall, the adaptive sampling outperformed the shotgun sequencing. While the use of a host removal DNA extraction kit and targeted sequencing using a database of foodborne pathogens allowed rapid detection of the pathogen, the most complete characterization was achieved when using solely a database of S. aureus combined with a conventional DNA extraction kit, enabling accurate placement of the strain on a phylogenetic tree alongside outbreak cases.

Discussion

This method shows great potential for strain-level analysis of foodborne outbreaks without the need for culture enrichment, thereby enabling faster investigations and facilitating precise pathogen characterization. The integration of adaptive sampling with metagenomics presents a valuable strategy for more efficient and targeted analysis of microbial communities in foodborne outbreaks, contributing to improved food safety and public health.

Selective whole-genome sequencing of Plasmodium parasites directly from blood samples by nanopore adaptive sampling

IMPORTANCE

Malaria is caused by parasites of the genus Plasmodium, and reached a global disease burden of 247 million cases in 2021. To study drug resistance mutations and parasite population dynamics, whole-genome sequencing of patient blood samples is commonly performed. However, the predominance of human DNA in these samples imposes the need for time-consuming laboratory procedures to enrich Plasmodium DNA. We used the Oxford Nanopore Technologies' adaptive sampling feature to circumvent this problem and enrich Plasmodium reads directly during the sequencing run. We demonstrate that adaptive nanopore sequencing efficiently enriches Plasmodium reads, which simplifies and shortens the timeline from blood collection to parasite sequencing. In addition, we show that the obtained data can be used for monitoring genetic markers, or to generate nearly complete genomes. Finally, owing to its inherent mobility, this technology can be easily applied on-site in endemic areas where patients would benefit the most from genomic surveillance.

Rapid detection of bacteria and antimicrobial resistant genes in intraamniotic infection using nanopore adaptive sampling

Nanopore adaptive sampling to diagnose intraamniotic infection

NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing

Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, NanoDeep, to overcome these limitations by incorporating convolutional neural network and squeeze and excitation. We first showed that the raw squiggle derived from native DNA sequences determines the origin of microbial and human genomes. Then, we demonstrated that NanoDeep successfully classified bacterial reads from the pooled library with human sequence and showed enrichment for bacterial sequence compared with routine nanopore sequencing setting. Further, we showed that NanoDeep improves the sequencing efficiency and preserves the fidelity of bacterial genomes in the mock sample. In addition, NanoDeep performs well in the enrichment of metagenome sequences of gut samples, showing its potential applications in the enrichment of unknown microbiota. Our toolkit is available at https://github.com/lysovosyl/NanoDeep.

Advancing animal tuberculosis surveillance using culture-independent long-read whole-genome sequencing

Animal tuberculosis is a significant infectious disease affecting both livestock and wildlife populations worldwide. Effective disease surveillance and characterization of Mycobacterium bovis (M. bovis) strains are essential for understanding transmission dynamics and implementing control measures. Currently, sequencing of genomic information has relied on culture-based methods, which are time-consuming, resource-demanding, and concerning in terms of biosafety. This study explores the use of culture-independent long-read whole-genome sequencing (WGS) for a better understanding of M. bovis epidemiology in African buffaloes (Syncerus caffer). By comparing two sequencing approaches, we evaluated the efficacy of Illumina WGS performed on culture extracts and culture-independent Oxford Nanopore adaptive sampling (NAS). Our objective was to assess the potential of NAS to detect genomic variants without sample culture. In addition, culture-independent amplicon sequencing, targeting mycobacterial-specific housekeeping and full-length 16S rRNA genes, was applied to investigate the presence of microorganisms, including nontuberculous mycobacteria. The sequencing quality obtained from DNA extracted directly from tissues using NAS is comparable to the sequencing quality of reads generated from culture-derived DNA using both NAS and Illumina technologies. We present a new approach that provides complete and accurate genome sequence reconstruction, culture independently, and using an economically affordable technique.

Attention-Aware Patch-Based CNN for Blind 360-Degree Image Quality Assessment

An attention-aware patch-based deep-learning model for a blind 360-degree image quality assessment (360-IQA) is introduced in this paper. It employs spatial attention mechanisms to focus on spatially significant features, in addition to short skip connections to align them. A long skip connection is adopted to allow features from the earliest layers to be used at the final level. Patches are properly sampled on the sphere to correspond to the viewports displayed to the user using head-mounted displays. The sampling incorporates the relevance of patches by considering (i) the exploration behavior and (ii) a latitude-based selection. An adaptive strategy is applied to improve the pooling of local patch qualities to global image quality. This includes an outlier score rejection step relying on the standard deviation of the obtained scores to consider the agreement, as well as a saliency to weigh them based on their visual significance. Experiments on available 360-IQA databases show that our model outperforms the state of the art in terms of accuracy and generalization ability. This is valid for general deep-learning-based models, multichannel models, and natural scene statistic-based models. Furthermore, when compared to multichannel models, the computational complexity is significantly reduced. Finally, an extensive ablation study gives insights into the efficacy of each component of the proposed model.

Comparative genomics analysis of three conserved plasmid families in the Western Hemisphere soft tick-borne relapsing fever borreliae provides insight into variation in genome structure and antigenic variation systems

Borrelia spirochetes, causative agents of Lyme disease and relapsing fever (RF), have uniquely complex genomes, consisting of a linear chromosome and both circular and linear plasmids. The plasmids harbor genes important for the vector-host life cycle of these tick-borne bacteria. The role of plasmids from Lyme disease causing spirochetes is more refined compared to RF Borrelia because of limited plasmid-resolved genome assemblies for the latter. We recently addressed this limitation and found that three linear plasmid families (F6, F27, and F28) were syntenic across all the RF Borrelia species that we examined. Given this conservation, we further investigated the three plasmid families. The F6 family, also known as the megaplasmid, contained regions of repetitive DNA. The F27 was the smallest, encoding genes with unknown function. The F28 family encoded the putative expression locus for antigenic variation in all species except Borrelia hermsii and Borrelia anserina. Taken together, this work provides a foundation for future investigations to identify essential plasmid-localized genes that drive the vector-host life cycle of RF Borrelia. IMPORTANCE Borrelia spp. spirochetes are arthropod-borne bacteria found globally that infect humans and other vertebrates. RF borreliae are understudied and misdiagnosed pathogens because of the vague clinical presentation of disease and the elusive feeding behavior of argasid ticks. Consequently, genomics resources for RF spirochetes have been limited. Analyses of Borrelia plasmids have been challenging because they are often highly fragmented and unassembled in most available genome assemblies. By utilizing Oxford Nanopore Technologies, we recently generated plasmid-resolved genome assemblies for seven Borrelia spp. found in the Western Hemisphere. This current study is an in-depth investigation into the linear plasmids that were conserved and syntenic across species. We identified differences in genome structure and, importantly, in antigenic variation systems between species. This work is an important step in identifying crucial plasmid-localized genetic elements essential for the life cycle of RF spirochetes.

Predicting rare events using neural networks and short-trajectory data

Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or measurement needed to resolve the elemental dynamics, accurate prediction from direct observations becomes challenging. In such cases a more effective approach is to cast statistics of interest as solutions to Feynman-Kac equations (partial differential equations). Here, we develop an approach to solve Feynman-Kac equations by training neural networks on short-trajectory data. Our approach is based on a Markov approximation but otherwise avoids assumptions about the underlying model and dynamics. This makes it applicable to treating complex computational models and observational data. We illustrate the advantages of our method using a low-dimensional model that facilitates visualization, and this analysis motivates an adaptive sampling strategy that allows on-the-fly identification of and addition of data to regions important for predicting the statistics of interest. Finally, we demonstrate that we can compute accurate statistics for a 75-dimensional model of sudden stratospheric warming. This system provides a stringent test bed for our method.

A complex rearrangement between APC and TP63 associated with familial adenomatous polyposis identified by multimodal genomic analysis: a case report

Genetic testing of the APC gene by sequencing analysis and MLPA is available across commercial laboratories for the definitive genetic diagnosis of familial adenomatous polyposis (FAP). However, some genetic alterations are difficult to detect using conventional analyses. Here, we report a case of a complex genomic APC-TP63 rearrangement, which was identified in a patient with FAP by a series of genomic analyses, including multigene panel testing, chromosomal analyses, and long-read sequencing. A woman in her thirties was diagnosed with FAP due to multiple polyps in her colon and underwent total colectomy. Subsequent examination revealed fundic gland polyposis. No family history suggesting FAP was noted except for a first-degree relative with desmoid fibromatosis. The conventional APC gene testing was performed by her former doctor, but no pathogenic variant was detected, except for 2 variants of unknown significance. The patient was referred to our hospital for further genetic analysis. After obtaining informed consent in genetic counseling, we conducted a multigene panel analysis. As insertion of a part of the TP63 sequence was detected within exon16 of APC, further analyses, including chromosomal analysis and long-read sequencing, were performed and a complex translocation between chromosomes 3 and 5 containing several breakpoints in TP63 and APC was identified. No phenotype associated with TP63 pathogenic variants, such as split-hand/foot malformation (SHFM) or ectrodactyly, ectodermal dysplasia, or cleft lip/palate syndrome (EEC) was identified in the patient or her relatives. Multimodal genomic analyses should be considered in cases where no pathogenic germline variants are detected by conventional genetic testing despite an evident medical or family history of hereditary cancer syndromes.

Practical strategies for operationalizing optimal allocation in stratified cluster-based outcome-dependent sampling designs

Cluster-based outcome-dependent sampling (ODS) has the potential to yield efficiency gains when the outcome of interest is relatively rare, and resource constraints allow only a certain number of clusters to be visited for data collection. Previous research has shown that when the intended analysis is inverse-probability weighted generalized estimating equations, and the number of clusters that can be sampled is fixed, optimal allocation of the (cluster-level) sample size across strata defined by auxiliary variables readily available at the design stage has the potential to increase efficiency in the estimation of the parameter(s) of interest. In such a setting, the optimal allocation formulae depend on quantities that are unknown in practice, currently making such designs difficult to implement. In this paper, we consider a two-wave adaptive sampling approach, in which data is collected from a first wave sample, and subsequently used to compute the optimal second wave stratum-specific sample sizes. We consider two strategies for estimating the necessary components using the first wave data: an inverse-probability weighting (IPW) approach and a multiple imputation (MI) approach. In a comprehensive simulation study, we show that the adaptive sampling approach performs well, and that the MI approach yields designs that are very near-optimal, regardless of the covariate type. The IPW approach, on the other hand, has mixed results. Finally, we illustrate the proposed adaptive sampling procedures with data on maternal characteristics and birth outcomes among women enrolled in the Safer Deliveries program in Zanzibar, Tanzania.

An Adaptive Hybrid Sampling Method for Free-Form Surfaces Based on Geodesic Distance

High precision geometric measurement of free-form surfaces has become the key to high-performance manufacturing in the manufacturing industry. By designing a reasonable sampling plan, the economic measurement of free-form surfaces can be realized. This paper proposes an adaptive hybrid sampling method for free-form surfaces based on geodesic distance. The free-form surfaces are divided into segments, and the sum of the geodesic distance of each surface segment is taken as the global fluctuation index of free-form surfaces. The number and location of the sampling points for each free-form surface segment are reasonably distributed. Compared with the common methods, this method can significantly reduce the reconstruction error under the same sampling points. This method overcomes the shortcomings of the current commonly used method of taking curvature as the local fluctuation index of free-form surfaces, and provides a new perspective for the adaptive sampling of free-form surfaces.

The Use of Adaptive Sampling to Reach Disadvantaged Populations for Immunization Programs and Assessments: A Systematic Review

Vaccines prevent 4-5 million deaths every year, but inequities in vaccine coverage persist among key disadvantaged subpopulations. Under-immunized subpopulations (e.g., migrants, slum residents) may be consistently missed with conventional methods for estimating immunization coverage and assessing vaccination barriers. Adaptive sampling, such as respondent-driven sampling, may offer useful strategies for identifying and collecting data from these subpopulations that are often "hidden" or hard-to-reach. However, use of these adaptive sampling approaches in the field of global immunization has not been systematically documented. We searched PubMed, Scopus, and Embase databases to identify eligible studies published through November 2020 that used an adaptive sampling method to collect immunization-related data. From the eligible studies, we extracted relevant data on their objectives, setting and target population, and sampling methods. We categorized sampling methods and assessed their frequencies. Twenty-three studies met the inclusion criteria out of the 3069 articles screened for eligibility. Peer-driven sampling was the most frequently used adaptive sampling method (57%), followed by geospatial sampling (30%), venue-based sampling (17%), ethnographic mapping (9%), and compact segment sampling (9%). Sixty-one percent of studies were conducted in upper-middle-income or high-income countries. Data on immunization uptake were collected in 65% of studies, and data on knowledge and attitudes about immunizations were collected in 57% of studies. We found limited use of adaptive sampling methods in measuring immunization coverage and understanding determinants of vaccination uptake. The current under-utilization of adaptive sampling approaches leaves much room for improvement in how immunization programs calibrate their strategies to reach "hidden" subpopulations.

Integrating gradient with scale in ecological and evolutionary studies

Gradient and scale are two key concepts in ecology and evolution that are closely related but inherently distinct. While scale commonly refers to the dimensional space of a specific ecological/evolutionary (eco-evo) issue, gradient measures the range of a given variable. Gradient and scale can jointly and interactively influence eco-evo patterns. Extensive previous research investigated how changing scales may affect the observation and interpretation of eco-evo patterns; however, relatively little attention has been paid to the role of changing gradients. Here, synthesizing recent research progress, we suggest that the role of scale in the emergence of ecological patterns should be evaluated in conjunction with considering the underlying environmental gradients. This is important because, in most studies, the range of the gradient is often part of its full potential range. The difference between sampled (partial) versus potential (full) environmental gradients may profoundly impact observed eco-evo patterns and alter scale-gradient relationships. Based on observations from both field and experimental studies, we illustrate the underlying features of gradients and how they may affect observed patterns, along with the linkages of these features to scales. Since sampled gradients often do not cover their full potential ranges, we discuss how the breadth and the starting and ending positions of key gradients may affect research design and data interpretation. We then outline potential approaches and related perspectives to better integrate gradient with scale in future studies.

Efficient real-time selective genome sequencing on resource-constrained devices

BACKGROUND

Third-generation nanopore sequencers offer selective sequencing or "Read Until" that allows genomic reads to be analyzed in real time and abandoned halfway if not belonging to a genomic region of "interest." This selective sequencing opens the door to important applications such as rapid and low-cost genetic tests. The latency in analyzing should be as low as possible for selective sequencing to be effective so that unnecessary reads can be rejected as early as possible. However, existing methods that employ a subsequence dynamic time warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone-sized MinION sequencer.

RESULTS

In this article, we present Hardware Accelerated Read Until (HARU), a resource-efficient hardware-software codesign-based method that exploits a low-cost and portable heterogeneous multiprocessor system-on-chip platform with on-chip field-programmable gate arrays (FPGA) to accelerate the sDTW-based Read Until algorithm. Experimental results show that HARU on a Xilinx FPGA embedded with a 4-core ARM processor is around 2.5× faster than a highly optimized multithreaded software version (around 85× faster than the existing unoptimized multithreaded software) running on a sophisticated server with a 36-core Intel Xeon processor for a SARS-CoV-2 dataset. The energy consumption of HARU is 2 orders of magnitudes lower than the same application executing on the 36-core server.

CONCLUSIONS

HARU demonstrates that nanopore selective sequencing is possible on resource-constrained devices through rigorous hardware-software optimizations. The source code for the HARU sDTW module is available as open source at https://github.com/beebdev/HARU, and an example application that uses HARU is at https://github.com/beebdev/sigfish-haru.

Targeted long-read sequencing allows for rapid identification of pathogenic disease-causing variants in retinoblastoma

BACKGROUND

Identification of disease-causing variants of the retinoblastoma gene (RB1), the predominant cause of retinoblastoma, is challenging. Targeted long-read genome sequencing offers a novel approach to resolve the diverse range of pathogenic variants in RB1 and provides haplotype information rapidly.

MATERIALS AND METHODS

Genomic DNA was isolated from a venipuncture blood draw of a retinoblastoma patient. Whole genome sequencing (WGS) was carried out using the short-read Ilumina platform. WGS and targeted sequencing of RB1 was accomplished using the long-read Oxford Nanopore Technologies (ONT) platform. Deep-learning frameworks allowed haplotagging, variant calling, and variant annotation of both short- and long-read data.

RESULTS

Targeted long-read sequencing of the RB1 gene allowed for enhanced depth of read coverage for discovery of rare variants and haplotype analysis. A duplication leading to a frameshift and early termination in RB1 was identified as the most deleterious variant by all sequencing methods, with long-read technology providing additional information of methylation signal and haplotype information. More importantly, there was greater than 98% concordance of RB1 variants identified between short-read and targeted long-read sequencing modalities.

CONCLUSIONS

Targeted long-read technology allows for focused sequencing effort for variant discovery. Application of this for the first time in a retinoblastoma patient allowed haplotagged variant identification and demonstrated excellent concordance with benchmark short-read sequencing. The added benefit of targeted long-read sequencing to resolve disease-causing genomic variation in RB1 rapidly from a blood draw will provide a more definitive diagnosis of heritable RB and guide management decisions for patients and their families.

Adaptive Partially Observed Sequential Change Detection and Isolation

High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev-Roberts procedure on the failure mode level and utilize the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.

Surveillance for endemic infectious disease outbreaks: Adaptive sampling using profile likelihood estimation

Outbreaks of an endemic infectious disease can occur when the disease is introduced into a highly susceptible subpopulation or when the disease enters a network of connected individuals. For example, significant HIV outbreaks among people who inject drugs have occurred in at least half a dozen US states in recent years. This motivates the current study: how can limited testing resources be allocated across geographic regions to rapidly detect outbreaks of an endemic infectious disease? We develop an adaptive sampling algorithm that uses profile likelihood to estimate the distribution of the number of positive tests that would occur for each location in a future time period if that location were sampled. Sampling is performed in the location with the highest estimated probability of triggering an outbreak alarm in the next time period. The alarm function is determined by a semiparametric likelihood ratio test. We compare the profile likelihood sampling (PLS) method numerically to uniform random sampling (URS) and Thompson sampling (TS). TS was worse than URS when the outbreak occurred in a location with lower initial prevalence than other locations. PLS had lower time to outbreak detection than TS in some but not all scenarios, but was always better than URS even when the outbreak occurred in a location with a lower initial prevalence than other locations. PLS provides an effective and reliable method for rapidly detecting endemic disease outbreaks that is robust to this uncertainty.

Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control

In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this paper, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev-Roberts-Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling

Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface-adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd-C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd₅₅ nanocluster and extended it to new geometries, Pd₇₉, and Pd₈₅, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy.

Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling

The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second-phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second-phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two-phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first-phase and second-phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.

Role of substrate recognition in modulating strigolactone receptor selectivity in witchweed

Witchweed, or Striga hermonthica, is a parasitic weed that destroys billions of dollars' worth of crops globally every year. Its germination is stimulated by strigolactones exuded by its host plants. Despite high sequence, structure, and ligand-binding site conservation across different plant species, one strigolactone receptor in witchweed, ShHTL7, uniquely exhibits a picomolar EC50 for downstream signaling. Previous biochemical and structural analyses have hypothesized that this unique ligand sensitivity can be attributed to a large binding pocket volume in ShHTL7 resulting in enhanced ability to bind substrates, but additional structural details of the substrate-binding process would help explain its role in modulating the ligand selectivity. Using long-timescale molecular dynamics simulations, we demonstrate that mutations at the entrance of the binding pocket facilitate a more direct ligand-binding pathway to ShHTL7, whereas hydrophobicity at the binding pocket entrance results in a stable “anchored” state. We also demonstrate that several residues on the D-loop of AtD14 stabilize catalytically inactive conformations. Finally, we show that strigolactone selectivity is not modulated by binding pocket volume. Our results indicate that while ligand binding is not the sole modulator of strigolactone receptor selectivity, it is a significant contributing factor. These results can be used to inform the design of selective antagonists for strigolactone receptors in witchweed.

DisSAGD: A Distributed Parameter Update Scheme Based on Variance Reduction

Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD. To increase the speed of convergence and improve stability, a distributed SGD algorithm based on variance reduction, named DisSAGD, is proposed in this study. DisSAGD corrects the gradient estimate for each iteration by using the gradient variance of historical iterations without full gradient computation or additional storage, i.e., it reduces the mean variance of historical gradients in order to reduce the error in updating parameters. We implemented DisSAGD in distributed clusters in order to train a machine learning model by sharing parameters among nodes using an asynchronous communication protocol. We also propose an adaptive learning rate strategy, as well as a sampling strategy, to address the update lag of the overall parameter distribution, which helps to improve the convergence speed when the parameters deviate from the optimal value—when one working node is faster than another, this node will have more time to compute the local gradient and sample more samples for the next iteration. Our experiments demonstrate that DisSAGD significantly reduces waiting times during loop iterations and improves convergence speed when compared to traditional methods, and that our method can achieve speed increases for distributed clusters.

Exploratory-Phase-Free Estimation of GP Hyperparameters in Sequential Design Methods-At the Example of Bayesian Inverse Problems

Methods for sequential design of computer experiments typically consist of two phases. In the first phase, the exploratory phase, a space-filling initial design is used to estimate hyperparameters of a Gaussian process emulator (GPE) and to provide some initial global exploration of the model function. In the second phase, more design points are added one by one to improve the GPE and to solve the actual problem at hand (e.g., Bayesian optimization, estimation of failure probabilities, solving Bayesian inverse problems). In this article, we investigate whether hyperparameters can be estimated without a separate exploratory phase. Such an approach will leave hyperparameters uncertain in the first iterations, so the acquisition function (which tells where to evaluate the model function next) and the GPE-based estimator need to be adapted to non-Gaussian random fields. Numerical experiments are performed exemplarily on a sequential method for solving Bayesian inverse problems. These experiments show that hyperparameters can indeed be estimated without an exploratory phase and the resulting method works almost as efficient as if the hyperparameters had been known beforehand. This means that the estimation of hyperparameters should not be the reason for including an exploratory phase. Furthermore, we show numerical examples, where these results allow us to eliminate the exploratory phase to make the sequential design method both faster (requiring fewer model evaluations) and easier to use (requiring fewer choices by the user).

Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes

Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide-MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide-MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide-MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.

Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks

The transmission of high-volume multimedia content (e.g., images) is challenging for a resource-constrained wireless multimedia sensor network (WMSN) due to energy consumption requirements. Redundant image information can be compressed using traditional compression techniques at the cost of considerable energy consumption. Fortunately, compressed sensing (CS) has been introduced as a low-complexity coding scheme for WMSNs. However, the storage and processing of CS-generated images and measurement matrices require substantial memory. Block compressed sensing (BCS) can mitigate this problem. Nevertheless, allocating a fixed sampling to all blocks is impractical since each block holds different information. Although solutions such as adaptive block compressed sensing (ABCS) exist, they lack robustness across various types of images. As a solution, we propose a holistic WMSN architecture for image transmission that performs well on diverse images by leveraging saliency and standard deviation features. A fuzzy logic system (FLS) is then used to determine the appropriate features when allocating the sampling, and each corresponding block is resized using CS. The combined FLS and BCS algorithms are implemented with smoothed projected Landweber (SPL) reconstruction to determine the convergence speed. The experiments confirm the promising performance of the proposed algorithm compared with that of conventional and state-of-the-art algorithms.

Visual Saliency Detection for Over-Temperature Regions in 3D Space via Dual-Source Images

To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform.

Markov State Model of Lassa Virus Nucleoprotein Reveals Large Structural Changes during the Trimer to Monomer Transition

Lassa virus contains a nucleoprotein (NP) that encapsulates the viral genomic RNA forming the ribonucleoprotein (RNP). The NP forms trimers that do not bind RNA, but a structure of only the NP N-terminal domain was co-crystallized with RNA bound. These structures suggested a model in which the NP forms a trimer to keep the RNA gate closed, but then is triggered to undergo a change to a form competent for RNA binding. Here, we investigate the scenario in which the trimer is disrupted to observe whether monomeric NP undergoes significant conformational changes. From multi-microsecond molecular dynamics simulations and an adaptive sampling scheme to sample the conformational space, a Markov state model (MSM) is constructed. The MSM reveals an energetically favorable conformational change, with the most significant changes occurring at the domain interface. These results support a model in which significant structural reorganization of the NP is required for RNP formation.

A Data-driven Adaptive Sampling Method Based on Edge Computing

The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.

Risk-Based Approach to Lot Release

In this work, a novel risk-based methodology for lot release is proposed. Its objective is to assess the risk that a lot declared to have passed truly meets product specifications. The methodology consists of 3 parts: adaptive sample size determination, estimation of the probability that the product was within specifications, and the lot-release decision. The methodology provides a probabilistic statement about the true quality of the batch. Having a probability estimate is the essential condition of risk-based decision-making. We demonstrate the proposed methodology on experimental data generated from 17 immediate-release solid oral drug products from a number of different manufacturers with 5 to 10 lots per manufacturer.

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process-such as the active-learning methods found in the machine-learning literature-will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.

Temporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species

Acoustic recordings of the environment can produce species presence-absence data for characterizing populations of sound-producing wildlife over multiple spatial scales. If a species is present at a site but does not vocalize during a scheduled audio recording survey, researchers may incorrectly conclude that the species is absent ("false negative"). The risk of false negatives is compounded when audio devices have sampling constraints, do not record continuously, and must be manually scheduled to operate at pre-selected times of day, particularly when research programs target multiple species with acoustic availability that varies across temporal conditions.We developed a temporally adaptive acoustic sampling algorithm to maximize detection probabilities for a suite of focal species amid sampling constraints. The algorithm combines user-supplied species vocalization models with site-specific weather forecasts to set an optimized sampling schedule for the following day. To test our algorithm, we simulated hourly vocalization probabilities for a suite of focal species in a hypothetical monitoring area for the year 2016. We conducted a factorial experiment that sampled from the 2016 acoustic environment to compare the probability of acoustic detection by a fixed (stationary) schedule versus a temporally adaptive optimized schedule under several sampling efforts and monitoring durations.We found that over the course of a study season, the probability of acoustically capturing a focal species (given presence) at least once via automated acoustic monitoring was greater (and acoustic capture occurred earlier in the season) when using the temporally adaptive optimized schedule as compared to a fixed schedule.The advantages of a temporally adaptive optimized acoustic sampling schedule are magnified when a study duration is short, sampling effort is low, and/or species acoustic availability is minimal. This methodology presents the opportunity to maximize acoustic monitoring sampling efforts amid constraints.

Exploration vs. Data Refinement via Multiple Mobile Sensors

We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.

BAR-based optimum adaptive steered MD for configurational sampling

The equilibrium and nonequilibrium adaptive alchemical free energy simulation methods optimum Bennett's acceptance ratio and optimum crooks' equation (OCE), based on the statistically optimal bidirectional reweighting estimator named Bennett's Acceptance Ratio or Crooks' equation, perform initial sampling in the staging alchemical transformation and then determine the importance rank of different states via the time-derivative of the variance. The method is proven to give speedups compared with the equal time rule. In the current work, we extend the time derivative of variance guided adaptive sampling method to the configurational space, falling in the term of steered MD (SMD). The SMD approach biasing physically meaningful collective variable (CV) such as one dihedral or one distance to pulling the system from one conformational state to another. By minimizing the variance of the free energy differences along the pathway in an optimized way, a new type of adaptive SMD (ASMD) is introduced. As exhibits in the alchemical case, this adaptive sampling method outperforms the traditional equal-time SMD in nonequilibrium stratification. Also, the method gives much more efficient calculation of potential of mean force than the selection criterion-based ASMD scheme, which is proven to be more efficient than traditional SMD. The OCE workflow is periodicity-of-CV dependent while ASMD is not. The performance is demonstrated in a dihedral flipping case and two distance pulling cases, accounting for periodic and nonperiodic CVs, respectively. © 2019 Wiley Periodicals, Inc.

Computational Study of Protein-Ligand Unbinding for Enzyme Engineering

The computational prediction of unbinding rate constants is presently an emerging topic in drug design. However, the importance of predicting kinetic rates is not restricted to pharmaceutical applications. Many biotechnologically relevant enzymes have their efficiency limited by the binding of the substrates or the release of products. While aiming at improving the ability of our model enzyme haloalkane dehalogenase DhaA to degrade the persistent anthropogenic pollutant 1,2,3-trichloropropane (TCP), the DhaA31 mutant was discovered. This variant had a 32-fold improvement of the catalytic rate toward TCP, but the catalysis became rate-limited by the release of the 2,3-dichloropropan-1-ol (DCP) product from its buried active site. Here we present a computational study to estimate the unbinding rates of the products from DhaA and DhaA31. The metadynamics and adaptive sampling methods were used to predict the relative order of kinetic rates in the different systems, while the absolute values depended significantly on the conditions used (method, force field, and water model). Free energy calculations provided the energetic landscape of the unbinding process. A detailed analysis of the structural and energetic bottlenecks allowed the identification of the residues playing a key role during the release of DCP from DhaA31 via the main access tunnel. Some of these hot-spots could also be identified by the fast CaverDock tool for predicting the transport of ligands through tunnels. Targeting those hot-spots by mutagenesis should improve the unbinding rates of the DCP product and the overall catalytic efficiency with TCP.

Sequential sampling strategy for extreme event statistics in nonlinear dynamical systems

Understanding the statistics of extreme events in dynamical systems of high complexity is of vital importance for reliability assessment and design. We formulate a method to pick samples optimally so that we have rapid convergence of the full statistics of a quantity of interest, including the tails that describe extreme events. This is important for large-scale problems in science and engineering, where we desire to predict the statistics of relevant quantities but can only afford a limited number of simulations or experiments due to their very expensive cost. We demonstrate our approach in a hydromechanical system with millions of degrees of freedom, where only 10–20 carefully selected samples can lead to accurate approximation of the extreme event statistics.

We develop a method for the evaluation of extreme event statistics associated with nonlinear dynamical systems from a small number of samples. From an initial dataset of design points, we formulate a sequential strategy that provides the “next-best” data point (set of parameters) that when evaluated results in improved estimates of the probability density function (pdf) for a scalar quantity of interest. The approach uses Gaussian process regression to perform Bayesian inference on the parameter-to-observation map describing the quantity of interest. We then approximate the desired pdf along with uncertainty bounds using the posterior distribution of the inferred map. The next-best design point is sequentially determined through an optimization procedure that selects the point in parameter space that maximally reduces uncertainty between the estimated bounds of the pdf prediction. Since the optimization process uses only information from the inferred map, it has minimal computational cost. Moreover, the special form of the metric emphasizes the tails of the pdf. The method is practical for systems where the dimensionality of the parameter space is of moderate size and for problems where each sample is very expensive to obtain. We apply the method to estimate the extreme event statistics for a very high-dimensional system with millions of degrees of freedom: an offshore platform subjected to 3D irregular waves. It is demonstrated that the developed approach can accurately determine the extreme event statistics using a limited number of samples.

Mechanisms of Lipid Scrambling by the G Protein-Coupled Receptor Opsin

Several class-A G protein-coupled receptor (GPCR) proteins act as constitutive phospholipid scramblases catalyzing the transbilayer translocation of >10,000 phospholipids per second when reconstituted into synthetic vesicles. To address the molecular mechanism by which these proteins facilitate rapid lipid scrambling, we carried out large-scale ensemble atomistic molecular dynamics simulations of the opsin GPCR. We report that, in the process of scrambling, lipid head groups traverse a dynamically revealed hydrophilic pathway in the region between transmembrane helices 6 and 7 of the protein while their hydrophobic tails remain in the bilayer environment. We present quantitative kinetic models of the translocation process based on Markov State Model analysis. As key residues on the lipid translocation pathway are conserved within the class-A GPCR family, our results illuminate unique aspects of GPCR structure and dynamics while providing a rigorous basis for the design of variants of these proteins with defined scramblase activity.

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring

Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.

How a fly photoreceptor samples light information in time

A photoreceptor's information capture is constrained by the structure and function of its light‐sensitive parts. Specifically, in a fly photoreceptor, this limit is set by the number of its photon sampling units (microvilli), constituting its light sensor (the rhabdomere), and the speed and recoverability of their phototransduction reactions. In this review, using an insightful constructionist viewpoint of a fly photoreceptor being an ‘imperfect’ photon counting machine, we explain how these constraints give rise to adaptive quantal information sampling in time, which maximises information in responses to salient light changes while antialiasing visual signals. Interestingly, such sampling innately determines also why photoreceptors extract more information, and more economically, from naturalistic light contrast changes than Gaussian white‐noise stimuli, and we explicate why this is so. Our main message is that stochasticity in quantal information sampling is less noise and more processing, representing an ‘evolutionary adaptation’ to generate a reliable neural estimate of the variable world.

Binding Kinetics in Drug Discovery

Over the last years, researchers have increasingly become interested in measuring and understanding drugs' binding kinetics, namely the time in which drug and its target associate and dissociate. Historically, drug discovery programs focused on the optimization of target affinity as a proxy of in-vivo efficacy. However, often the efficacy of a ligand is not appropriately described by the in-vitro measured drug-receptor affinity, but rather depends on the lifetime of the in-vivo drug-receptor interaction. In this review we review recent works that highlight the importance of binding kinetics, molecular determinants for rational optimization and the recent emergence of computational methods as powerful tools in measuring and understanding binding kinetics.

An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach

Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on "one-time" release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods.

Adaptive sampling for learning gaussian processes using mobile sensor networks

This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme.

Free-space fluorescence tomography with adaptive sampling based on anatomical information from microCT

Image reconstruction is one of the main challenges for fluorescence tomography. For in vivo experiments on small animals, in particular, the inhomogeneous optical properties and irregular surface of the animal make free-space image reconstruction challenging because of the difficulties in accurately modeling the forward problem and the finite dynamic range of the photodetector. These two factors are fundamentally limited by the currently available forward models and photonic technologies. Nonetheless, both limitations can be significantly eased using a signal processing approach. We have recently constructed a free-space panoramic fluorescence diffuse optical tomography system to take advantage of co-registered microCT data acquired from the same animal. In this article, we present a data processing strategy that adaptively selects the optical sampling points in the raw 2-D fluorescent CCD images. Specifically, the general sampling area and sampling density are initially specified to create a set of potential sampling points sufficient to cover the region of interest. Based on 3-D anatomical information from the microCT and the fluorescent CCD images, data points are excluded from the set when they are located in an area where either the forward model is known to be problematic (e.g., large wrinkles on the skin) or where the signal is unreliable (e.g., saturated or low signal-to-noise ratio). Parallel Monte Carlo software was implemented to compute the sensitivity function for image reconstruction. Animal experiments were conducted on a mouse cadaver with an artificial fluorescent inclusion. Compared to our previous results using a finite element method, the newly developed parallel Monte Carlo software and the adaptive sampling strategy produced favorable reconstruction results.

Flexible Time-Triggered Sampling in Smart Sensor-Based Wireless Control Systems

Wireless control systems (WCSs) often have to operate in dynamic environments where the network traffic load may vary unpredictably over time. The sampling in sensors is conventionally time triggered with fixed periods. In this context, only worse-than-possible quality of control (QoC) can be achieved when the network is underloaded, while overloaded conditions may significantly degrade the QoC, even causing system instability. This is particularly true when the bandwidth of the wireless network is limited and shared by multiple control loops. To address these problems, a flexible time-triggered sampling scheme is presented in this work. Smart sensors are used to facilitate dynamic adjustment of sampling periods, which enhances the flexibility and resource efficiency of the system based on time-triggered sampling. Feedback control technology is exploited for adapting sampling periods in a periodic manner. The deadline miss ratio in each control loop is maintained at/around a desired level, regardless of workload variations. Simulation results show that the proposed sampling scheme is able to deal with dynamic and unpredictable variations in network traffic load. Compared to conventional time-triggered sampling, it leads to much better QoC in WCSs operating in dynamic environments.