Multiple Pairs Research Articles

Fungal endophytes and origins of decay in beech (Fagus sylvatica) sapwood

Sapwood comprises much above-ground forest biomass, but its mycobiome in living trees is largely unknown. Here, we characterize the endophytic fungal communities of the functional sapwood of young and mature living beech trees ( Fagus sylvatica) at multiple scales, from within individual trees to woodland sites across the southern United Kingdom. Fungal community composition was determined using both culture-based and molecular approaches across two loci. Wood decay fungi, including those that cause heart rot, were detected in approximately 80% of all samples. Fungal community composition differed according to the survey approach (high throughput sequencing vs. isolation of fungi into culture) and between geographic location and individual trees, but no significant patterns were detected at different heights in individual trees or around their circumferences. ITS and LSU sequencing detected more distinct taxa than culturing. However, LSU primers yielded more OTUs than did ITS primers, though both identified unique OTUs. This highlights the importance of multiple survey approaches, including multiple primer pairs, for better characterisation of communities and confidence in results of endophyte studies.

Structural basis for Cas9 off-target activity

The target DNA specificity of the CRISPR-associated genome editor nuclease Cas9 is determined by complementarity to a 20-nucleotide segment in its guide RNA. However, Cas9 can bind and cleave partially complementary off-target sequences, which raises safety concerns for its use in clinical applications. Here, we report crystallographic structures of Cas9 bound to bona fide off-target substrates, revealing that off-target binding is enabled by a range of noncanonical base-pairing interactions within the guide:off-target heteroduplex. Off-target substrates containing single-nucleotide deletions relative to the guide RNA are accommodated by base skipping or multiple noncanonical base pairs rather than RNA bulge formation. Finally, PAM-distal mismatches result in duplex unpairing and induce a conformational change in the Cas9 REC lobe that perturbs its conformational activation. Together, these insights provide a structural rationale for the off-target activity of Cas9 and contribute to the improved rational design of guide RNAs and off-target prediction algorithms.

Using a source of opportunity for self-localization of mobile underwater vector sensor platforms

Transforming a network of few compact mobile underwater platforms, each equipped with a single acoustic sensor, into a distributed sensing array requires precise positioning of each mobile sensor. But conventional accurate underwater positioning tools rely on active acoustic sources (e.g., acoustic pingers), which imposes additional hardware and operational complexity. Hence self-localization (i.e., totally passive) methods using only acoustic sources of opportunity (such as surface vessels) for locating the mobile sensors of a distributed array appear as an attractive alternative. Existing underwater self-localization methods have mainly been developed for mobile platform equipped with time-synchronized hydrophones and rely only on the time-difference of arrivals between multiple pairwise combinations of the mobile hydrophones as inputs for a complex non-linear inversion procedure. Instead, we introduce a self-localization method, which uses a linear least square formulation, for two mobile time-synchronized vector sensor platforms based on their acoustic recordings of a distant surface vessel and their inertial navigation systems (INS) measurements. This method can be generalized to multiple vector sensor pairs to provide additional robustness towards input parameter errors (e.g., due to a faulty INS) as demonstrated experimentally using drifting buoys with inertial vector sensors deployed ∼100 m apart in shallow water.

Secure wireless communications in the multi-user MISO interference channel assisted by multiple reconfigurable intelligent surfaces

This paper exploits reconfigurable intelligent surfaces (RIS) to enhance physical layer security (PLS) in a challenging radio environment. By adjusting the reflecting coefficients, RIS can provide a programmable wireless environment so that the electromagnetic wave can propagate in the desired way. Specifically, we consider a scenario where multiple user pairs communicate simultaneously over the same channel in a multiuser MISO interference channel and each pair of transceivers keeps their confidential information secret from other receivers. We investigate the secrecy rate balance of the system aided by multiple RISs and exploit the additional design degrees of freedom provided by the coordinated RISs to increase the secrecy capacity, achieving secrecy transmission. In particular, we adopt cooperative beamforming among the collaborative transmitters to maximize all users' minimum secrecy rate, achieving the secrecy rate balance. Due to the non-convexity of the formulated optimization problem, we propose alternating optimization (AO) to solve it iteratively and optimize active the transmit beamforming at the transmitters and passive phase shifts at the RISs based on positive semi-definite relaxation (SDR) and successive convex approximation (SCA). In each iteration, we solve a semi-definite programming (SDP) problem and finally get the optimal local solution to the optimization problem. Finally, the simulation results validate that the proposed algorithm is effective and the introduced RISs can significantly enhance the system secrecy performance compared to conventional baseline schemes.

Thinking Inside Uncertainty: Interest Moment Perception for Diverse Temporal Grounding

Given a language query, temporal grounding task is to localize temporal boundaries of the described event in an untrimmed video. There is a long-standing challenge that multiple moments may be associated with one same video-query pair, termed label uncertainty. However, existing methods struggle to localize diverse moments due to the lack of multi-label annotations. In this paper, we propose a novel Diverse Temporal Grounding framework (DTG) to achieve diverse moment localization with only single-label annotations. By delving into the label uncertainty, we find the diverse moments retrieved tend to involve similar actions/objects, driving us to perceive these interest moments. Specifically, we construct soft multi-label through semantic similarity of multiple video-query pairs. These soft labels reveal whether multiple moments in the intra-videos contain similar verbs/nouns, thereby guiding interest moment generation. Meanwhile, we put forward a diverse moment regression network (DMRNet) to achieve multiple predictions in a single pass, where plausible moments are dynamically picked out from the interest moments for joint optimization. Moreover, we introduce new metrics that better reveal multi-output performance. Extensive experiments conducted on Charades-STA and ActivityNet Captions show that our method achieves state-of-the-art performance in terms of both standard and new metrics.

On the use of machine learning for ray-based ocean acoustic tomography

Underwater sound propagation is primarily driven by a non-linear forward model relating variability of the ocean sound speed profile (SSP) to the acoustic observations (e.g., eigenray arrival times). Ocean acoustic tomography (OAT) methods aim at reconstructing SSPs variations (with respect to a reference environment) from changes of the acoustic measurements between multiple source-receiver pairs. We investigated the performance of three different OAT methods: (1) model-based methods (i.e., classical ray-based OAT using a linearized forward model), (2) data-driven methods (such as deep learning) to directly learn the inverse model, and (3) a hybrid solution (i.e., the Neural Adjoint-NA- method) which combines deep learning of the forward model followed by a standard recursive optimization to estimate SSPs. Additionally, synthetic SSPs were generated to augment the variability of the training set. We tested these methods with modeled ray arrivals calculated for a downward refracting environment with mild fluctuations of the thermocline using idealized towed and fixed source configurations. Results indicate that merging data-driven and model-based methods can benefit OAT predictions depending on the selected sensing configurations and actual ray coverage of the water column. But ultimately, the robustness of the OAT predictions depends on the actual dynamics of the SSP variations.

Numerical investigation of a propeller operating under different inflow conditions

This work investigates the flow physics in propeller wakes to better understand how propeller wakes evolve under different inflow conditions from near field to far field. A rotating propeller is numerically modeled by using a dynamic overset technique that involves the improved delayed detached-eddy simulation method. To validate the numerical approach, its results are compared against experimentally determined thrust and torque coefficients and flow fields. The results show that, compared with uniform inflow, turbulent inflow significantly modifies the morphology of the vortex system behind the propeller. Under turbulent-inflow conditions, turbulent structures appear around the boundary layer of the propeller blades and interact with the boundary layer flow of the propeller blades, leading to instability and diffusion of primary tip vortices shed by the blade tips. Multiple local pairing in the circumferential direction leads to the rapid breakdown of the tip vortex system, accompanied by the generation of numerous secondary vortex structures. Tip vortices quickly lose coherence in the middle field and far field and tend to be homogeneously distributed when there is inflow turbulence. The present study gives a deeper insight into the flow physics driving the tip vortex pairing process for a propeller operating under uniform- and turbulent-inflow conditions.

Placement and Resource Allocation of Wireless-Powered Multiantenna UAV for Energy-Efficient Multiuser NOMA

This paper investigates a new downlink nonorthogonal multiple access (NOMA) system, where a multiantenna unmanned aerial vehicle (UAV) is powered by wireless power transfer (WPT) and serves as the base station for multiple pairs of ground users (GUs) running NOMA in each pair. An energy efficiency (EE) maximization problem is formulated to jointly optimize the WPT time and the placement for the UAV, and the allocation of the UAV’s transmit power between different NOMA user pairs and within each pair. To efficiently solve this nonconvex problem, we decompose the problem into three subproblems using block coordinate descent. For the subproblem of intra-pair power allocation within each NOMA user pair, we construct a supermodular game with confirmed convergence to a Nash equilibrium. Given the intra-pair power allocation, successive convex approximation is applied to convexify and solve the subproblem of WPT time allocation and inter-pair power allocation between the user pairs. Finally, we solve the subproblem of UAV placement by using the Lagrange multiplier method. Simulations show that our approach can substantially outperform its alternatives that do not use NOMA and WPT techniques or that do not optimize the UAV location.

Vortex structures in the wake of a marine propeller operating close to a free surface

The present paper analyses the vortical structures in the wake of a naval propeller operating underneath a free surface using detached-eddy simulation. We investigate the flow topology for several loading conditions and compare it with analogous observations behind a propeller operating in open water. We show that the wake topology is similar to that observed in open water only for low-loading conditions. For mild blade loading, the free surface's presence seems to stabilize the flow. On the contrary, for high blade loading, the mutual interaction between the vortex system and the free surface leads to vortex breakdown that overshadows the multiple pairing mechanisms observed in open-water conditions.

Time-Lagged Correlation Analysis of Shellfish Toxicity Reveals Predictive Links to Adjacent Areas, Species, and Environmental Conditions.

Diarrhetic Shellfish Poisoning (DSP) is an acute intoxication caused by the consumption of contaminated shellfish, which is common in many regions of the world. To safeguard human health, most countries implement programs focused on the surveillance of toxic phytoplankton abundance and shellfish toxicity levels, an effort that can be complemented by a deeper understanding of the underlying phenomena. In this work, we identify patterns of seasonality in shellfish toxicity across the Portuguese coast and analyse time-lagged correlations between this toxicity and various potential risk factors. We extend the understanding of these relations through the introduction of temporal lags, allowing the analysis of time series at different points in time and the study of the predictive power of the tested variables. This study confirms previous findings about toxicity seasonality patterns on the Portuguese coast and provides further quantitative data about the relations between shellfish toxicity and geographical location, shellfish species, toxic phytoplankton abundances, and environmental conditions. Furthermore, multiple pairs of areas and shellfish species are identified as having correlations high enough to allow for a predictive analysis. These results represent the first step towards understanding the dynamics of DSP toxicity in Portuguese shellfish producing areas, such as temporal and spatial variability, and towards the development of a shellfish safety forecasting system.

Relaxed risk of predation drives parallel evolution of stickleback behavior.

The occurrence of similar phenotypes in multiple independent populations derived from common ancestral conditions (viz. parallel evolution) is a testimony of evolution by natural selection. Parallel evolution implies that populations share a common phenotypic response to a common selection pressure associated with habitat similarity. Examples of parallel evolution at genetic and phenotypic levels are fairly common, but the driving selective agents often remain elusive. Similarly, the role of phenotypic plasticity in facilitating early stages of parallel evolution is unclear. We investigated whether the relaxation of predation pressure associated with the colonization of freshwater ponds by nine-spined sticklebacks (Pungitius pungitius) likely explains the divergence in complex behaviors between marine and pond populations, and whether this divergence is parallel. Using laboratory-raised individuals exposed to different levels of perceived predation risk, we calculated vectors of phenotypic divergence for four behavioral traits between habitats and predation risk treatments. We found a significant correlation between the directions of evolutionary divergence and phenotypic plasticity, suggesting that divergence in behavior between habitats is aligned with the response to relaxation of predation pressure. Finally, we show alignment across multiple pairs of populations, and that relaxation of predation pressure has likely driven parallel evolution of behavior in this species.

USIS: A unified semantic image synthesis model trained on a single or multiple samples

Semantic image synthesis methods learn to generate new images conditioned on predefined semantic label maps. Existing methods require access to large-volume samples labeled with semantic maps, which limits their applications. We propose USIS, a Unified Semantic Image Synthesis model which can be trained on only a single or multiple pairs of images and semantic maps. Once trained, a USIS model can generate new images according to unseen semantic maps, as existing semantic image synthesis methods do. Specifically, we design a hierarchical architecture to reconstruct training samples and gradually learn the distributions of multi-scale patches in samples from coarse to fine. To avoid the error accumulation across scales, we propose a mixed training strategy to stabilize the training process. Extensive experiments on one- or multiple-sample datasets show our proposed model achieves state-of-the-art performance in terms of visual fidelity.

Multiple Differential Distinguisher of SIMECK32/64 Based on Deep Learning

Currently, deep learning has provided an important means to solve problems in various fields. Intelligent computing will bring a new solution to the security analysis of lightweight block cipher as its analysis becomes more and more intelligent and automatic. In this study, the novel multiple differential distinguishers of round-reduced SIMECK32/64 based on deep learning are proposed. Two kinds of SIMECK32/64’s 6–11 rounds deep learning distinguishers are designed by using the neural network to simulate the case of the multiple input differences and multiple output differences in multiple differential cryptanalysis. The general models of the two distinguishers and the neural network structures are presented. The random multiple ciphertext pairs and the associated multiple ciphertext pairs are exploited as the input of the model. The generation method of the data set is given. The performance of the two proposed distinguishers is compared. The experimental results confirm that the proposed distinguishers have higher accuracy and rounds than the distinguisher with a single difference. The relationship between the quantity of multiple differences and the performance of the distinguishers is also verified. The differential distinguisher based on deep learning needs less time complexity and data complexity than the traditional distinguisher. The accuracy of filtering error ciphertext of our 8-round neural distinguisher is up to 96.10%.

Quantitative Analysis and Structural Elucidation of Fatty Acids by Isobaric Multiplex Labeling Reagents for Carbonyl-Containing Compound (SUGAR) Tags and m-CPBA Epoxidation.

In this study, a novel analytical method was developed to investigate fatty acids (FAs) for relative quantification, carbon-carbon double-bond localization, and cis-/trans-geometry differentiation by isobaric multiplex labeling reagents for carbonyl-containing compound (SUGAR) tag conjugation and meta-chloroperoxybenzoic acid (m-CPBA) epoxidation. FAs are essential components of cells and have diverse functions in energy storage and as complex lipid constituents. It has been reported that FAs play different roles in various biological processes such as the functional development of the brain. The comprehensive characterization and quantification of FAs are crucial to further elucidate their biological roles. However, it is challenging to perform relative quantification and structural elucidation of FAs using integrated mass spectrometry (MS)-based methods. Recently, our group developed isobaric multiplex SUGAR tags for quantitative glycomics. Besides aldehyde/ketone groups on glycans, hydrazide groups also possess reactivity toward carboxylic acids on FAs. In this study, we extended SUGAR tag labeling with FAs for the quantitative analysis by liquid chromatography (LC)-MS/MS in the positive ion mode and applied this strategy for the comparative analysis of FAs hydrolyzed from oil samples. In addition, to comprehensively elucidate the structures of unsaturated FAs, epoxidation by m-CPBA was performed before SUGAR tag labeling to enable carbon-carbon double-bond localization. Moreover, the cis- and trans-geometries of carbon-carbon double bonds in multiple pairs of monounsaturated FAs could also be differentiated in higher-energy collisional dissociation (HCD)-MS/MS. This study developed a high-throughput comprehensive FA analysis platform, which could be widely applied and utilized in biological and clinical studies.

Hybrid 3DMA for multi-user MIMO-VLC

In this paper, we propose and investigate a hybrid three-dimensional multiple access (3DMA) scheme for multi-user multiple-input multiple-output visible light communication (MU-MIMO-VLC) systems that can fully exploit the 3D resources of the system including frequency, space, and power. Particularly, all users in the MU-MIMO-VLC system applying hybrid 3DMA are first divided into multiple user groups (UGs) in the spatial domain, and users within each UG are further divided into multiple user pairs (UPs) in the frequency domain. In each UP, two users are multiplexed in the power domain via superposition coding. Due to the efficient 3D resource allocation in the MU-MIMO-VLC system applying hybrid 3DMA, the available bandwidth of each user can be substantially increased. Moreover, the impact of error propagation caused by imperfect successive interference cancellation (SIC) is considered, and the optimal power allocation (OPA) strategy is also derived to maximize the achievable sum rate of each UP. The feasibility and superiority of the proposed hybrid 3DMA with OPA have been successfully verified by the obtained analysis and simulation results. It is shown that the achievable average sum rate of an indoor 4 × 4 MU-MIMO-VLC system can be substantially improved by applying the proposed hybrid 3DMA with OPA, in comparison to benchmark schemes such as orthogonal frequency division multiple access and space division multiple access. Moreover, it is further revealed that hybrid 3DMA with OPA exhibits high tolerance against imperfect SIC induced error propagation, which also shows excellent robustness and performance consistency to support multiple randomly located users.

Constructing overlap functions via multiplicative generators on complete lattices

Multiplicative generators of lattice-valued overlap functions are investigated throughout this paper. To be more specific, we first obtain the multiplicative generator pairs maintaining the standard approach, and then we define the generalized multiplicative generator triples by using overlap functions instead of the multiplication operator. Conditions for multiplicative generator pairs and triples are obtained and several examples are given to illustrate the conclusions. Finally, we investigate some properties including extended homogeneity and idempotency of overlap functions constructed by generalized multiplicative generator triples.

Wi-Learner

Contactless RF-based sensing techniques are emerging as a viable means for building gesture recognition systems. While promising, existing RF-based gesture solutions have poor generalization ability when targeting new users, environments or device deployment. They also often require multiple pairs of transceivers and a large number of training samples for each target domain. These limitations either lead to poor cross-domain performance or incur a huge labor cost, hindering their practical adoption. This paper introduces Wi-Learner, a novel RF-based sensing solution that relies on just one pair of transceivers but can deliver accurate cross-domain gesture recognition using just one data sample per gesture for a target user, environment or device setup. Wi-Learner achieves this by first capturing the gesture-induced Doppler frequency shift (DFS) from noisy measurements using carefully designed signal processing schemes. It then employs a convolution neural network-based autoencoder to extract the low-dimensional features to be fed into a downstream model for gesture recognition. Wi-Learner introduces a novel meta-learner to "teach" the neural network to learn effectively from a small set of data points, allowing the base model to quickly adapt to a new domain using just one training sample. By so doing, we reduce the overhead of training data collection and allow a sensing system to adapt to the change of the deployed environment. We evaluate Wi-Learner by applying it to gesture recognition using the Widar 3.0 dataset. Extensive experiments demonstrate Wi-Learner is highly efficient and has a good generalization ability, by delivering an accuracy of 93.2% and 74.2% - 94.9% for in-domain and cross-domain using just one sample per gesture, respectively.

Integrated Transcriptome and Targeted Metabolite Analysis Reveal miRNA-mRNA Networks in Low-Light-Induced Lotus Flower Bud Abortion.

Most Nelumbo nucifera (lotus) flower buds were aborted during the growing season, notably in low-light environments. How lotus produces so many aborted flower buds is largely unknown. An integrated transcriptome and targeted metabolite analysis was performed to reveal the genetic regulatory networks underlying lotus flower bud abortion. A total of 233 miRNAs and 25,351 genes were identified in lotus flower buds, including 68 novel miRNAs and 1108 novel genes. Further enrichment analysis indicated that sugar signaling plays a potential central role in regulating lotus flower bud abortion. Targeted metabolite analysis showed that trehalose levels declined the most in the aborting flower buds. A potential regulatory network centered on miR156 governs lotus flower bud abortion, involving multiple miRNA-mRNA pairs related to cell integrity, cell proliferation and expansion, and DNA repair. Genetic analysis showed that miRNA156-5p-overexpressing lotus showed aggravated flower bud abortion phenotypes. Trehalose-6-P synthase 1 (TPS1), which is required for trehalose synthase, had a negative regulatory effect on miR156 expression. TPS1-overexpression lotus showed significantly decreased flower bud abortion rates both in normal-light and low-light environments. Our study establishes a possible genetic basis for how lotus produces so many aborted flower buds, facilitating genetic improvement of lotus’ shade tolerance.

Understanding the differences between single‐ and multiobjective optimization for the conservation of multiple species

AbstractThe purpose of this study is to understand how solutions from single‐ and multiobjective optimization for the conservation of multiple species are different and what impacts these differences. We identify optimal conservation investment allocations maximizing expected species' habitat ranges for multiple pairs of species using two approaches in the central and southern Appalachian region. We find that disparities between the two approaches are affected by differences in the involved species' expected habitat ranges (i.e., contrasting and similar) and their correlation pattern (i.e., positive, negative, and insignificant). Using a single metric by aggregating species' habitats for multiple species to carry out single‐objective optimization is shown to favor the species with a larger habitat distribution more if the involved species' expected habitat distributions are negatively correlated and their distribution difference is larger. Framing multiple metrics of species' habitats separately using multiobjective optimization for the same set of multiple species, in contrast, does not show such a drastic disparity.

Scattering of the asymmetric $$\phi ^6$$ kinks from a $${\mathcal{PT}\mathcal{}}$$-symmetric perturbation: creating multiple kink–antikink pairs from phonons

Interaction of asymmetric $\phi^6$ kinks with a spatially localized $\mathcal{PT}$-symmetric perturbation is investigated numerically. It has been shown that when the kink (antikink) hits the defect from the gain side, a final velocity of the kink decreases (increases), while for the kink and antikink coming from the opposite direction their final velocities remain unchanged. It is also found that when the kink interacts with the defect from the gain side multiple pair of the kink-antikink are formed from small amplitude waves (phonons) in the final states depending on the initial velocity of the initial kink and parameter of the perturbation.