Local Distance Research Articles

Detection of copy number variations based on a local distance using next-generation sequencing data.

As one of the main types of structural variation in the human genome, copy number variation (CNV) plays an important role in the occurrence and development of human cancers. Next-generation sequencing (NGS) technology can provide base-level resolution, which provides favorable conditions for the accurate detection of CNVs. However, it is still a very challenging task to accurately detect CNVs from cancer samples with different purity and low sequencing coverage. Local distance-based CNV detection (LDCNV), an innovative computational approach to predict CNVs using NGS data, is proposed in this work. LDCNV calculates the average distance between each read depth (RD) and its k nearest neighbors (KNNs) to define the distance of KNNs of each RD, and the average distance between the KNNs for each RD to define their internal distance. Based on the above definitions, a local distance score is constructed using the ratio between the distance of KNNs and the internal distance of KNNs for each RD. The local distance scores are used to fit a normal distribution to evaluate the significance level of each RDS, and then use the hypothesis test method to predict the CNVs. The performance of the proposed method is verified with simulated and real data and compared with several popular methods. The experimental results show that the proposed method is superior to various other techniques. Therefore, the proposed method can be helpful for cancer diagnosis and targeted drug development.

Emulation of seismic-phase traveltimes with machine learning

SUMMARY We present a machine learning (ML) method for emulating seismic-phase traveltimes that are computed using a global-scale 3-D earth model and physics-based ray tracing. Accurate traveltime predictions based on 3-D earth models are known to reduce the bias of event location estimates, increase our ability to assign phase labels to seismic detections and associate detections to events. However, practical use of 3-D models is challenged by slow computational speed and the unwieldiness of pre-computed lookup tables that are often large and have prescribed computational grids. In this work, we train a ML emulator using pre-computed traveltimes, resulting in a compact and computationally fast way to approximate traveltimes that are based on a 3-D earth model. Our model is trained using approximately 850 million P-wave traveltimes that are based on the global LLNL-G3D-JPS model, which was developed for more accurate event location. The training-set consists of traveltimes between 10 393 global seismic stations and randomly sampled event locations that provide a prescribed, distance-dependent geographic sample density for each station. Prediction accuracy is dependent on event-station distance and whether the station was included in the training set. For stations included in the training set the mean absolute deviation (MAD) of the difference between traveltimes computed using ray tracing through the 3-D model and the ML emulator for local, regional, and teleseismic distances are 0.090, 0.125 and 0.121 s, respectively. For tested station locations not included in the training set, MAD values for the three distance ranges increase to 0.173, 0.219 and 0.210 s, respectively. Empirical traveltime residuals for a global reference data are indistinguishable when ML emulation or the 3-D model is used to compute traveltimes. This result holds regardless of whether the recording station is used in ML training or not.

Extended trajectory of spatial memory errors in typical and atypical development: The role of binding and precision.

Spatial reconstruction, a method for evaluating how individuals remember the placement of objects, has traditionally been evaluated through the aggregate estimation of placement errors. However, this approach may obscure the nature of task errors. Specifically, recent data has suggested the importance of examining the precision of responses, as well as absolute performance on item-context bindings. In contrast to traditional analysis approaches based on the distance between the target and the reconstructed item, in this study we further explored three types of errors (swap error, global error, and local distance) that may all contribute to the distance, with particular emphasis on swap errors and local distance due to their associations with item-context bindings and memory precision, respectively. We examined these errors in children aged 3-18 years, making comparisons between children with typical development (TD) and children with Down syndrome (DS), a population with known memory challenges. As expected, older children outperformed younger children in terms of overall memory accuracy. Of importance is that we measured uneven maturational trajectories of memory abilities across the various error types. Specifically, both remembered locations (irrespective of object identity) and swap errors (object-location binding errors) align with the overall memory accuracy. Memory precision, as measured by local distance in simpler set size 2 trials, mirrored overall memory accuracy. However, for more complex set size 3 trials, local distance remained stable before age 8 and showed age-related change thereafter. The group with DS showed reduced precision compared to a TD matched group, and measures of precision, and to a lesser extent binding errors, correlated with standard neuropsychological outcomes. Overall, our study contributed to a fine-grained understanding of developing spatial memory ability in a large sample of typical developing children and a memory impaired population. These findings contribute to a growing body of research examining precision as a key factor in memory performance.

Gene drives for invasive wasp control: Extinction is unlikely, with suppression dependent on dispersal and growth rates.

Gene drives offer a potentially revolutionary method for pest control over large spatial extents. These genetic modifications spread deleterious variants through a population and have been proposed as methods for pest suppression or even eradication. We examined the influence of local dispersal, long-distance and/or human-mediated dispersal, and variation in population growth on the success of a gene drive for the control of invasive social wasps (Vespula vulgaris). Our simulations incorporated a spatially realistic environment containing variable habitat quality in New Zealand. Pest eradication was not observed, except in extreme and unrealistic scenarios of constant, widespread, and spatially intense releases of genetically modified individuals every year for decades. Instead, the regional persistence of genetically modified and wild-type wasps was predicted. Simulations using spatially homogeneous versus realistic landscapes (incorporating uninhabitable areas and dispersal barriers) showed little difference in overall population dynamics. Overall, little impact on wasp abundance was observed in the first 15 years after introduction. After 25 years, populations were suppressed to levels <95% of starting populations. Populations exhibited "chase dynamics" with population cycles in space, with local extinction occurring in some areas while wasps became abundant in others. Increasing the wasps' local dispersal distance increased the spatial and temporal variability of the occupied area and population suppression. Varying levels of human-associated long-distance dispersal had little effect on population dynamics. Increasing intrinsic population growth rates interacted with local dispersal to cause higher mean populations and substantially higher levels of variation in population suppression and the total amount of landscape occupied. Gene drives appear unlikely to cause a rapid and widespread extinction of this and probably other pests but could offer long-term and cost-effective methods of pest suppression. The predicted level of <95% pest suppression would substantially reduce the predation pressure and competitive interactions of this invasive wasp on native species. However, the predicted long-term persistence of genetically modified pests will influence the ethics and likelihood of using gene drives for pest control, especially given concerns that modified wasps would eventually be transported back to their home range.

Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views.

View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible, annotation-free system for automatic CMR viewplanning. The system mines the spatial relationship-more specifically, locates the intersecting lines-between the target planes and source views, and trains U-Net-based deep networks to regress heatmaps defined by distances from the intersecting lines. On the one hand, the intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. On the other hand, as the spatial relationship is self-contained in properly stored data, for example, in the DICOM format, the need for additional manual annotation is eliminated. In addition, the interplay of the multiple target planes predicted in a source view is utilized in a stacked hourglass architecture consisting of repeated U-Net-style building blocks to gradually improve the regression. Then, a multiview planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. For performance evaluation, the retrospectively identified planes prescribed by the technologists are used as the ground truth, and the plane angle differences and localization distances between the planes prescribed by our system and the ground truth arecompared. The retrospective experiments include 181 clinical CMR exams, which are randomly split into training, validation, and test sets in the ratio of 64:16:20. Our system yields the mean angular difference and point-to-plane distance of 5.68 and 3.12 mm, respectively, on the held-out test set. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-orientedscout. The proposed system demonstrates accurate automatic CMR view plane prescription based on deep learning on properly archived data, without the need for further manual annotation. This work opens a new direction for automatic view planning of anatomy-oriented medical imaging beyondCMR.

A LOF-IDW based data cleaning method for quality assessment in intelligent compaction of soils

Intelligent compaction (IC) is attracting increasing interest in construction engineering involving earthwork compaction. However, outliers may exist in data sets collected during IC, which can lead to misinterpretation of the compaction status of soils and may further result in erroneous results for compaction quality assessment. This study proposes a novel method for cleaning the measured data sets by combining the density-based local outlier factor (LOF) and inverse distance weighted (IDW) method. In the proposed LOF-IDW based method, the effect of spatial variation in soil properties is taken into account, while a boxplot-based method is proposed to dynamically determine the suitable threshold for outlier diagnosis. The capability and performance of the proposed method are verified against three data sets collected from construction sites. The results indicate that compared with the commonly used 3σ criterion, the proposed method not only exhibits a better performance in outlier diagnosis but also can rehabilitate the relevant data. In addition, the proposed method is demonstrated to be capable of significantly reducing the coefficient of variation of the measured data sets, which provides a more reliable support for the quality assessment and decision-making in IC.

A combined VOF-RANS approach for studying the evolution of incipient two-dimensional wind-driven waves over a viscous liquid

Recent laboratory experiments have revealed that important insights into the physical processes involved in the wind-driven generation of surface waves may be obtained by varying the viscosity of the carrying liquid over several orders of magnitude. The present paper reports on the development of a companion approach aimed at studying similar phenomena through numerical simulation, a way expected to remove some of the experimental limitations, especially in the near-interface region, and to allow the relative influence of several physical processes to be assessed by disregarding or inactivating arbitrarily some of them. After reviewing available options, we select and approach based on the combination of a volume of fluid technique to track the evolution of the air–liquid interface, and a two-dimensional Reynolds-averaged version of the Navier–Stokes equations supplemented with a turbulence model to predict the velocity and pressure fields in both fluids. We examine the formal and physical frameworks in which such a time-dependent two-dimensional formulation is meaningful, and close the governing momentum equations with the one-equation Spalart–Allmaras model which directly solves a transport equation for the eddy viscosity. For this purpose, we assume the interface to behave as a rigid wall with respect to turbulent fluctuations in the air, and implement a versatile algorithm to compute the local distance to the interface whatever its shape. We first assess the performance of this model in unseparated and separated single-phase flows over a wavy rigid wall, which are of specific relevance with respect to wind-wave generation. Then, we discuss the initialization protocol used in two-phase simulations, which involves an impulse disturbance with a white noise distribution applied to the interface position. We finally present some examples of interface evolutions obtained at several wind speeds with liquids of various viscosities, and discuss the underlying physics revealed by the associated statistics of interface disturbances, streamline patterns and energy spectra.

Investment feasibilities of CCUS technology retrofitting China's coal chemical enterprises with different CO2 geological sequestration and utilization approaches

CO2 capture, utilization, and storage (CCUS) technology provides an efficient alternative for decarbonizing the coal chemical industry under China's carbon neutrality goal. However, uncertainties such as geological conditions, technology level, carbon markets, subsidy policies, etc., will greatly affect investor confidence in the CCUS applicability in this industry. This study develops a trinomial tree real options based CCUS investment decision model to explore CCUS investment feasibilities for China's coal chemical enterprises with different CO2 geological sequestration and utilization approaches, i.e., CO2 enhanced oil recovery (CO2-EOR) projects, CO2 enhanced coalbed methane (CO2-ECBM) projects, and deep saline aquifer (DSA) projects. The findings show that 1) CO2-ECBM projects have a larger investment benefit compared to CO2-EOR and DSA projects, with a critical coalbed methane (CBM) price for the immediate investment of 1.3 CNY/m³ under the current technology and carbon price level. 2) CO2-EOR projects are immediately investable at a critical oil price of 200 CNY/BBL; DSA projects require the execution of a delayed option with a critical carbon price of 361.8 CNY/t. 3) Provincial investment benefits vary substantially depending on CO2 transport distance and product revenue, particularly for CO2-ECBM projects in Henan and Guizhou, which have significant investment benefits due to high local CBM prices and short CO2 transport distances.

ZPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15.

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions. This module enables the extraction of protein-ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic-level protein-ligand feature encoding and fusion to output refined ligand poses and ligand per-atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF-2016 protein-ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein-ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side-chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein-ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

Theoretical and Experimental Study on Homologous Acoustic Emission Signal Recognition Based on Synchrosqueezed Wavelet Transform Coherence

The acoustic emission (AE) technique has been widely investigated for its ability to locate damage in structures. However, the selection of the arrival point of AE signals and the existence of nonhomologous AE signals can significantly affect the location accuracy of damages. The synchrosqueezed wavelet transform (SWT) was used in our previous research to pick the accurate arrival point, but the existence of the nonhomologous signals was neglected in the picking process. To address this limitation, the synchrosqueezed wavelet transform coherence (SWTC) method is proposed to improve the accuracy by recognizing homologous signals and suppressing the spectral leakage in this paper. Compared with the wavelet transform coherence (WTC) method previously used, the SWTC method using the squeezing wavelet coefficients obtained by the SWT can constitute a more explicit coherence graph of AE signals. This clear coherence graph can help reduce the effect of subjective factors in observing the coherence and improve the recognition accuracy of homologous signals. The effectiveness of the proposed method is experimentally verified on a steel pipe and a concrete beam. The results demonstrate that the SWTC accurately identifies homologous AE signals and effectively improves the localization accuracy across different signal densities, localization distances, and materials.

Coarse-grained model simulation-guided localized DNA signal amplification probe for miRNA detection

DNA-based enzyme-free signal amplification strategies are widely employed to detect biomarkers in low abundance. To enhance signal amplification, localized DNA reaction units which increases molecular collision probability is commonly utilized. However, the current understanding of the structure-function relationships in localized DNA signal amplification probes is limited, leading to unsatisfied performance. In this study, we introduced a coarse-grained molecular model to simulate the dynamic behavior of two DNA reaction units within a DNA enzyme-free signal amplification circuit called Localized Catalytic Hairpin Assembly (LCHA). We investigated the impact of localized distance and flexibility on reaction performance. The most efficient LCHA probe guided by simulation exhibits sensitivity 28 times greater that of free CHA, with a detection limit of miR-21 reaching 16 pM, while the least effective LCHA probe demonstrated a modest improvement of only 7 times. We successfully employed the optimized probe to differentiate cancer cells from normal cells based on their miR-21 expression levels, showcasing its quantification ability. By elucidating the mechanistic insights and structure-function relationship in our work, we aim to contribute valuable information that can save users' time and reduce costs when designing localized DNA probes. With a comprehensive understanding of how the localization affects probe performance, researchers can now make more informed and efficient decisions during the design process. This work would find broad applications of DNA nanotechnology in biosensing, biocomputing, and bionic robots.

Estimating protein complex model accuracy based on ultrafast shape recognition and deep learning in CASP15.

This article reports and analyzes the results of protein complex model accuracy estimation by our methods (DeepUMQA3 and GraphGPSM) in the 15th Critical Assessment of techniques for protein Structure Prediction (CASP15). The new deep learning-based multimeric complex model accuracy estimation methods are proposed based on the ensemble of three-level features coupling with deep residual/graph neural networks. For the input multimeric complex model, we describe it from three levels: overall complex features, intra-monomer features, and inter-monomer features. We designed an overall ultrafast shape recognition (USR) to characterize the relationship between local residues and the overall complex topology, and an inter-monomer USR to characterize the relationship between the residues of one monomer and the topology of other monomers. DeepUMQA3 (Group name: GuijunLab-RocketX) ranked first in the interface residue accuracy estimation of CASP15. The Pearson correlation between the interface residue Local Distance Difference Test (lDDT) predicted by DeepUMQA3 and the real lDDT is 0.570, the only method that exceeds 0.5. Among the top 5 methods, DeepUMQA3 achieved the highest Pearson correlation of lDDT on 25 out of 39 targets. GraphGPSM (Group name: GuijunLab-PAthreader) has TM-score Pearson correlations greater than 0.9 on 14 targets, showing a good ability to estimate the overall fold accuracy. The DeepUMQA3 server is available at http://zhanglab-bioinf.com/DeepUMQA/ and the GraphGPSM server is available at http://zhanglab-bioinf.com/GraphGPSM/.

A bottom-up approach for recurrence detection based on sampling distance

One of the major problems faced in the recurrence analysis of dynamical systems is the tangential motion effect affecting the structures in recurrence plots and their quantification. This issue roots to the choice of a threshold for recurrence, making it a crucial parameter for such analyses. It has been shown that a variable threshold following the dynamical changes of the system is more suited to the analysis of non-stationary data as it mitigates this effect. We study here the use of the distance separating successive points in the phase space as a reference for the recurrence threshold. The method relies on a single parameter while qualitatively and quantitatively providing stable recurrence structures as the previously suggested threshold based on the local maximum pairwise distance. This complete bottom-up approach is shown to be beneficial in the presence of abrupt transitions. It is also fairly noise-resistant and is not dependent on the sampling frequency in its normalized formulation. Furthermore, the sampling distance provides a clear reference for the occurrence of the tangential motion effect, allowing to define a default value for the threshold parameter to avoid it.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABSTRACT A structural understanding of entire cellular processes has been an uncharted realm, until now. The artificial intelligence-based tool AlphaFold2 (AF2) has substantially changed the prediction accuracy, and predicted models of entire proteomes are now available. Here, we have examined AF2’s prediction of 38 core macroautophagic/autophagic proteins and 378 interacting partners representing the human autophagic interactome. Prior to AF2, ~50% of the proteins lacked atomistic level resolution and we found significant improvement in structural coverage by AF2, with an addition of ~ 47% of the residues modeled with reasonable confidence. We also augmented this structural information with μs timescale molecular dynamics simulations, in particular, ATG2, ATG10, and ATG14. ATG2A, a bipartite membrane protein with rodlike architecture was predicted with high accuracy and our simulations revealed dynamic transitions of cavity-lining residues that might play a critical role in regulating lipid transfer. In addition, a promising approach of multimeric prediction by AF2 revealed the architecture of ATG7-ATG10, a tetrameric complex that participates in conjugation machinery in autophagy. By combining computational and experimental approaches, we demonstrated that three salt bridges were crucial to ATG7-ATG10 complex formation and mutating these residues abrogated the binding. We have also generated a web resource with curated AF2 structural models, simulated conformational ensemble, and structural analysis that will be highly pertinent to the autophagy community. Altogether, our work presents a robust pipeline to utilize AF2 as a tool for a starting point to provide the dynamic behavior of molecules in a given biological pathway. Abbreviations AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like

Automatic determination of focal depth with the optimal period of Rayleigh wave amplitude spectra at local distances

SUMMARY Focal depth of earthquakes is essential for studies of seismogenic processes and seismic hazards. In regions with dense seismic networks, focal depth can be resolved precisely based on the traveltime of P and S, which is less feasible in case of sparse networks. Instead, surface waves are usually the strongest seismic phases at local and regional distances, and its excitation is sensitive to source depth, thus theoretically important for estimating focal depth even with a limited number of seismic stations. In this study, short-period (0.5–20 s) Rayleigh waves are explored to constrain focal depths. We observe that the optimal period (the period corresponding to the maximum amplitude) of Rayleigh waves at local distances (≤200 km) shows an almost linear correlation with focal depth. Based on this finding, we propose an automated method for resolving the focal depth of local earthquakes using the linear regression relation between the optimal period of Rayleigh wave amplitude spectra and focal depth. Synthetic tests indicate the robustness of this method against source parameters (focal mechanism, source duration and non-double-couple component) and crustal velocity structure. Although the attenuation (Q factor) of shallow crust can introduce complexities in determining focal depth, it can be simultaneously estimated if a sufficient number of stations are available. The proposed method is applied to tens of small-to-moderate earthquakes (Mw 3.5–5.0) in diverse tectonic settings, including locations in the United States (Oklahoma, South Carolina, California, Utah, etc.) and China (Sichuan, Shandong). Results demonstrate that reliable focal depth, with uncertainty of 1–2 km, can be determined even with one or a few seismic stations. This highlights the applicability of the method in scenarios characterized by sparse network coverage or historical events.

An overview on density peaks clustering

Density peaks clustering (DPC) algorithm is a new algorithm based on density clustering analysis, which can quickly obtain the cluster centers by drawing the decision diagram by using the calculation of local density and relative distance. Without prior knowledge and iteration, the parameters and structure are simple and easy to implement. Since it was proposed in 2014, it has attracted a large number of researchers to explore experiments and improve applications in recent years. In this paper, we first analyze the theory of DPC and its performance advantages and disadvantages. Secondly, it summarizes the improvement of DPC in recent years, analyzes the improvement effect, and shows it with experimental data. The related application research of DPC in different fields is introduced. Finally, the clustering results of DPC, LCDPC, DCHDPC and NADPC algorithms in different data sets are analyzed. Experiments show that the improved algorithm can divide the clustering more accurately, which provides new ideas for improving DPC algorithm in the future. At the same time, this paper summarizes and prospects the improvement and development of DPC.

Intrinsic disorder may drive the interaction of PROS1 and MERTK in uveal melanoma

BackgroundClass 2 uveal melanomas are associated with the inactivation of the BRCA1 ((breast cancer type 1 susceptibility protein)-associated protein 1 (BAP1)) gene. Inactivation of BAP1 promotes the upregulation of vitamin K-dependent protein S (PROS1), which interacts with the tyrosine-protein kinase Mer (MERTK) receptor on M2 macrophages to induce an immunosuppressive environment. MethodsWe simulated the interaction of PROS1 with MERTK with ColabFold. We evaluated PROS1 and MERTK for the presence of intrinsically disordered protein regions (IDPRs) and disorder-to-order (DOT) regions to understand their protein-protein interaction (PPI). We first evaluated the structure of each protein with AlphaFold. We then analyzed specific sequence-based features of the PROS1 and MERTK with a suite of bioinformatics tools. ResultsWith high-resolution, moderate confidence, we successfully modeled the interaction between PROS1 and MERTK (predicted local distance difference test score (pDLLT) = 70.68). Our structural analysis qualitatively demonstrated IDPRs (i.e., spaghetti-like entities) in PROS1 and MERK. PROS1 was 23.37 % disordered, and MERTK was 23.09 % disordered, classifying them as moderately disordered and flexible proteins. PROS1 was significantly enriched in cysteine, the most order-promoting residue (p-value <0.05). Our IUPred analysis demonstrated that there are two disorder-to-order transition (DOT) regions in PROS1. MERTK was significantly enriched in proline, the most disorder-promoting residue (p-value <0.05), but did not contain DOT regions. Our STRING analysis demonstrated that the PPI network between PROS1 and MERTK is more complex than their assumed one-to-one binding (p-value <2.0 × 10−6). ConclusionOur findings present a novel prediction for the interaction between PROS1 and MERTK. Our findings show that PROS1 and MERTK contain elements of intrinsic disorder. PROS1 has two DOT regions that are attractive immunotherapy targets. We recommend that IDPRs and DOT regions found in PROS1 and MERTK should be considered when developing immunotherapies targeting this PPI.

Plunging breakers. Part 1. Analysis of an ensemble of wave profiles

An experimental study of the dynamics and droplet production in three mechanically generated plunging breaking waves is presented in this two-part paper. In the present paper (Part 1), the dynamics of the three breakers are studied through measurements of the evolution of their free surface profiles during 10 repeated breaking events for each wave. The waves are created from dispersively focused wave packets generated with three wave maker motions that differ primarily by small changes in their overall amplitude. Breaker profiles are measured with a cinematic laser-induced fluorescence technique covering a streamwise region of approximately one breaker wavelength and over a time of 3.4 breaker periods. The aligned profile data is used to create spatio-temporal maps of the ensemble average surface height and the standard deviation of both the local normal distance and the local arc length relative to the instantaneous mean profile. It is found that the mean and standard deviation maps contain strongly correlated localized features and indicate that the transition from laminar to turbulent flow is a highly repeatable process. Regions of high standard deviation include the splash created by the plunging jet impact and subsequent splash impacts at the front of the breaking region as well as the site where the air pocket entrained under the plunging jet at the moment of jet tip impact comes to the surface and pops. In Part 2 (Erinin et al., J. Fluid Mech., vol. 967, 2023, A36), these features are used to interpret various features of the distributions of droplet number, diameter and velocity.

NIT: Searching for rumors in social network through neighborhood information transmission

Rumor detection has been a hot issue in current online public opinion governance. Most of the recent rumor detection methods tend to be supervised learning, while research about unsupervised detection methods is relatively rare, which focuses on deep learning models. For both interpretability and high performance, an unsupervised rumor detection method based on neighborhood information transmission (NIT) is proposed. Firstly, the initial importance regarded as the initial information of the object is evaluated from the perspective of local and global heterogeneous distance. Then the neighborhood information graph is constructed based on the neighborhood relationship on mixed data. On the neighborhood graph, NIT performs information transmission between neighbors and constantly updates the importance score by paying attention to the correlation between objects. Finally, the rumor object can be sought out by importance score ranking. In addition, to enhance the rumor identification effect, a propagation-based enhancement strategy is proposed to improve the performance of the unsupervised rumor detection algorithm by the propagation index of each object. Experiments were conducted on two UCI datasets and Weibo real-world rumor dataset, and the results proved the effectiveness of the NIT algorithm and the feasibility of the enhancement strategy.

Case in Wholesale Late Merger: Evidence from Mongolian Scrambling

Takahashi and Hulsey (2009) suggest that wholesale late merger is controlled by case. This article presents novel evidence for this idea from Condition C reconstruction effects in Mongolian local and long-distance scrambling. Departing from previous accounts, I argue that the complexity of the phenomenon reveals that Condition C connectivity is related neither to the position of underlying binders nor to A/Ā properties—scrambling bleeds Condition C, so long as the case requirement of the late-merged NP can be satisfied. Crucially, I motivate a hybrid case system for Mongolian in which accusative case is assigned as a dependent case. I show that this is both necessary and independently motivated, thereby introducing a fine-grained view of case into the wholesale-late-merger mechanism.