Signal Recognition Research Articles

Identification of crucial drought-tolerant genes of barley through comparative transcriptomic analysis and yeast-based stress assay

Drought is a persistent and serious threat to crop yield and quality. The identification and functional characterization of drought tolerance-related genes is thus vital for efforts to support the genetic improvement of drought-tolerant crops. Barley is highly adaptable and renowned for its robust stress resistance, making it an ideal subject for efforts to explore genes related to drought tolerance. In this study, two barley materials with different drought tolerance were subjected to soil drought treatment, including a variety with strong drought tolerance (Hindmarsh) and a genotype with weaker drought tolerance (XZ5). Transcriptomic sequencing data from the aboveground parts of these plants led to the identification of 1,206 differentially expressed genes associated with drought tolerance. These genes were upregulated in Hindmarsh following drought stress exposure but downregulated or unchanged in XZ5 under these same conditions, or were unchanged in Hindmarsh but downregulated in XZ5. Pathway enrichment analyses suggested that these genes are most closely associated with defense responses, signal recognition, photosynthesis, and the biosynthesis of various secondary metabolites. Using protein-protein interaction networks, the ankyrin repeat domain-containing protein 17-like isoform X2 was predicted to impact other drought tolerance-related protein targets in Hindmarsh. In MapMan metabolic pathway analyses, genes found to be associated with the maintenance of drought tolerance in Hindmarsh under adverse conditions were predicted to include genes involved in the abscisic acid, cytokinin, and gibberellin phytohormone signaling pathways, genes associated with redox homeostasis related to ascorbate and glutathione S-transferase, transporters including ABC and AAAP, transcription factors such as AP2/ERF and bHLH, the heat shock proteins HSP60 and HSP70, and the sucrose non-fermenting-1-related protein kinase. Heterologous HvSnRK2 (one of the identified genes, which encodes the sucrose non-fermenting-1-related protein kinase) gene expression in yeast conferred significant drought tolerance, highlighting the functional importance of this gene as one linked with drought tolerance. This study revealed the drought tolerance mechanism of Hindmarsh by comparing transcriptomes while also providing a set of candidate genes for genetic efforts to improve drought tolerance in this and other crop species.

Information Technology for Sound Analysis and Recognition in the Metropolis based on Machine Learning Methods

The goal of designing and implementing an intelligent information system for the recognition and classification of sound signals is to create an effective solution at the software level, which would allow analysis, recognition, classification and forecasting of sound signals in megacities and smart cities using machine learning methods. This system can help people in various fields to simplify their lives, for example, it can help farmers protect their crops from animals, in the military it can help with the identification of weapons and the search for flying objects, such as drones or missiles, in the future there is a possibility for recognizing the distance to sound, also, in cities can help with security, so a preventive response system can be built, which can check if everything is in order based on sounds. Also, it can make life easier for people with impaired hearing to detect danger in everyday life. In the part of the comparison of analogues of the developed product, 4 analogues were found: Shazam, sound recognition from Apple, Vocapia, and SoundHound. A table of comparisons was made for these analogues and the product under development. Also, after comparing analogues, a table for evaluating the effects of the development was built. During the system analysis section, a variety of audio research materials were developed to indicate the characteristics that can be used for this design: period, amplitude, and frequency, and, as an example, an article on real-world audio applications is shown. A precedent scenario is described using the RUP methodology and UML diagrams are constructed: Diagram of use cases; Class diagram; Activity chart; Sequence diagram; Diagram of components; and Deployment diagram. Also, sound data analysis was performed, sound data was visualized as spectrograms and sound waves, which clearly show that the data are different, so it is possible to classify them using machine learning methods. An experimental selection of the machine learning method as staandart clasificers for building a sound recognition model was made. The best method turned out to be SVC, the accuracy of which reflects more than 30 per cent. A neural network was also implemented to improve the obtained results. The result of training a model based on a neural network during 100 epochs achieved a result of 97.7% accuracy for training data and 47.8% accuracy when checking performance on test data. This result should be higher, so it is necessary to consider improving recognition algorithms, increasing the amount of data, and changing the recognition method. Testing of the project was carried out, showing its operation and pointing out shortcomings that need to be corrected in the future.

Elucidation of the late steps in the glycan-dependent ERAD of soluble misfolded glycoproteins.

The endoplasmic reticulum (ER) utilizes ER-associated degradation (ERAD), a highly conserved eukaryotic pathway, to eliminate misfolded or unassembled proteins and maintain protein homeostasis in cells. The clearance of misfolded glycoproteins involves several distinct steps, including the recognition of a specific glycan signal, retrotranslocation to the cytosol, and subsequent degradation of the misfolded protein by the ubiquitin proteasome system. Confocal microscopy was used to track the fate of a well-characterized ERAD substrate via a self-complementing split fluorescent protein assay. The results demonstrate that a misfolded variant of the STRUBBELIG (SUB) extracellular protein domain (SUBEX-C57Y) is retrotranslocated to the cytosol when transiently expressed in Nicotiana benthamiana leaf epidermal cells. Retrotranslocation requires a protein domain with a lesion that is exposed in the lumen of the ER, N-glycan trimming by α-mannosidases, HRD1-mediated ubiquitination, and the ATPase function of CDC48. The retrotranslocated SUBEX-C57Y ERAD substrate undergoes deglycosylation, and proteasomal degradation is blocked by a catalytically inactive cytosolic peptide N-glycanase. These findings define distinct aspects of ERAD that have been elusive until now and may represent the default pathway for degrading misfolded glycoproteins in plants.

Impact of Diverse Musical Genres on Blood-Feeding and Mating Behavior in Aedes aegypti Mosquitoes.

Sound plays an important role in mosquito foraging and sexual interactions. Mosquitoes utilize acoustic signals for host location, sexual recognition, and mating, which is achieved through the rhythmic beating of their wings to generate distinct sounds characterized by specific flight tone frequencies. In this study, electronic music (Tremor by Dimitri Vegas, Martin Garrix and Like Mike) and country music (Country Road by John Denver) were played in high and low volumes to investigate the influence of different genres of music on the blood feeding and mating responses of Ae. aegypti. The observations encompassed monitoring the landing attempts, blood-feeding endeavors, and mating behavior of Ae. aegypti. High-volume electronic and country music with elevated pitch and amplitude significantly reduced host attacks and blood-feeding attempts by female mosquitoes compared to low-volume or music-off conditions. However, the significant reduction of successful copula was only observed in Ae. aegypti mosquitoes exposed under electronic music with high volume. As transmission of dengue is through blood feeding and mating, this discovery may open new possibilities for developing music-based control strategies against mosquito-borne diseases.

A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy

A feature extraction method based on the combination of improved empirical modal decomposition (IEMD) and multi-scale permutation entropy (MPE) is proposed to address the problem of inaccurate recognition and classification of ship noise signals under complex environmental conditions. In order to eliminate the end effects, this paper proposes an extended model based on the principle of peak cross-correlation for improved empirical modal decomposition (EMD). In this paper, the IEMD method is used to decompose three ship underwater noise signals to extract the MPE features of the highest order intrinsic modal function (IMF) of energy. The results show that the IEMD-MPE method performs well in extracting the feature information of the signals and has a strong discriminative ability. Compared with the IEMD-aligned entropy (IEMD-PE) method, which describes the signals only at a single scale, the IEMD-MPE method achieves an improvement in the minimum difference distance ranging from 101.36% to 212.98%. In addition, two sets of highly similar ship propulsion noise signals were applied to validate the IEMD-MPE method, and the minimum differences of the experimental results were 0.0814 and 0.0057 entropy units, which verified the validity and generality of the method. This study provides theoretical support for the development of ship target recognition technology for propulsion.

An adaptive learning method for long-term gesture recognition based on surface electromyography.

The surface electromyography (EMG) signal reflects the user's intended actions and has become the important signal source for human-computer interaction. However, classification models trained on EMG signals from the same day cannot be applied for different days due to the time-varying characteristics of the EMG signal and the influence of electrodes shift caused by device wearing for different days, which hinders the application of commercial prosthetics. This type of gesture recogni-tion for different days is usually referred to as long-term gesture recognition.
Approach. To address this issue, we propose a long-term gesture recognition method by optimizing feature extraction, dimensionality reduction, and classification model calibration in EMG signal recognition. Our method extracts differential common spa-tial patterns (CSP) features and then conduct dimensionality reduction with non-negative matrix factorization (NMF), effectively reducing the influence of the non-stationarity of the EMG signals. Based on clustering and classification self-training(CCST) scheme, we select samples with high confidence from unlabeled sam-ples to adaptively updates the model before daily formal use. 
Main results. We verify the feasibility of our method on a dataset consisting of 30 days of gesture data. The proposed gesture recognition scheme achieves accuracy over 90%, similar to the performance of daily calibration with labeled data. However, our method needs only one repetition of unlabeled gestures samples to update the classifi-cation model before daily formal use. 
Significance. From the results we can conclude that the proposed method can not only ensure superior performance, but also greatly facilitate the daily use, which is especially suitable for long-term application.&#xD.

Super signal-enhancement biosensing platform for precise target recognition based on rolling circle-hybridization chain dual linear cascade amplification technology

This paper presents a self-powered biosensing platform based on graphdiyne@Au (2D GDY@Au) nanoparticles and rolling circle-hybridization chain (RC-HC) dual linear cascade amplification technology, which significantly enhances target recognition and signal amplification efficiency for miRNA-141. Specifically, the target on bioanode outputs a large amount of single-stranded DNA (T1) through the strand displacement amplification (SDA) mechanism. This efficient target recycling process triggers RC-HC dual linear cascade reaction. The RCA product and H2 form the L-Liner/H2 hybridized chain through a hybridization chain reaction, and then are immobilized on a flexible electrode using a Y-DNA capture handle. [Ru(NH3)6]3+ is precisely anchored in the grooves of the DNA double helix. The 2D GDY@Au enhances the electron mobility of the system to form a rich electron-donating center. The [Ru(NH3)6]3+ on the biocathode receives electrons and is reduced to [Ru(NH3)6]2+, producing a significantly amplified open-circuit voltage signal. Dual linear cascade amplification technology realizes precise target recognition, exponential amplification, and efficient conversion of biological signals. This technique displays an extensive linear range (0.0001–10000 pM) with a detection limit of 25.9 aM (S/N = 3), and it provides an innovative method for developing sensors based on nucleic acid amplification and presents a promising novel approach for the sensitive and precise detection of low-abundance target molecules, highlighting a new tactic for the creation of compact and portable analytical devices.

Nucleic Acid Covalent Tags.

The selective and site-specific chemical labeling of proteins has emerged as a pivotal research area in chemical biology and cell biology. An effective protein labeling typically meets several criteria, including high specificity, rapid and robust conjugation under physiological conditions, operation at low concentrations with biocompatibility, and minimal perturbation of the protein function and activity. The conjugation of nucleic acids with proteins has garnered significant attention recently due to the rapid advancements in nucleic acid probe technologies, leveraging the programmable nature of nucleic acids alongside the multifaceted functionalities of proteins. It helps to convert protein-specific information into nucleic acid signals, facilitating upstream versatile recognition and downstream signal amplification for the target protein. This review critically evaluates the recent progress in nucleic acid-based protein labeling methodologies, with a specific focus on covalent labeling using aptamer tags, protein fusion tags or the technique of metabolic oligosaccharide engineering. The tags establish covalent linkages with target proteins through various modalities such as small molecules or metabolic glycan engineering. The insights presented in the review highlight promising avenues for the development of highly specific and versatile protein labeling techniques, which is essential for the improvement of protein-targeted detection and imaging across diverse biological contexts.

Diagnostic Difficulty in a Girl With Anti–Signal Recognition Particle Myopathy With a Slow Progressive Course

Diagnostic Difficulty in a Girl With Anti–Signal Recognition Particle Myopathy With a Slow Progressive Course

Eliminating noise from a speech signal based on a pair of filters

Today, the increase in the number of devices working based on speech interfaces increases the importance of speech quality. However, even a minimal noise level can seriously affect the accuracy of speech signal recognition and processing. Therefore, denoising speech signals is an important task in signal processing, and it also serves to improve speech quality in telecommunications, speech recognition systems, and other speech-related applications. Applying existing noise reduction filters separately may not always be effective. Therefore, in this research, a noise reduction approach based on the sequential application of filters is proposed. Based on literature analysis, filters such as low-pass, band-pass, Kalman, Butterworth, and elliptic filters were selected, and pairs were formed based on them. Pairs of filters were applied to speech signals with different levels of noise, and the resulting filtered speech signals were evaluated based on the PESQ evaluation criterion. The purpose of this study is to determine the optimal pair of filters that can minimize the impact of noise on the quality of speech signals using the PESQ evaluation criterion. The results of the experiments showed that it is optimal to use a pair of band-pass and Butterworth filters at a low level of noise, a pair of low-pass and elliptic filters at a medium level, and a pair of band-pass and elliptic filters at a high level of noise. The results obtained are important and practical in the development of other hybrid methods of noise reduction.

A novel multi-morphological representation approach for multi-source EEG signals

Advances in artificial intelligence have significantly enhanced intelligent assistance and rehabilitation medicine by leveraging electroencephalogram (EEG) signal recognition. Nevertheless, eliminating cross-subject variability remains a significant challenge in expending the application of EEG signal recognition to the broader society. The transfer learning strategy has been utilized to address this issue; however, multi-source domains are often treated as a single entity in transfer learning, leading to underutilization of the information from multiple sources. Furthermore, many EEG signal transfer approaches overlook the low-dimensional structural information and multivariate statistical features inherent in EEG signals, leading to inadequate interpretability and suboptimal performance. Thus, in this study, a novel multi-morphological representation approach (MMRA) was proposed for multi-source EEG signal recognition to address these issues. MMRA utilized multi-manifold mapping to extract the common invariant representation shared between the multi-source domains and target domain. It took into account the low-dimensional structure and multivariate statistical features of EEG signals to enhance the acquisition of high-quality common invariant representations. Subsequently, the multi-source domains were decomposed to extract one-to-one features. The Maximum Mean Discrepancy (MMD) loss was further applied to guide the model in obtaining high-quality private invariant representations. The performance of the proposed MMRA method was evaluated using three publicly available motor imagery datasets and a driving fatigue dataset. Experimental results demonstrated that our proposed MMRA method outperformed other state-of-the-art methods in scenarios involving multiple subjects. In conclusion, the MMRA method developed in this study can serve as a novel tool offering enhanced performance to analyze EEG signals across various subjects.

De novo functional discovery of peptide-MHC restricted CARs from recombinase-constructed large-diversity monoclonal T cell libraries.

Chimeric antigen receptors (CAR) that mimic T cell receptors (TCR) on eliciting peptide-major histocompatibility complex (pMHC) specific T cell responses hold great promise in the development of immunotherapies against solid tumors, infections, and autoimmune diseases. However, broad applications of TCR-mimic (TCRm) CARs are hindered to date largely due to lack of a facile approach for the effective isolation of TCRm CARs. Here, we establish a highly efficient process for de novo discovery of TCRm CARs from human naïve antibody repertories by combining recombinase-mediated large-diversity monoclonal library construction with T cell activation-based positive and negative screenings. Panels of highly functional TCRm CARs with peptide-specific recognition, minimal cross-reactivity, and low tonic signaling were rapidly identified towards MHC-restricted intracellular tumor-associated antigens MAGE-A3, NY-ESO-1, and MART-1. Transduced TCRm CAR-T cells exhibited pMHC-specific functional avidity, potent cytokine release, and efficacious and persistent cytotoxicity. The developed approach could be used to generate safe and potent immunotherapies targeting MHC-restricted antigens.

Identification of a factor that accelerates substrate release from the signal recognition particle.

The eukaryotic signal recognition particle (SRP) cotranslationally recognizes the first hydrophobic segment of nascent secretory and membrane proteins and delivers them to a receptor at the endoplasmic reticulum (ER). How substrates are released from SRP at the ER to subsequently access translocation factors is not well understood. We found that TMEM208 can engage the substrate binding domain of SRP to accelerate release of its bound cargo. Without TMEM208, slow cargo release resulted in excessive synthesis of downstream polypeptide before engaging translocation factors. Delayed access to translocation machinery caused progressive loss of insertion competence, particularly for multipass membrane proteins, resulting in their impaired biogenesis. Thus, TMEM208 facilitates prompt cargo handover from the targeting to translocation machinery to minimize biogenesis errors and maintain protein homeostasis.

Multimodal driver emotion recognition using motor activity and facial expressions

Driving performance can be significantly impacted when a person experiences intense emotions behind the wheel. Research shows that emotions such as anger, sadness, agitation, and joy can increase the risk of traffic accidents. This study introduces a methodology to recognize four specific emotions using an intelligent model that processes and analyzes signals from motor activity and driver behavior, which are generated by interactions with basic driving elements, along with facial geometry images captured during emotion induction. The research applies machine learning to identify the most relevant motor activity signals for emotion recognition. Furthermore, a pre-trained Convolutional Neural Network (CNN) model is employed to extract probability vectors from images corresponding to the four emotions under investigation. These data sources are integrated through a unidimensional network for emotion classification. The main proposal of this research was to develop a multimodal intelligent model that combines motor activity signals and facial geometry images to accurately recognize four specific emotions (anger, sadness, agitation, and joy) in drivers, achieving a 96.0% accuracy in a simulated environment. The study confirmed a significant relationship between drivers' motor activity, behavior, facial geometry, and the induced emotions.

Engineering stimuli-responsive CRISPR-Cas systems for versatile biosensing.

The precise target recognition and nuclease-mediated effective signal amplification capacities of CRISPR-Cas systems have attracted considerable research interest within the biosensing field. Guided by insights into their structural and biochemical mechanisms, researchers have endeavored to engineer the key biocomponents of CRISPR-Cas systems with stimulus-responsive functionalities. By the incorporation of protein/nucleic acid engineering techniques, a variety of conditional CRISPR-Cas systems whose activities depend on the presence of target triggers have been established for the efficient detection of diverse types of non-nucleic acid analytes. In this review, we summarized recent research progress in engineering Cas proteins, guide RNA, and substrate nucleic acids to possess target analyte-responsive abilities for diverse biosensing applications. Furthermore, we also discussed the challenges and future possibilities of the stimulus-responsive CRISPR-Cas systems in versatile biosensing.

Impact of SVM-based Poisoning on the Semantic Recognition of Sounds

Abstract. Machine learning is a technique that enables computers to learn from data and make predictions or decisions, data poisoning is the process of machine learning training where malicious samples are put in to make the model predictions or classifications less accurate. Data poisoning attacks help to reveal security vulnerabilities in AI systems. In this paper, we study Support Vector Machine (SVM) poisoning for sound recognition techniques, using AISHELL-3 dataset training data, from which we find the most vulnerable features for SVM poisoning. In the field of speech recognition, SVM can be applied to speech feature extraction, speech classification and speech synthesis to find the best hyperplane by finding the maximum margin optimization for effective classification and recognition of speech signals. Experiments have resulted in biased semantic recognition of sounds, output of incorrect speech, reduced accuracy of model classification and generation of incorrect decision boundaries. The role of this research paper is to investigate whether SVM poisoning affects the semantic recognition of sounds, and the result of the research is that it does cause semantic bias.

Nutrient-dependent regulation of symbiotic nitrogen fixation in legumes

Abstract Mineral nutrients are essential for plant growth and development, playing a critical role in the mutualistic symbiosis between legumes and rhizobia. Legumes have evolved intricate signaling pathways that respond to various mineral nutrients, selectively activating genes involved in nodulation and nutrient uptake during symbiotic nitrogen fixation (SNF). Key minerals, including nitrogen (N), calcium (Ca), and phosphorus (P), are vital throughout the SNF process, influencing signal recognition, nodule formation, the regulation of nodule numbers, and the prevention of nodule early senescence. Here we review recent advancements in nutrient-dependent regulation of root nodule symbiosis, focusing on the systemic autoregulation of nodulation (AON) in nitrate-dependent symbiosis, the roles of NIN-like proteins (NLPs), and the function of essential nutrients and their associated transporters in legume symbiosis. Additionally, we discuss several key research areas that require further exploration to deepen our understanding of nutrient-dependent mechanisms in SNF.

Wi-Fi Sensing Techniques for Human Activity Recognition: Brief Survey, Potential Challenges, and Research Directions

Recent advancements in wireless communication technologies have made Wi-Fi signals indispensable in both personal and professional settings. The utilization of these signals for Human Activity Recognition (HAR) has emerged as a cutting-edge technology. By leveraging the fluctuations in Wi-Fi signals for HAR, this approach offers enhanced privacy compared to traditional visual surveillance methods. The essence of this technique lies in detecting subtle changes when Wi-Fi signals interact with the human body, which are then captured and interpreted by advanced algorithms. This paper initially provides an overview of the key methodologies in HAR and the evolution of non-contact sensing, introducing sensor-based recognition, computer vision, and Wi-Fi signal-based approaches, respectively. It then explores tools for Wi-Fi-based HAR signal collection and lists several high-quality datasets. Subsequently, the paper reviews various sensing tasks enabled by Wi-Fi signal recognition, highlighting the application of deep learning networks in Wi-Fi signal detection. The fourth section presents experimental results that assess the capabilities of different networks. The findings indicate significant variability in the generalization capacities of neural networks and notable differences in test accuracy for various motion analyses.

Hardware-Feasible and Efficient N-Type Organic Neuromorphic Signal Recognition via Reservoir Computing.

Organic electrochemical synaptic transistors (OESTs), inspired by the biological nervous system, have garnered increasing attention due to their multifunctional applications in neuromorphic computing. However, the practical implementation of OESTs for signal recognition-particularly those utilizing n-type organic mixed ionic-electronic conductors (OMIECs)-still faces significant challenges at the hardware level. Here, a state-of-the-art small-molecule n-type OEST integrated within a physically simple and hardware feasible reservoir-computing (RC) framework for practical temporal signal recognition is presented. This integration is achieved by leveraging the adjustable synaptic properties of the n-OEST, which exhibits tunable nonlinear short-term memory, transitioning from volatility to nonvolatility, and demonstrating adaptive temporal specificity. Additionally, the nonvolatile OEST offers 256 conductance levels and a wide dynamic range (≈147) in long-term potentiation/depression (LTP/LTD), surpassing previously reported n-OESTs. By combining volatile n-OESTs as reservoirs with a single-layer perceptron readout composed of nonvolatile n-OEST networks, this physical RC system achieves substantial recognition accuracy for both handwritten-digit images (94.9%) and spoken digit (90.7%), along with ultrahigh weight efficiency. Furthermore, this system demonstrates outstanding accuracy (98.0%) by grouped RC in practical sleep monitoring, specifically in snoring recognition. Here, a reliable pathway for OMIEC-driven computing is presented to advance bioinspired hardware-based neuromorphic computing in the physical world.

Regionally extensive ejecta layer of the Australasian tektite strewn field: the MIS 20/19 large meteorite impact in mainland South-East Asia

Aspects of the Quaternary sedimentary geology of South-East Asia have proven problematic in terms of interpretation as to the origins and relationships of the surface sediment layers. The MIS 20 large meteorite impact (c., 788 to 785 ka) occurred within mainland South-East Asia, evident from the well-researched ‘Australasian Tektite Strewn Field’ which extends over at least one tenth of the surface of the Earth. Key questions include: 1) whether the sedimentary impact signature is preserved in the Quaternary sediment cover of the region and 2) whether stratigraphic indicators and dating methods can discriminate meteorite impact-related associations of sedimentary strata, despite subsequent reworking and diagenesis. The importance of the questions raised relate to the search for the impact site, which has not been located conclusively. Moreover, the sedimentary signatures of meteorite impacts are not well known and the descriptions in this study should aid the recognition of impact signatures elsewhere in the world. An hypothesis was developed: Surface Quaternary sediments across a wide area of mainland South-East Asia represent the effects of a regionally significant meteorite impact. Over one hundred sedimentary sections were logged across five countries in mainland South-East Asia. Methods used, defining the stratigraphy and sedimentology, include computed tomography and X-ray scanning, geochemistry, magnetic susceptibility, and environmental luminescence as well as conventional grain size analyses. Luminescence analyses were applied to samples from key strata to provide age constraints and indications of reworking through dose distributional analysis of quartz fractions. The results of the investigation explain the nature of the stratigraphy and relate it specifically to the meteorite impact. In this manner, the strata and sedimentary signatures of the ejecta from a large cosmic impact are defined across a broad region, rather than being described at singular and isolated sections. The novelty is the spatial scale of the investigation which nevertheless remains detailed. A summary model of impact stratigraphy is presented that applies to the regional ejecta blanket covering at least 300,000 km2. Tektites were co-deposited with the ejecta and not introduced by surface processes reworking the deposits. Similar models may be applicable outside of mainland South-East Asia, wherever other large impacts are suspected to have occurred.