Series-Spatial Transcriptome Profiling of Leafy Head Reveals the Key Transition Leaves for Head Formation in Chinese Cabbage

Chinese cabbage is an important leaf heading vegetable crop. At the heading stage, its leaves across inner to outer show significant morphological differentiation. However, the genetic control of this complex leaf morphological differentiation remains unclear. Here, we reported the transcriptome profiling of Chinese cabbage plant at the heading stage using 24 spatially dissected tissues representing different regions of the inner to outer leaves. Genome-wide transcriptome analysis clearly separated the inner leaf tissues from the outer leaf tissues. In particular, we identified the key transition leaf by the spatial expression analysis of key genes for leaf development and sugar metabolism. We observed that the key transition leaves were the first inwardly curved ones. Surprisingly, most of the heading candidate genes identified by domestication selection analysis obviously showed a corresponding expression transition, supporting that key transition leaves are related to leafy head formation. The key transition leaves were controlled by a complex signal network, including not only internal hormones and protein kinases but also external light and other stimuli. Our findings provide new insights and the rich resource to unravel the genetic control of heading traits.

Information Processing Methods of Electronic Warfare Events Based on Communication Technology

Traditional electronic countermeasure incident intelligence processing has problems such as low accuracy and stability and long processing time. A method of electronic countermeasure incident intelligence processing based on communication technology is proposed. First, use the integrated digital signal receiver to identify various modulation methods in the complex signal environment to facilitate the processing and transmission of communication signals, then establish an electronic countermeasure intelligence processing framework with Esper as the core, and flow the situation to the processing conclusion through the PROTOBUF interactive format Redis cache. The data can realize the intelligent processing of electronic countermeasure incidents. The experimental results show that the method proposed in this paper increases the recall rate by 5 to 20% compared with other methods. This method has high accuracy and stability for electronic countermeasure incident intelligence processing and can effectively shorten the time for electronic countermeasure incident intelligence processing.

The Rhizobium-Legume Symbiosis: Co-opting Successful Stress Management

The interaction of bacteria with plants can result in either a positive, negative, or neutral association. The rhizobium-legume interaction is a well-studied model system of a process that is considered a positive interaction. This process has evolved to require a complex signal exchange between the host and the symbiont. During this process, rhizobia are subject to several stresses, including low pH, oxidative stress, osmotic stress, as well as growth inhibiting plant peptides. A great deal of work has been carried out to characterize the bacterial response to these stresses. Many of the responses to stress are also observed to have key roles in symbiotic signaling. We propose that stress tolerance responses have been co-opted by the plant and bacterial partners to play a role in the complex signal exchange that occurs between rhizobia and legumes to establish functional symbiosis. This review will cover how rhizobia tolerate stresses, and how aspects of these tolerance mechanisms play a role in signal exchange between rhizobia and legumes.

Balancing Complex Signals for Robust Predictive Modeling

Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before building predictive models. Such models, however, may have poor predictive performance on the unseen testing data involving outliers. In modern machine learning, outliers are regarded as complex signals because of their significant role and are not suggested for removal from the training dataset. Models trained in modern regimes are interpolated (over trained) by increasing their complexity to treat outliers locally. However, such models become inefficient as they require more training due to the inclusion of outliers, and this also compromises the models’ accuracy. This work proposes a novel complex signal balancing technique that may be used during preprocessing to incorporate the maximum number of complex signals (outliers) in the training dataset. The proposed approach determines the optimal value for maximum possible inclusion of complex signals for training with the highest performance of the model in terms of accuracy, time, and complexity. The experimental results show that models trained after preprocessing with the proposed technique achieve higher predictive accuracy with improved execution time and low complexity as compared to traditional predictive modeling.

isomiRs-specific differential expression is the rule, not the exception: Are we missing hundreds of species in microRNA analysis?

MicroRNAs (miRNAs) are small RNA molecules that act as regulators of gene expression through targeted mRNA degradation. They are involved in many biological and pathophysiological processes and are widely studied as potential biomarkers and therapeutics agents for human diseases, including cardiovascular disorders. Recently discovered isoforms of miRNAs (isomiRs) exist in high quantities and are very diverse. Despite having few differences with their corresponding reference miRNAs, they display specific functions and expression profiles, across tissues and conditions. However, they are still overlooked and understudied, as we lack a comprehensive view on their condition-specific regulation and impact on differential expression analysis. Here, we show that isomiRs can have major effects on differential expression analysis results, as their expression is independent of their host miRNA genes or reference sequences. We present two miRNA-seq datasets from human umbilical vein endothelial cells, and assess isomiR expression in response to senescence and compartment-specificity (nuclear/cytosolic) under hypoxia. We compare three different methods for miRNA analysis, including isomiR-specific analysis, and show that ignoring isomiRs induces major biases in differential expression. Moreover, isomiR analysis permits higher resolution of complex signal dissection, such as the impact of hypoxia on compartment localization, and differential isomiR type enrichments between compartments. Finally, we show important distribution differences across conditions, independently of global miRNA expression signals. Our results raise concerns over the quasi exclusive use of miRNA reference sequences in miRNA-seq processing and experimental assays. We hope that our work will guide future isomiR expression studies, which will correct some biases introduced by golden standard analysis, improving the resolution of such assays and the biological significance of their downstream studies.

Search for Continuous Gravitational-wave Signals in Pulsar Timing Residuals: A New Scalable Approach with Diffusive Nested Sampling

Detecting continuous nanohertz gravitational waves (GWs) generated by individual close binaries of supermassive black holes (CB-SMBHs) is one of the primary objectives of pulsar timing arrays (PTAs). The detection sensitivity is slated to increase significantly as the number of well-timed millisecond pulsars will increase by more than an order of magnitude with the advent of next-generation radio telescopes. Currently, the Bayesian analysis pipeline using parallel tempering Markov Chain Monte Carlo has been applied in multiple studies for CB-SMBH searches, but it may be challenged by the high dimensionality of the parameter space for future large-scale PTAs. One solution is to reduce the dimensionality by maximizing or marginalizing over uninformative parameters semianalytically, but it is not clear whether this approach can be extended to more complex signal models without making overly simplified assumptions. Recently, the method of diffusive nested (DNest) sampling has shown capability in coping with high dimensionality and multimodality effectively in Bayesian analysis. In this paper, we apply DNest to search for continuous GWs in simulated pulsar timing residuals and find that it performs well in terms of accuracy, robustness, and efficiency for a PTA including  ( 10 2 ) pulsars. DNest also allows a simultaneous search of multiple sources elegantly, which demonstrates its scalability and general applicability. Our results show that it is convenient and also highly beneficial to include DNest in current toolboxes of PTA analysis.

FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count

Top-down proteomics (TDP) has gained a lot of interest in biomedical application for detailed analysis and structural characterization of proteoforms. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complex signal of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. We present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In analysis in E. coli lysates, FLASHIda increased the number of unique proteoform level identifications from 800 to 1,500, or generated a near-identical number of identifications in ⅓ of instrument time when compared to standard DDA mode. Furthermore, FLASHIda enabled sensitive mapping of post translational modifications and detection of chemical adducts. As an extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

End-to-End SAR Deep Learning Imaging Method Based on Sparse Optimization

Synthetic aperture radar (SAR) imaging has developed rapidly in recent years. Although the traditional sparse optimization imaging algorithm has achieved effective results, its shortcomings are slow imaging speed, large number of parameters, and high computational complexity. To solve the above problems, an end-to-end SAR deep learning imaging algorithm is proposed. Based on the existing SAR sparse imaging algorithm, the SAR imaging model is first rewritten to the SAR complex signal form based on the real-value model. Second, instead of arranging the two-dimensional echo data into a vector to continuously construct an observation matrix, the algorithm only derives the neural network imaging model based on the iteration soft threshold algorithm (ISTA) sparse algorithm in the two-dimensional data domain, and then reconstructs the observation scene through the superposition and expansion of the multi-layer network. Finally, through the experiment of simulation data and measured data of the three targets, it is verified that our algorithm is superior to the traditional sparse algorithm in terms of imaging quality, imaging time, and the number of parameters.

UAV detection algorithm based on the difference of power dispersion

With the development and popularization of UAVs, illegal UAV accidents occur frequently. The complex signal environment in the city brings difficulties to detect UAV signal. In order to detect the UAV signal in the complex environment, this paper proposes a UAV detection algorithm based on the difference of power dispersion in time. Firstly, the algorithm performs short-time Fourier transform on the signal to obtain the time-frequency matrix. Secondly, The fixed frequency interference in the matrix is filtered by the local adaptive threshold. Finally, the UAV image transmission signal is detected by the discrete difference in time between image transmission signal and WiFi. Simulation results show that the algorithm can detect UAV signal under constant frequency interference and WiFi interference.

Advanced computation in cardiovascular physiology: new challenges and opportunities

Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes’ may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a specific focus on cardiovascular control physiology and pathology. This includes the development and adaptation of complex signal processing methods, multivariate cardiovascular models, multiscale and nonlinear models for central-peripheral dynamics, as well as deep and transfer learning algorithms applied to large datasets. The width of this perspective highlights the issues of specificity in heartbeat-related features and supports the need for an imminent transition from the black-box paradigm to explainable and personalized clinical models in cardiovascular research. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.