Automatic Detection of Slow Slip Events Using the PICCA: Application to Chilean GNSS Data

The detection of transient events related to slow earthquakes in GNSS positional time series is key to understanding seismogenic processes in subduction zones. Here, we present a novel Principal and Independent Components Correlation Analysis (PICCA) method that allows for the temporal and spatial detection of transient signals. The PICCA is based on an optimal combination of the principal (PCA) and independent component analysis (ICA) of positional time series of a GNSS network. We assume that the transient signal is mostly contained in one of the principal or independent components. To detect the transient, we applied a method where correlations between sliding windows of each PCA/ICA component and each time series are calculated, obtaining the stations affected by the slow slip event and the onset time from the resulting correlation peaks. We first tested and calibrated the method using synthetic signals from slow earthquakes of different magnitudes and durations and modelled their effect in the network of GNSS stations in Chile. Then, we analyzed three transient events related to slow earthquakes recorded in Chile, in the areas of Iquique, Copiapó, and Valparaíso. For synthetic data, a 150 days event was detected using the PCA-based method, while a 3 days event was detected using the ICA-based method. For the real data, a long-term transient was detected by PCA, while a 16 days transient was detected by ICA. It is concluded that simultaneous use of both signal separation methods (PICCA) is more effective when searching for transient events. The PCA method is more useful for long-term events, while the ICA method is better suited to recognize events of short duration. PICCA is a promising tool to detect transients of different characteristics in GNSS time series, which will be used in a next stage to generate a catalog of SSEs in Chile.

Respiration aligns perception with neural excitability

Recent studies from the field of interoception have highlighted the link between bodily and neural rhythms during action, perception, and cognition. The mechanisms underlying functional body-brain coupling, however, are poorly understood, as are the ways in which they modulate behaviour. We acquired respiration and human magnetoencephalography (MEG) data from a near-threshold spatial detection task to investigate the trivariate relationship between respiration, neural excitability, and performance. Respiration was found to significantly modulate perceptual sensitivity as well as posterior alpha power (8 - 13 Hz), a well-established proxy of cortical excitability. In turn, alpha suppression prior to detected vs undetected targets underscored the behavioural benefits of heightened excitability. Notably, respiration-locked excitability changes were maximised at a respiration phase lag of around -30° and thus temporally preceded performance changes. In line with interoceptive inference accounts, these results suggest that respiration actively aligns sampling of sensory information with transient cycles of heightened excitability to facilitate performance.

Analysis of Net Erosion Using a Physics-Based Erosion Model for the Doam Dam Basin in Korea

In Korea, approximately 70% of the country is mountainous, with steep slopes and heavy rainfall in summer from June to September. Korea is classified as a high-risk country for soil erosion, and the rate of soil erosion is rapidly increasing. In particular, the operation of Doam dam was suspended in 2001 because of water quality issues due to severe soil erosion from the upstream areas. In spite of serious dam sediment problems in this basin, in-depth studies on the origin of sedimentation using physic-based models have not been conducted. This study aims to analyze the spatial distribution of net erosion during typhoon events using a spatially distributed physics-based erosion model and to improve the model based on a field survey. The spatially uniform erodibility constants of the surface flow detachment equation in the original erosion model were replaced by land use erodibility constants based on benchmarking experimental values to reflect the effect of land use on net erosion. The results of the upgraded model considering spatial erodibility show a significant increase in soil erosion in crop fields and bare land, unlike the simulation results before model improvement. The total erosion and deposition for Typhoon Maemi in 2003 were 36,689.0 and 9893.3 m3, respectively, while the total erosion and deposition for Typhoon Rusa in 2002 were 142,476.6 and 44,806.8 m3, respectively, despite about twice as much rainfall and 1.2 times as high rainfall intensity. However, there is a limitation in quantifying the sources of erosion in the study watershed, since direct comparison of the simulated net erosion with observed spatial information from aerial images, etc., is impossible due to nonperiodic image photographing. Therefore, continuous monitoring of not only sediment yield but also periodic spatial detection on erosion and deposition is critical for reducing data uncertainty and improving simulation accuracy.

Geographies of Twitter debates

Over the last years, the prodigious success of online social media sites has marked a shift in the way people connect and share information. Coincident with this trend is the proliferation of location-aware devices and the consequent emergence of user-generated geospatial data. From a social scientific perspective, these location data are of incredible value as it can be mined to provide researchers with useful information about activities and opinions across time and space. However, the utilization of geo-located data is a challenging task, both in terms of data management and in terms of knowledge production, which requires a holistic approach. In this paper, we implement an integrated knowledge discovery in cyberspace framework for retrieving, processing and interpreting Twitter geolocated data for the discovery and classification of the latent opinion in user-generated debates on the internet. Text mining techniques, supervised machine learning algorithms and a cluster spatial detection technique are the building blocks of our research framework. As real-word example, we focus on Twitter conversations about Brexit, posted on Uk during the 13 months before the Brexit day. The experimental results, based on various analysis of Brexit-related tweets, demonstrate that different spatial patterns can be identified, clearly distinguishing pro- and anti-Brexit enclaves and delineating interesting Brexit geographies.

24-nt phasiRNAs move from tapetal to meiotic cells in maize anthers

In maize, 24-nt phased, secondary small interfering RNAs (phasiRNAs) are abundant in meiotic stage anthers, but their distribution and functions are not precisely known. Using laser capture microdissection we analyzed tapetal cells, meiocytes, and other somatic cells at several stages of anther development to establish the timing of 24-PHAS precursor transcripts and the 24-nt phasiRNA products. By integrating RNA and small RNA (sRNA) profiling plus single-molecule and sRNA FISH (smFISH or sRNA-FISH) spatial detection, we demonstrate that the tapetum is the primary site of 24-PHAS precursor and Dcl5 transcripts and the resulting 24-nt phasiRNAs. Interestingly, 24-nt phasiRNAs accumulate in all cell types, with the highest levels in meiocytes, followed by tapetum. Our data support the conclusion that 24-nt phasiRNAs are mobile from tapetum to meiocytes and to other somatic cells. We discuss possible roles for 24-nt phasiRNAs in anther cell types.

Spectral-spatial detection of buildings with special roofing in hyperspectral images

One of the analyses performed on hyperspectral images is target detection. Given the recent developments and the creation of images with high spatial resolution, the need for both use of spectral and spatial information in the detection of hyperspectral images has increased. The present research was conducted to introduce a new method for spectral-spatial detection of hyperspectral images. In the proposed method, the spectral image was primarily segmented using the watershed algorithm. Afterwards, for the objects resulting from segmentation, five spatial properties of area, perimeter, strength, meaning intensity, and entropy were extracted. Finally, the detection operation was performed utilizing the marker-based minimum spanning forest (MSF) algorithm. The above-mentioned techniques were applied to two sets of CASI sensor image data taken from the urban area of ​​Toulouse in southern France. The results of quantitative and qualitative evaluations showed that the proposed method improved the kappa coefficient by 40% and 34% in comparison with the spectral angle measurement (SAM) algorithm in the two tested images.

Perturb-map enables CRISPR genomics with spatial resolution and identifies regulators of tumor immune composition

The cellular architecture of a tumor, particularly immune composition, has a major impact on cancer outcome, and thus there is an interest in identifying genes that control the tumor microenvironment (TME). While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying gene functions operating extracellularly or within a tissue context. To address this, we developed an approach for spatial functional genomics called Perturb-map, which utilizes protein barcodes (Pro-Code) to enable spatial detection of barcoded cells within tissue. We show >120 Pro-Codes can be imaged within a tumor, facilitating spatial mapping of 100s of cancer clones. We applied Perturb-map to knockout dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Additionally, we paired Perturb-map and spatial transcriptomics for unbiased molecular analysis of Pro-Code/CRISPR lesions. Our studies found that in Tgfbr2 knockout lesions, the TME was converted to a mucinous state and T-cells excluded, which was concomitant with increased TGFb expression and pathway activation, suggesting Tgfbr2 loss on lung cancer cells enhanced suppressive effects of TGFb on the TME. These studies establish Perturb-map for functional genomics within a tissue at single cell-resolution with spatial architecture preserved.

Classification of Tennis Video Types Based on Machine Learning Technology

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.