Detection Of Rapid Changes Research Articles

Multiple fluorescence approaches to identify rapid changes in microbial indicators at karst springs

Karst springs are globally important for drinking water supply but are often also exceptionally vulnerable to contamination. Such springs usually exhibit strong variation in microbial water quality in sharp response to rainfall events, thus, posing a health hazard to consumers of water supplied from these sources. The rapid detection of such changes is extremely important as well as being able to establish a link to the sources of such pollution, so that appropriate measures can be taken both in terms of immediate protection of human health and the management of karst aquifers. In this study, a fluorescence-based multi-parameter approach was trialed in order to evaluate which methods can be used to monitor rainfall-induced rapid changes in microbial water quality at karst springs, as well as determine whether such changes can be linked to sources of human effluent contamination. The results from three monitoring periods at two karst springs revealed marked responses to rainfall events for all of the microbial parameters measured. Total cell count (TCC) measurements using flow cytometry (FCM) showed very strong positive correlations with the more conventionally monitored faecal indicator bacteria (FIB) and total coliforms (TC), indicating that such a fluorescence-based and cultivation-independent technique can be very useful to indicate rapid changes in microbial water quality at karst springs. Furthermore, very strong positive correlations were also found between tryptophan-like fluorescence (TLF) measurements and concentrations of all monitored microbial parameters, again demonstrating that such a fluorescence-based approach can also be useful for detecting rapid changes in concentrations of traditional faecal indicators. Interestingly, it was found that fluorescent whitening compounds (FWCs) signals do not necessarily follow temporal variations of microbial indicators. However, the frequency of detection of positive FWCs signals may still reveal useful information about the overall magnitude of human wastewater effluent impacts on karst aquifer systems.

Rapid online plant leaf area change detection with high-throughput plant image data

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

CAOM: Change-aware online 3D mapping with heterogeneous multi-beam and push-broom LiDAR point clouds

A High-Definition (HD) map is an essential component not only for autonomous vehicles but also for surveyors in favor of city management. Moreover, the HD map must be up-to-date by reflecting environmental changes continuously. The development of LiDAR Simultaneous Localization And Mapping (SLAM) techniques has provided an effective way to collect precise point clouds for the construction of HD maps, while traditional methods are time-consuming and labor-intensive on account of the complex street scene. Furthermore, life-long 3D mapping is quite necessary and challenging nowadays. In this paper, we introduce a novel change-aware 3D online mapping framework CAOM,11Code at https://github.com/DCSI2022/CAOM. using point clouds collected by Multi-Beam LiDAR (MBL) and Push-Broom LiDAR (PBL) for rapid city map update and change detection. Concerning the prior map with higher precision, heterogenous fusion is performed to rectify the SLAM drift, based on the View-Traced Clouds (ViTC) derived according to their view in common. Meanwhile, corresponding virtual range images are integrated with distance images to detect the 3D changes between the current map and the historical reference map offline. The detection results are later fused in a probabilistic way along with the homogeneous fusion process where a resilient graph consisting of diverse factors is established to maintain the local and global consistency of the final point cloud map. Compared with 3D-CSTM, the real-world experiments have shown an average 0.5 m and max 1.5 m improvement in the ATE (Absolute Translation Error) of the trajectory generated by our system. Quantitative measurements on the individual point accuracy are also conducted to verify the performance of 3D mapping with up to 16% improvement in comparison with respect to the 3D-CSTM method. The effects of the disparity detection and probabilistic fusion can be revealed from the small objects distinguished in the point cloud map as well as > 95% overall accuracy.

Rapid detection and online analysis of microbial changes through flow cytometry.

Short-read 16 S rRNA gene sequencing is the dominating technology to profile microbial communities in different habitats. Its uncontested taxonomic resolution paved the way for major contributions to the field. Sample measurement and analysis, that is, sequencing, is rather slow-in order of days. Alternatively, flow cytometry can be used to profile the microbiota of various sources within a few minutes per sample. To keep up with high measurement speed, we developed the open source-analyzing tool FlowSoFine. To validate the ability to distinguish microbial profiles, we examined human skin samples of three body sites (N=3× 54) with flow cytometry and 16 S rRNA gene amplicon sequencing. Confirmed by sequencing of the very same samples, body site was found to be significantly different by flow cytometry. For a proof-of-principle multidimensional approach, using stool samples of patients (N=40) with/without inflammatory bowel diseases, we could discriminate the health status by their bacterial patterns. In conclusion, FlowSoFine enables the generation and comparison of cytometric fingerprints of microbial communities from different sources. The implemented interface supports the user through all analytical steps to work out the biological relevant signals from raw measurements to publication ready figures. Furthermore, we present flow cytometry as a valid method for skin microbiota analysis.

A Bayesian nonparametric method for detecting rapid changes in disease transmission

Whether an outbreak of infectious disease is likely to grow or dissipate is determined through the time-varying reproduction number, Rt. Real-time or retrospective identification of changes in Rt following the imposition or relaxation of interventions can thus contribute important evidence about disease transmission dynamics which can inform policymaking. Here, we present a method for estimating shifts in Rt within a renewal model framework. Our method, which we call EpiCluster, is a Bayesian nonparametric model based on the Pitman–Yor process. We assume that Rt is piecewise-constant, and the incidence data and priors determine when or whether Rt should change and how many times it should do so throughout the series. We also introduce a prior which induces sparsity over the number of changepoints. Being Bayesian, our approach yields a measure of uncertainty in Rt and its changepoints. EpiCluster is fast, straightforward to use, and we demonstrate that it provides automated detection of rapid changes in transmission, either in real-time or retrospectively, for synthetic data series where the Rt profile is known. We illustrate the practical utility of our method by fitting it to case data of outbreaks of COVID-19 in Australia and Hong Kong, where it finds changepoints coinciding with the imposition of non-pharmaceutical interventions. Bayesian nonparametric methods, such as ours, allow the volume and complexity of the data to dictate the number of parameters required to approximate the process and should find wide application in epidemiology. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Research on Urban Road Network Extraction Based on Web Map API Hierarchical Rasterization and Improved Thinning Algorithm

Most existing research on the vector road network is based on GPS trajectory travel information extraction, and urban GPS trajectory data are large and difficult to obtain. Based on this, this study proposes a road network extraction method based on network map API and designs a vector road network based on an improved image-processing algorithm using trajectory data. Firstly, a large number of trajectory data are processed by hierarchical rasterization. The trajectory points of the regional OD matrix are obtained by using the map API interface to generate the trajectory. Then, the image expansion processing is performed on the road network raster image to complete the information loss problem. The improved Zhang–Suen refinement algorithm is used to refine the idea to obtain the road center line, and the vector road network in the study area is obtained. Finally, taking the Harbin City of Heilongjiang Province as an example, compared with the road network of the network map, it has been demonstrated that using this technology may improve the traveler experience and the sustainability of urban traffic flow while reducing the number of manual procedures required, performing online incremental rapid change detection, and updating the present road network at a cheaper cost.

Automated Detection of coronaL MAss Ejecta origiNs for Space Weather AppliCations (ALMANAC)

AbstractAlerts of potentially hazardous coronal mass ejection (CME) are based on the detection of rapid changes in remote observations of the solar atmosphere. This paper presents a method that detects and estimates the central coordinates of CME eruptions in Extreme Ultraviolet data, with the dual aim of providing an early alert, and giving an initial estimate of the CME direction of propagation to a CME geometrical model. In particular, we plan to link the Automated detection of coronaL MAss ejecta origiNs for space weather AppliCations (ALMANAC) method to the CME detection and characterization module of the Space Weather Empirical Ensemble Package, which is a fully automated modular software package for operational space weather capability currently being developed for the UK Meteorological Office. In this work, ALMANAC is applied to observations by the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). As well as presenting the method, a proof of concept test is made on a limited set of data associated with 20 halo CMEs recorded by the Coordinated Data Analysis Workshop (CDAW) catalog near the activity maximum of solar cycle 24. SDO/AIA data for each event is processed at 6 min cadence to identify the on‐disk location and time of each CME. The absolute mean deviance between the ALMANAC and CDAW source event coordinates are within 37.05 ± 29.71 min and 11.01 ± 10.39°. These promising results give a solid foundation for future work, and will provide initial constraints to an automated CME alert and forecasting system.

Rapid detection of isthmus block and rhythm change using local electrogram changes during complex atrial flutter ablation.

Multiple re-entry circuits may operate simultaneously in the atria in the form of dual loop re-entry using a common isthmus, or multiple re-entrant loops without a common isthmus. When two or more re-entrant circuits coexist, ablation of an individual isthmus may lead to a seamless transition (without significant changes in surface electrocardiogram, coronary sinus activation or tachycardia cycle length) to a second rhythm, and the isthmus block can go unnoticed. We hypothesize and subsequently illustrate in three patient cases, methods to rapidly identify a transition in the rhythm and isthmus block using local electrogram changes at the ablation site. Local activation sequence changes, electrogram timing, and the behaviour of pre-existing double potentials can reveal isthmus block promptly when rhythm transitions occur during ablation of multiloop re-entry tachycardias.

THE USE OF REMOTE SENSING IN GHANA TO SUSTAINABLY MANAGE THE ENVIRONMENT

A prerequisite for achieving the objectives of sustainable development is knowledge of the nature, extent, geographical distribution, along with the potentials and limits of natural resources. The mapping, monitoring, measuring, and management of many elements of the built, natural, and biophysical environments are all made possible through satellite remote sensing. Remote sensing is a crucial tool for advancing solutions and offers useful insights into urgent environmental concerns. Thus, data collection for efficient resource management in Ghana has shown to be greatly aided by remote sensing, which may also be used for environmental monitoring and management. Because of this, it is crucial for remote sensing technology to progress in order to acquire data more effectively and efficiently, which helps with rapid change detection, sustainable environmental management, and sustainability. This review's goal was to provide a thorough analysis of the literature on Ghana's use of remote sensing for environmental management and sustainability. Wiley, Taylor & Francis, Science Direct (Elsevier), and Scopus (Elsevier) were a few of the databases used for the review. The findings indicate that there are numerous remote sensing applications for sustainable environmental management in Ghana, including environmental impact assessments of various projects, analyses of changes in land use and land cover, mapping of watersheds and hydrologic features, estimation of soil loss, management of water resources, management of forestry resources, and management of natural disasters. The outcomes also demonstrate that natural, biophysical, and constructed ecosystems might be appropriately monitored and sustainably managed for the benefit of both the present and future generations with the use of remote sensing technology. Governments, municipal managers, environmentalists, and planners may develop useful plans and strategies for sustainable development and effective environmental management with the aid of the information supplied by remote sensing. Keywords: Ghana, Sustainable Management, Environment, Remote Sensing.

Modeling the mucinous glycocalyx to unravel receptor pattern recognition by influenza A viruses

Influenza A viruses (IAVs) exploit host glycans in airway epithelial mucosa for entry and infection. Rapid detection of changes in IAV glycan‐binding phenotype can provide early indication of virus transmissibility and infection potential. IAVs use hemagglutinins (HAs) to bind sialic acids, and a switch in HA specificity is considered a prerequisite for transmission from birds to humans. Likewise, understanding the molecular mechanisms used by the viruses to overcome the mucin barrier may guide the development of more effective antiviral therapies. This presentation will describe the development of a glycan array platform, which uses synthetic mimetics of mucin glycoproteins to model how receptor presentation in the mucinous glycocalyx (i.e., glycan type, valency of the glycoconjugates, and their surface density) may impact IAV recognition. This platform utilizes machine learning to identify preferred receptor displays in the mucinous glycocalyx for adhesion of H1N1 and H3N2 viruses. Intriguingly, the arrays revealed a change in receptor pattern recognition by H1N1 upon transition from avian to mammalian cell culture. Without altering its HA affinity, the mammalian cell‐produced virus showed reduced sensitivity to surface crowding of the mucin mimetics. Using our mucin mimetics, we have also evaluated the physical protective functions of the glycocalyx on viral adhesion to cells. We found that bulky mucin mimetics lacking sialoglycan receptors inhibited the rate of H1N1 adhesion in a size‐ and density‐dependent manner, consistent with current view of the mucins as providing a protective shield against pathogens. Counterintuitively, increasing density of the mucin mimetics enhanced the retention of bound viruses. Careful characterization of SNA behavior at the RBC surface using a range of biophysical and imaging techniques revealed lectin‐induced crowding and reorganization of the glycocalyx with concomitant enhancement in lectin clustering, presumably through the formation of a more extensive glycan receptor patch at the cell membrane. Our findings indicate that glycan‐targeting pathogens may exploit the biophysical and biomechanical properties of mucins to overcome the mucosal glycocalyx barrier.

The diagnostic importance of pathogenic variants and variant coexistence determined by NGS-based liquid biopsy approach in patients with lung adenocarcinoma

BackroundIdentification of driver mutations and rapid detection of genetic changes in lung cancer are critical in the management of the disease. Genetic structures of tumor tissues tend to change constantly and the possibility of emergence of new pathogenic variants that will create resistance to treatment. Liquid biopsy analysis has been one of the most effective approaches used to monitor and identify individual genetic changes. MethodsIn this study, TP53, EGFR, MET, ALK, PIK3CA, MAP2K, ERBB2 and ROS genes in cf DNA samples of 324 patients with lung adenocarcinoma were screened for genetic variations by NGS method. Analysis of the data showed that there were a total of 755 variations in 324 patients. ResultsPathogenic and possibly pathogenic variations were identified in 178 patients (54.9%) on TP53, 118 (36.4%) on EGFR, 55 (17.0%) on MET, 46 (14.2%) on ALK, 39 (12.0%) on MAP2K, 6 (1.9%) on ERBB2 and in 2 (0,6%) patients ROS genes. The detailed variant data of the genes included in the study were compared with the patients' stage status, metastasis status, smoking, age distribution and life span data, and the presence of possible significant relationships and candidate biomarkers for the molecular pathogenesis of the disease were investigated. ConclusionAs a result of data analysis, genetic changes associated with metastasis and adenocarcinoma formation were identified. It has been shown that variations identified in TP53, PIK3CA, MAP2K1 and EGFR genes can play critical roles in the pathogenesis and development of the disease.

A myrtenal-based colorimetric and fluorescent probe for reversibly monitoring alkaline pH and bioimaging in living cells and zebrafish

Acid-base balance in cells is vital to maintain the normal biological processes, and its disorders could lead to various diseases. It is significant to develop a reliable and portable method for tracing intracellular pH changes. Herein, a novel colorimetric and fluormetric dual-modal probe 2-(((6,6-dimethylbicyclo [3.1.1]hept-2-en-2-yl)methylene)hydrazono)-3-ethyl-5-(2-hydroxybenzylidene)thiazolidine-4-one (MTHB) was constructed for sensitive and rapid detection of alkaline pH changes by employing myrtenal as a starting material. MTHB showed a significantly enhanced yellow fluorescence upon increasing pH from 7.45 to 12.89, and the pKa was determined as 9.84. The probe MTHB was featured with large Stokes shift, high selectivity, good reversibility, and excellent photostability. The sensing mechanism of MTHB for alkaline pH was verified by HRMS, 1H NMR titration, and theoretical calculations. Additionally, in view of its low toxicity and good biocompatibility, MTHB was successfully utilized to visualize the pH variations in living HeLa cells and zebrafish.

Automatic change detection of multiple facial expressions: A visual mismatch negativity study

The rapid detection of changes in facial expressions is an important social and survival skill. The detection of multiple facial emotions includes not only the information of emotional valence but also differences in emotional valence, that is, emotional valence consistency and inconsistency. Thus, we explored whether changes in multiple facial expressions could be automatically detected, as indexed by the visual mismatch negativity (vMMN) response. Participants were presented with a set of facial stimuli while performing a visual facial identity detection task; the stimulus was presented in the center of the visual field. The facial stimuli set consisted of five different facial identities and were presented in an oddball sequence, with four peripherally expressing the same positive or negative emotion and one in the center expressing congruent or incongruent emotions. We found vMMN responses to changes in positive congruent deviant emotions between 210 and 320 ms and in all deviant emotions between 480 and 560 ms over bilateral temporal-occipital sites. In addition, at 480–520 ms, the positive congruent stimulus versus the incongruent stimulus and the negative incongruent stimulus versus the congruent stimulus induced more negative vMMN amplitude in the left temporal-occipital electrodes. This shows that individuals can automatically identify the changes in multiple faces’ emotional differences (emotional valence inconsistency), and that the emotional valence of the target face affects the automatic processing of multi-face emotional valence differences information. Furthermore, these results can be utilized in future research investigating automatic processing mechanisms.

HaptiTemp: A Next-Generation Thermosensitive GelSight-Like Visuotactile Sensor

This study describes the creation of a new type of compact skin-like silicone-based thermosensitive visuotactile sensor based on GelSight technology. The easy integration of this novel sensor into a complex visuotactile system capable of very rapid detection of temperature change (30°C/s) is unique in providing a system that parallels the withdrawal reflex of the human autonomic system to extreme heat. To the best of authors’ awareness, this is the first time a sensor that can trigger a sensory impulse like a withdrawal reflex of humans in robotics community. To attain this, we used thermochromic pigments color blue, orange, and black with a threshold of 31°C, 43°C, and 50°C, respectively on the gel material. Each pigment has the property of becoming translucent when its temperature threshold is reached, making it possible to stack thermochromic pigments of different colors and thresholds. The pigments were air-brushed on a low-cost commercially available transparent silicone sponge. We used MobileNetV2 and transfer learning to simulate tactile preprocessing in order to recognize five different objects. The new thermosensitive visuotactile sensor helped to achieve 97.3% tactile image classification accuracy of five different objects. Our novel thermosensitive visuotactile sensor could be of benefit in material texture analysis, telerobotics, space exploration, and medical applications.

Rational construction of a novel probe for the rapid detection of butyrylcholinesterase stress changes in apoptotic cells

The occurrence of numerous neurodegenerative diseases is associated with abnormal levels of butyrylcholinesterase (BChE).

Molecular Diagnostic Strategies for the Detection of Toxicological Changes in Animals: A Review

Rapid industrialization and successful green revolution have introduced a wide array of chemicals into our environment; some of these chemicals entered in ecosystem; gets accumulated and exert serious health and ecological problems. These toxic substances can enter the food chain and emphasize pathological changes which damage either cell, organ or system (circulatory, immune, respiratory, digestive, nervous, reproductive and musculo-skeletal etc.) by altering structure and/or function of biological components; DNA, RNA, proteins, lipids and carbohydrates. This review article provides certain molecular diagnostic techniques used for their robust and accurate detection at molecular level. Investigations conducted during 2020-2021 where various review and research articles were surveyed and then extracted to enlist congestive datum for rapid detection of toxicological changes in animals. In our investigations we concluded that toxic substances present in our environment affects health of animals by altering structure and functioning of biomolecules and their concerned system. These cytological and systemic changes can be detected with the help of molecular diagnostic techniques including dideoxysequencing, pyrosequencing, allele specific RT-PCR, CRISPER/Cas, genotyping and microarrays etc. present collection of data will provide congestive information for rapid toxicological detection at molecular level.

River classification and change detection from landsat images by using a river classification toolbox

<span>Water bodies especially rivers are vital to existence of all lifeforms on Earth. Therefore, monitoring river areas and water bodies is essential. In the past, the monitoring relied essentially on manpower in surveying individual areas. However, there are limitations associated wih such surveys, e.g., tremendous amount of time and labour involved in expeditions. Presently, there have been accelerated development in remote sensing (RS) and artificial intelligence (AI) technology, particularly for change monitoring and detection in different areas globally. This research presents technical development of a toolbox for rivers classification and their change detection from Landsat images, by using water index analysis and four machine learning algorithms, which are K-Means, ISODATA, maximum likelihood classification (MLC), and support vector machine (SVM). Experimental findings indicated that all presented techniques were effective in detecting hydrological changes. The most accurate algorithm, nevertheless, for river classification was the SVM, with accuracy of 96.89%, precision of 98.61%, recall of 96.59%, and F-measure of 97.59%. Herein, it was demonstrated, in addition, that the developed toolbox was versatile and could be applied in rapid river change detection in other areas.</span>

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This paper reports a self‐healable superhydrophobic hydrogel which not only exhibited a rapid and stable detection of strain changes but also demonstrated super‐robust superhydrophobicity, which retained superhydrophobicity after many kinds of damage (such as 1000% stretch, 10000 stretching‐releasing cycles, hand‐rub, tape‐peeling, and high‐speed drop/jet impact). Therefore, this superhydrophobic hydrogel might have bright future in electronic skin, smart grid, etc.. More details are discussed in the article by Wang et al. on page 3393—3398.image

Recent Advances in Fluorescence Imaging of Pulmonary Fibrosis in Animal Models.

Pulmonary fibrosis (PF) is a lung disease that may cause impaired gas exchange and respiratory failure while being difficult to treat. Rapid, sensitive, and accurate detection of lung tissue and cell changes is essential for the effective diagnosis and treatment of PF. Currently, the commonly-used high-resolution computed tomography (HRCT) imaging has been challenging to distinguish early PF from other pathological processes in the lung structure. Magnetic resonance imaging (MRI) using hyperpolarized gases is hampered by the higher cost to become a routine diagnostic tool. As a result, the development of new PF imaging technologies may be a promising solution. Here, we summarize and discuss recent advances in fluorescence imaging as a talented optical technique for the diagnosis and evaluation of PF, including collagen imaging, oxidative stress, inflammation, and PF-related biomarkers. The design strategies of the probes for fluorescence imaging (including multimodal imaging) of PF are briefly described, which can provide new ideas for the future PF-related imaging research. It is hoped that this review will promote the translation of fluorescence imaging into a clinically usable assay in PF.

Rapid detection of changes in the riverbanks of Laigiang river of the South Central Coast Vietnam: A methodology based on Digital Shoreline Analysis System and Sentinel-2

The research focuses on using Sentinel-2 that can be integrated with the Digital Shoreline Analysis System (DSAS) as an effective tool for the determination of changes in the riverbanks and using linear regression to predict shoreline changes. The research applied the assessment of shoreline changes in the period of 2015- 2020 and forecast to 2025 in Laigiang river of the South Central Coast region of Vietnam. Based on the DSAS tool, parameters such as Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR) and Linear Regression Rate (LRR) were determined. The analysis results show that the accretion process in the Laigiang river in the period of 2015-2020 with the accretion area ranges from 81.47 ha. Meanwhile, the area of shoreline erosion only fluctuates around 54.42 ha. The rhythm of evolution is a determinant element for this transitional system.