Event Detection Research Articles

Detection and treatment of string events in the limit

A string event is a pattern that occurs in a stream of characters. The need to detect and handle string events in infinite texts emerges in many scenarios, including online treatment of logs, web crawling, and syntax highlighting. This paper describes a technique to specify and treat string events. Users determine patterns of interest via a markup language. From such examples, tokens are generalized via a semi-lattice of regular expressions. Such tokens are combined into a context-free language that recognizes patterns in the text stream. These techniques are implemented in a text processing system called Lushu, which runs on the Java Virtual Machine (JVM). Lushu intercepts strings emitted by the JVM. Once patterns are detected, it invokes a user-specified action handler. As a proof of concept, this paper shows that Lushu outperforms state-of-the-art parsers and parser generators, such as Comby, BeautifulSoup4 and ZheFuscator, in terms of memory consumption and running time.

Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots

Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in an edge computing mobile device using an intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an improvement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.

Training for outbreak response through the Global Outbreak Alert and Response Network

Abstract The WHO designated the Information Centre for International Health Protection (INIG/ZIG 1) at the Robert Koch Institute as the first WHO Collaborating Centre for Global Outbreak Alert and Response (WHO CC GOARN). Its mandate is to aid WHO’s efforts in improving the detection of public health events and enhancing preparedness, readiness, and capabilities for alerts and rapid responses. The main focus of WHO CC GOARN at RKI include supporting WHO in the development and implementation of the GOARN training programme on international outbreak alert and response, as well as facilitating knowledge exchange and information dissemination. GOARN’s extensive engagement in international public health response underscores the need to complement technical expertise with human-centred competencies like effective communication and teamwork, while respecting diversity. On 18 -22 September 2023, WHO CC GOARN at RKI hosted the first “GOARN Outbreak Response Leadership Training Programme”, inviting 24 public health leaders plus highly experienced mentors from all WHO. The training was designed to enhance leadership mind-sets and practices, including advanced interpersonal and situational awareness, managing polarised tension, establishing trusted relationships with stakeholders, and influencing collective decision-making. Alongside expert facilitators and experienced mentors from various fields, participants engaged in open discussion, shared experiences and reflected on lessons from the COVID-19 pandemic and other recent outbreaks. This course provided an opportunity to engage with diverse, experienced and highly regarded leaders, sharing their views and personal experiences on what it means to practise leadership in health emergency response. This training also aimed to build a network of diverse, well-trained and interconnected public health leaders, upholding the principles of diversity, equity and inclusion to ensure that our response efforts resonate with the communities we serve.

Methodological framework for early detection of dengue outbreaks in Tanzania

Abstract Background Dengue fever is a major public health problem because it is a widespread and rapidly transmitted mosquito-borne viral disease. We developed a framework for early detection of dengue outbreaks in Tanzania, starting with detection of suspected and/or confirmed cases, followed by site-specific risk characterization, both useful for planning and prioritizing outbreak interventions or epidemic preparedness. Methods Our initial focus was on the identification and categorization of indicators that are specifically tailored to the early detection and classification of outbreak events. The framework was then evaluated using (i) available cases with syndromic and laboratory-confirmed disease information from ProMED emails using decision tree analysis, and (ii) available historical dengue epidemic data at the local level from 2019 with 6,795 confirmed cases using negative binomial regression analysis adjusted for month and area. Results The laboratory-confirmed diagnosis (dengue yes or no) was consistent with the results of the suspected case classification algorithm for clinically defined syndromic cases. There was strong evidence of an increase in dengue cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])) when regressing confirmed dengue cases in 2019 as the dependent variable and site-specific risk as the independent variable. Conclusions The rapid dengue outbreak risk assessment developed may be useful in controlling the epidemic in Tanzania. The suspected case classification algorithm can be a very useful tool to better assess whether a potential dengue case is present and thus requires laboratory confirmation. The framework can be used to rapidly predict the risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. Key messages • Framework for a novel rapid risk assessment for early detection of dengue outbreaks in Tanzania. • Framework relevant to epidemic preparedness.

Time to detection of serious adverse events by continuous vital sign monitoring versus clinical practice.

Continuous vital sign monitoring detects far more severe vital sign deviations (SVDs) than intermittent clinical rounds, and deviations are to some extent related to subsequent serious adverse events (SAEs). Early detection of SAEs is pivotal to allow for effective interventions but the time relationship between detection of SAEs by continuous vital sign monitoring versus clinical practice is not well-described at the general ward. To quantify the time difference between detection of SAEs by continuous vital sign monitoring and clinical suspicion of deterioration (CSD) in major abdominal surgery patients. Five hundred and five patients had their vital signs continuously monitored in combination with usual clinical practice consisting of National Early Warning Score assessments at least every 8'th hour, assessments during rounds, and other kinds of staff-patient interactions. The primary outcome was the time difference between the first chart note of CSD versus the first SVD, detected by continuous vital sign monitoring, in patients with a subsequent confirmed SAE during or up to 48 h after end of continuous vital sign monitoring. Out of the 505 continuously monitored patients, 142 patients had a combination of both postoperative SAE, CSD and SVD, and thus were included in the primary analysis. The median time from the first SVD to SAE was 42.8 h (interquartile range 19.8-72.1 h) compared to 13 minutes (interquartile range - 4.8 to 3.5 h) for CSD with a median difference of 48.1 h (95% confidence interval 43.0-54.8 h), p-value < .001. Continuous vital sign monitoring detects signs of oncoming SAEs in the form of SVD hours before CSD, potentially allowing for earlier and more effective treatments to reduce the extent of SAEs.

Disruption Dynamics and Charge Transfer of a Single Attoliter Emulsion Droplet Revealed by Combined Fast-Scan Sinusoidal Voltammetry and Short Time Fourier Transform Analysis.

Single-entity electrochemistry has gained significant attention for the analysis of individual cells, nanoparticles, and droplets, which is leveraged by robust electrochemical techniques such as chronoamperometry and cyclic voltammetry (CV) to extract information about single entities, including size, kinetics, mass transport, etc. For an in-depth understanding such as dynamic interaction between the electrode and a single entity, the unconventional fast electrochemical technique is essential for time-resolved analysis. This fast experimental technique is unfortunately hindered by a substantial nonfaradaic response. In this work, we introduce fast-scan sinusoidal voltammetry (FSSV) combined with a short-time Fourier transform (STFT) for analyzing single emulsion droplets. Utilizing ultramicroelectrode and fast potential sweeps up to apparent 200 V/s, we achieved high temporal resolution (8 ms per voltammogram) to capture the current signals during droplet collisions. STFT analysis reveals the amplitude and phase changes, allowing for the accurate detection of collision events even in the absence of redox species. By adopting an algorithm of drift-free baseline subtraction, a conventional CV shape was obtained in FSSV. The reacted charge from the single-entity voltammogram at every 8 ms was also plotted. This method effectively addresses limitations in traditional techniques, providing insights into emulsion dynamics such as droplet contact and droplet breakdown.

Fuzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events

Providing an accurate prediction of Significant Wave Height (SWH), and specially of extreme SWH events, is crucial for coastal engineering activities and holds major implications in several sectors as offshore renewable energy. With the aim of overcoming the challenge of skewness and imbalance associated with the prediction of these extreme SWH events, a fuzzy-based cascade ensemble of regression models is proposed. This methodology allows to remarkably improve the predictive performance on the extreme SWH values, by using different models specialised in different ranges on the target domain. The method’s explainability is enhanced by analysing the contribution of each model, aiding in identifying those predictor variables more characteristic for the detection of extreme SWH events. The methodology has been validated tackling a long-term SWH prediction problem, considering two case studies over the southwest coast of the United States of America. Both reanalysis data, providing information on various meteorological factors, and SWH measurements, obtained from the nearby stations and the station under examination, have been considered. The goodness of the proposed approach has been validated by comparing its performance against several machine learning and deep learning regression techniques, leading to the conclusion that fuzzy ensemble models perform much better in the prediction of extreme events, at the cost of a slight deterioration in the rest of the samples. The study contributes to advancing the SWH prediction field, specially, to understanding the behaviour behind extreme SWH events, critical for various sectors reliant on oceanic conditions.

An adaptive 6-dimensional floating-search multi-station seismic-event detector (A6-DFMSD) and its application to low-frequency earthquakes in the East Eifel Volcanic Field, Germany

We introduce a seismic event detector that applies signal analysis in the time and frequency domains. Signals are searched for with matching coincidences at neighbouring recording stations in space and time. No a priori waveform information is needed for the Adaptive 6-Dimensional Floating-search Multi-station Seismic-event Detector (A6-DFMSD). It combines a short / long time average algorithm (STA/LTA), frequency range selection, energy envelope matching, and backprojection techniques to find a robust detection model. As a challenging test example, the new detector is tuned and applied to a dataset with five months of microearthquake (ML < 2) recordings in the East Eifel Volcanic Field (EEVF), Germany. There, both magmatic and tectonic earthquakes occur in a depth range between 3 km and 43 km. A6-DFMSD detected 4.3 times as many events as were already known and it discovered a previously unknown event cluster. After manual localization and classification of the events, we show that A6-DFMSD finds events of different origins: tectonic, magmatic, atmospheric, and anthropogenic. In particular, low-frequency (LF) earthquakes of magmatic origin with a complicated waveform coda are very well identified. We suggest that seismological networks monitoring local seismicity in similar target zones would benefit from the use of A6-DFMSD to allow the detection of a wide range of different seismic signals.

A real scale application of a novel set of spatial and similarity features for detection and classification of natural seismic sources from Distributed Acoustic Sensing data

Summary Distributed Acoustic Sensing (DAS) turns a fiber optic into a very dense network of equally-distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyze DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatio-temporal and similarity features for the event classification, and Markov Random Field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalog. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along à 91 km fiber optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fiber optic path, the proposed method succeeded in detecting earthquakes of magnitude &amp;gt;0.4 and quarry blasts of magnitude &amp;gt;1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fiber optic systems.

Equipment Sounds’ Event Localization and Detection Using Synthetic Multi-Channel Audio Signal to Support Collision Hazard Prevention

Construction workplaces often face unforeseen collision hazards due to a decline in auditory situational awareness among on-foot workers, leading to severe injuries and fatalities. Previous studies that used auditory signals to prevent collision hazards focused on employing a classical beamforming approach to determine equipment sounds’ Direction of Arrival (DOA). No existing frameworks implement a neural network-based approach for both equipment sound classification and localization. This paper presents an innovative framework for sound classification and localization using multichannel sound datasets artificially synthesized in a virtual three-dimensional space. The simulation synthesized 10,000 multi-channel datasets using just fourteen single sound source audiotapes. This training includes a two-staged convolutional recurrent neural network (CRNN), where the first stage learns multi-label sound event classes followed by the second stage to estimate their DOA. The proposed framework achieves a low average DOA error of 30 degrees and a high F-score of 0.98, demonstrating accurate localization and classification of equipment near workers’ positions on the site.

IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms

Airtime is crucial for high-rotation tricks in snowboard halfpipe performance, significantly impacting trick difficulty, the primary judging criterion. This study aims to enhance the detection of take-off and landing events using inertial measurement unit (IMU) data in conjunction with machine learning algorithms since manual video-based methods are too time-consuming. Eight elite German National Team snowboarders performed 626 halfpipe tricks, recorded by two IMUs at the lateral lower legs and a video camera. The IMU data, synchronized with video, were labeled manually and segmented for analysis. Utilizing a 1D U-Net convolutional neural network (CNN), we achieved superior performance in all of our experiments, establishing new benchmarks for this binary segmentation task. In our extensive experiments, we achieved an 80.34% lower mean Hausdorff distance for unseen runs compared with the threshold approach when placed solely on the left lower leg. Using both left and right IMUs further improved performance (83.37% lower mean Hausdorff). For data from an algorithm-unknown athlete (Zero-Shot segmentation), the U-Net outperformed the threshold algorithm by 67.58%, and fine-tuning on athlete-specific (Few-Shot segmentation) runs improved the lower mean Hausdorff to 78.68%. The fine-tuned model detected takeoffs with median deviations of 0.008 s (IQR 0.030 s), landing deviations of 0.005 s (IQR 0.020 s), and airtime deviations of 0.000 s (IQR 0.027 s). These advancements facilitate real-time feedback and detailed biomechanical analysis, enhancing performance and trick execution, particularly during critical events, such as take-off and landing, where precise time-domain localization is crucial for providing accurate feedback to coaches and athletes.

Utility of syndromic surveillance for the surveillance of healthcare-associated infections in resource-limited settings: a narrative review

Globally, Healthcare-associated infections (HCAIs) pose a significant threat to patient safety and healthcare systems. In low- and middle-income countries (LMICs), the lack of adequate resources to manage HCAIs, as well as the weak healthcare system, further exacerbate the burden of these infections. Traditional surveillance methods that rely on laboratory tests are cost-intensive and impractical in these settings, leading to ineffective monitoring and delayed management of HCAIs. The rates of HCAIs in resource-limited settings have not been well established for most LMICs, despite their negative consequences. This is partly due to costs associated with surveillance systems. Syndromic surveillance, a part of active surveillance, focuses on clinical observations and symptoms rather than laboratory confirmation for HCAI detection. Its cost-effectiveness and efficiency make it a beneficial approach for monitoring HCAIs in LMICs. It provides for early warning capabilities, enabling timely identification and response to potential HCAI outbreaks. Syndromic surveillance is highly sensitive and this helps balance the challenge of low sensitivity of laboratory-based surveillance systems. If syndromic surveillance is used hand-in-hand with laboratory-based surveillance systems, it will greatly contribute to establishing the true burden of HAIs in resource-limited settings. Additionally, its flexibility allows for adaptation to different healthcare settings and integration into existing health information systems, facilitating data-driven decision-making and resource allocation. Such a system would augment the event-based surveillance system that is based on alerts and rumours for early detection of events of outbreak potential. If well streamlined and targeted, to monitor priority HCAIs such as surgical site infections, hospital-acquired pneumonia, diarrheal illnesses, the cost and burden of the effects from these infections could be reduced. This approach would offer early detection capabilities and could be expanded into nationwide HCAI surveillance networks with standardised data collection, healthcare worker training, real-time reporting mechanisms, stakeholder collaboration, and continuous monitoring and evaluation. Syndromic surveillance offers a promising strategy for combating HCAIs in LMICs. It provides early warning capabilities, conserves resources, and enhances patient safety. Effective implementation depends on strategic interventions, stakeholder collaboration, and ongoing monitoring and evaluation to ensure sustained effectiveness in HCAI detection and response.

Optimizing Football Formation Analysis via LSTM-Based Event Detection

The process of manually annotating sports footage is a demanding one. In American football alone, coaches spend thousands of hours reviewing and analyzing videos each season. We aim to automate this process by developing a system that generates comprehensive statistical reports from full-length football game videos. Having previously demonstrated the proof of concept for our system, here, we present optimizations to our preprocessing techniques along with an inventive method for multi-person event detection in sports videos. Employing a long short-term memory (LSTM)-based architecture to detect the snap in American football, we achieve an outstanding LSI (Levenshtein similarity index) of 0.9445, suggesting a normalized difference of less than 0.06 between predictions and ground truth labels. We also illustrate the utility of snap detection as a means of identifying the offensive players’ assuming of formation. Our results exhibit not only the success of our unique approach and underlying optimizations but also the potential for continued robustness as we pursue the development of our remaining system components.

Real-World Data Mining for Signal Detection of Antipsychotics-Associated Adverse Events Using the Korea Adverse Event Reporting System (KAERS) Database.

Background and Objectives: Recent studies suggest that the binary categorization of first-generation antipsychotics (FGAs) as being primarily responsible for extrapyramidal symptoms (EPSs) and second-generation antipsychotics (SGAs) for cardiometabolic abnormalities is an oversimplification. SGAs also demonstrate antagonistic affinity for D2 receptors, indicating their potential to induce EPSs. This study utilized the Korea Adverse Event Reporting System (KAERS) database to explore adverse drug event (ADE) signals related to both FGAs and SGAs. Materials and Methods: Relevant ADE reports from January 2013 to December 2022 were extracted from the KAERS database and analyzed using disproportionality analysis, employing the proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) with its 95% lower confidence interval (LCI) indices. Results: Of the initial dataset of 2,890,702 ADE reports, those with insufficient data and duplicates were removed, resulting in a final dataset of 5249 reports for analysis. Aripiprazole, an SGA, showed signals for movement disorders, including EPSs (PRR 4.7, ROR 4.8, IC 2.2), tremors (PRR 5.3, ROR 5.4, IC 2.4), and akathisia (PRR 18.6, ROR 19.3, IC 3.5). Notably, for quetiapine, cardiovascular signals were detected, including increased blood pressure (PRR 2.1, ROR 2.3, IC 0.5), and tachyarrhythmia (PRR 13.9, ROR 14.1, IC 1.8), along with peripheral edema (PRR 2.5, ROR 2.5, IC 0.2). Metabolic abnormalities, such as weight gain and increased appetite, were identified for four SGAs: aripiprazole, olanzapine, quetiapine, and risperidone. Safety signals related to movement disorders were not detectable for FGAs, likely due to the limited number of ADE reports available for analysis. Conclusions: Our study findings support that the distribution of ADEs between FGAs and SGAs is not strictly binary. Aripiprazole, despite being an SGA, showed signals for extrapyramidal movement disorders. Four SGAs (aripiprazole, olanzapine, quetiapine, and risperidone) were linked to metabolic side effects, while quetiapine was associated with cardiovascular safety signals.

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.

G.A.I.T: gait analysis interactive tool a pipeline for automatic detection of gait events across different motor impairments

AbstractWe introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson’s disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson’s disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects’ age and motor impairments highlights the versatility of our approach.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

CHA: Conditional Hyper-Adapter method for detecting human–object interaction

Human–object interactions (HOI) detection aims at capturing human–object pairs in images and predicting their actions. It is an essential step for many visual reasoning tasks, such as VQA, image retrieval and surveillance event detection. The challenge of this task is to tackle the compositional learning problem, especially in a few-shot setting. A straightforward approach is designing a group of dedicated models for each specific pair. However, the maintenance of these independent models is unrealistic due to combinatorial explosion. To address the above problems, we propose a new Conditional Hyper-Adapter (CHA) method based on meta-learning. Different from previous works, our approach regards each <verb, object> as an independent sub-task. Meanwhile, we design two kinds of Hyper-Adapter structures to guide the model to learn “how to address the HOI detection”. By combining the different conditions and hypernetwork, the CHA can adaptively generate partial parameters and improve the representation and generalization ability of the model. Finally, our proposed method can be viewed as a plug-and-play module to boost existing HOI detection models on the widely used HOI benchmarks.

Advancing early detection of biological events by digital holographic microscopy and simulation of microorganisms

There is a global need to advance bio-aerosol sensing for CBRN (Chemical, Biological, Radiological, and Nuclear) applications by compact and cost-effective devices. Employing digital holographic microscopy (DHM) and deep learning, we developed a system called HoloZcan to automate the analysis of airborne microbial pathogens and particles. DHM provides valuable information, but obtaining data from biological specimens for robust investigations is challenging. This paper introduces a custom simulation approach using the open-source software Meep and the finite-difference time-domain (FDTD) method to overcome limitations of existing Mie-based simulators, especially when dealing with complex microbial shapes. The simulation tool enables the modelling of specific microorganisms, offering a safer and more flexible alternative for CBRN research by bypassing ethical and logistical constraints associated with live pathogens. The study details the simulation workflow, built upon the construction of a database of optical properties of biological materials, for realistic simulations of light-microbe interactions. Evaluations on homogeneous and non-homogeneous objects demonstrate the tool’s limited intrinsic errors and superior sensitivity to refractive index changes compared to traditional Mie-based simulations. This work significantly advances our capability to accurately simulate and analyse CBRN-related scenarios, enhancing comprehensive research in bio-aerosol sensing.Graphical abstract

Tracking and Measuring Local Protein Synthesis In Vivo.

Detecting when and how much a protein molecule is synthesized is important for understanding cell function, but current methods either cannot be performed in vivo or have poor temporal resolution. Here, we developed a technique to detect and quantify subcellular protein synthesis events in real time in vivo. This Protein Translation Reporting (PTR) technique uses a genetic tag that produces a stoichiometric ratio of a small peptide portion of a split fluorescent protein and the protein of interest during protein synthesis. We show that the split fluorescent protein peptide can generate fluorescence within milliseconds upon binding the larger portion of the fluorescent protein, and that the fluorescence intensity is directly proportional to the number of molecules of the protein of interest synthesized. Using PTR, we tracked and measured protein synthesis events in single cells over time in vivo. We use different color split fluorescent proteins to detect multiple genes or alleles in single cells simultaneously. We also split a photoswitchable fluorescent protein to photoconvert the reconstituted fluorescent protein to a different channel to continually reset the time of detection of synthesis events.