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To avoid the critical problems of effective drugs not being carried to their targeted cancers and their quantity and location not being sensed in situ, this work presents a completely new innovative strategy to achieve both smart cancer targeting (SCT) and super-sensitive sensing (SSS), where one drug carrier works for effective drug loading and release. Herein, malignant melanoma treatment is used as an example of reliable detection and effective therapy. We report two characteristic dumbbell-like nano-micelles and spherical-like nano-micelles of hyaluronan induced by the Eu3+/Tb3+ complexes for effective drug loading and release, respectively. These special Eu3+/Tb3+-loaded nano-micelles (marked as ENM and TNM) have strong and sharp red/green luminescence that can sensitively detect the malignant melanoma drug dacarbazine through changes in fluorescence intensity. Cytotoxicity experiments confirmed that both ENM and TNM are not toxic to normal cells at very high concentrations of 4 mM. However, when loaded with cancer drugs (D-ENM and D-ENM), they both killed cancer cells with more than 40% efficacy at this concentration. The in vivo experiments confirmed that D-ENM and D-TNM can effectively target cancer cells in tissue and effectively impede cancer growth. The detection limits of ENM and TNM in sensing cancer drugs can reach 0.456 μg/mL and 0.139 μg/mL, respectively. Therefore, the reported Eu3+/Tb3+-induced hyaluronan nano-micelles (ENM and TNM) are distinguished carriers of this cancer drug and excellent in situ sensors, and they have highly therapeutic effects with extremely low toxicity to normal cells. [ABSTRACT FROM AUTHOR]

Addressing the current reliance on manual sorting and grading of spray rose cut flowers, this paper proposed an improved YOLOv5s model for intelligent recognition and grading detection of rose color series and flowering index of spray rose cut flowers. By incorporating small-scale anchor boxes and small object feature output, the model enhanced the annotation accuracy and the detection precision for occluded rose flowers. Additionally, a convolutional block attention module attention mechanism was integrated into the original network structure to improve the model's feature extraction capability. The WIoU loss function was employed in place of the original CIoU loss function to increase the precision of the model's post-detection processing. Test results indicated that for two types of spray rose cut flowers, Orange Bubbles and Yellow Bubbles, the improved YOLOv5s model achieved an accuracy and recall improvement of 10.2% and 20.0%, respectively. For randomly collected images of spray rose bouquets, the model maintained a detection accuracy of 95% at a confidence threshold of 0.8. [ABSTRACT FROM AUTHOR]

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 d B for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 d B to 16 d B , respectively. [ABSTRACT FROM AUTHOR]

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either "explosion", "ambient", or "other" with true positive rates (recall) greater than 96% for all three categories. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of mitigating unknown partial path overlaps in communication systems. This study demonstrates that by utilizing the front-end insight of communication systems along with the sum–product algorithm applied to factor graphs, it is possible not only to track these overlapping components accurately, but also to detect all multipath channel impairments simultaneously. The proposed methodology involves discretizing channel parameters, such as channel paths and attenuation coefficients, to ensure the most accurate computation of means of Gaussian observations. These parameters are modeled as Bernoulli random variables with priors set to 0.5 . A notable aspect of the algorithm is its integration of the received signal power into the calculation of noise variance, which is critical for its performance. To further reduce the receiver complexity, a novel implementation strategy, based on provided pre-defined look up tables (LOTs) to the reciver, is introduced. The simulation results, covering both distributed and concentrated pilot scenarios, reveal that the algorithm performs almost equally under both conditions and surpasses the established upper bound in performance. [ABSTRACT FROM AUTHOR]

Seafood is an important source of food and protein for humans. However, it is highly susceptible to microbial contamination, which has become a major challenge for the seafood processing industry. Bacteriophages are widely distributed in the environment and have been successfully used as biocontrol agents against pathogenic microorganisms in certain food processing applications. However, due to the influence of environmental factors and seafood matrices, using bacteriophages for commercial-scale biocontrol strategies still faces some challenges. This article briefly introduces the current processes used for the production and purification of bacteriophages, lists the latest findings on the application of phage-based biocontrol in seafood, summarizes the challenges faced at the current stage, and provides corresponding strategies for solving these issues. [ABSTRACT FROM AUTHOR]

Recently, planar and broadband hot-electron photodetectors (HE PDs) were established but exhibited degraded performances due to the adoptions of the single-junction configurations and the utilizations of absorbable films with thicknesses larger than the electronic mean free path. In this work, we present a five-layer design for planar HE PDs assisted by triple junctions in which an ultrathin Pt layer dominates the broadband and displays strong optical absorption (>0.9 from 900 nm to 1700 nm). Optical studies reveal that the optical admittance matching between optical admittances of designed device and air at all interested wavelengths is responsible for broadband light-trapping that induces prominent energy depositions in Pt layers. Electrical investigations show that, benefitting from suppressed hot-electron transport losses and increased hot-electron harvesting junctions, the predicted responsivity of the designed HE PD is up to 8.51 mA/W at 900 nm. Moreover, the high average absorption (responsivity) of 0.96 (3.66 mA/W) is substantially sustained over a broad incidence angle regardless of the polarizations of incident light. The comparison studies between five-layer and three-layer devices emphasize the superiority of five-layer design in strong optical absorption in Pt layers and efficient hot-electron extraction. [ABSTRACT FROM AUTHOR]

Nitrite, a collective term for a group of inorganic compounds containing nitrite ions (NO2−), is widely present in the natural environment and in the human body. It has a wide range of applications in the medical, food and environmental fields, such as food additives, water treatment agents and drugs. However, the excessive intake of nitrite poses indirect carcinogenic, teratogenic and mutagenic risks to humans. With the in-depth study of the functional properties of nitrite, there is an increasing demand for accurate and efficient methods for its detection. This paper presents a review of methods for the detection of nitrite, which will cover different principles and technologies, including traditional methods, optical methods, electrochemical sensors, and biosensors, and their prospects. By comparing and evaluating the different methods, it will provide references and valuable suggestions for choosing the most suitable nitrite detection methods and the scientific selection of alternatives for nitrite. [ABSTRACT FROM AUTHOR]

Simple Summary: Camels are one of the important economic animals in China; however, their health is often threatened by helminth. This study investigated the prevalence of helminth in camels by collecting 435 samples from five regions in Xinjiang using two methods. The results showed that 18.2% of the camels were infected with helminths, with the highest prevalence observed in Urumqi. Camels under three years old were found to be more susceptible. This study identified three major parasites (Trichostrongylus spp., Chabertia ovina, and Haemonchus contortus) in camels, and most camels were found to be simultaneously infected with multiple parasites. This research is crucial for formulating effective strategies to prevent and control camel parasites, and it contributes to improving the health and production efficiency of camels. Camels, vital to economies in Asia, Africa, and the Arabian Peninsula, have been domesticated for over 4000 years. They thrive in arid regions like Xinjiang, China, but face challenges from internal and external parasites, particularly gastrointestinal parasites, which impact health, meat and milk quality, and production efficiency. This study investigates the prevalence of gastrointestinal helminth infections in camels from five major regions in Xinjiang. We collected 435 fresh fecal samples and used the saturated saline flotation method and McMaster's method for detection. Molecular examination followed. The overall prevalence was 18.2% (95% confidence interval [CI]: 14.7–22.2%), with Urumqi showing the highest prevalence (29%, 95% CI: 23.4–35.1%) compared to other regions (odds ratio [OR]: 4.62, 95% CI: 2.63–8.41%). Younger camels (≤3 years old) were more likely infected by the parasites after adjusting for the region differences (OR: 10.53, 5.12–24.65%). However, we found no evidence that the prevalence was different between male and female camels. PCR analysis identified Trichostrongylus spp., Chabertia ovina, and Haemonchus contortus as predominant parasites, with observed co-infections indicating a complex parasitic landscape. The findings provide essential epidemiological data for effective parasite control strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: Pigeon Newcastle disease is a severe infectious disease in pigeons, characterized by a high infection rate and mortality rate. Quick and accurate detection of this disease is crucial for preventing outbreaks. In our research, we developed a quick and accurate method to diagnose this disease using a combination of two advanced techniques: recombinase-aided amplification (RAA) and CRISPR/Cas12a. This method first amplifies parts of the virus's genetic material and then detects it, allowing for simple visualization using a lateral flow dipstick (LFD). Our tests showed that this method is very specific, meaning it does not react with other viruses, and it can detect very low amounts of the virus. When tested on real clinical samples, our new approach was performed, as well as the current gold standard test, quantitative real-time PCR. This new approach provides an important technical means for the rapid diagnosis of pigeon Newcastle disease onsite, enabling the timely prevention and control of Newcastle disease outbreaks. Pigeon Newcastle disease, caused by pigeon paramyxovirus type 1 (PPMV-1), is a significant infectious disease in pigeons that can result in substantial mortality and poses a severe threat to the pigeon industry. The rapid and accurate onsite diagnosis of pigeon disease is crucial for timely diagnosis and the implementation of effective prevention and control measures. In this study, we established a rapid detection method for PPMV-1 based on recombinase-aided amplification (RAA) and CRISPR/Cas12a. The RAA primers target the conserved regions of the L gene for preamplification in clinical nucleic acid samples, followed by CRISPR/Cas12a detection of the target gene. Visualization could be achieved by combination with a lateral flow dipstick (LFD). This method demonstrated high specificity, showing no cross-reactivity with non-PPMV-1 samples. The sensitivity of the method assessed by fluorescence analysis reached 100 copies/µL, and when it was combined with an LFD, the sensitivity was 103 copies/µL. The constructed RAA-CRISPR/Cas12a-LFD visual detection method was applied to clinical sample testing and was found to enable the rapid and accurate detection of swab samples and tissue specimens. Its sensitivity was consistent with the current gold standard, quantitative real-time PCR results. The RAA-CRISPR/Cas12a-LFD detection method we developed provides a novel approach for the rapid, simple, precise, and specific onsite diagnosis of pigeon Newcastle disease. [ABSTRACT FROM AUTHOR]

Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of automated systems for efficient monitoring. The study presented in this paper focuses on detecting biofouling on tidal stream turbine blades using camera-based monitoring. The process begins with dividing the video into a series of images, which are then annotated to identify and select the bounding boxes containing objects to be detected. These annotated images are used to train YOLO version 8 to detect biofouled and clean blades in the images. The proposed approach is evaluated using metrics that demonstrate the superiority of this YOLO version compared to previous ones. To address the issue of misdetection, a data augmentation approach is proposed and tested across different YOLO versions, showing its effectiveness in improving detection quality and robustness. [ABSTRACT FROM AUTHOR]

Over the past years, transport mode recognition has become a large field of research. However, flight as a type of transportation has been mostly overlooked. A system for flight detection might be useful for context-aware applications, but more importantly, it can be used to automatically manage airplane mode on smartphones. Smartphones transmit radio frequency signals which could potentially interfere with aircraft systems, and it is therefore important that devices enable airplane mode to avoid this problem. This paper proposes flyDetect, a method for automatic flight mode detection and an embodiment in the form of an app that demonstrates the viability of the method. Thus, the system uses the accelerometer and barometer in an Android smartphone, can detect the start and end of a flight, and notify other apps or systems on the device when this happens. Our evaluation shows that flyDetect meets the requirements set for the solution, and the results are very promising. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading cause of cancer-related deaths worldwide, emphasizing the significance of early detection. Computer-aided diagnostic systems have emerged as valuable tools for aiding radiologists in the analysis of medical images, particularly in the context of lung cancer screening. A typical pipeline for lung cancer diagnosis involves pulmonary nodule detection, segmentation, and classification. Although traditional machine learning methods have been deployed in the previous years with great success, this literature review focuses on state-of-the-art deep learning methods. The objective is to extract key insights and methodologies from deep learning studies that exhibit high experimental results in this domain. This paper delves into the databases utilized, preprocessing steps applied, data augmentation techniques employed, and proposed methods deployed in studies with exceptional outcomes. The reviewed studies predominantly harness cutting-edge deep learning methodologies, encompassing traditional convolutional neural networks (CNNs) and advanced variants such as 3D CNNs, alongside other innovative approaches such as Capsule networks and transformers. The methods examined in these studies reflect the continuous evolution of deep learning techniques for pulmonary nodule detection, segmentation, and classification. The methodologies, datasets, and techniques discussed here collectively contribute to the development of more efficient computer-aided diagnostic systems, empowering radiologists and dfhealthcare professionals in the fight against this deadly disease. [ABSTRACT FROM AUTHOR]

Modeling the response of gamma detectors has long been a challenge within the nuclear community. Significant research has been conducted to digitally replicate instruments that can cost over USD 100,000 and are difficult to operate outside of a laboratory setting. The large cost and availability prevent some from making use of such equipment. Subsequently, there have been multiple attempts to create cost-effective codes that replicate the response of sodium-iodide and high-purity germanium detectors for data derivation related to gamma-ray interaction with matter. While robust programs do exist, they are often subject to export controls and/or they are not intuitive to use. Through the use of hybrid Monte Carlo methods, MATLAB can be used to produce a fast first-order response of various gamma-ray detectors. The combination of a graphical user interface with a numerical-based script allows for open-source and intuitive code. When benchmarked with experimental data from Co-60, Cs-137, and Na-22, the code can numerically calculate a response comparable to experimental and industry-standard response codes. Evidence supports both savings in computational requirements and the inclusion of an intuitive user experience that does not heavily compromise data when compared to other standard codes, such as MCNP and GADRAS, or experimental results. When the application is installed on a Dell Intel i7 computer with 16 cores, the average time to simulate the benchmarked isotopes is 0.26 s. Installation on an HP Intel i7 four-core machine runs the same isotopes in 1.63 s. The results indicate that simple gamma detectors can be modeled in an open-source format. The anticipation for the MATLAB application is to be a tool that can be easily accessible and provide datasets for use in an academic setting requiring gamma-ray detectors. Ultimately, this article provides evidence that hybrid Monte Carlo codes in an open-source format can benefit the nuclear community in both computational time and up-front cost for access. [ABSTRACT FROM AUTHOR]

The youcai mosaic virus (YoMV) can infect a diverse array of crop species, such as Raphanus sativus, Brassica napus, Solanum nigrum, and Rehmannia glutinosa, causing substantial economic damage. This study aimed to develop a rapid, sensitive, and economical diagnostic method for YoMV. We successfully expressed and purified the recombinant His-CPYoMV-YZ protein in E. coli BL21, which was used to immunize New Zealand White rabbits, generating high-titer polyclonal antibodies (PAb-CPYoMV-YZ). Additionally, a serological-based reverse transcription loop-mediated isothermal amplification (S-RT-LAMP) assay was refined, combining serological and molecular detection techniques to enhance practicality. Utilizing PAb-CPYoMV-YZ, we developed four techniques for detecting YoMV: Western blot, dot immunoblotting assay, enzyme-linked immunosorbent assay (ELISA), and S-RT-LAMP. YoMV isolates from various regions and hosts were analyzed. The results indicated that PAb-CPYoMV-YZ was highly effective in detecting YoMV across a range of hosts and isolates from diverse regions. This study fills an important gap in the serological detection of YoMV and provides a practical tool for on-site diagnosis and control of YoMV infection. [ABSTRACT FROM AUTHOR]

Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease. Current detection methods can only confirm the diagnosis at the onset of the disease, missing the critical window for early treatment. Recent studies using animal models have found that detecting changes in miRNA sites can predict the onset and severity of the disease in its early stages, facilitating early diagnosis and treatment. miRNAs show expression changes in motor neurons that connect the brain, spinal cord, and brain stem, as well as in the skeletal muscle in mouse models of ALS. Clinically, expression changes in some miRNAs in patients align with those in mouse models, such as the upregulation of miR-29b in the brain and the upregulation of miR-206 in the skeletal muscle. This study provides an overview of some miRNA study findings in humans as well as in animal models, including SOD1, FUS, TDP-43, and C9orf72 transgenic mice and wobbler mice, highlighting the potential of miRNAs as diagnostic markers for ALS. miR-21 and miR-206 are aberrantly expressed in both mouse model and patient samples, positioning them as key potential diagnostic markers in ALS. Additionally, miR-29a, miR-29b, miR-181a, and miR-142-3p have shown aberrant expression in both types of samples and show promise as clinical targets for ALS. Finally, miR-1197 and miR-486b-5p have been recently identified as aberrantly expressed miRNAs in mouse models for ALS, although further studies are needed to determine their viability as diagnostic targets. [ABSTRACT FROM AUTHOR]

DNA adductomics is the global study of all DNA adducts and was first proposed in 2006 by the Matsuda group. Its development has been greatly credited to the advances in mass spectrometric techniques, particularly tandem and multiple-stage mass spectrometry. In fact, liquid chromatography-mass spectrometry (LC-MS)-based methods are virtually the sole technique with practicality for DNA adductomic studies to date. At present, DNA adductomics is primarily used as a tool to search for DNA adducts, known and unknown, providing evidence for exposure to exogenous genotoxins and/or for the molecular mechanisms of their genotoxicity. Some DNA adducts discovered in this way have the potential to predict cancer risks and/or to be associated with adverse health outcomes. DNA adductomics has been successfully used to identify and determine exogenous carcinogens that may contribute to the etiology of certain cancers, including bacterial genotoxins and an N-nitrosamine. Also using the DNA adductomic approach, multiple DNA adducts have been observed to show age dependence and may serve as aging biomarkers. These achievements highlight the capability and power of DNA adductomics in the studies of medicine, biological science, and environmental science. Nonetheless, DNA adductomics is still in its infancy, and great advances are expected in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Validation of plant pathogens' diagnostic assays requires positive and negative controls. High Throughput Sequencing (HTS) has shown capabilities for detecting multiple pathogens simultaneously. However, accurate pathogen identification from HTS data depends on the bioinformatic pipeline used. There is currently no consensus on the best pipeline, leading to inconsistent results. To address this, standardized artificial HTS datasets are proposed as benchmarks to evaluate the performance of bioinformatic pipelines used in detection scenarios using HTS. These datasets will aid in resolving challenges like unknown sensitivity and specificity of the bioinformatic tool, contributing to advancing plant pathogen detection using HTS. The validation of diagnostic assays in plant pathogen detection is a critical area of research. It requires the use of both negative and positive controls containing a known quantity of the target pathogen, which are crucial elements when calculating analytical sensitivity and specificity, among other diagnostic performance metrics. High Throughput Sequencing (HTS) is a method that allows the simultaneous detection of a theoretically unlimited number of plant pathogens. However, accurately identifying the pathogen from HTS data is directly related to the bioinformatic pipeline utilized and its effectiveness at correctly assigning reads to their associated taxa. To this day, there is no consensus about the pipeline that should be used to detect the pathogens in HTS data, and results often undergo review and scientific evaluation. It is, therefore, imperative to establish HTS resources tailored for evaluating the performance of bioinformatic pipelines utilized in plant pathogen detection. Standardized artificial HTS datasets can be used as a benchmark by allowing users to test their pipelines for various pathogen infection scenarios, some of the most prevalent being multiple infections, low titer pathogens, mutations, and new strains, among others. Having these artificial HTS datasets in the hands of HTS diagnostic assay validators can help resolve challenges encountered when implementing bioinformatics pipelines for routine pathogen detection. Offering these purely artificial HTS datasets as benchmarking tools will significantly advance research on plant pathogen detection using HTS and enable a more robust and standardized evaluation of the bioinformatic methods, thereby enhancing the field of plant pathogen detection. [ABSTRACT FROM AUTHOR]

Simple Summary: The number of invasive pests has increased worldwide, mainly due to international trade. This problem is further exacerbated by climate change and the continued restrictions on the quantity and number of pesticides imposed by international authorities. Delottococcus aberiae, a mealybug native to South Africa, was first detected in Europe in 2009, specifically in the Valencia Community (Spain). The identification of its sex pheromone allowed the development of a monitoring network for this pest a decade later. This network facilitated the mapping of the affected citrus area and its subsequent expansion over time. The monitoring network has revealed that from 2019 until 2023, the citrus area affected by the pest has considerably increased. This network, in conjunction with fruit damage assessments conducted at the end of the crop cycle, has also allowed us to know that the damage to the fruit is independent of the maximum annual population level of the pest. The male catches obtained during the months of March to June, when fruit setting occurs, seem to be related to economic losses due to fruit damage. The invasive mealybug Delottococcus aberiae De Lotto (Hemiptera: Pseudococcidae) has rapidly spread in the Mediterranean basin since its detection in 2009 in the Valencia Community in eastern Spain. The use of sticky traps baited with its sex pheromone, (4,5,5-trimethyl-3-methylenecyclopent-1-en-1-yl)methyl acetate, has allowed to determine the geographical distribution of D. aberiae by means of the surveillance network described in the present work. The population monitoring of the pest over a five-year period (2019–2023) has revealed an increase from 31% to 70% of the affected citrus-growing area. The monitoring network has also allowed a better understanding of the pests' biological cycle throughout the year. The populations start growing from March to June and reach their maximum in July–August. During autumn, there is a gradual decline in the population. Although the highest annual populations were detected in 2022 and 2023, the greatest crop losses were recorded in 2020 and 2021, with mean values near 18%. Data suggest that the damage responsible for fruit deformation, and thus the economic losses, are related to the population levels in spring (April–May) rather than those in summer (July–August). The findings of this study can be valuable for future research and development of effective pest control strategies. [ABSTRACT FROM AUTHOR]

Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning. [ABSTRACT FROM AUTHOR]

Since the discovery of the phenomenon of surface-enhanced Raman scattering (SERS), it has gradually become an important tool for the analysis of material compositions and structures. The applications of SERS have been expanded from the fields of environmental and materials science to biomedicine due to the extremely high sensitivity and non-destructiveness of SERS-based analytical technology that even allows single-molecule detection. This article provides a comprehensive overview of the surface-enhanced Raman scattering (SERS) phenomenon. The content is divided into several main sections: basic principles and the significance of Raman spectroscopy; historical advancements and technological progress in SERS; and various practical applications across different fields. We also discuss how electromagnetic fields contribute to the SERS effect, the role of chemical interactions in enhancing Raman signals, a modeling and computational approaches to understand and predict SERS effects. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in response to hemorrhage and resuscitation with fluids, which makes hemorrhage detection during resuscitation challenging. We developed two sequential inference algorithms for detection of internal hemorrhage based on the Luenberger observer and the extended Kalman filter. The sequential inference algorithms use fluid resuscitation dose and hematocrit measurement as inputs to generate signatures to enable detection of internal hemorrhage. In the case of the extended Kalman filter, the signature is nothing but inferred hemorrhage rate, which allows it to also estimate internal hemorrhage rate. We evaluated the proof-of-concept of these algorithms based on in silico evaluation in 100 virtual patients subject to diverse hemorrhage and resuscitation rates. The results showed that the sequential inference algorithms outperformed naïve internal hemorrhage detection based on the decrease in hematocrit when hematocrit noise level was 1% (average F1 score: Luenberger observer 0.80; extended Kalman filter 0.76; hematocrit 0.59). Relative to the Luenberger observer, the extended Kalman filter demonstrated comparable internal hemorrhage detection performance and superior accuracy in estimating the hemorrhage rate. The analysis of the dependence of the sequential inference algorithms on measurement noise and plant parametric uncertainty showed that small (≤1%) hematocrit noise level and personalization of sequential inference algorithms may enable continuous noninvasive detection of internal hemorrhage and estimation of its rate. [ABSTRACT FROM AUTHOR]

Simple Summary: Porcine circovirus type 3 (PCV3) is a newly discovered pathogen that causes porcine dermatitis and nephrotic syndrome (PDNS)-like clinical signs. The current lack of effective vaccines and therapies for PCV3 has led to a main reliance on early diagnosis and detection for the prevention and control of PCV3 infection. Nucleic acid detection is commonly used for the early identification of pathogens, and the sensitivity and specificity of two-pot RPA-CRISPR/Casa-Ps, the rapid, effective, sensitive, and specific detection method for PCV3 is, indeed, needed. Here, we develop one- and two-pot methods with a combination of recombinant polymerase amplification (RPA) and a CRISPR/Cas12a detection system for the simple, rapid, sensitive, specific, and visual detection of PCV3 clinical samples within 30 min. These two detection methods have the potential to detect clinical PCV3 infections. The porcine circovirus type 3 (PCV3) infection is an emerging disease associated with clinical signs of porcine dermatitis and nephropathy syndrome (PDNS)-like clinical signs. Currently, there is a lack of effective vaccines and therapeutics against this disease. Therefore, rapid, effective, sensitive, and specific detection methods are crucial for the timely identification, prevention, and control of PCV3. In this study, we developed one- and two-pot visual detection methods for PCV3 using a clustered regularly interspaced short palindromic repeat (CRISPR)/Cas12a detection system combined with recombinase polymerase amplification (RPA). These two methods demonstrated no cross-reactivity with eight other swine viruses and exhibited minimum detection limits of five and two copies of viral DNA, respectively, revealing their high specificity and sensitivity. During a clinical sample detection within 30 min, the coincidence rates between the one- and two-pot detection methods and real-time quantitative polymerase chain reaction (qPCR) were 100%. In conclusion, both one- and two-pot RPA-CRISPR/Cas12a detection methods have significant potential for the rapid, sensitive, and specific visual detection of PCV3. [ABSTRACT FROM AUTHOR]

This paper presents the application of the Comparison of Flow Pattern Distribution (CFPD) method for detecting water leakage and understanding consumption behaviors at both microscale and macroscale. Implemented at Lille University's Scientific Campus, this research utilizes Automated Meter Reading (AMR) to collect real-time water supply and consumption data. The research successfully identified several significant leak events by analyzing this data with the CFPD method on weekly and daily scales. The analysis of the data resulted in identifying the seasonal and operational consumption patterns across different periods of the year. The findings highlight the effectiveness of the CFPD method in achieving water conservation and operational efficiency, consequently contributing to the UN Sustainable Development Goal (SDG) 6 concerning clean water and sanitation. [ABSTRACT FROM AUTHOR]

This paper presents the results of an experimental study of the dynamic characteristics of the process of laser fragmentation/laser-induced fluorescence (LF/LIF) of trinitrotoluene traces on a paper surface under synchronized double-pulse laser irradiation. An Nd:YAG-laser (266 nm) was used for the fragmentation of TNT molecules, while fluorescence excitation of their NO fragments was performed using a KrF laser with a generation line of 247.867 nm in the region of the location of the bandhead of the P12 branch of the γ(0, 2) absorption band of the NO molecule. It was shown that the dissociation process of TNT traces has an inertial character and continues after the cessation of the fragmenting laser pulse. It was found that with the delay values between the fragmenting and probing laser pulses in the region of 200 ns, the efficiency of the LF/LIF method can be increased by 12 times. This paper presents the results of an experimental evaluation of the efficiency of two-pulse LF/LIF compared to single-pulse laser exposure, where the fragmentation of TNT molecules and excitation of their NO fragments were simultaneously performed by KrF laser pulses. The possibility of multiple increases in the efficiency of two-pulse LF/LIF with an increase in the energy density of the fragmenting laser radiation was shown. The obtained results are important in terms of increasing the sensitivity and/or range of the LF/LIF method for remote detection of traces of nitrocompounds. [ABSTRACT FROM AUTHOR]

Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station's presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment's connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. We designed and built a defense scheme which detects and blacklists a fake base station and then, informed by the detection, avoids it through link routing for connectivity availability. For detection and blacklisting, our scheme uses the real-time information of both the time duration and the number of request transmissions, the features of which are directly impacted by the fake base station's threat and which have not been studied in previous research. Upon detection, our scheme takes an active measure called link routing, which is a novel concept in mobile/4G/5G networking, where the user equipment routes the connectivity request to another base station. To defend against a Sybil-capable fake base station, we use a history–reputation-based link routing scheme for routing and base station selection. We implemented both the base station and the user on software-defined radios using open-source 5G software (srsRAN v23.10 and Open5GS v2.6.6) for validation. We varied the base station implementation to simulate legitimate vs. faulty but legitimate vs. fake and malicious base stations, where a faulty base station notifies the user of the connectivity disruption and releases the session, while a fake base station continues to hold the session. We empirically analyzed the detection and identification thresholds, which vary with the fake base station's power and the channel condition. By strategically selecting the threshold parameters, our scheme provides zero errors, including zero false positives, to avoid blacklisting a temporarily faulty base station that cannot provide connectivity at the time. Furthermore, our link routing scheme enables the base station to switch in order to restore the connectivity availability and limit the threat impact. We also discuss future directions to facilitate and encourage R&D in securing telecommunications and base station security. [ABSTRACT FROM AUTHOR]

The pinewood nematode (PWN), Bursaphelenchus xylophilus, is the causal agent of pine wilt disease and is considered an A2 quarantine organism by the European Plant Protection Organisation. In Europe, this nematode has been reported in Pinus pinaster, P. radiata, and P. nigra. In May 2024, severe wilting symptoms were observed in P. sylvestris trees at Serra da Lousã (Coimbra, the central area of continental Portugal). Wood samples were collected from six wilted trees, and the presence of PWN was investigated. From these, B. xylophilus specimens were detected in five out of the six trees. Species identification was performed based on species-specific morphological diagnostic characters, and this was confirmed by real-time PCR using species-specific primers targeting the B. xylophilus satellite DNA region. This study presents the first detection of B. xylophilus in P. sylvestris in Portugal and in Europe. [ABSTRACT FROM AUTHOR]

Simple Summary: Lameness is a big issue within UK dairy herds that can lead to significant welfare and financial implications. Early detection and treatment can result in a better cure of this painful condition. Cows enjoy grooming each other and will use automatic brushes to groom themselves. However, grooming is a luxury behaviour that is performed if everything else is fine with the cow. Lame cows may use brushes less because it could be painful to be on their feet. This study aimed to assess if the possibility of monitoring automated brush use within a loose-housed dairy herd could aid in the early identification of lameness. This study found that if accurate monitoring of individuals' brush use (duration per visit to a brush) could be collected, then this could act as an indicator for lameness. This information could then be utilised on the farm to allow prompt identification of the individual(s) for direct assessment, which in turn allows for prompt action to be taken with the aim of improved outcomes for the cow(s) and the farm. This study focused on a group of 49 high-yielding dairy cows (primarily Holstein Friesians) and how their interactions with wall-mounted automated brushes correlated to their mobility (also described as lameness) score (AHDB 0–3 system. Of the 49 animals in the study, 48 were mobility scored with a sample lameness prevalence of 14.6% (n = 22 score 0, n = 19 score 1, n = 6 score 2 and n = 1 score 3 (score 2 and 3 combined due to low numbers identified)). There was no statistical difference in the number of visits between the lame (score 2 and 3) and sound cows (score 0 and 1); however, there was a statistically relevant decrease in the duration that the lame cows spent brushing per visit (sound 91.7 ± 6.06 s compared to lame 63.0 ± 9.22 s, p = 0.0097). No significant difference was identified in how the lame cows interacted with the brushes (i.e., which body part) when compared to the group. The group, in general, showed a significant preference towards interacting with the brush with their head area (63.95% of interactions observed over the 72 h involved the head). In conclusion, monitoring brush use (duration of use per visit) could aid with the identification of clinically lame animals. [ABSTRACT FROM AUTHOR]

The development of a rapid, sensitive, specific method for detecting foodborne pathogens is paramount for supplying safe food to enhance public health safety. Despite the significant improvement in pathogen detection methods, key issues are still associated with rapid methods, such as distinguishing living cells from dead, the pathogenic potential or health risk of the analyte at the time of consumption, the detection limit, and the sample-to-result. Mammalian cell-based assays analyze pathogens' interaction with host cells and are responsive only to live pathogens or active toxins. In this study, a human embryonic kidney (HEK293) cell line expressing Toll-Like Receptor 5 (TLR-5) and chromogenic reporter system (HEK dual hTLR5) was used for the detection of viable Salmonella in a 96-well tissue culture plate. This cell line responds to low concentrations of TLR5 agonist flagellin. Stimulation of TLR5 ligand activates nuclear factor-kB (NF-κB)—linked alkaline phosphatase (AP-1) signaling cascade inducing the production of secreted embryonic alkaline phosphatase (SEAP). With the addition of a ρ-nitrophenyl phosphate as a substrate, a colored end product representing a positive signal is quantified. The assay's specificity was validated with the top 20 Salmonella enterica serovars and 19 non-Salmonella spp. The performance of the assay was also validated with spiked food samples. The total detection time (sample-to-result), including shortened pre-enrichment (4 h) and selective enrichment (4 h) steps with artificially inoculated outbreak-implicated food samples (chicken, peanut kernel, peanut butter, black pepper, mayonnaise, and peach), was 15 h when inoculated at 1–100 CFU/25 g sample. These results show the potential of HEK-DualTM hTLR5 cell-based functional biosensors for the rapid screening of Salmonella. [ABSTRACT FROM AUTHOR]

When uncrewed agricultural machinery performs autonomous operations in the field, it inevitably encounters obstacles such as persons, livestock, poles, and stones. Therefore, accurate recognition of obstacles in the field environment is an essential function. To ensure the safety and enhance the operational efficiency of autonomous farming equipment, this study proposes an improved YOLOv8-based field obstacle detection model, leveraging depth information obtained from binocular cameras for precise obstacle localization. The improved model incorporates the Large Separable Kernel Attention (LSKA) module to enhance the extraction of field obstacle features. Additionally, the use of a Poly Kernel Inception (PKI) Block reduces model size while improving obstacle detection across various scales. An auxiliary detection head is also added to improve accuracy. Combining the improved model with binocular cameras allows for the detection of obstacles and their three-dimensional coordinates. Experimental results demonstrate that the improved model achieves a mean average precision (mAP) of 91.8%, representing a 3.4% improvement over the original model, while reducing floating-point operations to 7.9 G (Giga). The improved model exhibits significant advantages compared to other algorithms. In localization accuracy tests, the maximum average error and relative error in the 2–10 m range for the distance between the camera and five types of obstacles were 0.16 m and 2.26%. These findings confirm that the designed model meets the requirements for obstacle detection and localization in field environments. [ABSTRACT FROM AUTHOR]

Over recent decades, natural and artificial colloids, as well as nanoparticles, have been increasingly used in various applications. Consequently, with this rising consumption, surface and subsurface environments are more exposed to these particles. The presence of these particles and the colloid-facilitated transport of microorganisms, the interactions between dissolved contaminants and mobile colloids in porous media, and the fate and transport of colloids through groundwater—one of the primary sources of water supply for human societies—have attracted extensive research. This study investigates the performance of several image processing methods in the field of colloid detection, which is a prerequisite for the subsequent steps in porous media research. We employed four different categories of image processing approaches on microscopy images—segmentation-based methods, background-detection-based methods, filter-based methods, and morphology-based methods—to conduct the detection process of colloids. Eight methods were applied and subsequently analyzed in terms of their drawbacks and advantages to determine the best ones in this domain. Finally, we proposed an ensemble approach that leverages the strengths of the three best methods using a majority vote to detect colloids more accurately. In experiments, Precision, Recall, F-measure, and TCR criteria were considered as evaluation tools. Experimental results demonstrate the high accuracy of image processing methods in recognizing colloids. Among all these methods, morphology-based methods were the most successful, achieving the best detection performance and improving the limited distinguishing features of small colloids. Moreover, our ensemble approach, achieving perfect scores across all evaluation criteria, highlights its superiority compared with other detection methods. [ABSTRACT FROM AUTHOR]

This paper introduces a resource management approach for distributed multiple-input multiple-output (MIMO) radar systems equipped with phased array antennas. The approach focuses and adjusts narrow beams from all transmit and receive nodes to generate a regularly shaped observation area for reliable detection. Based on this, a structured early warning framework can be built by evenly arranging sufficient observation areas to cover the surveillance region and periodically scanning these areas for continuous monitoring. Observation area generation, a core technique for this framework, involves the joint optimization of beamforming weights for both transmit and receive nodes, as well as the beam dwell time. Our optimization strategy is designed to achieve two key objectives: minimizing beam dwell time to ensure rapid alerts for defense systems, and minimizing node transmit power to extend operational time while reducing the risk of intercept. To address the problem of observation area generation, we propose a two-stage method. The first stage uses the signal-to-clutter-plus-noise ratio (SCNR) as a new criterion to determine the transmit and receive beamforming weights. The second stage employs a power factor as an additional variable to scale the transmit beamforming weights under various beam dwell times, constructing a Pareto solution set for the problem. Numerical simulations validate the effectiveness of our method, demonstrating improved detection capabilities compared to monostatic phased array radar systems. [ABSTRACT FROM AUTHOR]

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publication trends, (3) progress of remote sensing and learning algorithms, and (4) application of remote sensing techniques and learning models for landslide susceptibility mapping, detections, prediction, inventory and deformation monitoring, assessment, and extraction and management. The literature selections were based on keyword searches using title/abstract and keywords from Web of Science and Scopus. A total of 186 research articles published between 2011 and 2024 were critically reviewed to provide answers to research questions related to the recent advances in the use of remote sensing technologies combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms. The review revealed that these methods have high efficiency in landslide detection, prediction, monitoring, and hazard mapping. A few current issues were also identified and discussed. [ABSTRACT FROM AUTHOR]

Excessive copper (Cu2+) causes adverse effects on human health and the ecological environment. Traditional methods for detecting Cu2+ have drawbacks such as high detection costs, complex operating conditions, and being time consuming. Therefore, there is an urgent need to develop simple detection methods to better meet specific health and environment quality needs. In this work, a paper-based fluorescence sensor was prepared (herein referred to as the as-prepared method) by immersing filter paper in aqueous polyethyleneimine (PEI) solution, and its potential use in Cu2+ detection was investigated. The results showed that the as-prepared paper samples, with fluorescence properties obtained by aggregation-induced luminescence of PEI, have selective recognition of Cu2+ based on the internal filtration effect, and the lowest detection limit is 0.03 μM. In addition, the relative error of this method is in the range of 1.80~2.23%, which is relatively comparable to the national standard method (0.63~630 μM), demonstrating high accuracy. Therefore, paper-based sensors with a simple preparation method have potential applications in the detection of Cu2+ in water. [ABSTRACT FROM AUTHOR]

As the consumption of antibiotics rises, they have generated some negative impacts on organisms and the environment because they are often unable to be effectively degraded, and seeking effective detection methods is currently a challenge. Covalent–organic frameworks (COFs) are new types of crystalline porous crystals created based on the strong covalent interactions between blocked monomers, and COFs demonstrate great potential in the detection of antibiotics from aqueous solutions because of their large surface area, adjustable porosity, recyclability, and predictable structure. This review aims to present state-of-the-art insights into COFs (properties, classification, synthesis methods, and functionalization). The key mechanisms for the detection of antibiotics and the application performance of COFs in the detection of antibiotics from water are also discussed, followed by the challenges and opportunities for COFs in future research. [ABSTRACT FROM AUTHOR]

Brain tumors are a leading cause of death globally, with numerous types varying in malignancy, and only 12% of adults diagnosed with brain cancer survive beyond five years. This research introduces a hyperparametric convolutional neural network (CNN) model to identify brain tumors, with significant practical implications. By fine-tuning the hyperparameters of the CNN model, we optimize feature extraction and systematically reduce model complexity, thereby enhancing the accuracy of brain tumor diagnosis. The critical hyperparameters include batch size, layer counts, learning rate, activation functions, pooling strategies, padding, and filter size. The hyperparameter-tuned CNN model was trained on three different brain MRI datasets available at Kaggle, producing outstanding performance scores, with an average value of 97% for accuracy, precision, recall, and F1-score. Our optimized model is effective, as demonstrated by our methodical comparisons with state-of-the-art approaches. Our hyperparameter modifications enhanced the model performance and strengthened its capacity for generalization, giving medical practitioners a more accurate and effective tool for making crucial judgments regarding brain tumor diagnosis. Our model is a significant step in the right direction toward trustworthy and accurate medical diagnosis, with practical implications for improving patient outcomes. [ABSTRACT FROM AUTHOR]

Studies of the virome of olive trees with symptoms of leaf mottling by high-throughput sequencing (HTS) revealed the presence of a new virus. Full coding genome sequences of two isolates were determined and consisted of a single RNA segment of 16,516 nt and 16,489, respectively. The genomic organization contained 10 open reading frames (ORFs) from 5′ to 3′: ORF1a, ORF1b (RdRp), ORF2 (p22), ORF3 (p7), ORF4 (HSP70h), ORF5 (HSP90h), ORF6 (CP), ORF7 (p19), ORF8 (p12), ORF9 (p23) and ORF10 (p9). Phylogenetic analyses clustered this virus in the genus Olivavirus, family Closteroviridae, with the closest species being Olivavirus flaviolae, commonly named olive leaf yellowing-associated virus (OLYaV). However, amino acid sequences of all taxonomically relevant proteins showed, in all cases, a divergence higher than 25% between OLYaV and the new virus, indicating that it represents a new species in the genus Olivavirus for which the common name of olive leaf mottling virus (OLMV) is proposed. This study represents an advance in the genus Olivavirus and provides new insights into the olive virome. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), cardiovascular illnesses, including arrhythmia, are the primary cause of mortality globally, responsible for over 31% of all fatalities each year. To reduce mortality, early and precise diagnosis is essential. Although the analysis of electrocardiograms (ECGs) is the primary means of detecting arrhythmias, it depends significantly on the expertise and subjectivity of the health professional reading and interpreting the ECG, and errors may occur in detection. Artificial intelligence provides tools, techniques, and models that can support health professionals in detecting arrhythmias. However, these tools are based only on ECG data, of which the process of obtaining is an invasive, high-cost method requiring specialized equipment and personnel. Smartwatches feature sensors that can record real-time signals indicating the heart's behavior, such as ECG signals and heart rate. Using this approach, we propose a machine learning- and deep learning-based approach for detecting arrhythmias using heart rate data obtained with smartwatches. Heart rate data were collected from 252 patients with and without arrhythmias who attended a clinic in Arequipa, Peru. Heart rates were also collected from 25 patients who wore smartwatches. Ten machine learning algorithms were implemented to generate the most effective arrhythmia recognition model, with the decision tree algorithm being the most suitable. The results were analyzed using accuracy, sensitivity, and specificity metrics. Using Holter data yielded values of 93.2%, 91.89%, and 94.59%, respectively. Using smartwatch data yielded values of 70.83%, 91.67%, and 50%, respectively. These results indicate that our model can effectively recognize arrhythmias from heart rate data. The high sensitivity score suggests that our model adequately recognizes true positives; that is, patients with arrhythmia. Likewise, its specificity suggests an adequate recognition of false positives. [ABSTRACT FROM AUTHOR]

Infant meningitis remains a severe burden on global health, particularly for young infants. Traditional ultrasound imaging techniques are limited in spatial resolution to visualize white blood cells (WBCs) in the cerebrospinal fluid (CSF), which is considered a well-established marker for meningitis detection. This work presents a novel platform that uses high-resolution ultrasound to detect the backscatter signals from microscopic CSF WBCs through the anterior fontanelle of neonates and young infants. The whole system was built around a custom probe that allows for a 20 MHz focused transducer to be mechanically controlled to map the area of interest in the CSF. Data processing can be performed internally in the device without the need to extract the images for further analysis. The in vitro feasibility of the proposed solution was evaluated in imaging 7 μ m particle suspensions at different concentrations relevant to meningitis diagnosis ranging from 7- to 646-particles (pp)/ μ L. The experimental tests were conducted from a simple setup using a sample container to a more realistic setup based on an anatomical phantom of the neonatal head. The results show high-quality images, where 7 μ m particles can be resolved for the different concentrations. [ABSTRACT FROM AUTHOR]

The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various classifiers. The results were categorized into three groups to discern differences between general hand movements and no movement; specific movements and no movement; and specific movements and other specific movements (between five different finger movements and no movement). Testing was conducted on the sampling frequencies, trials, number of electrodes, algorithms, and their parameters. The preferred algorithm was determined to be the FastICACorr algorithm with 20 components. The optimal sampling frequency is 1 kHz to avoid adding excessive noise and to ensure efficient handling. Twenty trials are deemed sufficient for training, and the number of electrodes will range from one to three, depending on the wearable EEG's ability to handle the algorithm parameters with good performance. [ABSTRACT FROM AUTHOR]

Enteroviruses are RNA viruses that initiate infections through the gastrointestinal (GI) tract and are associated with enteric illness in individuals of all ages. Most serious infections of enteroviruses are in infants and young children where it is the common cause of aseptic meningitis and other systemic diseases, leading to a high mortality rate. Enteroviruses belong to the small non-enveloped family of the Picornaviridae family. The virus can spread mainly through fecal–oral and respiratory routes. In the Arabian Gulf countries, the incidence of enteroviral infections is only restricted to a few reports, and thus, knowledge of the epidemiology, characteristics, and pathogenesis of the virus in the gulf countries remains scarce. In this minireview, we sought to provide an overview of the characteristics of enterovirus and its pathogenesis, in addition to gathering the reports of enterovirus infection prevalence in Gulf Cooperation Council (GCC) countries. We also present a summary of the common methods used in its detection. [ABSTRACT FROM AUTHOR]

Landslides are a widely recognized phenomenon, causing huge economic and human losses worldwide. The detection of spatial and temporal landslide deformation, together with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Advanced landslide monitoring systems based on remote sensing techniques (RSTs) play a crucial role in risk management and provide important support for early warning systems (EWSs) at local and regional scales. The purpose of this article is to present a review of the current state of knowledge in the development of RSTs used for identifying landslide precursors, as well as detecting, monitoring, and predicting landslides. Almost 200 articles from 2010 to 2024 were analyzed, in which the authors utilized RSTs to detect potential precursors for early warning of hazards. The applications, challenges, and trends of RSTs, largely dependent on the type of landslide, deformation pattern, hazards posed by the landslide, and the size of the area of interest, were also discussed. Although the article indicates some limitations of the RSTs used so far, integrating different techniques and technological developments offers the opportunity to create reliable EWSs and improve existing ones. [ABSTRACT FROM AUTHOR]

Nowadays, technology allows people to connect and communicate with each other even from miles away, no matter the distance. With the increased use of social networks that were rapidly adopted in human beings' lives, they can chat and share different media files. While the intent for which they have been created may be positive, they can be abused and utilized in a negative way. One form in which they can be maliciously used is represented by cyberbullying. This is a form of bullying where an aggressor shares, posts, or sends false, harmful, or negative content about someone else by electronic means. In this paper, we propose a solution for bullying detection in GIFs. We employ a hybrid architecture that comprises a Convolutional Neural Network (CNN) and three Recurrent Neural Networks (RNNs). For the feature extractor, we used the DenseNet-121 model that was pre-trained on the ImageNet-1k dataset. The obtained results give an accuracy of 99%. [ABSTRACT FROM AUTHOR]

Even with 100% certainty of a complete cure for breast cancer (BC), there is still a long way to go toward more efficient treatment because it requires sensitive and timely detection and accurate pre/post-clinical characterizations. Despite the availability of advanced diagnostic tools, many cancer patients lack access to efficient diagnostics that are both highly reliable and affordable. The fluorescence-based optical technique aims to make another significant leap forward in improving patient safety. It offers a convenient operation that reduces healthcare costs compared to visual examination tools (VETs). The primary and metastatic stages of BC consider different cancerous cell lines (MDAs), meaning the highest number of cells in this research (up to 300,000) represents the metastatic stages of BC, and 50,000 represents the primary level of BC. Developments have been studied based on fluorescence-enhanced photodynamic characterizations. The ability to characterize the fluorescence caused by MDA with 50,000 cells compared to the dominant radiation of MDA with 300,000 cells is emphatic proof of the high potential of fluorescence technique in timely BC detections, specifically before it spreads to the axillary lymph nodes. The specific cell numbers of 50,000 and 300,000 were chosen arbitrarily based on the cultivation of common biological limitations. Comparing the outcomes between 50,000 and 300,000 cells allows for evaluating the fluorescence technique's diagnostic capability across various stages of breast cancer. This assessment provides valuable insights into the effectiveness of the fluorescence-based characterizing approach in detecting cancerous cells at different stages of the disease. Here, we have assessed fluorescence's spectral shift and intensity difference as a diagnostic approach to distinguish between cancerous and normal breast cells. This study also presents a two-way structure of the 5-aminolevulinic acid (5-ALA) prodrug and Fluorescein Sodium (FS) effect in BC cell characterization from the perspective of photodynamical procedures and the detection side. 5-ALA induces an accumulation of protoporphyrin IX (PpIX) photosensitizer through a biosynthetic pathway, leading to red radiation of fluorescence measurements depending on different factors, such as temperature, incubation time, added glucose of the culturing medium, as well as photosynthesis processes. The presence and progression of breast cancer can be indicated by elevated levels of Reactive Oxygen Species (ROS), associated with the production of PpIX in cells following the administration of 5-ALA. In addition, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) fluorophores are recognized as the main factors for fluorescence emissions at around 420–580 nm emission intervals. Considering the MDA's high metastatic potential, the impact of 5-ALA on MDA's cellular morphology and viability has been investigated. The molecular fluorophores are the primary probes to MDA's cellular photodynamic considerations, allowing this widespread pre/post-clinical approach. The fluorescence signal reduction due to decreased cell viability and increased MDA's cellular death rate after 24 h of the 5-ALA-induced staining corresponds to the changes in lipid metabolism enzymes of MDAs cultured at different doses, which could be known as a cell death inducer function. Furthermore, statistical concerns have been studied using PCA multivariate component analysis to differentiate MDA cell lines administrated by 5-ALA. [ABSTRACT FROM AUTHOR]

In surface-enhanced Raman scattering (SERS) detection research, the shape, structure, surface modification, and material selection of nanoparticles can significantly impact the SERS intensity. Petal-like gap-enhanced Raman tags (P-GERTs) possess numerous sharp tips and edges, which generate localized electric field enhancements, further amplifying the electric field enhancement effect on neighboring molecules and enhancing the SERS signal. Additionally, the surface of P-GERTs can be modified with functional molecules, enabling their application in the detection of disease biomarkers. Using COVID-19 as an example, the performance of P-GERTs in disease biomarker detection was validated, demonstrating that the signal intensity of this probe can reach 55 times that of regular gold nanoparticles and 36.7 times that of smooth shell gap-enhanced Raman tags (S-GERTs). Furthermore, in combination with magnetically retrievable magnetic bead substrates, the N-protein antigen was specifically detected in a one-step process. N-protein was detected within 15 min using a portable Raman spectrometer. The limit of detection (LOD) for the standard sample was 4.28 pg/mL, and the LOD for the actual throat swab sample system was 25.4 pg/mL. This workflow can be extended to the detection of other biomarkers, making it widely applicable. [ABSTRACT FROM AUTHOR]

Arsenic, the 20th most common element in Earth's crust and historically regarded as the King of Poisons, occurs naturally in two oxidation states, Arsenate (V) and Arsenite (III), and is prevalent worldwide through natural and anthropogenic means. The cations of the metalloid exhibit unique chemical behaviour in water and are found to be components of approximately 245 natural minerals, making its occurrence in drinking water a compelling challenge, especially in groundwater. This comprehensive review collates information regarding the prevalence of arsenic contamination in water worldwide and its impact on human health, its chemical behaviour, methods for detection and quantification, and treatment strategies. A comprehensive search was conducted, and the selection of eligible studies was carried out using the PRISMA (the preferred reporting items for systematic reviews and meta-analyses) guidelines. Essential characteristics of eligible research studies were extracted based on geographical areas, origins, concentration levels and the magnitude of populations vulnerable to arsenic contamination in groundwater sources. Arsenic contamination of water affects over 100 countries including Canada, the United States, Pakistan, China, India, Brazil and Bangladesh, where hydrogeological conditions favour prevalence and groundwater is the primary water source for food preparation, irrigation of food crops and drinking water. This leads to human exposure through absorption, ingestion and inhalation, causing numerous health disorders affecting nearly all systems within the human body, with acute and chronic toxicity including cancers. The presence of arsenic in water poses a considerable challenge to humanity, prompting scientists to devise diverse mitigation approaches categorized as (a) oxidation processes, (b) precipitation methods, (c) membrane technologies, (d) adsorption and ion exchange methods, and (e) social interventions. This comprehensive review is expected to be a valuable source for professionals in the water industry, public management, and policymaking, aiding their ongoing and future research and development efforts. [ABSTRACT FROM AUTHOR]

Rock burst is a worldwide prevention and control problem, and the main reason for its occurrence is the concentration of stress in the surrounding rock of the coal roadway. Therefore, it is of great significance to realize the rapid and accurate detection of the stress distribution in the surrounding rock of the roadway for the prevention and control of rock burst. Based on the principle of charge induction, this paper adopts a research method combining theoretical analysis and indoor and field tests to carry out a study on the charge induction detection of stress distribution of surrounding rock in coal seam roadways using the self-developed coal rock charge induction monitor. A theoretical analysis of the charge induction intensity in relation to the stress level is carried out. Indoor tests on the law of charge induction for graded loading of large sized coal samples are carried out. Field detection tests of the charge induction law at different drilling depths on the solid coal side and the large coal pillar side of the coal seam roadway are carried out. The results show a positive correlation between the charge signal intensity and the stress magnitude. The induced charge of coal samples has a tendency to increase with the increase in graded loading stress level. The magnitude of the induced charge can reflect the stress level of the coal body. On the solid coal side, the induced charge has a tendency of increasing and then decreasing with the increase in detection depth. The final results are in good agreement with the results of the drill chip method, which better reflects the distribution of the lateral support pressure of the roadway. On the side of the large coal pillar, the induced charge has a tendency to increase, then decrease, and then increase with the increase in probing depth, which is in good agreement with the distribution of lateral support pressure formed in the elastic core area of the large coal pillar. Therefore, the charge induction technology can be used as a fast, non-contact detection means for the partitioning and stress distribution of the roadway enclosure, which can provide guidance for the target prevention and controlling rock burst and for designing roadway support. [ABSTRACT FROM AUTHOR]

For the monitoring of chimeric antigen receptor (CAR) T-cell therapies, antigen-based CAR detection methods are usually applied. However, for each target-antigen, a separate detection system is required. Furthermore, when monitored CAR T-cells in the blood of patients treated with bispecific antibodies or T-cell engagers (bsAbs/BiTEs) recognize the same antigen, these methods produce false-positive results in clinical diagnostics. Anti-CAR-linker monoclonal antibodies (mAbs) targeting the linker sequence between the variable domains of the antigen binding CAR fragment promise a universal and unbiased CAR detection. To test this, we analyzed clinical specimens of all BCMA- and CD19-targeting CAR T-cell products currently approved for clinical use. We found a highly specific and sensitive CAR detection using anti-CAR-linker mAb in blood cells from patients treated with Ide-cel, Tisa-cel, Axi-cel, Brexu-cel, and Liso-cel. For Ide-cel and Tisa-cel, the sensitivity was significantly lower compared to that for antigen-based CAR detection assays. Strikingly, the specificity of anti-CAR linker mAb was not affected by the simultaneous presence of bispecific blinatumomab or teclistamab for Axi-cel, Brexu-cel, Liso-cel, or Ide-cel, respectively. Cilta-cel (containing a monomeric G4S-CAR linker) could not be detected by anti-CAR linker mAb. In conclusion, anti-CAR-linker mAbs are highly specific and useful for CAR T-cell monitoring but are not universally applicable. [ABSTRACT FROM AUTHOR]

Citrus tristeza virus (CTV) is a globally pervasive and economically significant virus that negatively impacts citrus trees, leading to substantial reductions in fruit yield. CTV occurs within the phloem of infected plants, causing a range of disease phenotypes, such as stem pitting (SP), quick decline (QD), and other detrimental diseases. Research on CTV is challenging due to the large size of its RNA genome and the diversity of CTV populations. Comparative genomic analyses have uncovered genetic diversity in multiple regions of CTV isolates' genomes, facilitating the classification of the virus into distinct genotypes. Despite these challenges, notable advancements have been made in identifying and controlling CTV strains through serological and molecular methods. The following review concentrates on the techniques of nucleic acid identification and serological analysis for various CTV isolates, assisting in the comparison and evaluation of various detection methods, which are crucial for the effective management of CTV diseases, and so contributes to the innovation and development of CTV detection methods. [ABSTRACT FROM AUTHOR]