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Background Verticilium dahliae is the most important wilt pathogen of olive trees with a broad host range causing devastating diseases currently without any effective chemical control. Traditional detection methodologies are based on symptoms-observation or lab-detection using time consuming culturing or molecular techniques. Therefore, there is an increasing need for portable tools that can detect rapidly V. dahliae in the field. Results In this work, we report the development of a novel method for the rapid, reliable and on-site detection of V. dahliae using a newly designed isothermal LAMP assay and crude extracts of olive wood. For the detection of the fungus, LAMP primers were designed targeting the internal transcribed spacer (ITS) region of the rRNA gene. The above assay was combined with a purpose-built prototype portable device which allowed real time quantitative colorimetric detection of V. dahliae in 35 min. The limit of detection of our assay was found to be 0.8 fg/μl reaction and the specificity 100% as indicated by zero cross-reactivity to common pathogens found in olive trees. Moreover, detection of V. dahliae in purified DNA gave a sensitivity of 100% (Ct < 30) and 80% (Ct > 30) while the detection of the fungus in unpurified crude wood extracts showed a sensitivity of 80% when multisampling was implemented. The superiority of the LAMP methodology regarding robustness and sensitivity was demonstrated when only LAMP was able to detect V. dahliae in crude samples from naturally infected trees with very low infection levels, while nested PCR and SYBR qPCR failed to detect the pathogen in an unpurified form. Conclusions This study describes the development of a new real time LAMP assay, targeting the ITS region of the rRNA gene of V. dahliae in olive trees combined with a 3D-printed portable device for field testing using a tablet. The assay is characterized by high sensitivity and specificity as well as ability to operate using directly crude samples such as woody tissue or petioles. The reported methodology is setting the basis for the development of an on-site detection methodology for V. dahliae in olive trees, but also for other plant pathogens.

With growing consumer concerns about food safety, developing methods for the field-based, non-destructive, and rapid detection of pesticide residues is becoming increasingly critical. This study introduces a field-based, non-destructive, and rapid method for detecting pesticide residues on kumquat surfaces. Initially, spectral data from the visible/near-infrared (VNIR) light bands were collected using a handheld spectrometer from kumquats treated with three pesticides at various gradient concentrations and water. The data were then preprocessed and analyzed using machine learning (SPA-SVM) and deep learning models (1D-CNN, 1D-ResNet) to determine the optimal model. Features from the convolutional layer of the 1D-ResNet model were extracted for visualization and analysis, highlighting significant differences in features between the different pesticides and across varying concentrations. The results indicate that the 1D-ResNet model achieved 97% overall accuracy, with a macro average of 0.96 and a weighted average of 0.97, and that precision, recall, and F1-score approached 1.00 for most pesticide treatment gradients. The results of this research verified the feasibility of the handheld spectrometer combined with 1D-Resnet for the detection of pesticide residues on the surface of kumquat, realized the visualization of pesticide residue characteristics, and also provided a reference for the detection of pesticide residues on the surface of other fruits.

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area.

The field of ecology has undergone a molecular revolution, with researchers increasingly relying on DNA‐based methods for organism detection. Unfortunately, these techniques often require expensive equipment, dedicated laboratory spaces and specialized training in molecular and computational techniques; limitations that may exclude field researchers, underfunded programmes and citizen scientists from contributing to cutting‐edge science. It is for these reasons that we have designed a simplified, inexpensive method for field‐based molecular organism detection—FINDeM (Field‐deployable Isothermal Nucleotide‐based Detection Method). In this approach, DNA is extracted using chemical cell lysis and a cellulose filter disc, followed by two body‐heat inducible reactions—recombinase polymerase amplification and a CRISPR‐Cas12a fluorescent reporter assay—to amplify and detect target DNA, respectively. Here, we introduce and validate FINDeM in detecting Batrachochytrium dendrobatidis, the causative agent of amphibian chytridiomycosis, and show that this approach can identify single‐digit DNA copies from epidermal swabs in under 1 h using low‐cost supplies and field‐friendly equipment. This research signifies a breakthrough in ecology, as we demonstrate a field‐deployable platform that requires only basic supplies (i.e. micropipettes, plastic consumables and a UV flashlight), inexpensive reagents (~ $1.29 USD/sample) and emanated body heat for highly sensitive, DNA‐based organism detection. By presenting FINDeM in an ecological system with pressing, global biodiversity implications, we aim to not only highlight how CRISPR‐based applications promise to revolutionize organism detection but also how the continued development of such techniques will allow for additional, more diversely trained researchers to answer the most pressing questions in ecology. Resumen El campo de la ecología ha experimentado una revolución molecular, con investigadores que dependen cada vez más de métodos moleculares para la detección de organismos. Sin embargo, estas técnicas a menudo requieren de equipos costosos, laboratorios de biología molecular y entrenamiento especializado en técnicas moleculares y computacionales; lo cual puede limitar a los investigadores de campo, programas académicos con bajos recursos y a la ciencia ciudadana de contribuir en los avances de la ciencia. Es por estas razones que hemos diseñado un método simple y económico para la detección a nivel molecular de organismos en campo: FINDeM (Método de Detección Desplegable en Campo basado en Nucleótidos Isotermales). En este método, se extrae el ADN mediante una lisis celular química y un disco de filtro de celulosa, seguido de dos reacciones inducidas por el calor corporal: la amplificación de la polimerasa recombinasa y un ensayo de fluorescencia CRISPR‐Cas12a, para amplificar y detectar el ADN del organismo de interés, respectivamente. Aquí, presentamos y validamos FINDeM para la detección de Batrachochytrium dendrobatidis, el agente causante de la quitridiomicosis en anfibios, y mostramos que este método puede identificar copias de ADN de un solo dígito en muestras de hisopados de piel en menos de una hora utilizando suministros económicos y equipos fácil uso en campo. Esta investigación representa un avance en ecología, ya que demostramos una plataforma desplegable en el campo que solo requiere suministros básicos (es decir, micropipetas, insumos de plástico y una linterna de rayos ultravioleta), reactivos económicos (~$ 1.29 USD por muestra) y calor corporal para una detección altamente sensitiva del ADN del organismo de interés. Al presentar FINDeM en un sistema ecológico con implicaciones globales para la biodiversidad, nuestro objetivo es destacar no solo cómo los métodos que aplican la técnica CRISPR prometen revolucionar el desarrollo de técnicas de detección de organismos, sino también cómo el desarrollo de estas técnicas permitirá que investigadores de diversas disciplinas puedan tener acceso a estas tecnologías para responder grandes preguntas en ecología.

With the rapid rising of global population, the demand for improving breeding techniques to greatly increase the worldwide crop production has become more and more urgent. Most researchers believe that the key to new breeding techniques lies in genetic improvement of crops, which leads to a large quantity of phenotyping spots. Unfortunately, current phenotyping solutions are not powerful enough to handle so many spots with satisfying speed and accuracy. As a result, high-throughput phenotyping is drawing more and more attention. In this paper, we propose a new field-based sensing solution to high-throughput phenotyping. We mount a LiDAR (Velodyne HDL64-S3) on a mobile robot, making the robot a \"phenomobile.\" We develop software for data collection and analysis under Robotic Operating System using open source components and algorithm libraries. Different from conducting phenotyping observations with an in-row and one-by-one manner, our new solution allows the robot to move around the parcel to collect data. Thus, the 3D and 360° view laser scanner can collect phenotyping data for a large plant group at the same time, instead of one by one. Furthermore, no touching interference from the robot would be imposed onto the crops. We conduct experiments for maize plant on two parcels. We implement point cloud merging with landmarks and Iterative Closest Points to cut down the time consumption. We then recognize and compute the morphological phenotyping parameters (row spacing and plant height) of maize plant using depth-band histograms and horizontal point density. We analyze the cloud registration and merging performances, the row spacing detection accuracy, and the single plant height computation accuracy. Experimental results verify the feasibility of the proposed solution.

Background: Ground contact (GC) detection is essential for sprint performance analysis. Inertial measurement units (IMUs) enable field-based assessment, but their reliability during sprint acceleration remains limited when using heuristic and recently used machine learning algorithms. This study introduces a deep learning one-dimensional convolutional neural network (1D-CNN) to improve GC event and GC times detection in sprint acceleration. Methods: Twelve sprint-trained athletes performed 60 m sprints while bilateral shank-mounted IMUs (1125 Hz) and synchronized high-speed video (250 Hz) captured the first 15 m. Video-derived GC events served as reference labels for model training, validation, and testing, using resultant acceleration and angular velocity as model inputs. Results: The optimized model (18 inception blocks, window = 100, stride = 15) achieved mean Hausdorff distances ≤ 6 ms and 100% precision and recall for both validation and test datasets (Rand Index ≥ 0.977). Agreement with video references was excellent (bias < 1 ms, limits of agreement ± 15 ms, r > 0.90, p < 0.001). Conclusions: The 1D-CNN surpassed heuristic and prior machine learning approaches in the sprint acceleration phase, offering robust, near-perfect GC detection. These findings highlight the promise of deep learning-based time-series models for reliable, real-world biomechanical monitoring in sprint acceleration tasks.

Background Porcine Sapelovirus (PSV) infection has been confirmed in pigs worldwide, mostly asymptomatic, but in some cases, it can lead to significant issues in the gastrointestinal, respiratory, neurological, or reproductive systems. PSV is considered an emerging pathogen of porcine species. Recombinase polymerase amplification (RPA) is a simple and fast isothermal technique that uses three enzymes for amplification without the use of any sophisticated equipment. Methods and results The reverse transcription recombinase polymerase amplification (RT-RPA) assay was developed and optimized for field based detection of PSV. The assay was developed by targeting 5´UTR region of PSV genome and optimized for reaction time, temperature, primer and MgOAc concentration. The analytical sensitivity and specificity of assay was determined. The assay was evaluated on 85 porcine faecal samples collected from field. In addition to conventional format, this assay was also optimized for visual dye-based detection format and lateral flow strips based detection (in combination with probe). The developed assay works at constant temperature of 35 °C for 20 min with forward primer concentration 20pm, reverse primer concentration 10pm and MgOAc concentration of 14mM. This assay is highly sensitive and detects up to 28 copies of viral nucleic acid both in the conventional as well as in fluorescent dye based detection format. Using the newly developed assay 21 samples out of 85 samples were found positive, showing positivity rate of 24.7%. The positivity rate of RT-RPA assay corroborated with the gold standard RT-PCR test. Conclusions This study presented the development of an RT-RPA isothermal assay for rapid and accurate detection of PSV. The assay is highly sensitive, specific, works at a low and constant temperature, does not require any high-end instrument and can be a potential diagnostics tool for pen-side testing of PSV in the field conditions. The newly developed RT-RPA assay could successfully detect PSV circulating in swine population of Haryana, India. This is a first report of this kind from the region.

•Forensic techniques have shifted from equipped laboratories to decentralised areas.•Enabling factors of this paradigm shift and current obstacles are discussed.•Historical usage of field-based forensic tests and their development are reviewed.•Emerging technologies for forensic analysis are presented. Field based forensic tests commonly provide information on the presence and identity of biological stains and can also support the identification of species. Such information can support downstream processing of forensic samples and generate rapid intelligence. These approaches have traditionally used chemical and immunological techniques to elicit the result but some are known to suffer from a lack of specificity and sensitivity. The last 10 years has seen the development of field-based genetic profiling systems, with specific focus on moving the mainstay of forensic genetic analysis, namely STR profiling, out of the laboratory and into the hands of the non-laboratory user. In doing so it is now possible for enforcement officers to generate a crime scene DNA profile which can then be matched to a reference or database profile. The introduction of these novel genetic platforms also allows for further development of new molecular assays aimed at answering the more traditional questions relating to body fluid identity and species detection. The current drive for field-based molecular tools is in response to the needs of the criminal justice system and enforcement agencies, and promises a step-change in how forensic evidence is processed. However, the adoption of such systems by the law enforcement community does not represent a new strategy in the way forensic science has integrated previous novel approaches. Nor do they automatically represent a threat to the quality control and assurance practices that are central to the field. This review examines the historical need and subsequent research and developmental breakthroughs in field-based forensic analysis over the past two decades with particular focus on genetic methods Emerging technologies from a range of scientific fields that have potential applications in forensic analysis at the crime scene are identified and associated issues that arise from the shift from laboratory into operational field use are discussed.

Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and are densely arranged. Therefore, using an oriented bounding box to mark the target is a mainstream choice. However, this general method is designed based on horizontal box annotation, while the improved method for detecting an oriented bounding box has a high computational complexity. In this paper, we propose a method called ellipse field network (EFN) to organically integrate semantic segmentation and object detection. It predicts the probability distribution of the target and obtains accurate oriented bounding boxes through a post-processing step. We tested our method on the HRSC2016 and DOTA data sets, achieving mAP values of 0.863 and 0.701, respectively. At the same time, we also tested the performance of EFN on natural images and obtained a mAP of 84.7 in the VOC2012 data set. These extensive experiments demonstrate that EFN can achieve state-of-the-art results in aerial image tests and can obtain a good score when considering natural images.

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.