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Unmanned Aerial Vehicle (UAV) thermal imagery is rapidly becoming an essential tool in precision agriculture. Its ability to enable widespread crop status assessment is increasingly critical, given escalating water demands and limited resources, which drive the need for optimizing water use and crop yield through well-planned irrigation and vegetation management. Despite advancements in crop assessment methodologies, including the use of vegetation indices, 2D mapping, and 3D point cloud technologies, some aspects remain less understood. For instance, mission plans often capture nadir and oblique images simultaneously, which can be time- and resource-intensive, without a clear understanding of each image type's impact. This issue is particularly critical for crops with specific growth patterns, such as woody crops, which grow vertically. This research aims to investigate the role of nadir and oblique images in the generation of CWSI (Crop Water Stress Index) maps and CWSI point clouds, that is 2D and 3D products, in woody crops for precision agriculture. To this end, products were generated using Agisoft Metashape, ArcGIS Pro, and CloudCompare to explore the effects of various flight configurations on the final outcome, seeking to identify the most efficient workflow for each remote sensing product. A linear regression analysis reveals that, for generating 2D products (orthomosaics), combining flight angles is redundant, while 3D products (point clouds) are generated equally from nadir and oblique images. Volume calculations show that combining nadir and oblique flights yields the most accurate results for CWSI point clouds compared to LiDAR in terms of geometric representation (R2 = 0.72), followed by the nadir flight (R2 = 0.68), and, finally, the oblique flight (R2 = 0.54). Thus, point clouds offer a fuller perspective of the canopy. To our knowledge, this is the first time that CWSI point clouds have been used for precision viticulture, and this knowledge can aid farm managers, technicians, or UAV pilots in optimizing the capture of UAV image datasets in line with their specific goals. [ABSTRACT FROM AUTHOR]

The fungus Botrytis cinerea causes severe diseases in many crops. In grapevines, it causes Botrytis bunch rot (BBR), one of the most reported diseases worldwide. It affects all herbaceous organs of the vine, especially the ripe berries, causing significant reductions in yield and wine quality. Botrytis detection models traditionally focus on temporal analysis at a specific spatial location, ignoring the study of the spatial variability of the crop. Unmanned aerial vehicles (UAVs) equipped with multispectral cameras can provide high-resolution images that can be valuable information to develop a tool for aerial pest detection. This paper proposes an algorithm to assess the risk of Botrytis development in a vineyard in Spain, using as input products generated by UAV imagery: DTM (Digital Terrain Model), NDVI (Normalised Difference Vegetation Index), CHM (Canopy Height Model) and LAI (Leaf Area Index). They represent the height and architecture of the canopy, the topography and the plant status. Healthy vines were significantly different from vines affected by Botrytis (p < 0.05) in each of these variables, supporting the consistency of using these inputs for the model. This methodology combines photogrammetric, spatial analysis techniques, and machine learning classification methods with deep vineyard-related agronomic knowledge to produce heatmaps with acceptable accuracy (R² > 0.7) that may support vineyard managers in understanding the spatial variability of the disease, allowing the spatial 2D visualisation of the risk of BBR disease development and, potentially, resulting in higher operational efficiency and reducing phytosanitary treatments, as well as economic costs. Furthermore, the present work takes advantage of imaging technologies that provide information about any location in the field, not only about specific points in the vineyard, suggesting that UAV imagery is appropriate to measure the likelihood of BBR development within the vineyard, highlighting the importance of efficient disease management based on spatial variability. • Multispectral imagery was employed to classify the vineyard in several botrytis risk zones. • It uses as input products generated by UAV imagery: DTM, NDVI, CHM and LAI. • Inputs represent the height and architecture of the canopy, the topography and the plant status. • Heatmaps may support vineyard managers in understanding the spatial variability of the disease. • The methodology provides information about any location in the field, not only about specific points. [ABSTRACT FROM AUTHOR]

• The framework enables enhanced UAV data acquisition for fruit detection purposes in vineyards considering the biophysical environment. • The optimization algorithm provides paths up to 24% shorter compared to conventional mission planners. • The proposed framework is designed for both single and multiple UAVs flying synchronously over the field. • The algorithm can be applied to woody crops other than vineyards trained in vertical trellis since they share similar biophysical characteristics and crop structures. Typically, commercial orchards and vineyards consist of large fields that encounter similar development phases at once. Thus, it becomes necessary to efficiently fly over all fields to detect fruit and identify their status in a very limited timeframe. For this purpose, Unmanned Aerial Vehicles (UAVs) path planning plays a pivotal role in agriculture as it enables optimal coverage of agricultural fields, leading to enhanced data acquisition and improved precision agriculture practices, for instance, disease assessment and pesticide application. In addition, deep learning techniques offer precise image analysis. On the one hand, object detection has been applied to agricultural fields to carry out a wide range of operations, such as detecting apples and predicting yield in vineyards, with higher detection accuracy when the fruits are fully visible. On the other hand, when crops present leaf-occlusion, the algorithms face difficulties and are unable to adapt to the specific characteristics of the field. Therefore, this study seeks to address this issue by developing a novel framework to enhance UAV path planning for data collection in vineyards, considering the current biophysical environment. To this end, the proposed framework requires two flights: i) a first flight (survey) to acquire insights on the crop structure and environment, and ii) a second flight using the Ant Colony Optimization Max-Min Ant System (ACO-MMAS) algorithm to enhance image acquisition by considering multiple angles to overcome partial leaf-occlusion. Further, the optimisation algorithm can potentially boost the acquisition of datasets for fruit detection by considering single and multiple UAVs flying synchronously while ensuring a safe distance between platforms and efficient coverage. The method was tested in two vineyards with different environmental characteristics, increasing levels of difficulty and acquired during two different growing seasons. It improved the length of the computed paths by up to 24%, compared to a base algorithm that considers only the closest point without any optimisation, improving the decision-making processes and resource allocation in crop management. [ABSTRACT FROM AUTHOR]

• We found that the machine learning used in farm risk management falls into 5 major risk types and 4 risk components. • Production risk is the most studied risk type in the selected papers covering 96% of the papers. • Only 35 and 11 of 746 papers studied farm vulnerability and resilience, respectively. • In the last 4 years, machine learning usage for farm risk management is tripled. • There is a transition trend in recent years from regression analysis to deep learning-based approaches including CNNs. Farms face various risks such as uncertainties in the natural growth process, obtaining adequate financing, volatile input and output prices, unpredictable changes in farm-related policy and regulations, and farmers' personal health problems. Accordingly, farmers have to make decisions to be prepared for such situations under risk or mitigate their impacts to maintain essential functions. Increasingly, a data-driven perspective is warranted where machine learning (ML) has become an essential tool for automatic extraction of useful information to support decision-making in farm management as well as risk management. ML's role in farm risk management (FRM) has recently increased with advances in technology and digitalization. This paper provides a literature review in the form of a systematic mapping study to identify the publications, trends, active research communities, and detailed reviews on the use of ML methods for FRM. Accordingly, nine research/mapping questions are designed to extract the required information. In total, we retrieved 1819 papers, of which 746 papers were selected based on the defined exclusion criteria for a detailed review. We categorized the studies based on the addressed risk types (e.g., production risk), assessments that addressed risk components (e.g., resilience), used ML types (e.g., supervised learning) and algorithms ranging from regression modeling to deep learning, addressed ML tasks (e.g., classification), data types (e.g., images), and farm types (e.g., crop-based farm). The results reveal that there is a significant increase in employing ML methods including deep learning and convolutional neural networks for FRM in recent years. The production risk and impact/damage assessment are the most frequently addressed risk type and assessment that addressed risk components in ML-FRM, respectively. In addition, research gaps and open problems are identified and accordingly insights and recommendations from risk management and machine learning perspectives are provided for future studies including the need for ML methods for different risk types (e.g., financial risk), assessments addressing different risk components (e.g., resilience assessment), and developing more advanced ML methods (e.g., reinforcement learning) for FRM. [ABSTRACT FROM AUTHOR]

• Detailed review of over 300 research papers. • Object Detection and Tracking are suitable techniques to enhance Precision Farming. • Critical discussion regarding the challenges of Object Detection and Tracking. • Potential research lines to improve Object Detection and Tracking are identified. Object Detection and Tracking have gained importance in recent years because of the great advances in image and video analysis techniques and the accurate results these technologies are producing. Moreover, they have successfully been applied to multiple fields, including the agricultural domain since they offer real-time monitoring of the status of the crops and animals while counting how many are present within a field/barn. This review aims to review the current literature on Object Detection and Tracking within the field of Precision Farming. For that, over 300 research articles were explored, from which 150 articles from the last five years were systematically reviewed and analysed regarding the algorithms they implemented, the domain they belong to, the difficulties they faced, and which limitations should be tackled in the future. Lastly, it examines potential issues that this approach might have, for instance, the lack of open-source datasets with labelled data. The findings of this study indicate that Object Detection and Tracking are critical techniques to enhance Precision Farming and pave the way for robotization for the agricultural sector since they provide accurate results and insights on crop and animal management, and optimize resource allocation. Future work should focus on the optimal acquisition of the datasets prior to Object Detection and Tracking, along with the consideration of the biophysical environment of the farming scenarios. [ABSTRACT FROM AUTHOR]

• Recognition of apnea and regular breathing patterns in respiratory gating training and a reference dataset. • A morphological analysis was followed through autoencoders, where supervised learning was used to evaluate input-output correlation of the autoencoder. • This morphological approach led to promising results in both datasets, hence avoiding feature engineering and enhancing transferability to other time series analysis. Background and Objective: Respiratory gating training is a common technique to increase patient proprioception, with the goal of (e.g.) minimizing the effects of organ motion during radiotherapy. In this work, we devise a system based on autoencoders for classification of regular, apnea and unconstrained breathing patterns (i.e. multiclass). Methods: Our approach is based on morphological analysis of the respiratory signals, using an autoencoder trained on regular breathing. The correlation between the input and output of the autoencoder is used to train and test several classifiers in order to select the best. Our approach is evaluated in a novel real-world respiratory gating biofeedback training dataset and on the Apnea-ECG reference dataset. Results: Accuracies of 95 ± 3.5% and 87 ± 6.6% were obtained for two different datasets, in the classification of breathing and apnea. These results suggest the viability of a generalised model to characterise the breathing patterns under study. Conclusions: Using autoencoders to learn respiratory gating training patterns allows a data-driven approach to feature extraction, by focusing only on the signal's morphology. The proposed system is prone to be used in real-time and could potentially be transferred to other domains. [ABSTRACT FROM AUTHOR]

Soil erosion, rapid geomorphological change and vegetation degradation are major threats to the human and natural environment. Unmanned Aerial Systems (UAS) can be used as tools to provide detailed and accurate estimations of landscape change. The effect of flight strategy on the accuracy of UAS image data products, typically a digital surface model (DSM) and orthophoto, is unknown. Herein different flying altitudes (126-235 m) and area coverage orientations (N-S and SW-NE) are assessed in a semi-arid and medium-relief area where terraced and abandoned agricultural fields are heavily damaged by piping and gully erosion. The assessment was with respect to cell size, vertical and horizontal accuracy, absolute difference of DSM, and registration of recognizable landscape features. The results show increasing cell size (5-9 cm) with increasing altitude, and differences between elevation values (10-20 cm) for different flight directions. Vertical accuracy ranged 4-7 cm but showed no clear relationship with flight strategy, whilst horizontal error was stable (2-4 cm) for the different orthophotos. In all data sets, geomorphological features such as piping channels, rills and gullies and vegetation patches could be labeled by a technician. Finally, the datasets have been released in a public repository. [ABSTRACT FROM AUTHOR]

• UAV RGB videos were employed to extract grape phenotypic traits at an early stage of development. • It detects and tracks white grape bunches in a vineyard with leaf occlusion and challenging sunny conditions. • It detects grape berries within the detected bunches. • Early extraction of phenotyping traits may support vineyard managers to monitor the sanitary status of the vine and get information on the yield to be harvested. Grapevine phenotyping is the process of determining the physical properties (e.g., size, shape, and number) of grape bunches and berries. Grapevine phenotyping information provides valuable characteristics to monitor the sanitary status of the vine. Knowing the number and dimensions of bunches and berries at an early stage of development provides relevant information to the winegrowers about the yield to be harvested. However, the process of counting and measuring is usually done manually, which is laborious and time-consuming. Previous studies have attempted to implement bunch detection on red bunches in vineyards with leaf removal and surveys have been done using ground vehicles and handled cameras. However, Unmanned Aerial Vehicles (UAV) mounted with RGB cameras, along with computer vision techniques offer a cheap, robust, and timesaving alternative. Therefore, Multi-object tracking and segmentation (MOTS) is utilized in this study to determine the traits of individual white grape bunches and berries from RGB videos obtained from a UAV acquired over a commercial vineyard with a high density of leaves. To achieve this goal two datasets with labelled images and phenotyping measurements were created and made available in a public repository. PointTrack algorithm was used for detecting and tracking the grape bunches, and two instance segmentation algorithms - YOLACT and Spatial Embeddings - have been compared for finding the most suitable approach to detect berries. It was found that the detection performs adequately for cluster detection with a MODSA of 93.85. For tracking, the results were not sufficient when trained with 679 frames.This study provides an automated pipeline for the extraction of several grape phenotyping traits described by the International Organization of Vine and Wine (OIV) descriptors. The selected OIV descriptors are the bunch length, width, and shape (codes 202, 203, and 208, respectively) and the berry length, width, and shape (codes 220, 221, and 223, respectively). Lastly, the comparison regarding the number of detected berries per bunch indicated that Spatial Embeddings assessed berry counting more accurately (79.5%) than YOLACT (44.6%). [ABSTRACT FROM AUTHOR]

Purpose: The internal auditory canal (IAC) plays a key role in lateral skull base surgery. Although several approaches to the IAC have been proposed, endoscope-assisted transcanal corridors to the IAC have rarely been studied. We sought to provide a step-by-step description of the transcanal transpromontorial approach to the IAC and analyze anatomic relationships that might enhance predictability and safety of this approach., Methods: Ten cadaveric specimens were dissected and the extended transcanal transpromontorial approach to the IAC was established. Various morphometric measurements and anatomic landmarks were reviewed and analyzed., Results: The proposed technique proved feasible and safe in all specimens. There was no inadvertent injury to the jugular bulb or internal carotid artery. The chorda tympani, a key landmark for the mastoid segment of the facial nerve, was identified in all dissections. The spherical recess of the vestibule and middle turn of cochlea are important landmarks for identification of the labyrinthine segment of the facial nerve. Identification of all boundaries of the working area is also essential for safe access. Among various morphometric measurements, the modiolus-IAC angle (≈ 150°) proved particularly consistent; given its ease of use and low variability, we believe it could serve as a landmark for identification and subsequent dissection of the IAC., Conclusions: The extended transcanal transpromontorial approach to the IAC is feasible and safe. Relying on anatomic landmarks to ensure preservation of the involved neurovascular structures is essential for a successful approach. The modiolus-IAC angle is a consistent, reproducible landmark for IAC identification and dissection., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Humulus lupulus extracts have in their composition different molecules, such as polyphenols, α-acids, β-acids, and hydrocarbons, which contribute to the plant's medicinal properties. These molecules are associated with antimicrobial, antioxidant and anti-inflammatory activities., Objective: This work focuses on the evaluation of H. lupulus biological activities, with the aim of evaluating its potential for inclusion in cosmetic formulations., Methods: Two distinct aqueous extracts and two hydrolates obtained via hydrodistillation were evaluated. These include the flower parts (FE, FH) and the mix of aboveground parts (ME, MH). The chemical profiles for both aqueous extracts and hydrolates were identified by high performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). Antimicrobial, antioxidant, cytotoxicity, and anti-inflammatory activity were tested in vitro using standard methods., Results: Rutin was the major compound found in FE (40.041 μg mg -1 of extract) and ME (2.909 μg mg -1 of extract), while humulenol II was the most abundant compound in hydrolates (FH: 20.83%; MH: 46.80%). Furthermore, FE was able to inhibit the growth of Staphylococcus aureus and Staphylococcus epidermis with MIC values of 50% and 25% (v/v), respectively. FH showed the same effect in Staphylococcus aureus (50% v/v). FH evidenced poor antioxidant potential in DPPH scavenging test and demonstrated significant antioxidant and anti-inflammatory effects by reducing (***p < 0.001) intracellular reactive oxygen species (ROS), NO (nitric oxide) levels (***p < 0.001) and cyclooxygenase-2 (COX-2) protein expression (***p < 0.001) in lipopolysaccharide (LPS)-stimulated macrophages. Nevertheless, it is important to note that FH exhibited cytotoxicity at high concentrations in 3T3 fibroblasts and RAW 264.7 macrophages., Conclusion: The studied H. lupulus aqueous extracts and hydrolates revealed that FH stands out as the most promising bioactive source for cosmetic formulations. However, future research addressing antimicrobial activity is necessary to confirm its potential incorporation into dermatological and cosmetic formulations., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

The "EscaYard" dataset comprises multimodal data collected from vineyards to support agricultural research, specifically focusing on vine health and productivity. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing multispectral images and 3D point clouds, and (2) smartphones for detailed ground-level photography. The UAV used was DJI Matrice 210 V2 RTK, equipped with a Micasense Altum sensor, flying at 30 m above ground level to ensure detailed coverage. Ground-level data were collected using smartphones (iPhone X and Xiaomi Poco X3 Pro), which provided high-resolution images of individual plants. These images were geotagged, enabling location mapping, and included data on the phytosanitary status and number of grape clusters per plant. Additionally, the dataset contains RTK GNSS data, offering high-precision location information for each vine, enhancing the dataset's value for spatial analysis. Moreover, the dataset is structured to support various research applications, including agronomy, remote sensing, and machine learning. It is particularly suited for studying disease detection, yield estimation, and vineyard management strategies. The high-resolution and multispectral nature of the data allows for a detailed analysis of vineyard conditions. Potential reuse of the dataset spans multiple disciplines, enabling studies on environmental monitoring, geographic information systems (GIS), and precision agriculture. Its comprehensive nature makes it a valuable resource for developing and testing algorithms for disease classification, yield prediction, and plant phenotyping. For instance, the images of bunches and grape leaves can be used to train object detection algorithms for accurate disease detection and consequent precise spraying. Moreover, yield prediction algorithms can be trained by extracting the phenotypic traits of the grape bunches. The "EscaYard" dataset provides a foundation for advancing research in sustainable farming practices, optimising crop health, and improving productivity through precise agricultural technologies., (© 2024 The Author(s).)

Object Detection and Tracking have provided a valuable tool for many tasks, mostly time-consuming and prone-to-error jobs, including fruit counting while in the field, among others. Fruit counting can be a challenging assignment for humans due to the large quantity of fruit available, which turns it into a mentally-taxing operation. Hence, it is relevant to use technology to ease the task of farmers by implementing Object Detection and Tracking algorithms to facilitate fruit counting. However, those algorithms suffer undercounting due to occlusion, which means that the fruit is hidden behind a leaf or a branch, complicating the detection task. Consequently, gathering the datasets from multiple viewing angles is essential to boost the likelihood of recording the images and videos from the most visible point of view. Furthermore, the most critical open-source datasets do not include labels for certain fruits, such as grape bunches. This study aims to unravel the scarcity of public datasets, including labels, to train algorithms for grape bunch Detection and Tracking by considering multiple angles acquired with a UAV to overcome fruit occlusion challenges., (© 2024 The Author(s).)

The advent of robotics in schools and universities curricula are preparing students to encompass new didactic fields. This article presents ReFiBot which is an education robot that has been used to increase the technical literacy on robotics and bring more awareness to open science at Wageningen University. The ReFiBot combines open-source hardware and software, integrated with a chassis made from recycled plastic from fishnets. The ReFiBot was carefully designed to be easily assembled with off-the-shelf electronic parts and programmed using the Arduino IDE. Moreover, a software library is facilitated to ease its adoption in educational activities from any curricula level. The ReFiBot has been mainly used for education but can also be used for research on swarm robotics. The CAD files, components list, software files, and tutorial within this contribution will guide the reader through the assemblage and best practices of this circular robotics kit., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Fig. 17Selected responses from the questionnaire after tutorial 2., (© 2023 The Author(s).)

LiDAR (Light Detection and Ranging) technology's precision in data collection has gained immense traction in the field of remote sensing, particularly in Precision Agriculture using Unmanned Aerial Vehicles (UAVs). To fulfill the pressing need for public UAV LiDAR datasets in the domain of Agricultural Sciences, especially for woody crops such as vineyards, this study presents an extensive dataset of LiDAR data collected from vineyards in northern Spain. The DJI M300 multi-rotor platform, equipped with a DJI Zenmuse L1 LiDAR sensor, conducted UAV flights at 20, 30, and 50 meters above ground level (AGL) across two vineyards during three development stages in 2021 and 2022. This dataset is composed of ten high-density 3D LiDAR point clouds stored in .laz format with embedded RGB information in each point. It provides insights into vineyard morphology and development, thereby aiding in the optimization of vineyard management strategies. Furthermore, it serves as a valuable tool for agricultural robotics, offering comprehensive terrain information for developing efficient flight paths and navigation algorithms. Finally, it serves as a reliable "ground truth" dataset to validate satellite-derived models, facilitating the creation of highly accurate digital elevation models (DEMs) and other derived models., (© 2023 The Author(s).)

There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled "Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard". The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Inc.)

Remote sensing makes it possible to gather data rapidly, precisely, accurately, and non-destructively, allowing it to assess grapevines accurately in near real-time. In addition, multispectral cameras capture information in different bands, which can be combined to generate vegetation indices useful in precision agriculture. This dataset contains 16,504 multispectral images from a 1.06 ha vineyard affected by Botrytis cinerea , in the north of Spain. The photos were taken throughout four UAV flights at 30 m height with varying camera angles on 16 September 2021, the same date as the grape harvest. The first flight took place with the camera tilted at 0° (nadir angle), the second flight at 30°, the third flight at 45°, and the fourth flight was also performed at 0° but was scheduled in the afternoon to capture the shadows of the plants projected on the ground. This dataset was created to support researchers interested in disease detection and, in general, UAV remote sensing in vineyards and other woody crops. Moreover, it allows digital photogrammetry and 3D reconstruction in the context of precision agriculture, enabling the study of the effect of different tilt angles on the 3D reconstruction of the vineyard and the generation of orthomosaics., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Counting the number of grape bunches at an early stage of development offers relevant information to the winegrower about the potential yield to be harvested. However, manual counting on the fields is laborious and time-consuming. Remote sensing, and more precisely unmanned aerial vehicles mounted with RGB or multispectral cameras, facilitate this task rapidly and accurately. This dataset contains 40 RGB videos from a 1.06-ha vineyard located in northern Spain. Moreover, the dataset includes mask labels of visible grape bunches. The videos were acquired throughout four UAV flights with an RGB camera tilted at 60 degrees. Each flight recorded one side of a row of the vineyard. The grape berries were between pea-size (BBCH75) and bunch closure (BBCH79) stage, which is two months before harvesting. No operations other than those usual in a commercial vineyard, such as pruning, cane tying, fertilization, and pest treatment, have been carried out, hence, the dataset presents leaf occlusion. The dataset was gathered and labelled to train object detection and tracking algorithms for grape bunch counting. Furthermore, it eases the work of winegrowers to check the sanitary status of the vineyard., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The Chikungunya virus (CHIKV) can cause long lasting symptoms and manifestations. However, there is little information on which ocular ones are most frequent following infection. We performed a systematic review (registered in the International Prospective Register of Systematic Reviews; no CRD42020171928) to establish the most frequent ocular manifestations of CHIKV infection and their associations with gender and age. Articles published until September 2020 were selected from PubMed, Scielo, Cochrane and Scopus databases. Only studies with CHIKV-infected patients and eye alterations were included. Reviews, descriptive studies, or those not investigating the human ocular manifestations of CHIKV, those with patients with other diseases and infections, abstracts and studies without relevant data were excluded. Twenty-five studies were selected for inclusion. Their risk of bias was evaluated by a modified Newcastle-Ottawa scale. The most frequent ocular symptoms of CHIKV infection included ocular pain, inflammation and reduced visual acuity, whilst conjunctivitis and optic neuritis were the most common manifestations of the disease. These occurred mostly in individuals of 42 ± 9.5 years of age and woman. The few available reports on CHIKV-induced eye manifestations highlight the need for further research in the field to gather more substantial evidence linking CHIKV infection, the eye and age/gender. Nonetheless, the data emphasizes that ocular alterations are meaningful occurrences of CHIKV infection which can substantially affect quality of life.

The term 'neurogenic inflammation' is commonly used, especially with respect to the role of sensory nerves within inflammatory disease. However, despite over a century of research, we remain unclear about the role of these nerves in the vascular biology of inflammation, as compared with their interacting role in pain processing and of their potential for therapeutic manipulation. This chapter attempts to discuss the progress in understanding, from the initial discovery of sensory nerves until the present day. This covers pioneering findings that these nerves exist, are involved in vascular events and act as important sensors of environmental changes, including injury and infection. This is followed by discovery of the contents they release such as the established vasoactive neuropeptides substance P and CGRP as well as anti-inflammatory peptides such as the opioids and somatostatin. The more recent emergence of the importance of the transient receptor potential (TRP) channels has revealed some of the mechanisms by which these nerves sense environmental stimuli. This knowledge enables a platform from which to learn of the potential role of neurogenic inflammation in disease and in turn of novel therapeutic targets.

While metamaterials offer engineered static optical properties, future artificial media with dynamic random-access control over shape and position of meta-molecules will provide arbitrary control of light propagation. The simplest example of such a reconfigurable metamaterial is a nanowire grid metasurface with subwavelength wire spacing. Recently we demonstrated computationally that such a metadevice with individually controlled wire positions could be used as dynamic diffraction grating, beam steering module and tunable focusing element. Here we report on the nanomembrane realization of such a nanowire grid metasurface constructed from individually addressable plasmonic chevron nanowires with a 230 nm × 100 nm cross-section, which consist of gold and silicon nitride. The active structure of the metadevice consists of 15 nanowires each 18 μm long and is fabricated by a combination of electron beam lithography and ion beam milling. It is packaged as a microchip device where the nanowires can be individually actuated by control currents via differential thermal expansion.

Objective: The aims of this study were to determine the frequency of asymptomatic sensorineural hearing loss (SNHL) in systemic lupus erythematosus (SLE) and to determine the association between SNHL and demographic, clinical, and laboratory features and cardiovascular risk factors., Methods: We conducted a cross-sectional study including consecutive female SLE patients. We performed audiometry and clinical and laboratory evaluation and determined cardiovascular risk factors in all patients. Statistical analysis included principal component analysis and logistic regression., Results: Eighty-nine women were included with mean age of 38.98 (SD, 7.77) years and mean disease duration of 10.29 (SD, 9.19) years. Asymptomatic SNHL was observed in 14 patients (16%). In logistic regression model, only low-density lipoprotein levels (z = 2.64; P = 0.008) were associated with SNHL., Conclusions: We observed asymptomatic SNHL in 16% of SLE and an association with low-density lipoprotein levels suggesting atherosclerosis as a mechanism. Follow-up is needed to determine clinical implications.

There are many outdoor robotic applications where a robot must reach a goal position or explore an area without previous knowledge of the environment around it. Additionally, other applications (like path planning) require the use of known maps or previous information of the environment. This work presents a system composed by a terrestrial and an aerial robot that cooperate and share sensor information in order to address those requirements. The ground robot is able to navigate in an unknown large environment aided by visual feedback from a camera on board the aerial robot. At the same time, the obstacles are mapped in real-time by putting together the information from the camera and the positioning system of the ground robot. A set of experiments were carried out with the purpose of verifying the system applicability. The experiments were performed in a simulation environment and outdoor with a medium-sized ground robot and a mini quad-rotor. The proposed robotic system shows outstanding results in simultaneous navigation and mapping applications in large outdoor environments.

Unlabelled: A new era has arrived in auditory rehabilitation with the introduction of new technologies such as electroacoustic stimulation (EAS). EAS is indicated for patients with residual hearing at low frequencies and severe or profound hearing loss at high frequencies. These patients have no indication for conventional cochlear implant and have difficulties in adapting to individual sound amplification devices. Preservation of hearing is vital in this process; the surgical technique must be based on this concept., Objectives: To present the cochlear implant surgical technique with MED-EL Mand FlexEAS to preserve hearing in patients with hearing loss at high frequencies and to maintain low frequency hearing. We are the first institution to carry out this treatment in Brazil., Methods: A case report of the surgical technique carried out in four patients; the procedure was carried out by the cochlear implant group of a specialized clinical hospital., Results: The procedures were successful and uneventful., Conclusion: We described the technique used at our institution for implants using EAS; the surgical technique is complex and includes steps for preservation of hearing.

Introduction: The perception of tinnitus varies among individuals. The limitations caused by tinnitus are related to psychological factors, mood changes and psychiatric conditions, while other factors related to discomfort caused by tinnitus are being studied. Hearing loss is an important factor for the onset of tinnitus., Objectives: To evaluate the correlation between the degree of discomfort caused by tinnitus and the hearing loss level., Materials and Methods: A retrospective study of the patients treated at the Otolaryngology Service of the State University from Campinas for 15 months, using the Visual-Analogue Scale to classify the degree of discomfort by tinnitus., Results: 107 patients were studied and there was no correlation between the degree of annoyance of tinnitus with hearing loss, age, gender, presence of dizziness, of neck pain, headache, changes of the temporomandibular joint, the use of caffeine or excessive intake of carbohydrates. The discomfort was slightly higher in patients without hearing loss and in women., Conclusion: Dizziness, neck pain, headache and caffeine abuse are prevalent complaints in patients with tinnitus.