Animal Movement Research Articles

Flying cats and dogs: Relationality, empathy and care in the transportation of animals by air

The carrying of companion animals by air has grown into an international business involving approved vets, pet travel agencies, airlines, and animal terminals. This study investigates how the experience of transportation is shaped and draws theoretically on an approach of more-than-human mobility. In detail, I approach mobility through relationality, empathy, and caring practices by considering air transportation as an anthropocentric means of animal movement. Movement, including the specific conditions used to carry companion animals, influences the experience of mobility, triggering multispecies empathy and caring practices. Empirical accounts provide 16 online interviews with cat and dog caregivers analyzed via a qualitative content analysis. The interviewees shared transportation experiences in the aircraft cabin, in the hold, or when carrying animals as cargo. The analysis reveals that caregivers and companion animals experience air transportation relationally and the separation of caregivers and companion animals causes negative transport experiences.

Modelling individual variability in habitat selection and movement using integrated step‐selection analysis

Abstract Integrated step‐selection analysis (ISSA) is frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSA have been developed, making it possible to quantify variability among animals in their space‐use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused on the former despite the widely acknowledged and important role that movement plays in determining ecological processes from the individual to ecosystem level. One potential explanation for this omission is the absence of readily available software or examples demonstrating methods for estimating movement parameters in ISSA with random effects. We demonstrated methods for characterizing among‐individual variability in both movement and habitat‐selection parameters using a simulated data set and by fitting two models to an acoustic telemetry data set containing locations of 35 red snapper (Lutjanus campechanus). Movement kernels were assumed to depend on either the type of benthic reef habitat in which the fish was located (model 1) or the distance between the fish's current location and the nearest edge habitat (model 2). In both models, we also quantified habitat selection for different benthic habitat classes and distance to edge habitat, and we allowed for individual variability in movement and habitat‐selection parameters using random effects. The simulation example highlights the benefits of a mixed‐effects specification, namely, we can increase precision when estimating individual‐specific movement parameters by borrowing information across like individuals. In our applied example, we found substantial among‐individual variability in both habitat selection and movement parameters. Nonetheless, most red snapper selected for hardbottom habitat and for locations nearer to edge habitat. They also moved less when in hardbottom habitat. Turn angles were frequently near ± π but were more dispersed when fish were far away from edge habitat. We provide code templates and functions for quantifying variability in movement and habitat‐selection parameters when implementing ISSA with random effects. In doing so, we hope to encourage ecologists conducting ISSA to take full advantage of their ability to model among‐individual variability in both habitat‐selection and movement patterns.

Studying animal locomotion with multiple data loggers: quantifying time drift between tags

AbstractTemporal accuracy is a fundamental characteristic of logging technology and is needed to correlate data streams. Single biologgers sensing animal movement (accelerometers, gyroscope, magnetometers, collectively inertial measurement unit; IMU) have been extensively used to study the ecology of animals. To better capture whole body movement and increase the accuracy of behavior classification, there is a need to deploy multiple loggers on a single individual to capture the movement of multiple body parts. Yet due to temporal drift, accurately aligning multiple IMU datasets can be problematic, especially as deployment duration increases. In this paper we quantify temporal drift and errors in commercially available IMU data loggers using a combination of robotic and animal borne experiments. The variance in drift rate within a tag is over an order of magnitude lower (σ = 0.001 s h−1) than the variance between tags (σ = 0.015 s·h−1), showing that recording frequency is a characteristic of each tag and not a random variable. Furthermore, we observed a large offset (0.54 ± 0.016 s·h−1) between two groups of tags that had differing recording frequencies, and we observed three instances of instantaneous temporal jumps within datasets introducing errors into the data streams. Finally, we show that relative drift rates can be estimated even when deployed on animals displaying various behaviors without the tags needing to be simultaneously moved. For the tags used in this study, drift rates can vary significantly between tags, are repeatable, and can be accurately measured in the field. The temporal alignment of multiple tag datasets allows researchers to deploy multiple tags on an individual animal which will greatly increase our knowledge of movement kinematics and expand the range of movement characteristics that can be used for behavioral classification.

ENSO dataset & comparison of deep learning models for ENSO forecasting

AbstractForecasting the El Nino-Southern Oscillation (ENSO) is a challenging task in climatology. It is one of the main factors responsible for the Earth’s interannual climatic fluctuation and can result in many climatic anomalies. The impacts include natural disasters (floods, droughts), low & high agriculture yields, price fluctuation, energy demand, availability of water resources, animal movement, and many more. This study presents a comprehensive ENSO dataset containing standard indicators and other relevant data to facilitate ENSO analysis and forecasting. To ensure the dataset's validity and reliability, we performed extensive data analysis and trained four basic deep models for time series forecasting (i.e. CNN, RNN, LSTM, and hybrids). The data analysis confirmed the accuracy and suitability of the dataset for ENSO forecasting. The LSTM model achieved the best fit to the data, leading to superior performance in forecasting ENSO events.

Scaling of buccal mass growth and muscle activation determine the duration of feeding behaviours in the marine mollusc Aplysia californica.

The mechanical forces experienced during movement and the time constants of muscle activation are important determinants of the durations of behaviours, which may both be affected by size-dependent scaling. The mechanics of slow movements in small animals are dominated by elastic forces and are thus quasistatic (i.e. always near mechanical equilibrium). Muscular forces producing movement and elastic forces resisting movement should scale identically (proportional to mass2/3), leaving the scaling of the time constant of muscle activation to play a critical role in determining behavioural duration. We tested this hypothesis by measuring the duration of feeding behaviours in the marine mollusc Aplysia californica whose body sizes spanned three orders of magnitude. The duration of muscle activation was determined by measuring the time it took for muscles to produce maximum force as A. californica attempted to feed on tethered inedible seaweed, which provided an in vivo approximation of an isometric contraction. The timing of muscle activation scaled with mass0.3. The total duration of biting behaviours scaled identically, with mass0.3, indicating a lack of additional mechanical effects. The duration of swallowing behaviour, however, exhibited a shallower scaling of mass0.17. We suggest that this was due to the allometric growth of the anterior retractor muscle during development, as measured by micro-computed tomography (micro-CT) scans of buccal masses. Consequently, larger A. californica did not need to activate their muscles as fully to produce equivalent forces. These results indicate that muscle activation may be an important determinant of the scaling of behavioural durations in quasistatic systems.

The role of memory-based movements in the formation of animal home ranges.

Most animals live in spatially-constrained home ranges. The prevalence of this space-use pattern in nature suggests that general biological mechanisms are likely to be responsible for their occurrence. Individual-based models of animal movement in both theoretical and empirical settings have demonstrated that the revisitation of familiar areas through memory can lead to the formation of stable home ranges. Here, we formulate a deterministic, mechanistic home range model that includes the interplay between a bi-component memory and resource preference, and evaluate resulting patterns of space-use. We show that a bi-component memory process can lead to the formation of stable home ranges and control its size, with greater spatial memory capabilities being associated with larger home range size. The interplay between memory and resource preferences gives rise to a continuum of space-use patterns-from spatially-restricted movements into a home range that is influenced by local resource heterogeneity, to diffusive-like movements dependent on larger-scale resource distributions, such as in nomadism. Future work could take advantage of this model formulation to evaluate the role of memory in shaping individual performance in response to varying spatio-temporal resource patterns.

Features of the ecology of <i>Rhinolophus mehelyi</i> (Chiroptera: Rhinolophidae) during hibernation in the north‐east of the Caucasus

The cave was surveyed and the animals were counted 1–2 times a month. Microclimatic indicators were recorded by automatic loggers.During the research, it was found that the place of hibernation of R. mehelyi in the north‐east of the area is the Karabudakhkentskaya Cave. The minimum, maximum and average temperatures in the cave during the wintering period of R. mehelyi are equal: 6.9 oC, 15.6 oC and 12.4 oC accordingly. The minimum humidity was 89.1 %, the maximum was 98.2 % and the average was 92.7 %. It was found that the temperature inside the shelter affects the number of individuals in the visible part of the cave and the formation of individual groupings. In addition, there is an increase in the number of individuals in April before the start of migration.It was found that humidity does not affect the movement of the species inside the cave, but temperature is a factor determining the movement of animals and their crowding. Thus, to accurately record the number of species, which is important for carrying out environmental measures, it is necessary to take into account both indicators – temperature and time of research.

Modified Overset Method in OpenFOAM for Simultaneous Motion and Deformation: A Case Study of a Flexible Flapping Foil

The influence of wing deformation in animal propulsion and motion is an intriguing phenomenon that has contributed to a growing interest in biomimetics in both academia and industry. The study focuses on developing a robust numerical tool for analyzing the motion of biological systems in fluid flows, crucial to understanding fluid-structure interaction (FSI) phenomena. The research introduces a new mesh deformation solver, dynamicOversetZoneFvMesh, designed to address limitations in OpenFOAM's conventional Overset method. This solver enables the application of both rigid body motion and deformation at specified patches within certain zones, allowing for more complex motion scenarios. Through meticulous verification, parallelization, validation, and mesh convergence studies, the modified Overset solver's reliability is confirmed. Two cases are examined: a rigid-body flapping foil and a flexible foil with leading-edge deformation. Results demonstrate the adapted solver's proficiency in managing complex fluid dynamics and accommodating simultaneous motion and deformation. Furthermore, the study reveals that leading-edge flexibility improves the power extraction efficiency of flapping foils, aligning with previous literature. Overall, the research lays the foundation for advancing FSI simulations and opens avenues for addressing previously challenging problems in the field.

Smart Agriculture: Enhancing Security Through Animal Detection Via Deep Learning and Computer Vision

Agriculture stands as a crucial sector, making significant contributions to the economies of many countries. Nevertheless, it encounters various challenges, one of which is animal disruption. This poses a considerable threat to crops, leading to financial losses for farmers. In response to this concern, we have engineered an animal disruption warning system for agricultural settings based on YOLOv6 technology.The system operates by analyzing live video feeds from strategically placed cameras. Utilizing deep learning algorithms, it can detect and classify animals in real-time. The computer vision algorithms enable tracking and prediction of animal movements. Upon detection, the system promptly sends alerts, enabling timely and appropriate actions.In this paper, we periodically monitor the entire farm through a camera that continuously records its surroundings. The identification of animal entry is achieved using a deep learning model, and alarm systems serve as a deterrent, notifying forest officials. This report provides details on the libraries and convolutional neural networks employed in constructing the model.This research focuses on the implementation of a robust animal detection system in agricultural environments, leveraging the capabilities of deep learning. The project utilizes state-of-the-art deep neural networks and computer vision algorithms to analyze live video feeds from strategically positioned cameras across the farm. The deep learning model is trained to detect and classify various animals in real-time, contributing to the early identification of potential threats to crops.The system employs sophisticated computer vision techniques, enabling accurate tracking and prediction of animal movements within the monitored areas. Upon detection, the system triggers timely alerts, providing farmers with the necessary information to take swift and appropriate actions, thereby mitigating potential damage to crops.To achieve these objectives, the project involves periodic monitoring of the entire farm through a camera that continuously records its surroundings. The deep learning model, supported by alarm systems, effectively identifies animal entries, serving as a proactive deterrent. This research report outlines the libraries, frameworks, and convolutional neural networks employed in the development of the animal detection model, shedding light on the technical aspects of its implementation.The integration of deep learning and computer vision in agriculture not only enhances crop protection but also contributes to the sustainable and efficient management of farming practices. This research offers insights into the potential of advanced technologies to address challenges in agriculture and opens avenues for further exploration in the intersection of technology and agriculture.              

Diverse habitats shape the movement ecology of a top marine predator, the white shark Carcharodon carcharias

AbstractAn animal's movement is influenced by a plethora of internal and external factors, leading to individual‐ and habitat‐specific movement characteristics. This plasticity is thought to allow individuals to exploit diverse environments efficiently. We tested whether the movement characteristics of white sharks Carcharodon carcharias differ across ontogeny and among habitats along the coast of Central California. In doing so, we elucidate how changes in internal state (physiological changes coinciding with body size) and external environments (differing seascapes and/or diel phases) shape the movement of this globally distributed predator. Twenty‐one white sharks, from small juveniles to large adults, were equipped with motion‐sensitive biologging tags at four contrasting seascapes: two islands, a headland, and an inshore cove. From multisensor biologging data, 20 metrics characterizing movement (i.e., depth use, vertical velocities, activity, turning rates, and bursting events) were derived and subjected to multivariate analyses. Movement characteristics were most different across seascapes, followed by ontogeny and diel phase. Juvenile sharks, which were only encountered at the cove, displayed the most distinct movement characteristics. Sharks at this seascape remained close to the shore traveling over smaller areas, shallower depth ranges, and with lower levels of tail beat frequencies, when corrected for size, than sub‐adult and adult sharks tagged elsewhere. Distinct tortuous daytime versus linear nighttime horizontal movements were recorded from sharks at island seascapes but not from those at the headland or inshore cove. At the offshore islands, the linear nighttime swimming patterns coincided with repeated dives to and from deeper water. The availability of prey and access to deeper water are likely drivers of the differences in movement characteristics described, with varying demographics of pinniped prey found at the subadult and adult aggregation areas and juvenile sharks being piscivorous and their habitat neither adjacent to pinniped haul out areas nor deeper water. This study demonstrates plasticity in the movements of a top predator, which adapts its routine to suit the habitat it forages within.

Estimating migration timing and abundance in partial migratory systems by integrating continuous antenna detections with physical captures.

Many populations migrate between two different habitats (e.g. wintering/foraging to breeding area, mainstem-tributary, river-lake, river-ocean, river-side channel) as part of their life history. Detection technologies, such as passive integrated transponder (PIT) antennas or sonic receivers, can be placed at boundaries between habitats (e.g. near the confluence of rivers) to detect migratory movements of marked animals. Often, these detection systems have high detection probabilities and detect many individuals but are limited in their ability to make inferences about abundance because only marked individuals can be detected. Here, we introduce a mark-recapture modelling approach that uses detections from a double-array PIT antenna system to imply movement directionality from arrays and estimate migration timing. Additionally, when combined with physical captures, the model can be used to estimate abundances for both migratory and non-migratory groups and help quantify partial migration. We first test our approach using simulation, and results indicate our approach displayed negligible bias for total abundance (less than ±1%) and slight biases for state-specific abundance estimates (±1%-6%). We fit our model to array detections and physical captures of three native fishes (humpback chub [Gila cypha], flannelmouth sucker [Catostomus latipinnis] and bluehead sucker [Catostomus discobolus]) in the Little Colorado River (LCR) in Grand Canyon, AZ, a system that exhibits partial migration (i.e. includes residents and migrants). Abundance estimates from our model confirm that, for all three species, migratory individuals are much more numerous than residents. There was little difference in movement timing between 2021 (a year without preceding winter/spring floods) and 2022 (a year with a small flood occurring in early April). In both years, flannelmouth sucker arrived in mid-March whereas humpback chub and bluehead sucker arrivals occurred early- to mid-April. With humpback chub and flannelmouth sucker, movement timing was influenced by body size so that large individuals were more likely to arrive early compared to smaller individuals. With more years of data, this model framework could be used to evaluate ecological questions pertaining to flow cues and movement timing or intensity, relative trends in migrants versus residents and ecological drivers of skipped spawning.

Vegetation biomass and topography are associated with seasonal habitat selection and fall translocation behavior in Arctic hares.

Habitat selection theory suggests that environmental features selected at coarse scales reveal fundamental factors affecting animal fitness. When these factors vary across seasons, they may lead to large-scale movements, including long-distance seasonal migrations. We analyzed the seasonal habitat selection of 25 satellite-tracked Arctic hares from a population on Ellesmere Island (Nunavut, Canada) that relocated over 100 km in the fall. Since no other lagomorph is known to perform such extensive movements, this population offered an ideal setting to test animal movement and habitat selection theory. On summer grounds hares selected low elevation areas, while on winter grounds they selected high vegetation biomass, high elevation, and steep slopes. During fall relocation, they alternated between stopover and traveling behavioral states (ratio 2:1). Stopover locations were characterized by higher vegetation heterogeneity and lower rugosity than traveling locations, while vegetation biomass and elevation interacted to explain stopover locations in a more complex way. The selected combination of environmental features thus varied across seasons and behavioral states, in a way broadly consistent with predictions based on the changing food and safety needs of hares. Although causality was not demonstrated, our results improve our understanding of long-distance movements and habitat selection in Arctic hares, as well as herbivore ecology in the polar desert. Results also provide strong support to animal movement and habitat selection theory, by showing how some important hypotheses hold when tested in a species phylogenetically distinct from most animal models used in this research field.

KRISHI MITRA – A SMART FARMING SYSTEM THROUGH AI

The concept of smart agriculture is still in its infancy, since Internet of Things (IOT) sensors may gather data about agricultural fields and behave accordingly based on human input. The development of a smart agriculture system that takes advantage of cutting edge technologies like Arduino, IOT, and wireless sensor networks is suggested in this paper. The goal of the article is to employ automation and emerging technologies, such as IOT and smart agriculture. One of the main ways to increase the production of productive crops is to monitor the surrounding conditions. This paper's highlight is the development of a system that uses sensors utilizing an Arduino board to monitor temperature, humidity, moisture, and even the movement of animals that could kill crops in agricultural fields. If there is any disparity, the system will be notified.

Changes in microflora at high concentrations of pets in urban environments: assessment of risk to the environment and human health

Microbiological studies on soil samples from gardens for walking people and pets in two large neighbourhoods of the city of Sofia were carried out. The quantities of microorganisms from main groups, including pathogenic species, were determined with a view to evaluating the epizootological safety of these areas. Especially high were the values of the isolated Gram-negative bacteria (4.33x104 ±1.88 and 2.70x105 ±2.54), of Pseudomonas aeruginosa (1.75x104 ±3.03 and 3.00x104 ±4.58), of staphylococci, including pathogenic mannitol-positive species (3.48x106 ±2.99 and 4.95 x106 ±3.96), as well as of the fungi. Oval fungi (5.50x104 ±3.77 and 1.55x105 ±1.12) and the causative agents of dermatomycosis tinea or ringworm (2.75x104 ±1.92 and 7.60x104 ±3.72) were present in significant quantities. However, the important sanitary indicative bacteria E. coli and C. perfringens were below the detectable minimum. It was found that in the area with greater movement of people and animals, the amounts of microorganisms from all the studied groups were higher. The results obtained show that urban areas with high traffic from pets and their owners can be a source of infection for animals and humans.

Performance of Cow Evaluation System Elements in Simulated Environmental Conditions

Animal phenotype assessment plays a crucial role in enhancing the genetic potential of domestic breeds. Currently, research is underway to digitize and automate phenotyping through optical systems, enabling the recognition and evaluation of animals’ linear parameters. A prototype of a non-contact monitoring system for livestock phenotype indicators is currently under development. The movement of animals within the model (a laboratory measuring stand) is restricted by a specially designed transparent barrier made of polyethylene terephthalate, enabling the capture of three-dimensional photographs of the animals’ exterior. To validate the suitability of glass for this purpose, laboratory tests were conducted. (Research purpose) The research aims to determine the degree of relationship between protective glass contamination and the accuracy of the optical animal recognition system. (Materials and methods) The paper outlines the specifications of the laboratory stand, facilities, and equipment used, along with the methods employed and experimental conditions. The probability of determining the degree of protective glass contamination was quantified on a scale of 0 to 1, with a value of 0.78 indicating a high likelihood of accurate determination. (Results and discussion) The findings reveal that the system for determining the linear parameters of animals can operate reliably even when the protective glass is contaminated up to 30 percent. When the contamination reaches 50 percent due to inadequate glass cleaning, the system’s ability to recognize points of interest reduces by a factor of 1.625. Furthermore, at 80 percent contamination, achieving high-quality data collection becomes unfeasible as the camera fails to recognize the object. Proper cleaning of the glass is imperative to maintain the system stability. (Conclusions) The optical system enables the recognition and evaluation of animals’ linear parameters, provided that the protective glass of the laboratory stand is contaminated by no more than 50 percent and undergoes high-quality cleaning. At lower levels of glass contamination, up to 30 percent, these estimates exhibit a 2.6-38 percent increase compared to other contamination levels.

Identification of suspicious behavior through anomalies in the tracking data of fishing vessels

Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources.

Assessing One Health capacities for transboundary zoonotic diseases at the Libya-Tunisia border

BackgroundThe dynamic nature of zoonotic emergence, spillover and spread necessitates multisectoral coordination beyond national borders to encompass cross-boundary and regional cooperation. Designated points of entry (POEs), specifically ground crossings, serve as critical locales for establishing and maintaining robust prevention, detection, notification, coordination, and response mechanisms to transboundary emerging and re-emerging disease threats. In order to better assess One Health capacities for transboundary zoonotic diseases (TZD) prevention, detection and response we adapted an existing tool, One Health Systems Assessment for Priority Zoonoses (OHSAPZ), for a cross-border, POE setting in North Africa.MethodsThe One Health Transboundary Assessment for Priority Zoonoses (OHTAPZ) tool was used to support prioritization of transboundary zoonoses and analyze operational capacities between national and subnational-level human and animal health stakeholders from Libya and Tunisia. Country partners jointly identified and prioritized five TZDs of concern. Case study scenarios for each priority pathogen were used to elicit current disease operations, as well as multisectoral and bilateral engagement networks. Finally, a gap analysis was performed to determine bilateral strengths and weaknesses to TZDs.ResultsThe five priority TZDs jointly confirmed to undergo One Health assessment were avian influenza (low and high pathogenic strains); brucellosis; Rift Valley fever; Crimean-Congo hemorrhagic fever; and rabies. Using the qualitative information collected, a transboundary systems map schematic was developed outlining the movement of human patients, animals, diagnostic samples, and routes of communication and coordination both within and between countries for zoonotic diseases.ConclusionsAnalysis of current operations (prevention, detection, surveillance, laboratory capacity, quarantine/isolation, and response) and the resulting transboundary systems map schematic helped identify existing capacity strengths for certain priority pathogens, as well as challenges to timely information-sharing and coordination. We developed targeted recommendations to address these limitations for joint action planning between Libya and Tunisia.

Learning Rich Feature Representation and State Change Monitoring for Accurate Animal Target Tracking.

Animal tracking is crucial for understanding migration, habitat selection, and behavior patterns. However, challenges in video data acquisition and the unpredictability of animal movements have hindered progress in this field. To address these challenges, we present a novel animal tracking method based on correlation filters. Our approach integrates hand-crafted features, deep features, and temporal context information to learn a rich feature representation of the target animal, enabling effective monitoring and updating of its state. Specifically, we extract hand-crafted histogram of oriented gradient features and deep features from different layers of the animal, creating tailored fusion features that encapsulate both appearance and motion characteristics. By analyzing the response map, we select optimal fusion features based on the oscillation degree. When the target animal's state changes significantly, we adaptively update the target model using temporal context information and robust feature data from the current frame. This updated model is then used for re-tracking, leading to improved results compared to recent mainstream algorithms, as demonstrated in extensive experiments conducted on our self-constructed animal datasets. By addressing specific challenges in animal tracking, our method offers a promising approach for more effective and accurate animal behavior research.

Potent small molecule inhibitors against the 3C protease of foot-and-mouth disease virus.

Foot-and-mouth disease (FMD) is one of the most devastating diseases of livestock which can cause significant economic losses, especially when introduced to FMD-free countries. FMD virus (FMDV) belongs to the family Picornaviridae and is antigenically heterogeneous with seven established serotypes. The prevailing preventive and control strategies are limited to restriction of animal movement and elimination of infected or exposed animals, which can be potentially combined with vaccination. However, FMD vaccination has limitations including delayed protection and lack of cross-protection against different serotypes. Recently, antiviral drug use for FMD outbreaks has increasingly been recognized as a potential tool to augment the existing early response strategies, but limited research has been reported on potential antiviral compounds for FMDV. FMDV 3C protease (3Cpro) cleaves the viral-encoded polyprotein into mature and functional proteins during viral replication. The essential role of viral 3Cpro in viral replication and the high conservation of 3Cpro among different FMDV serotypes make it an excellent target for antiviral drug development. We have previously reported multiple series of inhibitors against picornavirus 3Cpro or 3C-like proteases (3CLpros) encoded by coronaviruses or caliciviruses. In this study, we conducted structure-activity relationship studies for our in-house focused compound library containing 3Cpro or 3CLpro inhibitors against FMDV 3Cpro using enzyme and cell-based assays. Herein, we report the discovery of aldehyde and α-ketoamide inhibitors of FMDV 3Cpro with high potency. These data inform future preclinical studies that are related to the advancement of these compounds further along the drug development pathway.IMPORTANCEFood-and-mouth disease (FMD) virus (FMDV) causes devastating disease in cloven-hoofed animals with a significant economic impact. Emergency response to FMD outbreaks to limit FMD spread is critical, and the use of antivirals may overcome the limitations of existing control measures by providing immediate protection for susceptible animals. FMDV encodes 3C protease (3Cpro), which is essential for virus replication and an attractive target for antiviral drug discovery. Here, we report a structure-activity relationship study on multiple series of protease inhibitors and identified potent inhibitors of FMDV 3Cpro. Our results suggest that these compounds have the potential for further development as FMD antivirals.

ORTEGA v1.0: an open-source Python package for context-aware interaction analysis using movement data

BackgroundInteraction analysis via movement in space and time contributes to understanding social relationships among individuals and their dynamics in ecological systems. While there is an exciting growth in research in computational methods for interaction analysis using movement data, there remain challenges regarding reproducibility and replicability of the existing approaches. The current movement interaction analysis tools are often less accessible or tested for broader use in ecological research.ResultsTo address these challenges, this paper presents ORTEGA, an Object-oRiented TimE-Geographic Analytical tool, as an open-source Python package for analyzing potential interactions between pairs of moving entities based on the observation of their movement. ORTEGA is developed based on one of the newly emerged time-geographic approaches for quantifying space-time interaction patterns among animals. A case study is presented to demonstrate and evaluate the functionalities of ORTEGA in tracing dynamic interaction patterns in animal movement data. Besides making the analytical code and data freely available to the community, the developed package also offers an extension of the existing theoretical development of ORTEGA for incorporating a context-aware ability to inform interaction analysis.ConclusionsORTEGA contributes two significant capabilities: (1) the functions to identify potential interactions (e.g., encounters, concurrent interactions, delayed interactions) from movement data of two or more entities using a time-geographic-based approach; and (2) the capacity to compute attributes of potential interaction events including start time, end time, interaction duration, and difference in movement parameters such as speed and moving direction, and also contextualize the identified potential interaction events.