Black kites of different age and sex show similar avoidance responses to wind turbines during migration

Populations of soaring birds are often impacted by wind-power generation. Sex and age bias in turbine collisions can exacerbate these impacts through demographic changes that can lead to population decline or collapse. While several studies have reported sex and age differences in the number of soaring birds killed by turbines, it remains unclear if they result from different abundances or group-specific turbine avoidance behaviours, the latter having severer consequences. We investigated sex and age effects on turbine avoidance behaviour of black kites ( Milvus migrans ) during migration near the Strait of Gibraltar. We tracked the movements of 135 individuals with GPS data loggers in an area with high density of turbines and then modelled the effect of proximity of turbines on bird utilization distribution (UD). Both sexes and age classes showed similar patterns of displacement, with reduced UD values in the proximity of turbines and a clear peak at 700–850 m away, probably marking the distance at which most birds turn direction to avoid approaching the turbines further. The consistency of these patterns indicates that displacement range can be used as an accurate proxy for collision risk and habitat loss, and should be incorporated in environmental impact assessment studies.

Influence of Contextual Variables in the Changes of Direction and Centripetal Force Generated during an Elite-Level Soccer Team Season

The study of the contextual variables that affect soccer performance is important to be able to reproduce the competition context during the training sessions. Therefore, the aim of the present study was to evaluate the effect of match outcome as related to goal difference (large win, >2 goals, LW; narrow win, 1–2 goals, NW; drawing, D; narrow loss, 1–2 goals, NL; or large loss, >2 goals, LL), match location (home, H; away, A; neutral, N), type of competition (international, INT; national, NAT; friendly, F), phase of the season (summer preseason, SPS; in-season 1, IS1; winter preseason, WPS; in-season 2), and the field surface (natural grass, NG; artificial turf, TF) on the change of direction (COD) and centripetal force (CentF) generated during official games. Thirty male elite-level soccer players (age: 26.57 ± 5.56 years) were assessed while using WIMU PROTM inertial devices (RealTrack Systems, Almeria, Spain) in 38 matches during the 2017–2018 season, selecting for analysis the number of COD at different intensities and the CentF, depending on the turn direction. Statistical analyses comprised a one-way ANOVA with the Bonferroni post-hoc and t-test for independent samples. The main results showed that the match outcome (ωp2 = 0.01–0.04; NW = D = NL > LL), match location (ωp2 = 0.01–0.06; A = N > H), type of competition (ωp2 = 0.01–0.02; INT > NAT > F), and period of the season (ωp2 = 0.01–0.02; SPS = IS1 = WPS > IS2) all exert some influence. No effect was found for the playing surface. Therefore, match outcome, match location, type of competition, and period of the season influence the demands of centripetal force and changes of direction. These aspects should be considered in the design of training sessions and microcycle workload planning during the season to improve competitive success.

Hippocampal spatial memory representations in mice are heterogeneously stable

The population of hippocampal neurons actively coding space continually changes across days as mice repeatedly perform tasks. Many hippocampal place cells become inactive while other previously silent neurons become active, challenging the belief that stable behaviors and memory representations are supported by stable patterns of neural activity. Active cell replacement may disambiguate unique episodes that contain overlapping memory cues, and could contribute to reorganization of memory representations. How active cell replacement affects the evolution of representations of different behaviors within a single task is unknown. We trained mice to perform a Delayed Non-Match to Place (DNMP) task over multiple weeks, and performed calcium imaging in area CA1 of the dorsal hippocampus using head-mounted miniature microscopes. Cells active on the central stem of the maze “split” their calcium activity according to the animal’s upcoming turn direction (left or right), the current task phase (study or test), or both task dimensions, even while spatial cues remained unchanged. We found that different splitter neuron populations were replaced at unequal rates, resulting in an increasing number of cells modulated by turn direction and a decreasing number of cells with combined modulation by both turn direction and task phase. Despite continual reorganization, the ensemble code stably segregated these task dimensions. These results show that hippocampal memories can heterogeneously reorganize even while behavior is unchanging.Significance statementSingle photon calcium imaging using head-mounted miniature microscopes in freely moving animals, has enabled researchers to measure the long term stability of hippocampal pyramidal cells during repeated behaviors. Previous studies have demonstrated instability of neural circuit components including dendritic spines and axonal boutons. It is now known that single units in the neuronal population exhibiting behaviorally relevant activity eventually become inactive and that previously silent neurons can quickly acquire task-relevant activity. The function of such population dynamics is unknown. We show here that population dynamics differ for cells coding distinct task dimensions, suggesting such dynamics are part of a mechanism for latent memory reorganization. These results add to a growing body of work showing that maintenance of episodic memory is an ongoing and dynamic process.

NEW APPROACH TO ADDITIONAL LEFT-TURN SECTIONS OF THE TRAFFIC SIGNAL REGULATION

Introduction. The paper describes methods of using the additional left-turn phase of regulation. The authors view the main foreign and domestic methods used in the specialization of the left turn in a separate phase of regulation. To propose a new approach to the specialization of the left-turn direction in a separate phase of regulation the authors investigate one of the most loaded intersections in Voronezh and determine the main characteristics of the traffic flow. Therefore, the authors suggest new parameters in choosing of the necessary method of traffic organization at controlled intersections.Methods and materials. The paper analyses the main methods, which were conditionally related to two approaches: the accident rate of the site and the characteristics of the traffic flow (speed and intensity of the left-turn flow). The authors explained that the new approach, which took into account such parameters as speed, vehicle delays and queue length, allowed effectively controlling the intersection due to the optimal choice of the necessary traffic organization method and due to the result of changes in the parameters by combining the intensity of the left-turn flow and forward flow.Results. The authors developed the approach to the additional left-turn section introduction at the controlled intersection based on a comparison of the left-turn intensity and forward flow and on the analysis of changes in the main characteristics of the traffic flow (speed, vehicle delays and queue length).Discussion and conclusions. The authors concluded that it is necessary to use the proposed approach at the stage of modeling, design and reorganization of the regulated area on the basis of changes in the main characteristics of the traffic flow.

Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers

Changes of directions and cutting maneuvers, including 180-degree turns, are common locomotor actions in team sports, implying high mechanical load. While the mechanics and neurophysiology of turns have been extensively studied in laboratory conditions, modern inertial measurement units allow us to monitor athletes directly on the field. In this study, we applied four supervised machine learning techniques (linear regression, support vector regression/machine, boosted decision trees and artificial neural networks) to predict turn direction, speed (before/after turn) and the related positive/negative mechanical work. Reference values were computed using an optical motion capture system. We collected data from 13 elite female soccer players performing a shuttle run test, wearing a six-axes inertial sensor at the pelvis level. A set of 18 features (predictors) were obtained from accelerometers, gyroscopes and barometer readings. Turn direction classification returned good results (accuracy >98.4%) with all methods. Support vector regression and neural networks obtained the best performance in the estimation of positive/negative mechanical work (coefficient of determination R2 = 0.42–0.43, mean absolute error = 1.14–1.41 J) and running speed before/after the turns (R2 = 0.66–0.69, mean absolute error = 0.15–018 m/s). Although models can be extended to different angles, we showed that meaningful information on turn kinematics and energetics can be obtained from inertial units with a data-driven approach.

Bikeability in Basel

“Bikeability” is becoming increasingly relevant in the field of transport- and urban planning. However, it is often unclear how bikeability is defined, let alone how it can be modeled. The goal of this project was to develop a quantitative method to model bikeability. A case study area in the city of Basel, Switzerland was selected for assessing the model. Here “bikeability” is understood as a measure of the ability and convenience in reaching important destinations by bike, based on the travel distance weighted by the perceived safety, -comfort, and -attractiveness of the streets and intersections along the routes. The underlying assumption was that cyclists try to minimize the distance traveled and maximize the perceived safety, -comfort, and -attractiveness of their route of choice. Unlike most of the previous bikeability assessments we reviewed, our method used existing route choice studies to identify attributes for quantifying cycling quality, which presumably results in a model that more accurately reflects real-life behavior. Many relevant attributes that have not been captured by previous models are included in this work, such as the high curbs of tram stops, tram tracks, and the turn direction at intersections. The method is suitable for several applications in urban planning, such as the identification of locations that need improvement and the comparison of planning measures. The current model covers conventional bikes used by commuting cyclists. However, the method could be used for E-bikes and non-commuting cyclists by applying the appropriate input values.

Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion

In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments.

Preconscious Prediction of a Driver's Decision Using Intracranial Recordings

While driving, we make numerous conscious decisions such as route and turn direction selection. Although drivers are held responsible, the neural processes that govern such decisions are not clear. We recorded intracranial EEG signals from six patients engaged in a computer-based driving simulator. Patients decided which way to turn (left/right) and subsequently reported the time of the decision. We show that power modulations of gamma band oscillations (30–100 Hz) preceding the reported time of decision (up to 5.5 sec) allow prediction of decision content with high accuracy (up to 82.4%) on a trial-by-trial basis, irrespective of subsequent motor output. Moreover, these modulations exhibited a spatiotemporal gradient, differentiating left/right decisions earliest in premotor cortices and later in more anterior and lateral regions. Our results suggest a preconscious role for the premotor cortices in early stages of decision-making, which permits foreseeing and perhaps modifying the content of real-life human choices before they are consciously made.