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Social interactions involve multi-modal signaling. Here, we study interacting rats to investigate audio-haptic coordination and multisensory integration in the auditory cortex. We find that facial touch is associated with an increased rate of ultrasonic vocalizations, which are emitted at the whisking rate (∼8 Hz) and preferentially initiated in the retraction phase of whisking. In a small subset of auditory cortex regular-spiking neurons, we observed excitatory and heterogeneous responses to ultrasonic vocalizations. Most fast-spiking neurons showed a stronger response to calls. Interestingly, facial touch-induced inhibition in the primary auditory cortex and off-responses after termination of touch were twofold stronger than responses to vocalizations. Further, touch modulated the responsiveness of auditory cortex neurons to ultrasonic vocalizations. In summary, facial touch during social interactions involves precisely orchestrated calling-whisking patterns. While ultrasonic vocalizations elicited a rather weak population response from the regular spikers, the modulation of neuronal responses by facial touch was remarkably strong. Rats are highly social creatures, preferring to live in large groups within an established hierarchy. Social interactions range from play, mating, and parental care to displays of aggression and dominance and involve the use of odors, touch, and vocal calls. Touch typically takes the form of snout-to-snout contact, while most vocalizations are ultrasonic, with calls of different frequencies used to signal alarm or pleasure. To date, most studies of rat vocalizations have involved playback of recorded calls to anaesthetized animals, and relatively little is known about how freely moving rats respond to calls. Rao et al. have now addressed this question by recording video footage of rats interacting with other animals or with objects and then using electrodes to record signals in the brains of these rats. The video footage revealed that rats produce more vocal calls during social interactions than they do during non-social interactions. Moreover, bursts of calls appear to signal the beginning and end of bouts of snout-to-snout contact, suggesting that rodent communication involves the coordinated use of both tactile and vocal cues. Surprisingly, electrode recordings from the part of the brain that responds to sound—the auditory cortex—revealed that most neurons in this region did not respond to ultrasonic calls. However, a type of neuron called a fast-spiking neuron did respond strongly to these calls. The work of Rao et al. shows that information from multiple senses is directly combined early in the processing of sensory information. Exactly why tactile stimuli should inhibit the auditory cortex is not clear, but there is some evidence that this may increase the rat's sensitivity to sounds. Further experiments are required to test this possibility and to determine how integrating information from multiple senses affects rodent behavior. This will help us to understand how the brain generates coherent social behaviour from signals arriving through distinct sensory channels.

Locomotor kinematics have been challenging inputs for automated diagnostic screening of livestock. Locomotion is a highly variable behavior, and influenced by subject characteristics (e.g., body mass, size, age, disease). We assemble a set of methods from different scientific disciplines, composing an automatic, high through-put workflow which can disentangle behavioral complexity and generate precise individual indicators of non-normal behavior for application in diagnostics and research. For this study, piglets ( Sus domesticus ) were filmed from lateral perspective during their first 10 h of life, an age at which maturation is quick and body mass and size have major consequences for survival. We then apply deep learning methods for point digitization, calculate joint angle profiles, and apply information-preserving transformations to retrieve a multivariate kinematic data set. We train probabilistic models to infer subject characteristics from kinematics. Model accuracy was validated for strides from piglets of normal birth weight (i.e., the category it was trained on), but the models infer the body mass and size of low birth weight (LBW) piglets (which were left out of training, out-of-sample inference) to be “normal.” The age of some (but not all) low birth weight individuals was underestimated, indicating developmental delay. Such individuals could be identified automatically, inspected, and treated accordingly. This workflow has potential for automatic, precise screening in livestock management.

In conventional geometric morphometric analyses of limb long bones, differences in the evolutionary capacity of articular surfaces and non-articular structures often remain unrecognised. It can be shown that areas of high spatial variance dominate shape data, which is problematic for the functional interpretation of limb long bone shape. We herein introduce Procrustes superimposition by static reference (PSSR), a novel analysis strategy that aims to facilitate morpho-functional inference. This procedure exploits the spatial constraint of some reference structures (in our case, articular surfaces) for the superimposition of other subareas (e.g. muscle attachment sites) in relation to that static reference. PSSR allows for the transformation of raw scan data, enabling researchers to extract geometric models of two- and three-dimensional substructures that cannot effectively be integrated with landmarks. As we demonstrate by a simple model analysis for one muscle attachment site, this procedure can yield measures of direct functional relevance. Multivariate analysis of an extensive set of subareas indicates how this type of data relates to conventional shape coordinates. The shape evolution of xenarthran humeri, which has previously been subject to a detailed study (Milne et al., J Zool 278(1):48–56, 2009), serves as a test case. The concept of a variance-based separation of landmark subsets expands mathematical methods by incorporating knowledge about evolutionary constraints. PSSR could therefore find application far beyond the intuitive case study of long bone shape.

Digitization of video recordings often requires the laborious procedure of manually clicking points of interest on individual video frames. Here, we present progressive tracking, a procedure that facilitates manual digitization of markerless videos. In contrast to existing software, it allows the user to follow points of interest with a cursor in the progressing video, without the need to click. To compare the performance of progressive tracking with the conventional frame-wise tracking, we quantified speed and accuracy of both methods, testing two different input devices (mouse and stylus pen). We show that progressive tracking can be twice as fast as frame-wise tracking while maintaining accuracy, given that playback speed is controlled. Using a stylus pen can increase frame-wise tracking speed. The complementary application of the progressive and frame-wise mode is exemplified on a realistic video recording. This study reveals that progressive tracking can vastly facilitate video analysis in experimental research.

The 3D musculoskeletal motion of animals is of interest for various biological studies and can be derived from X-ray fluoroscopy acquisitions by means of image matching or manual landmark annotation and mapping. While the image matching method requires a robust similarity measure (intensity-based) or an expensive computation (tomographic reconstruction-based), the manual annotation method depends on the experience of operators. In this paper, we tackle these challenges by a strategic approach that consists of two building blocks: an automated 3D landmark extraction technique and a deep neural network for 2D landmarks detection. For 3D landmark extraction, we propose a technique based on the shortest voxel coordinate variance to extract the 3D landmarks from the 3D tomographic reconstruction of an object. For 2D landmark detection, we propose a customized ResNet18-based neural network, BoneNet, to automatically detect geometrical landmarks on X-ray fluoroscopy images. With a deeper network architecture in comparison to the original ResNet18 model, BoneNet can extract and propagate feature vectors for accurate 2D landmark inference. The 3D poses of the animal are then reconstructed by aligning the extracted 2D landmarks from X-ray radiographs and the corresponding 3D landmarks in a 3D object reference model. Our proposed method is validated on X-ray images, simulated from a real piglet hindlimb 3D computed tomography scan and does not require manual annotation of landmark positions. The simulation results show that BoneNet is able to accurately detect the 2D landmarks in simulated, noisy 2D X-ray images, resulting in promising rigid and articulated parameter estimations.

Digitization of video recordings often requires the laborious procedure of manually clicking points of interest on individual video frames. Here, we present progressive tracking, a procedure that facilitates manual digitization of markerless videos. In contrast to existing software, it allows the user to follow points of interest with a cursor in the progressing video, without the need to click. To compare the performance of progressive tracking with the conventional frame-wise tracking, we quantified speed and accuracy of both methods, testing two different input devices (mouse and stylus pen). We show that progressive tracking can be twice as fast as frame-wise tracking while maintaining accuracy, given that playback speed is controlled. Using a stylus pen can increase frame-wise tracking speed. The complementary application of the progressive and frame-wise mode is exemplified on a realistic video recording. This study reveals that progressive tracking can vastly facilitate video analysis in experimental research.

Locomotor kinematics have been challenging inputs for automated diagnostic screening of livestock. Locomotion is a highly variable behavior, and influenced by subject characteristics (e.g. body mass, size, age, disease). We assemble a set of methods from different scientific disciplines, composing an automatic, high through-put workflow which can disentangle behavioral complexity and generate precise individual indicators of non-normal behavior for application in diagnostics and research. For this study, piglets (/Sus domesticus/) were filmed from lateral perspective during their first ten hours of life, an age at which maturation is quick and body mass and size have major consequences for survival. We then apply deep learning methods for point digitization, calculate joint angle profiles, and apply information-preserving transformations to retrieve a multivariate kinematic data set. We train probabilistic models to infer subject characteristics from kinematics. Model accuracy is validated for strides from piglets of normal birth weight (i.e. the category it was trained on), but the models infer the body mass and size of low birth weight piglets (which were left out of training, out-of-sample inference) to be \"normal\". The age of some (but not all) low birth weight individuals is underestimated, indicating developmental delay. Such individuals could be identified automatically, inspected, and treated accordingly. This workflow has potential for automatic, precise screening in livestock management.Competing Interest StatementThe authors have declared no competing interest.Footnotes* The data and results have been re-worked (with minor technical changes) and the manuscript reflected to adjust this. There was an error in the data table, which affected the ankle and wrist joints, but results of the modeling are in overall agreement with the previous submission. The Intro and Discussion were revised for submission in a veterinary-focused journal. Methods were adjusted slightly to facilitate understanding for the new target audience. The previous review did suggest a major revision, including a split of the MS into a methods paper and a piglet-focused paper, which we found unjustified and therefore retracted the submission.* https://git.sr.ht/~falk/piglet_fcas

Altered mechanical loading is a known risk factor for osteoarthritis. Destabilization of the medial meniscus (DMM) is a preclinical gold standard model for post-traumatic osteoarthritis and is thought to induce instability and locally increased loading. However, the joint- and tissue-level mechanical environment underlying cartilage degeneration remains poorly documented. Using a custom multiscale modeling approach, we assessed joint and tissue biomechanics in rats undergoing sham surgery and DMM. High-fidelity experimental gait data were collected in a setup combining biplanar fluoroscopy and a ground reaction force plate. Knee poses and joint-level loading were estimated through musculoskeletal modeling, using bony landmarks, semi-automatically tracked via deep learning on fluoroscopic images, and ground reaction forces. A musculoskeletal model of the rat hindlimb was adapted to represent knee flexion-extension, valgus-varus, and internal-external rotation. The tissue-level cartilage mechanical environment was then spatially estimated, using the musculoskeletal modeling parameters as inputs into a dedicated finite element (FE) model of the rat knee, comprising cartilage and meniscal tissues. Experimental gait data and modeling workflows, including musculoskeletal models and FE meshes, are openly shared through a data repository. In rats with DMM, the frontal plane knee pose was altered, yet there was no indication of joint-level overloading. Tissue-level mechanical cues typically linked with cartilage degeneration were not increased in the medial tibial cartilage, despite evidence of tissue structural changes. DMM did not increase joint and tissue mechanical responses in the knee medial compartment, suggesting that mechanical loading alone does not explain the observed osteoarthritis-like structural changes.

Background Prolonged sitting time is a risk factor for chronic disease, yet recent global surveillance is not well described. The aims were to clarify: (i) the countries that have collected country-level data on self-reported sitting time; (ii) the single-item tools used to collect these data; and (iii) the duration of sitting time reported across low- to high-income countries. Methods Country-level data collected within the last 10 years using single-item self-report were included. The six-stage methodology: (1) reviewing Global Observatory for Physical Activity! Country Cards; (2–4) country-specific searches of PubMed, the Demographic and Health Survey website and Google; (5) analysing the Eurobarometer 88.4; and (6) country-specific searches for World Health Organization STEPwise reports. Results A total of 7641 records were identified and screened for eligibility. Sixty-two countries (29%) reported sitting time representing 47% of the global adult population. The majority of data were from high-income (61%) and middle income (29%) countries. The tools used were the International Physical Activity Questionnaire (IPAQ; n  = 34), a modified IPAQ ( n  = 1) or the Global Physical Activity Questionnaire (GPAQ; n  = 27). The median of mean daily sitting times was 4.7 (IQR: 3.5–5.1) hours across all countries. Higher-income countries recorded a longer duration of sitting time than lower-income countries (4.9 vs 2.7 h). Conclusions This study provides an updated collation of countries collecting self-reported sitting time data. The daily sitting time findings should be interpreted cautiously. Current surveillance of sitting time is limited by a lack of coverage. Measures of population sitting time that are valid, feasible and sensitive to change should be embedded within global surveillance systems, to help guide future policy, research and practice. Trial registration Not applicable.

The amount of methane released to the atmosphere from the Nord Stream subsea pipeline leaks remains uncertain, as reflected in a wide range of estimates1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18. A lack of information regarding the temporal variation in atmospheric emissions has made it challenging to reconcile pipeline volumetric (bottom-up) estimates1,2,3,4,5,6,7,8 with measurement-based (top-down) estimates8,9,10,11,12,13,14,15,16,17,18. Here we simulate pipeline rupture emission rates and integrate these with methane dissolution and sea-surface outgassing estimates9,10 to model the evolution of atmospheric emissions from the leaks. We verify our modelled atmospheric emissions by comparing them with top-down point-in-time emission-rate estimates and cumulative emission estimates derived from airborne11, satellite8,12,13,14 and tall tower data. We obtain consistency between our modelled atmospheric emissions and top-down estimates and find that 465 ± 20 thousand metric tons of methane were emitted to the atmosphere. Although, to our knowledge, this represents the largest recorded amount of methane released from a single transient event, it is equivalent to 0.1% of anthropogenic methane emissions for 2022. The impact of the leaks on the global atmospheric methane budget brings into focus the numerous other anthropogenic methane sources that require mitigation globally. Our analysis demonstrates that diverse, complementary measurement approaches are needed to quantify methane emissions in support of the Global Methane Pledge19.