Miracle-dz/UniFAR-FTUs huggingface dataset:
Maintenance of ankle power and decreased inter-limb asymmetry during sprint running in fatigued multi-speed athletes

The ability to shift from walking and jogging to sprinting gaits, even when fatigued after prolonged effort, would have been as useful to our hunter-gatherer ancestors as it is in modern day sports. During prolonged jogging, joint moment and work are reduced in the distal (ankle) joint but increased at proximal (hip/knee) joints as fatigue progresses, and might be expected to occur in sprinting. Fatigue is also thought to increase inter-limb kinematic and force production asymmetries, which are speculated to influence injury risk. However, the effects of running-related fatigue on sprint running gait have been incompletely studied, so these hypotheses remain untested. We studied 3-D kinematics and ground reaction force production in dominant (DL) and non-dominant (NDL) legs during both non-fatigued and fatigue sprinting in habitual but uncoached running athletes. Contrary to the tested hypotheses, relative between-leg differences were greater in non-fatigued than fatigued sprinting. When not fatigued, DL produced greater propulsive impulse through both greater positive and negative work being performed at the ankle, whilst NDL produced more vertical impulse, possibly resulting from the greater hip flexion observed prior to the downwards acceleration of the foot towards the ground. Whilst few changes were detected in DL once fatigued, NDL shifted towards greater horizontal force production, largely resulting from an increase in plantarflexion (distal-joint) moments and power. After fatiguing running, therefore, inter-limb asymmetry was reduced and no distal-to-proximal shift in work/power was detected during sprinting. Speculatively, these adaptations may help to attenuate decreases in running speed whilst minimising injury risk. Significance The ability to attain fast running speeds may critically determine success in tasks such as prey chase- and-capture in hunter-gatherer societies as well as success in modern sports competitions. At times, sprint running may have to be performed whilst fatigued from previous, longer-distance running, when speeds are reduced and injury risk may be higher. Previous work indicated that fatigue may trigger a proximal shift in joint work and power production and increase inter-limb asymmetry. On the contrary, we show that relative ankle positive and negative joint work was maintained in the face of fatigue and that inter-limb asymmetry was reduced in a group of runners experienced, but not formally instructed, in both long-distance and sprint running.
V-type H+ ATPase Activity is Required for Embryonic Dorsal-Ventral Symmetry Breaking

The mechanism for embryonic dorsal-ventral (DV) symmetry breaking is idiosyncratic to the species, then converges on polarized expression of BMP signaling ligands. Here, we show that V-ATPase (VHA) activity is an early requirement for DV symmetry breaking in sea urchin embryos. In these basal deuterostomes, DV specification is mediated by ventral Nodal expression that leads to the establishment of a BMP signaling gradient. Nodal expression occurs downstream from p38 MAPK, which is transiently asymmetrically active. We show that VHA activity is required for DV symmetry breaking upstream from both p38 MAPK and Nodal. We rescue VHA-mediated ventralization by enforcing Nodal signaling asymmetry. We identify a VHA-dependent DV voltage gradient and also find that VHA activity is required for hypoxia inducible factor (HIF) activation. However, neither hyperpolarization nor HIF activation account for the dorsalizing effects of VHA, implicating a third unknown pathway that connects VHA activity to p38 MAPK symmetry breaking. Graphical Abstract
Active reconfiguration of cytoplasmic lipid droplets governs migration of nutrient-limited phytoplankton

As open oceans continue to warm, modified currents and enhanced stratification exacerbate nitrogen and phosphorus limitation, constraining primary production. The ability to migrate vertically bestows motile phytoplankton a crucial–albeit energetically expensive–advantage toward vertically redistributing for optimal growth, uptake and resource storage in nutrient-limited water columns. However, this traditional view discounts the possibility that the phytoplankton migration strategy may be actively selected by the storage dynamics when nutrients turn limiting. Here we report that storage and migration in phytoplankton are coupled traits, whereby motile species harness energy storing lipid droplets (LDs) to biomechanically regulate migration in nutrient limited settings. LDs grow and translocate–directionally–within the cytoplasm to accumulate below the cell nucleus, tuning the speed, trajectory and stability of swimming cells. Nutrient reincorporation reverses the LD translocation, restoring the homeostatic migratory traits measured in population-scale millifluidic experiments. Combining intracellular LD tracking and quantitative morphological analysis of red-tide forming alga, Heterosigma akashiwo, along with a model of cell mechanics, we discover that the size and spatial localization of growing LDs govern the ballisticity and orientational stability of migration. The strain-specific shifts in migration which we identify here are amenable to a selective emergence of mixotrophy in nutrient-limited phytoplankton. We rationalize these distinct behavioral acclimatization in an ecological context, relying on concomitant tracking of the photophysiology and reactive oxygen species (ROS) levels, and propose a dissipative energy budget for motile phytoplankton alleviating nutrient limitation. The emergent resource acquisition strategies, enabled by distinct strain-specific migratory acclimatizing mechanisms, highlight the active role of the reconfigurable cytoplasmic LDs in guiding vertical movement. By uncovering the mechanistic coupling between dynamics of intracellular changes to physiologically-governed migration strategies, this work offers a tractable framework to delineate diverse strategies which phytoplankton may harness to maximize fitness and resource pool in nutrient-limited open oceans of the future. One sentence summary Phytoplankton harness reconfigurable lipid droplets to biomechanically tune migratory strategies in dynamic nutrient landscapes.
Circulatory Neutrophils Exhibit Enhanced Neutrophil Extracellular Trap Formation in Early Puerperium: NETs at the Nexus of Thrombosis and Immunity

Pregnancy is associated with elevated maternal levels of cell-free DNA of neutrophil extracellular trap (NET) origin, as circulatory neutrophils exhibit increased spontaneous NET formation, mainly driven by G-CSF and finely modulated by sex hormones. The postpartum period, on the other hand, involves physiological alterations consistent with the need for protection against infections and fatal haemorrhage. Our findings indicate that all relevant serum markers of neutrophil degranulation and NET release are substantially augmented postpartum. Neutrophil pro-NETotic activity in vitro is also upregulated particularly in post-delivery neutrophils. Moreover, maternal puerperal neutrophils exhibit a strong pro-NETotic phenotype, associated with increased levels of all key players in the generation of NETs – citH3, MPO, NE and ROS, compared to non-pregnant and pregnant controls. Intriguingly, post-delivery NET formation is independent of G-CSF in contrast to late gestation and complemented by the presence of TF on the NETs, alterations in the platelet activity status, and activation of the coagulation cascade, triggered by circulating microparticles. Taken together, our results reveal the highly pro-NETotic and potentially procoagulant nature of postpartum neutrophils, bridging an overt immune activation with possible harmful thrombotic incidence.
PathoClock and PhysioClock in mice recapitulate human multimorbidity and heterogeneous aging

Multimorbidity is a public health concern and an essential component of aging and healthspan but understudied because investigative tools are lacking that can be translatable to capture similarities and differences of the aging process across species and variability between individuals and individual organs. To help address this need, body organ disease number (BODN) borrowed from human studies was applied to C57BL/6 (B6) and CB6F1 mouse strains at 8, 16, 24 and 32 months of age, as a measure of systems morbidity based on pathology lesions to develop a mouse PathoClock resembling clinically based Body Clock in humans, using Bayesian inference. A mouse PhysioClock was also developed based on measures of physiological domains including cardiovascular, neuromuscular, and cognitive function in the same two mouse strains so that alignment with BODN was predictable. The results revealed between- and within-age variabilities in PathoClock and PhysioClock, as well as between-strain variabilities. Both PathoClock and PhysioClock correlated with chronological age more strongly in CB6F1 than C57BL/6. Prediction models were then developed, designated as PathoAge and PhysioAge, using regression models of pathology and physiology measures on chronological age. PathoAge better predicted chronological age than PhysioAge as the predicted chronological and observed chronological age for PhysioAge were complex rather than linear. In conclusion, PathoClock and PhathoAge can be used to capture biological changes that predict BODN, a metric developed in human, and compare multimorbidity across species. These mouse clocks are potential translational tools that could be used in aging intervention studies.
The role of oxytocin in modulating self-other distinction in human brain: a pharmacological fMRI study

The self-other distinction is crucial in human social cognition and social interaction. Studies have found that oxytocin (OT) sharpens the self-other perceptual boundary but with mixed results. Further, little is known if the effect of OT on self-resemblance facial perception exists, especially on its neural basis. Moreover, it is unclear if OT would influence the judgment in self-other discrimination when the other is a child or an adult. In the current double-blinded, placebo-controlled study, we investigated the effect of OT on self-face perception at both behavioral and neural levels. We morphed participants’ faces and strangers’ faces to create four stimuli conditions. After being treated by either OT or placebo (PL), participants reported whether a morphed face resembles themselves, or was morphed with their own faces, while being scanned with fMRI. Behavioral results showed that people judged adult-morphed faces better than child-morphed faces. fMRI results showed that the OT group exhibited generally increased activities in the visual area and IFG for self-morphed faces. Such difference was more pronounced in the adult face compared to child face conditions. Multivariate fMRI analysis revealed that the OT group showed better classification between self-morphed versus other-morphed faces, indicating that OT increased self-other distinction, especially for adult faces and in the left hemisphere. Our study shows the significant effect of OT on self-referential brain processes, providing evidence for the potential OT’s effect on a left hemisphere self network.
Aerosolization of Mycobacterium tuberculosis by Tidal Breathing

Rationale Interrupting tuberculosis (TB) transmission requires an improved understanding of how – and when – the causative organism, Mycobacterium tuberculosis (Mtb), is aerosolized. Although Cough is commonly assumed to be the dominant source of Mtb aerosols, recent evidence of Cough-independent Mtb release implies the contribution of alternative mechanisms. Objective To compare the aerosolization of Mtb and particulate matter from GeneXpert-positive patients during three separate respiratory manoeuvres: Tidal Breathing (TiBr), Forced Vital Capacity (FVC), and Cough. Methodology Bioaerosol sampling and Mtb detection were combined with real-time assessments of CO2 production and particle counts from 39 confirmed TB patients. Measurements and Main Results TiBr and FVC produced comparable numbers of particles, with Cough producing >4-fold more. For all manoeuvres, the proportions of particles detected across size categories from 0.5 – 5 μm were similar, with minor differences observed only in particles between 1.5 – 2 μm (p = 0.014) and >5 μm (p = 0.020). Viable Mtb bacilli were detected in 66%, 70%, and 65% of TiBr, FVC, and Cough samples, respectively. Notably, while Cough produced 3-fold more Mtb than TiBr, the relative infrequency of coughing compared to breathing implies that TiBr likely contributes >90% of the daily aerosolised Mtb across a range of Cough frequencies. Conclusions Our results suggest that, while Cough increases particle aerosolization compared to TiBr, this is not associated with increased Mtb aerosolization. Instead, TiBr produces more Mtb per particle than Cough. Assuming the number of viable Mtb organisms detected provides a proxy measure of patient infectiousness, these observations imply a significant contribution of TiBr to TB transmission.
A common neural currency account for social and non-social decisions

To date, social and non-social decisions have been studied in isolation. Consequently, the extent to which social and non-social forms of decision uncertainty are integrated using shared neurocomputational resources remains elusive. Here, we address this question using simultaneous EEG-fMRI and a task in which decision evidence in social and non-social contexts varies along comparable scales. First, we identify comparable time-resolved build-up of activity in the EEG, akin to a process of evidence accumulation. We then use the endogenous trial-by-trial variability in the slopes of these accumulating signals to construct parametric fMRI predictors. We show that a region of the posterior-medial frontal cortex (pMFC) uniquely explains trial-wise variability in the process of evidence accumulation in both the social and non-social contexts. We further demonstrate a task-dependent coupling between the pMFC and regions of the human valuation system in dorso- and ventro-medial prefrontal cortex (dmPFC/vmPFC) across both contexts. Finally, we report domain-specific representations in regions known to encode the early decision evidence for each context. These results are suggestive of a domain-general decision-making architecture, whereupon domain-specific information is likely converted into a “common currency” in the dmPFC/vmPFC and accumulated for the decision in the pMFC.
Technology Assessment of Aperture Coupled Slot Antenna Array in Groove Gapwaveguide for 5G Millimeter Wave Applications

A compact triple-layer series fed slot unit array using Groove Gap Waveguide (GGW) technology for 5G millimeter-wave applications is presented in this paper. The design employed a two-step coupling mechanism for the radiating slots. The aperture coupling design when combined with the groove gapWaveguide (GGW) technology allows achieving three different goals: First, a reduction of the array size in the transversal direction, so more unit arrays can be added without grating lobes. Secondly, low insertion losses with high radiation efficiency. And third, a reduction of the manufacturing complexity with good stability against fabrication tolerances of up to 0.1 mm, making the design feasible to be massively deployed. The antenna was fabricated and measured showing good agreement with the numerical simulation results, the antenna provides a peak realized gain of 12.15dB at the central frequency of operation and a fractional bandwidth (FBW) of 9%.
Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.
Modeling of the bulk tobacco flue-curing process using a deep learning-based method

The research of the bulk tobacco flue-curing process is helpful to model the intelligent bulk curing system, which is designed to implement the curing procedure without manual operation. An intelligent bulk curing method based on the convolutional neural network (CNN) named TobaccoNet was proposed, which could set the target dry-bulb temperature (TD) and the target wet-bulb temperature (TW) of the bulk curing barn according to the tobacco leaves image. The performance of the TobaccoNet is compared with the traditional manual image feature extraction method, the stacked-sparse-autoencoder (SSAE)-based deep learning method, and the other two methods applied in related references. The test results show that TobaccoNet outperforms the comparison methods in predicting TD and TW. Specifically, the correlation coefficient reaches 0.9965 and 0.9683, the mean relative error is 1.62% and 1.77%, and the root mean squared error achieves 1.061 °C and 0.8581 °C respectively. The promising results demonstrate that the TobaccoNet is effective and reliable for modeling the intelligent bulk tobacco flue-curing process. The influence of different CNN structures on the prediction accuracy of TD and TW was analyzed. From the perspective of the computational complexity and the prediction performance, the proposed sequential CNN structure is more suitable for analyzing bulk tobacco curing in this study.
A Confidence-Based Supervised-Autonomous Control Strategy for Robotic Vaginal Cuff Closure*

Autonomous robotic suturing has the potential to improve surgery outcomes by leveraging accuracy, repeatability, and consistency compared to manual operations. However, achieving full autonomy in complex surgical environments is not practical and human supervision is required to guarantee safety. In this paper, we develop a confidence-based supervised autonomous suturing method to perform robotic suturing tasks via both Smart Tissue Autonomous Robot (STAR) and surgeon collaboratively with the highest possible degree of autonomy. Via the proposed method, STAR performs autonomous suturing when highly confident and otherwise asks the operator for possible assistance in suture positioning adjustments. We evaluate the accuracy of our proposed control method via robotic suturing tests on synthetic vaginal cuff tissues and compare them to the results of vaginal cuff closures performed by an experienced surgeon. Our test results indicate that by using the proposed confidence-based method, STAR can predict the success of pure autonomous suture placement with an accuracy of 94.74%. Moreover, via an additional 25% human intervention, STAR can achieve a 98.1% suture placement accuracy compared to an 85.4% accuracy of completely autonomous robotic suturing. Finally, our experiment results indicate that STAR using the proposed method achieves 1.6 times better consistency in suture spacing and 1.8 times better consistency in suture bite sizes than the manual results.
Kernel-Based 3-D Dynamic Occupancy Mapping with Particle Tracking

Mapping three-dimensional (3-D) dynamic environments is essential for aerial robots but challenging to consider the increased dimensions in both space and time compared to 2-D static mapping. This paper presents a kernel-based 3-D dynamic occupancy mapping algorithm, K3DOM, that distinguishes between static and dynamic objects while estimating the velocities of dynamic cells via particle tracking. The proposed algorithm brings the benefits of kernel inference such as its simple computation, consideration of spatial correlation, and natural measure of uncertainty to the domain of dynamic mapping. We formulate the dynamic occupancy mapping problem in a Bayesian framework and represent the map through Dirichlet distribution to update posteriors in a recursive way with intuitive heuristics. The proposed algorithm demonstrates its promising performance compared to baseline in diverse scenarios simulated in ROS environments.
Automated Classification of Retinal OCT Images Using a Deep Multi-Scale Fusion CNN

The automated analysis of optical coherence tomography (OCT) images can play a crucial role in the diagnosis and management of retinal diseases. The wide variations of the retinal disease manifestations in terms of shape, size, texture and spatial location pose a huge challenge in designing reliable and efficient automated methods. Existing methods mostly use single-scale deep frameworks for encoding features from the OCT images to make a diagnosis decision. Such approaches potentially ignore the useful discriminative information in different scales. Therefore, in this paper, we propose a Deep Multi-scale Fusion Convolutional Neural Network (DMF-CNN) that can encode the multi-scaled disease characteristics and effectively combine them for reliable classification. Specifically, multiple CNNs with different receptive fields are used to obtain scale-specific feature representations from the OCT images. These representations are fused to mine the cross-scale powerful discriminative features for classification. A joint multi-loss optimization strategy is designed to collectively learn the scale-specific and cross-scale complementary information during training. The method is evaluated on two publicly available OCT databases (the UCSD and the NEH) and delivers state-of-the-art performance. An impressive overall accuracy of 96.03% and 99.60% is obtained for the UCSD and the NEH datasets, respectively. The outstanding performance and the improved generalization makes the method a reliable diagnostic aid for medical practitioners.
State Estimation for Hybrid Wheeled-Legged Robots Performing Mobile Manipulation Tasks

This paper introduces a general state estimation framework fusing multiple sensor information for hybrid wheeled-legged robots performing mobile manipulation tasks. At the core of the state estimator is a novel unified odometry for hybrid locomotion which can seamlessly maintain tracking and has no need to switch between stepping and rolling modes. To the best of our knowledge, the proposed odometry is the first work in this area. It is calculated based on the robot kinematics and instantaneous contact points of wheels with sensor inputs from IMU, joint encoders, joint torque sensors estimating wheel contact status, as well as RGB-D camera detecting geometric features of the terrain (e.g. elevation and surface normal vector). Subsequently, the odometry output is utilized as the motion model of a 3D Lidar map-based Monte Carlo Localization module for drift-free state estimation. As part of the framework, visual localization is integrated to provide high precision guidance for the robot movement relative to an object of interest. The proposed approach was verified thoroughly by two experiments conducted on the Pholus robot with OptiTrack measurements as ground truth.