Miracle-dz/UniFAR-FTUs huggingface dataset:
Current and potential future research activities in adaptive structures: an ARO perspective

The Army Research Office (ARO) has been supporting projects focusing on basic research in the area of smart materials and adaptive structures over recent years. A major emphasis of the ARO's Structures and Dynamics Program has been on the theoretical, computational, and experimental analysis of smart structures and structural dynamics, damping, active control, and health monitoring as applied to rotorcraft, electromagnetic antenna structures, missiles, land vehicles, and weapon systems. The variety of research projects supported by the program have been primarily directed towards improving the ability to predict, control, and optimize the dynamic response of complex, multi-body deformable structures. The projects in the field of smart materials and adaptive structures have included multi-disciplinary research conducted by teams of several faculty members as well as research performed by individual investigators. This paper begins with a brief discussion of smart or active materials, i.e. materials having capabilities of sensing changes from the surrounding environment and actively responding to those inputs in an effective manner. Integrating these materials in structures makes them `smart', i.e. it provides them with the capability to respond to the external stimuli to compensate for undesired effects and/or to enhance the desired effects. The terms `active', `smart', `adaptive', and `intelligent' are frequently used interchangeably in this context. This discussion is followed by illustrations from several current ARO-sponsored research projects related to smart materials and adaptive structures. A summary of significant results based upon these investigations is given next. Finally, directions of potential future research in the smart materials and adaptive structures area are discussed.
The genetic architecture of the sexually selected sword ornament and its evolution in hybrid populations

Biologists since Darwin have been fascinated by the evolution of sexually selected ornaments, particularly those that reduce viability. Uncovering the genetic architecture of these traits is key to understanding how they evolve and are maintained. Here, we investigate the genetic architecture of a sexually selected ornament, the “sword” fin extension that characterizes many species of swordtail fish (Xiphophorus). Using sworded and swordless sister species of Xiphophorus, we generated a mapping population and show that the sword ornament is polygenic – with ancestry across the genome explaining substantial variation in the trait. After accounting for the impacts of genome-wide ancestry, we identify one major effect QTL that explains ∼5% of the overall variation in the trait. Using a series of approaches, we narrow this large QTL interval to a handful of likely candidate genes, including the gene sp8. Notably, sp8 plays a regulatory role in fin regeneration and harbors several derived substitutions that are predicted to impact protein function in the species that has lost the sword ornament. Furthermore, we find evidence of selection on ancestry at sp8 in four natural hybrid populations, consistent with selection against the sword in these populations.
Ecological memory mitigates negative impacts of disturbance on biomass production in benthic diatom metacommunities

Disturbance events to coastal habitats such as extreme heat events, storms, or floods have increased in magnitude and frequency in recent years due to anthropogenic climate change and the destruction of habitats. These events constitute a major threat to many ecological communities and global biodiversity. Disturbance history influences ecosystem response to novel disturbances such that communities that have previously been exposed to disturbances should be more resilient to new disturbances compared to previously sheltered communities. This principle is defined as ecological memory. Resilience should also increase with access to a larger species pool, because a larger species pool increases species and response diversity of a community. One possibility of increasing the local species pool is connectivity via adequate dispersal between habitat patches with different species compositions in metacommunities. In a laboratory experiment, we exposed benthic diatom communities of different origin to a mechanical disturbance, simulated dispersal in half of the communities, and measured their chlorophyll a concentration over time. The local diatom communities originated from different locations on an intertidal flat that varied in hydrodynamic exposure history. Hydrodynamic exposure disturbs the sediment, and thereby determines sediment properties and the composition of intertidal diatom communities. In the experiment, disturbance negatively affected chlorophyll a concentration across all treatments. However, the response to disturbance depended on the ecological memory of the communities; the more exposed areas the communities originated from, the less negative was the effect of the mechanical disturbance. Interestingly, dispersal did not mitigate the negative impacts of disturbance in any of the communities. Our results highlight the importance of ecological memory for ecosystem functioning and demonstrate the limitations of patch connectivity to alleviate the impacts of disturbance events in metacommunities.
Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

With the rapid growth of Web services, the demand for discovering the optimal services to satisfy the users’ requirements is no longer an easy task. The critical issue in the process of service discovery is to conduct a similarity calculation. To solve such an issue, this study proposes an effective approach that combines the Embeddings from Language Models (ELMo) representation and Convolutional Neural Network (CNN) to obtain a more accurate similarity score for retrieving target Web services. More specifically, first, the study adopts the ELMo model to generate effective word representations for capturing the sufficient information from services and queries. Then, the word representations are used to compose a similarity matrix, which will be taken as the input for the CNN to learn the matching relationships. Finally, the combination of the ELMo representation and CNN is used to address the representation and interaction processes within the matching task to improve the service discovery performance. The results demonstrate the effectiveness of our proposed approach for retrieving better targeted Web services.
Revealing the unique features of each individual’s muscle activation signatures (Short) Unique features of muscle activation signatures

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation approach was implemented that enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the signatures, are highly robust. Additionally, we proposed a method for visualising each individual’s muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.
Specific changes in sleep oscillations after blocking human metabotropic glutamate receptor 5 in the absence of altered memory function

Background: Sleep consolidates declarative memory by repeated replay linked to the cardinal oscillations of non-rapid eye movement (NonREM) sleep. However, there is so far little evidence of classical glutamatergic plasticity induced by this replay. Rather, we have previously reported that blocking N-methyl-D-aspartate (NMDA) or α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors does not affect sleep-dependent consolidation of declarative memory. Aims: The aim of this study was to investigate the role of metabotropic glutamate receptor 5 (mGluR5) in memory processing during sleep. Methods: In two placebo-controlled within-subject crossover experiments with 20 healthy humans each, we used fenobam to block mGluR5 during sleep. In Experiment I, participants learned word-pairs (declarative task) and a finger sequence (procedural task) in the evening, then received the drug and recall was tested the next morning. To cover possible effects on synaptic renormalization processes during sleep, in Experiment II participants learned new word-pairs in the morning after sleep. Results/outcomes: Surprisingly, fenobam neither reduced retention of memory across sleep nor new learning after sleep, although it severely altered sleep architecture and memory-relevant EEG oscillations. In NonREM sleep, fenobam suppressed 12–15 Hz spindles but augmented 2–4 Hz delta waves, whereas in rapid eye movement (REM) sleep it suppressed 4–8 Hz theta and 16–22 Hz beta waves. Notably, under fenobam NonREM spindles became more consistently phase-coupled to the slow oscillation. Conclusions/interpretations: Our findings indicate that mGluR5-related plasticity is not essential for memory processing during sleep, even though mGlurR5 are strongly implicated in the regulation of the cardinal sleep oscillations.
Measuring Accessibility of Healthcare Facilities for Populations with Multiple Transportation Modes Considering Residential Transportation Mode Choice

Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily travel presents different residential transportation mode choices (RTMC). The purpose of our study was to measure the spatial accessibility of healthcare facilities based on MTM considering RTMC (MTM-RTMC). We selected the gravity two-step floating catchment area method (G2SFCA) as a fundamental model. Through the single transportation mode (STM), MTM, and MTM-RTMC, three aspects used to illustrate and redesign the G2SFCA, we obtained the MTM-RTMC G2SFCA model that integrates RTMC probabilities and the travel friction coefficient. We selected Nanjing as the experimental area, used route planning data of four modes (including driving, walking, public transportation, and bicycling) from a web mapping platform, and applied the three models to pediatric clinic services to measure accessibility. The results show that the MTM-RTMC mechanism is to make up for the traditional estimation of accessibility, which loses sight of the influence of residential transportation choices. The MTM-RTMC mechanism that provides a more realistic and reliable way can generalize to major accessibility models and offers preferable guidance for policymakers.
Study of B Cell Repertoire in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common antibody-mediated encephalitis. There are several studies on B cell repertoire of anti-NMDAR encephalitis in Caucasians. Here, the cerebrospinal fluid (CSF) samples of 12 Chinese patients with first-episode anti-NMDAR encephalitis were collected to investigate the B cell receptor (BCR) binding to NMDAR by single cell amplification of BCR and Sanger sequencing. BCR data of healthy persons, and of patients with anti-leucine-rich glioma inactivated 1 (anti-LGI1) encephalitis, multiple sclerosis (MS), and neuromyelitis optica spectrum disorder (NMOSD) from the public databases were used as control. A heavy chain common clone IGHV1-18*04,IGHD1-26*01/ IGHD2-2*03/IGHD2-8*01, IGHJ3*02_(CDR3) ARVGSKYGFETFDI was found in 11 of 12 enrolled patients but not in the comparison data set. In addition, 4 shared clonotypes were found among these patients, and three of them contained the common clone. This study also revealed that the antibody gene family usage preference between patients and healthy controls were different, while they had similar antibody mutation rate. Our findings may have potential clinical implications for the diagnosis of anti-NMDAR encephalitis.
Integrating tracking and resight data from breeding Painted Bunting populations enables unbiased inferences about migratory connectivity and winter range survival

Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. We show that integrating geolocator recovery data and mark-resight data enables unbiased estimates of both migratory connectivity between breeding and non-breeding populations and region-specific survival probabilities for wintering locations. Using simulations, we first demonstrate that an integrated Bayesian model returns unbiased estimates of transition probabilities between seasonal ranges and to determine how different sampling designs influence estimability of transition probabilities. We then parameterize the model with tracking data and mark-resight data from declining Painted Bunting (Passerina ciris) populations breeding in the eastern United States, hypothesized to be threatened by the illegal pet trade in parts of their Caribbean, non-breeding range. Consistent with this hypothesis, we found that male buntings wintering in Cuba were 20% less likely to return to the breeding grounds than birds wintering elsewhere in their range. Improving inferences from archival tags through proper data collection and further development of integrated models will advance our understanding of full annual cycle ecology of migratory species.
PecanPy: a fast, efficient and parallelized Python implementation of node2vec

Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. Node2vec is the most widely used method for node embedding. However, its original Python and C++ implementations scale poorly with network density, failing for dense biological networks with hundreds of millions of edges. We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and precomputing/parallelization to result in fast, high-quality node embeddings for biological networks of all sizes and densities. PecanPy software and documentation are available at https://github.com/krishnanlab/pecanpy.
Distinct brain networks coupled with the parietal cortex represent target location inside and outside the visual field

Our mental representation of the egocentric space is influenced by the disproportionate sensory perception along the ventral-dorsal (i.e., front-back) axis of the body. Previous studies typically investigated the neural architectures involved in the egocentric representations within the visual field and revealed a crucial involvement of the parietal cortex and its interaction with the frontal lobe. However, neural architectures involved in the space representation behind self-body are still unclear. To address this problem, we applied both functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) to a 3D spatial-memory paradigm for human participants, in which the participants remembered a target location within (left or right) or outside (back) their visual field. Both fMRI and MEG experiments showed that the involvements of the fronto-parietal network including the frontal eye field and supplementary motor area were larger in the representations of retrieved target object within the visual field than outside. Conversely, the medial temporal lobe (MTL)-parietal network, including the right entorhinal cortex, was more involved in the target representation when the target was outside the visual field in both the experiments. In the MEG experiment, the connectivity increased in alpha-band frequency (8–13 Hz) in both fronto-parietal and MTL-parietal networks, and the preferential enhancement of the MTL-parietal network for the back preceded that of the fronto-parietal network for left/right. These findings suggest that the parietal cortex may represent whole space around self-body by coordinating the two distinct brain networks to process the egocentric spatial representation inside and outside the visual field. Significance Statement Representations of external space around self-body are necessary for coordinated actions, and our sensory-motor system is strongly biased toward processing the space in front. However, space exists behind us too. We conducted both fMRI and MEG studies and found that the parietal cortex coupled with the frontal lobe (FEF and SMA) and medial temporal lobe (MTL) represents a target inside and outside the visual field, respectively, although the target location was retrieved from short-term memory in both conditions. These results suggest that the parietal cortex may represent the whole of the present external space by interacting (alpha rhythm) with the frontal lobe for subsequent actions and the MTL to inquire the back scene.
FlowScatt: enabling volume-independent flow cytometry data by decoupling fluorescence from scattering

Motivation Measuring fluorescence by flow cytometry is fundamental for characterising single-cell performance. While it is known that fluorescence and scattering values tend to positively correlate, the impact of cell volume on fluorescence is typically overlooked. This makes of fluorescence values alone an inaccurate measurement for high-precision characterisations. Results We developed FlowScatt, an open-source software package that removes volume-dependency in the fluorescence channel. Using FlowScatt, flourescence values are re-calculated based on the unified volume per cell that arises from scattering decomposition. Availability FlowScatt is openly available as a Python package on https://github.com/rstoof/FlowScatt. Experimental data for validation is available online. Contact angel.goni-moreno@newcastle.ac. uk
Increased isoDGR motifs in plasma fibronectin are associated with atherosclerosis through facilitation of vascular fibrosis

Abnormal matrix deposition on vessels and recruitment of inflammatory cells into the arterial wall are critical events in atherosclerotic plaque formation. Fibronectin protein is a key matrix component that exhibits high levels of deamidation in atherosclerotic plaques and blood plasma, but it is unclear how this structural change impacts on endothelial function or modifies interactions with recruited leukocytes. This study aimed to determine how deamidation-induced isoDGR motifs in fibronectin influence extracellular matrix accumulation on endothelial cells, and to investigate possible effects on integrin ‘outside-in’ signalling in matrix-bound monocytes which are key mediators of human atherosclerosis. Blood plasma fibrinogen and fibronectin displayed marked accumulation of isoDGR motifs in ischemic heart disease (IHD) as determined by ELISA analysis of patients undergoing coronary artery bypass grafting compared with age-matched healthy controls. Biochemical and functional assays confirmed that isoDGR-containing fibronectin promoted activation of integrin β1 in monocytes and facilitated protein deposition and fibrillogenesis on endothelial cell layers. In addition, isoDGR interactions with integrins on the monocyte cell surface triggered an ERK:AP-1 signalling cascade that induced potent secretion of chemotactic mediators (including CCL2, CCL4, IL-8, and TNFα), that promoted further leukocyte recruitment to the assembling plaque. Fibronectin deamidation forms isoDGR motifs that increase binding to β1 integrins on the surface of endothelial cells and monocytes. Subsequent activation of integrin ‘outside-in’ signalling pathways elicits a range of potent cytokines and chemokines that drive additional leukocyte recruitment to the developing atherosclerotic matrix and likely constitutes a key early event in progression to IHD.
Wireless AI-Powered IoT Sensors for Laboratory Mice Behavior Recognition

According to the U.S. Department of Agriculture in 2018, there are more than 100 million animals used in research, education, and testing per year. Of the laboratory animals used for research, 95 percent are mice and rats as reported by the Foundation for Biomedical Research (FBR). We present here our work in developing wireless Artificial Intelligent (AI)-powered IoT Sensors (AIIS) for laboratory mice motion recognition utilizing embedded micro-inertial measurement units (uIMUs). Based on the AIIS, we have demonstrated a small-animal motion tracking and recognition system that could recognize 5 common behaviors of mice in cages with accuracy of ~76.23%. The key advantage of this AIIS-based system is to enable high throughput behavioral monitoring of multiple to a large group of laboratory animals, in contrast to traditional video tracking systems that usually track only single or a few animals at a time. The system collects motion data (i.e., three axes linear accelerations and three axes angular velocities) from the IoT sensors attached to different mice, and classifies these data into different behaviors using machine learning algorithms. One of the challenging problems for data analysis is that the distribution of behavior samples is extremely imbalanced. Behaviors such as sleeping and walking dominate the entire sample set from different mice. However, machine learning algorithms often require a balanced sample set to achieve optimal performance. Thus, several methods are proposed to solve the imbalanced sample problem. Data processing methods for data segmentation, feature extraction, feature selection, imbalanced learning, and machine learning are explored to process motion data including sleeping, walking, rearing, digging, shaking, grooming, drinking and scratching. For example, by tuning the parameters of a machine-learning algorithm (i.e., Support Vector Machine (SVM)), the average accuracy of classifying five behaviors (i.e., sleeping, walking, rearing, digging and shaking) is 48.07% before solving the imbalance sample issue. To address this problem, an iteration of sample and feature selection is applied to improve the SVM performance. A combination of oversampling and undersampling is used to handle imbalanced classes, and feature selection provides the optimal number of features. The accuracy increases from 48.07% to 76.23% when the optimized combination is used. We further obtained an average accuracy of 86.46% by removing shaking, which is proved to have a negative effect on the overall performance, out of these five behaviors. Furthermore, we were able to classify less frequent behaviors including rearing, digging, grooming, drinking and scratching at an average accuracy of 96.35%.