Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Modeling: Human Dynamic Model

Measurements of human activity are useful for studying the neural computations underlying human behavior. Dynamic models of human behavior also support clinical efforts to analyze, rehabilitate, and predict movements. They are used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. This paper describes a physics-based movement analysis technique for analyzing and simulating bipedal humanoid movements. A 48 degree of freedom dynamic model of humans uses physical simulation software as a tool for synthesizing humanoid movement with sufficient speed and accuracy to allow the analysis and synthesis of real-time interactive applications such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to believably animate a humanoid character, control a simulated system, or estimate internal joint forces used during a movement for creating effort-contingent experimental stimuli. A virtual reality environment developed as part of this research supports controlled experiments for systematically recording human behaviors.
Pharmacological characterization of the alpha 2‐adrenergic receptor inhibiting rat hippocampal CA3 epileptiform activity: comparison of ligand efficacy and potency

Pharmacological characteristics of the α2 adrenergic receptor (AR) that inhibits epileptiform activity in the hippocampal CA3 region were studied in rat brain slices. The effects of a large number of αAR antagonists on epinephrine (EPI)‐mediated inhibition of epileptiform activity were scrutinized with Schild plot analysis and compared to binding values for the rat and human α1, α2, 5‐HT1A, D2, I1, and I2 receptor subtypes. A perfect correlation and slope was found for the rat α2AAR, but not the other receptors, indicating that α2AARs solely mediate this effect. The actions of different chemical classes of α2AAR agonists at inhibiting epileptiform activity were then determined. While the imidazolines and guanidines exhibited the highest potency, the catecholamines had greater intrinsic activity compared to norepinephrine (NE). Dexmedetomidine, an imidazoline and guanabenz, a guanidine, were the most potent. In contrast, the catecholamines EPI and α‐methyl‐NE were the most efficacious. Furthermore, the α2AAR‐mediated inhibitory actions of EPI on hippocampal CA3 epileptiform burst frequencies were neither sex nor age‐dependent. These findings suggest that the activation of α2AARs could provide a new pharmacotherapeutic strategy for treating epilepsy and highlight the need for selective α2A agonists. Supported by ND EPSCoR, NSF CAREER 0347259, NSF REU Site 0639227, NIH P20 RR016741, APS and ASPET SURF.
Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. Yet, the interconnection of both genotypes obscures individual contributions to phenotypic variation (i.e. rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as model fruit tree. We characterized 240 rootstocks from 8 avocado cv. Hass orchards in three regions of the province of Antioquia, in the northwest Andes of Colombia, using 13 microsatellite markers (simple sequence repeats – SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, eco-physiological, and fruit yield and quality traits) in the scions for three years (2015–2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a ‘genetic prediction’ model in order to calculate narrow-sense heritabilities (h2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (R2) that oscillated between 0.58 and 0.74 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e. total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), while the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in ‘Hass’ avocado. They also reinforce the utility of SSR markers for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work reinforces the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit tree breeding programs, while enhancing our understanding of the consequences of grafting.
A novel 3D atomistic-continuum cancer invasion model: In silico simulations of an in vitro organotypic invasion assay

We develop a three-dimensional genuinely hybrid atomistic-continuum model that describes the invasive growth dynamics of individual cancer cells in tissue. The framework explicitly accounts for phenotypic variation by distinguishing between cancer cells of an epithelial-like and a mesenchymal-like phenotype. It also describes mutations between these cell phenotypes in the form of epithelial-mesenchymal transition (EMT) and its reverse process mesenchymal-epithelial transition (MET). The model consists of a hybrid system of partial and stochastic differential equations that describe the evolution of epithelial-like and mesenchymal-like cancer cells, respectively, under the consideration of matrix-degrading enzyme concentrations and the extracellular matrix density. With the help of inverse parameter estimation and a sensitivity analysis, this three-dimensional model is then calibrated to an in vitro organotypic invasion assay experiment of oral squamous cell carcinoma cells.
The shrub Ephedra californica facilitates arthropod communities along a regional desert climatic gradient

Arthropods underpin arid community dynamics and provide many key ecosystem services. In arid ecosystems, the key habitat components that influence arthropod community structure are relatively understudied. Ephedra californica is a common and widespread shrub with established positive effects on plant and vertebrate animal communities within the drylands of Southern California. The capacity for these positive effects to further support arthropod communities has not been examined. We tested the hypothesis that the physical structure and cover vegetation enhances key measures of arthropod community assembly at nine Californian desert sites that comprise an extensive regional aridity gradient. We contrasted the effects of shrub canopies with ground-covering vegetation on structuring ground-active arthropod communities by surveying ground-active arthropods with pitfall traps and collecting vegetation on the soil surface in the form of residual dry matter (RDM). We collected a total of 5868 individual arthropod specimens for a total of 184 morphospecies. Arthropod abundance and morphospecies richness and RDM biomass and cover were significantly greater beneath the canopy of E. californica throughout the region. Total biomass of RDM did not significantly influence arthropod communities, but cover of RDM on the soil surface negatively influenced arthropod abundance. Neither climatic aridity nor downscaled evaporative stress estimates were significant mediators of the arthropodvegetation association patterns. Vegetation thus likely has direct and indirect physical effects on arthropod communities. These canopy versus soil surface vegetation differences will refine sampling of fine-scale patterns of arthropod diversity in drylands. Regional land managers can support arthropod diversity by maintaining populations of foundation shrub species such as E. californica.
Local coupling between sleep spindles and slow oscillations supports the consolidation of motor memories

The precise coupling between slow oscillations (SO) and spindles is critical for sleep-dependent consolidation of declarative memories. Here, we examined whether this mechanism also operates in the stabilization of human motor memories during NREM sleep. We hypothesized that if the coupling of these oscillations is instrumental to motor memory consolidation then only SO-coupled spindles would predict long-term memory. We found that sleep enhanced long-term memory retention by 34%. Motor learning increased the density of spindles but not their frequency, duration or amplitude during NREM sleep. This modulation was manifested locally over the hemisphere contralateral to the trained hand. Although motor learning did not affect the density of SOs, it substantially modulated the spindle-SO coupling in an inter-hemispheric manner, suggesting it may rather increase the ability of slow oscillations to promote thalamic spindles. The fact that only coupled spindles predicted long-term memory points to the association of these oscillations as a fundamental signature of motor memory consolidation. Our work provides evidence in favor of a common mechanism at the basis of the stabilization of declarative and non-declarative memories.
Epigenetic reprogramming of plasmacytoid dendritic cells drives type I interferon-dependent differentiation of acute myeloid leukemias for therapeutic benefit

Pharmacological inhibition of epigenetic enzymes can have therapeutic benefit, particularly against hematological malignancies. While these agents can affect tumor cell growth and proliferation, recent studies have demonstrated that pharmacological de-regulation of epigenetic modifiers may additionally mediate anti-tumor immune responses. Here we discovered a novel mechanism of immune regulation through the inhibition of histone deacetylases (HDACs). In a genetically engineered model of t(8;21) AML, leukemia cell differentiation and therapeutic benefit mediated by the HDAC inhibitor panobinostat required activation of the type I interferon (IFN) signaling pathway. Plasmacytoid dendritic cells (pDCs) were identified as the cells producing type I IFN in response to panobinostat, through transcriptional activation of IFN genes concomitant with increased H3K27 acetylation at these loci. Depletion of pDCs abrogated panobinostat-mediated activation of type I IFN signaling in leukemia cells and impaired therapeutic efficacy, while combined treatment of panobinostat and recombinant IFNα improved therapeutic outcomes. These discoveries offer a new therapeutic approach for t(8;21) AML and demonstrate that epigenetic rewiring of pDCs enhances anti-tumor immunity, opening the possibility of exploiting this cell type as a new target for immunotherapy.
StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.
Sub-millimetre resolution laminar fMRI using Arterial Spin Labelling in humans at 7 T

Laminar fMRI at ultra-high magnetic field strength is typically carried out using the Blood Oxygenation Level-Dependent (BOLD) contrast. Despite its unrivalled sensitivity to detecting activation, the BOLD contrast is limited in its spatial specificity due to signals stemming from intra-cortical ascending and pial veins. Alternatively, regional changes in perfusion (i.e., cerebral blood flow through tissue) are colocalised to neuronal activation, which can be non-invasively measured using arterial spin labelling (ASL) MRI. In addition, ASL provides a quantitative marker of neuronal activation in terms of perfusion signal, which is simultaneously acquired along with the BOLD signal. However, ASL for laminar imaging is challenging due to the lower SNR of the perfusion signal and higher RF power deposition i.e., specific absorption rate (SAR) of ASL sequences. In the present study, we present for the first time in humans, isotropic sub-millimetre spatial resolution functional perfusion images using Flow-sensitive Alternating Inversion Recovery (FAIR) ASL with a 3D-EPI readout at 7T. We show that robust statistical activation maps can be obtained with perfusion-weighting in a single session. We observed the characteristic BOLD amplitude increase towards the superficial laminae, and, in apparent discrepancy, the relative perfusion profile shows a decrease of the amplitude and the absolute perfusion profile a much smaller increase towards the cortical surface. Considering the draining vein effect on the BOLD signal using model-based spatial ‘convolution’, we show that the empirically measured perfusion and BOLD profiles are, in fact, consistent with each other. This study demonstrates that laminar perfusion fMRI in humans is feasible at 7T and that caution must be exercised when interpreting BOLD signal laminar profiles as direct representation of the cortical distribution of neuronal activity.
Divergent thermal challenges elicit convergent stress signatures in aposymbiotic Astrangia poculata

Anthropogenic climate change threatens corals globally and both high and low temperatures are known to induce coral bleaching. However, coral stress responses across wide thermal breadths are rarely explored. In addition, it is difficult to disentangle the role of symbiosis on the stress response of obligately symbiotic coral hosts. Here, we leverage aposymbiotic colonies of the facultatively symbiotic coral, Astrangia poculata, which lives naturally with and without its algal symbiont, to examine how broad thermal challenges influence coral hosts. A. poculata were collected from their northern range limit and thermally challenged in two independent 16-day common garden experiments (heat and cold challenge) and behavioral responses to food stimuli and genome-wide gene expression profiling (TagSeq) were performed. Both thermal challenges elicited significant reductions in polyp extension. However, five times as many genes were differentially expressed under cold challenge compared to heat challenge. Despite more genes responding to cold challenge, there was significant overlap in which genes were differentially expressed across thermal challenges. These convergently responding genes (CRGs) were associated with downregulation of motor functions and nematocysts while others were consistent with stress responses previously identified in tropical corals. The fact that these responses were observed in aposymbiotic colonies highlights that many genes previously implicated in stress responses in symbiotic species may simply represent the coral’s stress response in or out of symbiosis.
Asynchronous Remote Key Generation: An Analysis of Yubico's Proposal for W3C WebAuthn

WebAuthn, forming part of FIDO2, is a W3C standard for strong authentication, which employs digital signatures to authenticate web users whilst preserving their privacy. Owned by users, WebAuthn authenticators generate attested and unlinkable public-key credentials for each web service to authenticate users. Since the loss of authenticators prevents users from accessing web services, usable recovery solutions preserving the original WebAuthn design choices and security objectives are urgently needed. We examine Yubico's recent proposal for recovering from the loss of a WebAuthn authenticator by using a secondary backup authenticator. We analyse the cryptographic core of their proposal by modelling a new primitive, called Asynchronous Remote Key Generation (ARKG), which allows some primary authenticator to generate unlinkable public keys for which the backup authenticator may later recover corresponding private keys. Both processes occur asynchronously without the need for authenticators to export or share secrets, adhering to WebAuthn's attestation requirements. We prove that Yubico's proposal achieves our ARKG security properties under the discrete logarithm and PRF-ODH assumptions in the random oracle model. To prove that recovered private keys can be used securely by other cryptographic schemes, such as digital signatures or encryption schemes, we model compositional security of ARKG using composable games by Brzuska et al. (ACM CCS 2011), extended to the case of arbitrary public-key protocols. As well as being more general, our results show that private keys generated by ARKG may be used securely to produce unforgeable signatures for challenge-response protocols, as used in WebAuthn. We conclude our analysis by discussing concrete instantiations behind Yubico's ARKG protocol, its integration with the WebAuthn standard, performance, and usability aspects.
Assessing and Prioritizing the Climate Change Policy Objectives for Sustainable Development in Pakistan

The world is facing the observable and direct impacts of climate change in the shape of extreme weather events, irregular precipitation, glacial melt, sea-level rise, an asymmetric average temperature trends, higher oceanic temperatures, damage to natural ecosystems, and biodiversity. In 2015, collective wisdom stirred the global community to agree upon two unprecedented milestones in recent human history—the Paris agreement and sustainable development goals (SDGs). The twin global agreements set a platform for collective efforts to secure socio-economic and climatic sustainability. Pakistan also joined the board to put forth policy interventions for the mitigation and adaption of climate change risks to maintain symmetry between the environmental quality and economic development. The government of Pakistan (GOP) formulated the National Climate Change Policy (NCCP (2012)) and passed the Pakistan Climate Change Act (PCCA (2017)) to provide an impetus for the implementation of the NCCP. Yet, an appropriate assessment and prioritization of the objectives of the NCCP are indispensable for productive and effective policies to achieve the targets. This study serves this purpose by prioritizing the climate change policy objectives (CCPOs) based on symmetric principles, i.e., energy, transport, urban and town planning, industry, and agriculture as criteria, with 17 sub-criteria in total. The fuzzy analytical hierarchy process (AHP) results show that the energy sector is the most pivotal while implementing the NCCP. Energy production is the most critical area that needs mitigation policy intervention for shifting the energy mix of the economy from high-carbon to low-carbon energy. The fuzzy AHP analysis further revealed the railway, population and urbanization, air pollution, crop growing practices and techniques as the most important related to transport, urban planning, industry, and agriculture sub-criteria, respectively. The fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis showed the ranking of the CCPOs from high to low through the integration of policies, institutional capacity building, water security, natural resource management, natural disaster management, environmental financial structure development, and social sector development. The findings of the present research would be helpful for experts and policymakers to re-examine the NCCP and put forth action plans to achieve sustainable climate and SDGs.
Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition

A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.
Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping

In the event of a natural disaster, geo-tagged Tweets are an immediate source of information for locating casualties and damages, and for supporting disaster management. Topic modeling can help in detecting disaster-related Tweets in the noisy Twitter stream in an unsupervised manner. However, the results of topic models are difficult to interpret and require manual identification of one or more “disaster topics”. Immediate disaster response would benefit from a fully automated process for interpreting the modeled topics and extracting disaster relevant information. Initializing the topic model with a set of seed words already allows to directly identify the corresponding disaster topic. In order to enable an automated end-to-end process, we automatically generate seed words using older Tweets from the same geographic area. The results of two past events (Napa Valley earthquake 2014 and hurricane Harvey 2017) show that the geospatial distribution of Tweets identified as disaster related conforms with the officially released disaster footprints. The suggested approach is applicable when there is a single topic of interest and comparative data available.