Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure, dynamics, and stability of the globular domain of human linker histone H1.0 and the role of positive charges

Linker histone H1 (H1) is an abundant chromatin‐binding protein that acts as an epigenetic regulator binding to nucleosomes and altering chromatin structures and dynamics. Nonetheless, the mechanistic details of its function remain poorly understood. Recent work suggest that the number and position of charged side chains on the globular domain (GD) of H1 influence chromatin structure and hence gene repression. Here, we solved the solution structure of the unbound GD of human H1.0, revealing that the structure is almost completely unperturbed by complex formation, except for a loop connecting two antiparallel β‐strands. We further quantified the role of the many positive charges of the GD for its structure and conformational stability through the analysis of 11 charge variants. We find that modulating the number of charges has little effect on the structure, but the stability is affected, resulting in a difference in melting temperature of 26 K between GD of net charge +5 versus +13. This result suggests that the large number of positive charges on H1‐GDs have evolved for function rather than structure and high stability. The stabilization of the GD upon binding to DNA can thus be expected to have a pronounced electrostatic component, a contribution that is amenable to modulation by posttranslational modifications, especially acetylation and phosphorylation.
Designing of nanobodies against Dengue virus Capsid: a computational affinity maturation approach

Abstract Dengue virus, an arbovirus, is one of the most prevalent diseases in the tropical environment and leads to huge number of casualties every year. No therapeutics are available till date against the viral disease and the only medications provide symptomatic relief. In this study, we have focused on utilizing conventional nanobodies and repurposing them for Dengue. Computationally affinity matured, best binding nanobodies tagged with constant antibody regions, could be proposed as therapeutics. These could also be applied for drug delivery purposes due to their high specificity against the viral Capsid. Another application of these nanobodies has been thought to utilize them for diagnostic purposes, to use the nanobodies for viral detection from patient samples at the earliest stage using ELISA. This study may open a new avenue for immunologic study in foreseeable future with the usage of the same molecules for multiple purposes. Highlights Natural nanobodies against viruses were modified for use against Dengue virus Capsid conserved regions. Computational affinity maturation was performed making use of change in binding affinities upon mutating various residues in the complementary determining regions. Docking studies performed to inspect the docking groove, interface analysis and energy calculations. MM/GBSA calculations done to calculate binding free energy of the complex to determine stability of the complex. Communicated by Ramaswamy H. Sarma
Power priors with entropy balancing weights in data augmentation of partially controlled randomized trials

ABSTRACT In pediatric or orphan diseases, there are many instances where it is unfeasible to conduct randomized and controlled clinical trials. This is due in part to the difficulty of enrolling a sufficient number of patients over a reasonable time period to meet adequate statistical power to demonstrate the treatment efficacy. One solution to reduce the sample size or expedite the trial timeline is to complement the current trial with real-world data. To this end, several propensity score-based methods have been developed to create defined groups of patients that are controlled for confounding based on a set of measured covariates at baseline. However, balance checking on the measured covariates and tweaks to the propensity score models is usually inevitable to achieve the joint balance across all covariates. To mitigate this iterative procedure, we utilize the entropy balancing weighting technique which focuses on balancing the covariates of subjects between the experimental and control groups directly and augments the current trial with the external control data via a power prior. The finite-sample properties of the proposed method are assessed via simulations in the context of asymmetrically randomized controlled trials where only a small portion of patients are randomized to the control group. Other methods such as covariate-balancing propensity score (CBPS) and propensity score matching (PSM) and weighting (PSW) are also compared to provide context on the operating characteristics of the proposed method.
Mind Your Heart

Background Mindfulness training (MT) may promote medication adherence in outpatients with heart failure. Objective The aims of this study were to determine the feasibility and acceptability of MT (primary outcomes) and explore effects on medication adherence, functional capacity, cognitive function, depression, and mindfulness skills (secondary outcomes). Methods In this pre/post-design study, participants received a 30-minute phone-delivered MT session weekly for 8 weeks. Results We enrolled 33 outpatients (32% women; 69.7 White; mean age, 60.3 years). Retention was 100%, and session attendance was 91%. Overall, participants (97%) rated MT as enjoyable. Objectively assessed (P < .05) adherence decreased post intervention, whereas improvements were noted in functional capacity (P = .05), mindfulness (P < .05), and cognitive function (reaching significance for Flanker scores). Conclusions Phone-delivered MT was feasible and acceptable. Whereas no improvements were noted in medication adherence and depression, cognitive function, functional capacity, and mindfulness levels increased post intervention, suggesting MT may have beneficial effects in outpatients with heart failure.
Are alpha and beta oscillations spatially dissociated over the cortex in context‐driven spoken‐word production?

Abstract Decreases in oscillatory alpha‐ and beta‐band power have been consistently found in spoken‐word production. These have been linked to both motor preparation and conceptual‐lexical retrieval processes. However, the observed power decreases have a broad frequency range that spans two “classic” (sensorimotor) bands: alpha and beta. It remains unclear whether alpha‐ and beta‐band power decreases contribute independently when a spoken word is planned. Using a re‐analysis of existing magnetoencephalography data, we probed whether the effects in alpha and beta bands are spatially distinct. Participants read a sentence that was either constraining or non‐constraining toward the final word, which was presented as a picture. In separate blocks participants had to name the picture or score its predictability via button press. Irregular‐resampling auto‐spectral analysis (IRASA) was used to isolate the oscillatory activity in the alpha and beta bands from the background 1‐over‐f spectrum. The sources of alpha‐ and beta‐band oscillations were localized based on the participants’ individualized peak frequencies. For both tasks, alpha‐ and beta‐power decreases overlapped in left posterior temporal and inferior parietal cortex, regions that have previously been associated with conceptual and lexical processes. The spatial distributions of the alpha and beta power effects were spatially similar in these regions to the extent we could assess it. By contrast, for left frontal regions, the spatial distributions differed between alpha and beta effects. Our results suggest that for conceptual‐lexical retrieval, alpha and beta oscillations do not dissociate spatially and, thus, are distinct from the classical sensorimotor alpha and beta oscillations.
Supporting Veterans with dementia to remain in the community: strategies used in 12 Veterans Health Administration programs

ABSTRACT The Veterans Health Administration (VA) provides services to growing numbers of Veterans with dementIa, individuals at heightened risk for hospitalizations and nursing home placement. Beginning in 2010, the VA funded 12 innovative pilot programs to improve dementia care and help Veterans remain at home. We conducted a retrospective qualitative analysis of program materials and interviews with physicians, nurses, social workers, and other personnel (n = 33) to understand the strategies these programs adopted. Interviews were conducted every 6 months between 2010–2013 (4–5 interviews per program) and focused on factors affecting program design and implementation, challenges, and strategies to reduce hospitalizations and nursing home placements. Programs varied considerably yet shared three overarching strategies to improve dementia care: involving and supporting family caregivers; engaging interdisciplinary teams; and improving coordination with other healthcare providers. Our results highlight the importance of adapting common dementia care strategies based on the local context and needs of individuals served.
An effective double gene knock‐in strategy using small‐molecule L755507 in the medaka fish (Oryzias latipes)

Homology‐directed repair (HDR)‐mediated genome editing has become a powerful method for altering chromosomal sequences in a seamless and accurate manner. However, the low efficiency of HDR in most cells hinders the establishment of desired strains harboring accurately modified genomes. To enhance HDR‐mediated knock‐in events, we explored two approaches, namely low‐temperature incubation and chemical compound administration using medaka embryos after microinjection. We validated the performance of each method by calculating the knock‐in efficiencies according to the expression area of fluorescent protein in the embryos. The in vivo assay indicated that the reduction in temperature did not promote HDR events, whereas among the nine compounds screened, the small molecule L755507 could enhance the HDR‐mediated targeted integration of reporter cassettes. Additionally, the L755507‐based approach allowed for the simultaneous integration of two different DNA fragments into the two targeted loci, that is, double knock‐in. Our established knock‐in system combining L755507, donor plasmids, and the CRISPR/Cas9 nickase system can reduce the workload for genetically modified strain generation, thus accelerating studies on the molecular mechanisms of biological phenomena.
NEXT Study of Thruster Extended-Performance II (NEXT STEP II)

*Ion propulsion technology under NASA’s Evolutionary Xenon Thruster (NEXT) project progresses toward flight application. The existing capabilities of the NEXT xenon ion thruster are reviewed, including previous analyses and test data examining the possibilities of extending the NEXT thruster performance and life time capabilities beyond those demonstrated under the existing program and throttle table. Both high thrust-to-power operation and high thrust density high power operation were evaluated. These analyses were extended here to examine the performance and lifetime implications of increasing the discharge propellant efficiency above those values assumed in the NEXT throttle table. Additionally, enhanced high thrust-to-power operation was examined assuming the use of an improved-perveance ion optics design. Increases in discharge propellant efficiency are projected to provide modest increases in overall thruster efficiency, specific impulse, and thruster service life. Performance estimates for the NEXT thruster with enhanced perveance ion optics indicate thrust-to-power ratios of up to 58 mN/kW are feasible, yielding sub-3000 second specific impulse operation which compares favorably to the BPT-4000 Hall thruster.
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN

The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e.g., face parsers and face landmark detectors. These models provide the flexibility to construct various criterions with very concrete and interpretable semantic meanings (e.g., change face shape or change skin color) to restrict latent subspace disentanglement. Rich latent space controls unknown previously can be discovered using the constructed criterions. Second, we propose a new perspective to explain the behavior of a CNN classifier by generating counterfactuals in the interpretable latent subspaces we discovered. This explanation helps reveal whether the classifier learns semantics as intended. Experiments on various disentanglement criterions demonstrate the effectiveness of our approach. We believe this approach contributes to both areas of image manipulation and counterfactual explainability of CNNs. The code is available at \url{https://github.com/prclibo/ice}.
Cyber Mobility Mirror for Enabling Cooperative Driving Automation: A Co-Simulation Platform

—Endowed with automation and connectivity, Connected and Automated Vehicles (CAVs) are meant to be a revo-lutionary promoter for Cooperative Driving Automation (CDA). Nevertheless, CAVs need high-ﬁdelity perception information on their surroundings, which is available but costly to collect from various on-board sensors, such as radar, camera, and LiDAR, as well as vehicle-to-everything (V2X) communications. Therefore, precisely simulating the sensing process with high-ﬁdelity sensor inputs and timely retrieving the perception information via a cost-effective platform are of increasing signiﬁcance for enabling CDA-related research, e.g., development of decision-making or control module. Most state-of-the-art trafﬁc simulation studies for CAVs rely on the situation-awareness information by directly calling on intrinsic attributes of the objects, which impedes the reliability and ﬁdelity for testing and validation of CDA algorithms. In this study, a co-simulation platform is developed, which can simulate both the real world with a high-ﬁdelity sensor perception system and the cyber world (or “mirror” world) with a real-time 3D reconstruction system. Speciﬁcally, the real-world simulator is mainly in charge of simulating the road-users (such as vehicles, bicyclists, and pedestrians), infrastructure (e.g., trafﬁc signals and roadside sensors) as well as the object detection process. The mirror-world simulator is responsible for reconstructing 3D objects and their trajectories from the perceived information (provided by those roadside sensors in the real-world simulator) to support the development and evaluation of CDA algorithms. To illustrate the efﬁcacy of this co-simulation platform, a roadside LiDAR-based real-time vehicle detection and 3D reconstruction system is prototyped as a study case.
Reading-Strategy Inspired Visual Representation Learning for Text-to-Video Retrieval

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset. Code is available at https://github.com/LiJiaBei-7/rivrl.
Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection

Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these populations would require a scaled approach that combines mobility, low cost, speed, accuracy, and privacy. We can resolve these issues by combining the chest X-ray imaging mode with a federated deep-learning approach, provided that the federated model is trained on homogenous data to ensure that no single data source can adversely bias the model at any point in time. In this study we show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization, paving the way for a low-cost and accessible deep learning-based clinical system for lung cancer screening. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is performed using all combinations of lung field segmentation, close cropping, and rib suppression operators. We show that this pre-processing pipeline results in deep learning models that successfully generalize an independent lung nodule dataset using ablation studies to assess the contribution of each operator in this pipeline. In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.
Interprofessional education workshop on aging: student perceptions of interprofessional collaboration, aging, and cultural fluency

ABSTRACT Interprofessional education is vital to prepare students in healthcare-related fields for future practice to improve participation in teams and enhance client/patient outcomes. This study aimed to evaluate the impact of a half-day interprofessional education workshop on aging based on students’ self-rated confidence in working in interprofessional teams, with aging, and culturally diverse clients/patients. Students from eight healthcare-related disciplines at a private mid-western university attended an interprofessional workshop. Afterward, students completed a reflective survey to evaluate pre- and post-workshop perceptions about interprofessional collaboration and caring for aging and diverse clients/patients. Quantitative methods using paired sample t-tests revealed a statistically significant difference in students’ self-reported level of preparedness in the areas of interprofessional education, aging, and cultural fluency (P < .001). In addition, qualitative methods were used to organize data into themes. Additional insights were gained to inform future workshops.
Unsupervised Audio Source Separation Using Differentiable Parametric Source Models

—Supervised deep learning approaches to underde- termined audio source separation achieve state-of-the-art performance but require a dataset of mixtures along with their corre- sponding isolated source signals. Such datasets can be extremely costly to obtain for musical mixtures. This raises a need for unsupervised methods. We propose a novel unsupervised model-based deep learning approach to musical source separation. Each source is modelled with a differentiable parametric source-ﬁlter model. A neural network is trained to reconstruct the observed mixture as a sum of the sources by estimating the source models’ parameters given their fundamental frequencies. At test time, soft masks are obtained from the synthesized source signals. The experimental evaluation on a vocal ensemble separation task shows that the proposed method outperforms learning-free methods based on nonnegative matrix factorization and a supervised deep learning baseline. Integrating domain knowledge in the form of source models into a data-driven method leads to high data efﬁciency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio. This work makes powerful deep learning based separation usable in scenarios where training data with ground truth is expensive or nonexistent.
Neural Implicit Surfaces in Higher Dimension

This work investigates the use of neural networks admitting high-order derivatives for modeling dynamic variations of smooth implicit surfaces. For this purpose, it extends the representation of diﬀerentiable neural implicit surfaces to higher dimensions, which opens up mechanisms that allow to exploit geometric transformations in many settings, from animation and surface evolution to shape morphing and design galleries. The problem is modeled by a k - parameter family of surfaces S c , speciﬁed as a neural network function f : R 3 × R k → R , where S c is the zero-level set of the implicit function f ( · , c ) : R 3 → R , with c ∈ R k , with variations induced by the control variable c . In that context, restricted to each coordinate of R k , the underlying representation is a neural homotopy which is the solution of a general partial diﬀerential equation .
Interplay between Binary and Three-Body Interactions and Enhancement of Stability in Trapless Dipolar Bose–Einstein Condensates

We investigate the nonlocal Gross–Pitaevskii (GP) equation with long-range dipole-dipole and contact interactions (including binary and three-body collisions). We address the impact of the three-body interaction on stabilizing trapless dipolar Bose–Einstein condensates (BECs). It is found that the dipolar BECs exhibit stability not only for the usual combination of attractive binary and repulsive three-body interactions, but also for the case when these terms have opposite signs. The trapless stability of the dipolar BECs may be further enhanced by time-periodic modulation of the three-body interaction imposed by means of Feshbach resonance. The results are produced analytically using the variational approach and confirmed by numerical simulations.