Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomimetic In Vitro Model of Cell Infiltration into Skin Scaffolds for Pre-Screening and Testing of Biomaterial-Based Therapies

Due to great clinical need, research where different biomaterials are tested as 3D scaffolds for skin tissue engineering has increased. In vitro studies use a cell suspension that is simply pipetted onto the material and cultured until the cells migrate and proliferate within the 3D scaffold, which does not mimic the in vivo reality. Our aim was to engineer a novel biomimetic in vitro model that mimics the natural cell infiltration process occurring in wound healing, thus offering a realistic approach when pre-screening and testing new skin substitutes. Our model consists of porous membrane cell culture inserts coated with gelatin and seeded with human dermal fibroblasts, inside which two different commercially available dermal substitutes were placed. Several features relevant to the wound healing process (matrix contraction, cell infiltration and proliferation, integration of the biomaterial with the surrounding tissue, and secretion of exogenous cytokines and growth factors) were evaluated. Our results showed that cells spontaneously infiltrate the materials and that our engineered model is able to induce and detect subtle differences between different biomaterials. The model allows for room for improvements or “adds-on” and miniaturization and can contribute to the development of functional and efficient skin substitutes for burns and chronic wounds.
Hierarchical Multi-Label Classification of Scientific Documents

Automatic topic classification has been studied extensively to assist managing and indexing scientific documents in a digital collection. With the large number of topics being available in recent years, it has become necessary to arrange them in a hierarchy. Therefore, the automatic classification systems need to be able to classify the documents hierarchically. In addition, each paper is often assigned to more than one relevant topic. For example, a paper can be assigned to several topics in a hierarchy tree. In this paper, we introduce a new dataset for hierarchical multi-label text classification (HMLTC) of scientific papers called SciHTC, which contains 186,160 papers and 1,234 categories from the ACM CCS tree. We establish strong baselines for HMLTC and propose a multi-task learning approach for topic classification with keyword labeling as an auxiliary task. Our best model achieves a Macro-F1 score of 34.57% which shows that this dataset provides significant research opportunities on hierarchical scientific topic classification. We make our dataset and code for all experiments publicly available.
PPARβ/δ recruits NCOR and regulates transcription reinitiation of ANGPTL4

In the absence of ligands, the nuclear receptor PPARβ/δ recruits the NCOR and SMRT corepressors, which form complexes with HDAC3, to canonical target genes. Agonistic ligands cause dissociation of corepressors and enable enhanced transcription. Vice versa, synthetic inverse agonists augment corepressor recruitment and repression. Both basal repression of the target gene ANGPTL4 and reinforced repression elicited by inverse agonists are partially insensitive to HDAC inhibition. This raises the question of how PPARβ/δ represses transcription mechanistically. We show that the PPARβ/δ inverse agonist PT-S264 impairs transcription initiation by decreasing recruitment of activating Mediator subunits, RNA polymerase II, and TFIIB, but not of TFIIA, to the ANGPTL4 promoter. Mass spectrometry identifies NCOR as the main PT-S264–dependent interactor of PPARβ/δ. Reconstitution of knockout cells with PPARβ/δ mutants deficient in basal repression results in diminished recruitment of NCOR, SMRT, and HDAC3 to PPAR target genes, while occupancy by RNA polymerase II is increased. PT-S264 restores binding of NCOR, SMRT, and HDAC3 to the mutants, resulting in reduced polymerase II occupancy. Our findings corroborate deacetylase-dependent and -independent repressive functions of HDAC3-containing complexes, which act in parallel to downregulate transcription.
Approaches for measuring the dynamics of RNA–protein interactions

RNA–protein interactions are pivotal for the regulation of gene expression from bacteria to human. RNA–protein interactions are dynamic; they change over biologically relevant timescales. Understanding the regulation of gene expression at the RNA level therefore requires knowledge of the dynamics of RNA–protein interactions. Here, we discuss the main experimental approaches to measure dynamic aspects of RNA–protein interactions. We cover techniques that assess dynamics of cellular RNA–protein interactions that accompany biological processes over timescales of hours or longer and techniques measuring the kinetic dynamics of RNA–protein interactions in vitro.
Antifungal potency and modes of action of a novel olive tree defensin against closely related ascomycete fungal pathogens

Antimicrobial peptides play a pivotal role in the innate immunity of plants. Defensins are cysteine-rich antifungal peptides with multiple mechanisms of action (MOA). A novel Oleaceae-specific defensin gene family was discovered in the genome sequences of the wild and cultivated species of a perennial olive tree, Olea europaea. Antifungal properties of an olive tree defensin OefDef1.1 were investigated against a necrotrophic ascomycete fungal pathogen Botrytis cinerea in vitro and in planta. OefDef1.1 displayed potent antifungal activity against this pathogen by rapidly permeabilizing the plasma membrane of the conidial and germling cells. Interestingly, it was translocated to the cytoplasm and induced reactive oxygen species in the germlings, but not in the conidia. In medium containing high concentrations of Na1+, antifungal activity of OefDef1.1 against B. cinerea was significantly reduced. In contrast, OefDef1.1_V1 variant in which the γ-core motif of OefDef1.1 was replaced by that of a Medicago truncatula defensin MtDef4 displayed Na1+-tolerant antifungal activity and was more potent in reducing the virulence of B. cinerea in planta. OefDef1.1 also exhibited potent antifungal activity against three hemibiotrophic ascomycete pathogens Fusarium graminearum, F. oxysporum and F. virguliforme. Significant differences were observed among the four pathogens in their responses to OefDef1.1 in growth medium with or without the high concentrations of Na1+. The varied responses of closely related ascomycete pathogens to this defensin have implications for engineering disease resistance in plants.
Stochastic Solutions for Dense Subgraph Discovery in Multilayer Networks

Network analysis has played a key role in knowledge discovery and data mining. In many real-world applications in recent years, we are interested in mining multilayer networks, where we have a number of edge sets called layers, which encode different types of connections and/or time-dependent connections over the same set of vertices. Among many network analysis techniques, dense subgraph discovery, aiming to find a dense component in a network, is an essential primitive with a variety of applications in diverse domains. In this paper, we introduce a novel optimization model for dense subgraph discovery in multilayer networks. Our model aims to find a stochastic solution, i.e., a probability distribution over the family of vertex subsets, rather than a single vertex subset, whereas it can also be used for obtaining a single vertex subset. For our model, we design an LP-based polynomial-time exact algorithm. Moreover, to handle large-scale networks, we also devise a simple, scalable preprocessing algorithm, which often reduces the size of the input networks significantly and results in a substantial speed-up. Computational experiments demonstrate the validity of our model and the effectiveness of our algorithms.
Identification of Swimmers in Distress Using Unmanned Aerial Vehicles: Experience at the Mont-Tremblant IRONMAN Triathlon

Abstract Background: This preliminary report describes our experience using unmanned aerial vehicles (UAVs) to identify swimmers in distress at the 2018 Mont-Tremblant IRONMAN triathlon (Quebec, Canada). Methods: In a prospective pilot study, we sought to determine whether UAV surveillance could identify swimmers showing signs of distress quicker than conventional methods (i.e., lifeguards on the ground and on watercraft). In addition, we investigated the feasibility of using UAVs for medical surveillance at a triathlon event in terms of operations, costs, safety, legal parameters, and added value. Prior to the race, we screened participants for medical conditions that could elevate their risk of injury during the swim portion of the triathlon. Athletes deemed to be at increased risk were given a yellow swimming cap to enhance their surveillance by trained observers watching a live video feed from the UAVs. Results: On race day, a total of 3 UAVs (2 mobile, 1 tethered) were launched over Lake Tremblant and provided 3 observers with live video of the swimmers. Of the 2,473 race participants, there were 25 athletes with pre-identified medical conditions who wore a yellow cap during the swim. We did not detect any signs of distress among swimmers wearing yellow caps. Among the remaining 2,448 athletes, there were 5 swimmers who demonstrated signs of distress and required mobilization of water rescue boats; UAV surveillance identified 1 of these 5 distress events before it was seen by lifeguards on rescue boats. None of the athletes in the IRONMAN suffered an adverse event while swimming. Several technical and safety issues related to UAV surveillance arose including poor visibility, equipment loss, and flight autonomy. Conclusion: While our preliminary findings suggest that using UAVs to identify distressed swimmers during an IRONMAN race is feasible and safe, more research is necessary to determine how to optimize UAV surveillance at mass sporting events and integrate this technology within the existing emergency response teams.
Contrastive Weighted Learning for Near-Infrared Gaze Estimation

Appearance-based gaze estimation has been very successful with the use of deep learning. Many following works improved domain generalization for gaze estimation. However, even though there has been much progress in domain generalization for gaze estimation, most of the recent work have been focused on cross-dataset performance -- accounting for different distributions in illuminations, head pose, and lighting. Although improving gaze estimation in different distributions of RGB images is important, near-infrared image based gaze estimation is also critical for gaze estimation in dark settings. Also there are inherent limitations relying solely on supervised learning for regression tasks. This paper contributes to solving these problems and proposes GazeCWL, a novel framework for gaze estimation with near-infrared images using contrastive learning. This leverages adversarial attack techniques for data augmentation and a novel contrastive loss function specifically for regression tasks that effectively clusters the features of different samples in the latent space. Our model outperforms previous domain generalization models in infrared image based gaze estimation and outperforms the baseline by 45.6\% while improving the state-of-the-art by 8.6\%, we demonstrate the efficacy of our method.
Uncertainty-aware predictive modeling for fair data-driven decisions

Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems. While critical concepts like fairness and explainability are often addressed, the safety of systems is typically not sufficiently taken into account. By viewing data-driven decision systems as socio-technical systems, we draw on the uncertainty in ML literature to show how fairML systems can also be safeML systems. We posit that a fair model needs to be an uncertainty-aware model, e.g. by drawing on distributional regression. For fair decisions, we argue that a safe fail option should be used for individuals with uncertain categorization. We introduce semi-structured deep distributional regression as a modeling framework which addresses multiple concerns brought against standard ML models and show its use in a real-world example of algorithmic profiling of job seekers.
Remote Postischemic Conditioning Promotes Stroke Recovery by Shifting Circulating Monocytes to CCR2+ Proinflammatory Subset

Brain injury from stroke is typically considered an event exclusive to the CNS, but injury progression and repair processes are profoundly influenced by peripheral immunity. Stroke stimulates an acute inflammatory response that results in a massive infiltration of peripheral immune cells into the ischemic area. While these cells contribute to the development of brain injury, their recruitment has been considered as a key step for tissue repair. The paradoxical role of inflammatory monocytes in stroke raises the possibility that the manipulation of peripheral immune cells before infiltration into the brain could influence stroke outcome. One such manipulation is remote ischemic limb conditioning (RLC), which triggers an endogenous tolerance mechanism. We observed that mice subjected to poststroke RLC shifted circulating monocytes to a CCR2+ proinflammatory monocyte subset and had reduced acute brain injury, swelling, and improved motor/gait function in chronic stroke. The RLC benefits were observed regardless of injury severity, with a greater shift to a CCR2+ subset in severe stroke. Adoptive transfer of CCR2-deficient monocytes abolished RLC-mediated protection. The study demonstrates the importance of RLC-induced shift of monocytes to a CCR2+ proinflammatory subset in attenuating acute injury and promoting functional recovery in chronic stroke. The defined immune-mediated mechanism underlying RLC benefits allows for an evidence-based framework for the development of immune-based therapeutic strategies for stroke patients. SIGNIFICANCE STATEMENT Stroke is the leading cause of physical disability worldwide but has few treatment options for patients. Because remote ischemic limb conditioning (RLC) elicits endogenous tolerance in neither an organ- nor a tissue-specific manner, the immune system has been considered a mediator for an RLC-related benefit. Application of RLC after stroke increased a proinflammatory CCR2+ monocyte subset in the blood and the brain. RLC reduced acute stroke injury and promoted motor/gait function during the recovery phase. The RLC benefits were absent in mice that received CCR2-deficient monocytes. This preclinical study shows the importance of CCR2+ proinflammatory monocytes in RLC benefits in stroke and provides a therapeutic RLC platform as a novel immune strategy to improve outcomes in stroke patients.
Method to Convert Stem Cells into Cancer Stem Cells

The cancer stem cell (CSC) hypothesis suggests that tumors are sustained exclusively by a small population of the cells with stem cell properties. CSCs have been identified in most tumors and are responsible for the initiation, recurrence, and resistance of different cancers. In vitro CSC models will be of great help in revisiting the mechanism of cancer development, as well as the tumor microenvironment and the heterogeneity of cancer and metastasis. Our group recently described the generation of CSCs from induced pluripotent stem cells (iPSCs), which were reprogrammed from normal cells, and/or embryonic stem cells (ESCs). This procedure will improve the understanding of the essential niche involved in cancer initiation. The composition of this cancer-inducing niche, if identified, will let us know how normal cells convert to malignant in the body and how, in turn, cancer prevention could be achieved. Further, once developed, CSCs demonstrate the ability to differentiate into endothelial cells, cancer-associated fibroblasts, and other phenotypes establishing the CSC niche. These will be good materials for developing novel cancer treatments. In this protocol, we describe how to handle mouse iPSCs/ESCs and how to choose the critical time for starting the conversion into CSCs. This CSC generation protocol is essential for understanding the role of CSC in cancer initiation and progress.
Fast Quasi-Optimal Power Flow of Flexible DC Traction Power Systems

This paper proposes a quasi-optimal power flow (OPF) algorithm for flexible DC traction power systems (TPSs). Near-optimal OPF solutions can be solved with high computational efficiency by quasi-OPF. Unlike conventional OPF algorithms, quasi-OPF does not utilize mathematical optimization algorithms but adopts a new methodology. First, we adopt a new modeling method and successfully reveal the physical meaning of OPF solutions in flexible DC TPSs. Then, by converting the physical meaning of OPF solutions into mathematical expressions, a simple mapping from the power flow solution to the near-optimal OPF solution is obtained, and the quasi-OPF algorithm is designed based on power flow and this mapping. Since calculating power flow is computationally cheap and the mapping is based on simple arithmetic, the quasi-OPF algorithm can solve OPF with much less execution time, achieving subsecond level calculation and a speed-up of 57 times compared to the primal-dual interior point method. The effectiveness is verified by mathematical proofs and a case study with Beijing Metro Line 13. This study provides insight into the physical meaning of OPF solutions and is a powerful tool for flexible DC TPSs to analyze the effects of coordinated control, design real-time coordinated control strategies, and solve operational problems in planning.
Characteristic of Electron Density of Coaxial Dielectric Barrier Discharge of CH4 /Air Mixture

Numerical study of dielectric barrier discharge of CH4/air mixture were carried out by employing a coaxial cylindrical electrode with diameter of 10mm under the condition of the discharge voltage of 6000V. The influence of electrode structure parameter and equivalence ratio on electron density were analyzed. The result shows that the electron density which increases with the decrease of the distance from the cathode has a quick increase at the small position near the cathode (0.05~0.4mm), and the maximum value of electron density appears at the position of 0.1mm. The electron density has no obvious change with the equivalence ratio, it is benefit to the lean combustion of natural gas application.
The Fremantle Neck Awareness Questionnaire in Chronic Neck Pain Patients: Turkish Version, Validity and Reliability Study.

STUDY DESIGN Cross-cultural adaptation and psychometric analysis. OBJECTIVE To develop "Fremantle Neck Awareness Questionnaire Turkish (FreNAQ-T)" and to investigate the validity, reliability, and psychometric properties of FreNAQ-T in the Turkish population with chronic neck pain (CNP). SUMMARY OF BACKGROUND DATA Since the different beliefs about the nature of the problem in chronic pain (CP) and its future consequences lead to mis-stimulated neuroplastic changes, the perceptual level and proprioceptive-awareness should be examined in CP. The perceptual level and propriceptive awareness in individuals with chronic low back pain and in knee osteoarthritis were evaluated while the neck version hasn't been studied. METHODS The neck version was conducted using forward-backward method to translate the FreBAQ from English to Turkish. The number of the included patients was 111. The FreNAQ-T was applied to 37 patients after three days for test-retest reliability. Validity of the FreNAQ-T was assessed using Rasch analysis and Spearman's correlation coefficient. Reliability of the FreNAQ-T's internal consistency was tested by Person Separation Index (PSI) and Cronbach Alpha. Test-retest reliability was tested by differential item functioning (DIF) by time. RESULTS All items of FreNAQ-T were found to fit Rasch Model (given Bonferroni adjustment fit level of 0.006 and chi-square: 6 (df = 9), p = 0.739). Internal construct validity evaluation was good, overall mean item fit residual was 0.141 (SD 1.089) and mean person fit residual was -0.211 (SD 1.173). In reliability evaluation, the PSI was 0.69 (good) and Cronbach's alpha was 0.70 (acceptable). When the test-retest was examined via DIF by time, none of the items showed DIF. CONCLUSION The FreNAQ-T is a valid, reliable, unidimensional scale in CNP and suitable for the assessment of neck-specified awareness-perception in Turkish population with CNP. LEVEL OF EVIDENCE 3.
An Electronic-Based Curriculum to Train Acute Care Providers in Rural Haiti and India.

Background Access to a trained, competent health care workforce remains a challenge globally, particularly in rural settings. To bridge this gap, the World Health Organization calls for innovations in electronic learning and task shifting. Yet, these approaches are underutilized due to cost, challenges associated with implementing technology, and a lack of suitably educated trainees. Objective We explored the feasibility of the Acute Care Providers Project (ACPP) to remotely train community members to be health care providers in 2 sites: Haiti and India. Methods The ACP program is an asynchronous curriculum that provides core health content and a structured approach to clinical care through an electronic curriculum. The curriculum is reinforced with case-based practice and hands-on workshops for procedural skills. ACPP was deployed in rural Haiti and India. Evaluation of the program included multiple-choice pretests and posttests, an objective structured clinical examination (OSCE), and direct observation of skills. Results Four Haitian and 55 Indian trainees completed the course. In Haiti, mean scores were 34.8% (SD 12.4) on the pretest and 78.0% (SD 6.5) on the posttest (P = .004). Trainees scored 100% on the OSCE and passed the skills checklist. In India, mean scores were 16.5% (SD 3.9) on the pretest and 81.7% (SD 9.0) on the posttest (P < .001). Trainees scored a median of 91.8% (SD 3.95) on the OSCE and all passed the skills checklist. Conclusions The ACPP offers a scalable, replicable asynchronous curriculum to train lay individuals to provide basic health care in rural communities.