Miracle-dz/UniFAR-FTUs huggingface dataset:
Slowly Delivered Icariin/Allogeneic Bone Marrow-Derived Mesenchymal Stem Cells to Promote the Healing of Calvarial Critical-Size Bone Defects

Bone tissue engineering technique is a promising strategy to repair large-volume bone defects. In this study, we developed a 3-dimensional construct by combining icariin (a small-molecule Chinese medicine), allogeneic bone marrow-derived mesenchymal stem cells (BMSCs), and a siliceous mesostructured cellular foams-poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (SMC-PHBHHx) composite scaffold. We hypothesized that the slowly released icariin could significantly promote the efficacy of SMC-PHBHHx/allogeneic BMSCs for repairing critical-size bone defects in rats. In in vitro cellular experiments, icariin at optimal concentration (10−6 mol/L) could significantly upregulate the osteogenesis- and angiogenesis-related genes and proteins, such as Runx2, ALP, osteocalcin, vascular endothelial growth factors, and fibroblast growth factors, as well as the mineralization of BMSCs. Icariin that was adsorbed onto the SMC-PHBHHx scaffold showed a slow release profile within a 2-week monitoring span. Eight weeks after implantation in calvarial critical-size bone defects, the constructs with icariin were associated with significantly higher bone volume density, trabecular thickness, trabecular number, and significantly lower trabecular separation than the constructs without icariin. Histomorphometric analysis showed that icariin was also associated with a significantly higher density of newly formed blood vessels. These data suggested a promising application potential of the icariin/SMC-PHBHHx/allogeneic BMSCs constructs for repairing large-volume bone defects in clinic.
A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run

The operation of large‐scale water resources systems often involves several conflicting and noncommensurable objectives. The full characterization of tradeoffs among them is a necessary step to inform and support decisions in the absence of a unique optimal solution. In this context, the common approach is to consider many single objective problems, resulting from different combinations of the original problem objectives, each one solved using standard optimization methods based on mathematical programming. This scalarization process is computationally very demanding as it requires one optimization run for each trade‐off and often results in very sparse and poorly informative representations of the Pareto frontier. More recently, bio‐inspired methods have been applied to compute an approximation of the Pareto frontier in one single run. These methods allow to acceptably cover the full extent of the Pareto frontier with a reasonable computational effort. Yet, the quality of the policy obtained might be strongly dependent on the algorithm tuning and preconditioning. In this paper we propose a novel multiobjective Reinforcement Learning algorithm that combines the advantages of the above two approaches and alleviates some of their drawbacks. The proposed algorithm is an extension of fitted Q‐iteration (FQI) that enables to learn the operating policies for all the linear combinations of preferences (weights) assigned to the objectives in a single training process. The key idea of multiobjective FQI (MOFQI) is to enlarge the continuous approximation of the value function, that is performed by single objective FQI over the state‐decision space, also to the weight space. The approach is demonstrated on a real‐world case study concerning the optimal operation of the HoaBinh reservoir on the Da river, Vietnam. MOFQI is compared with the reiterated use of FQI and a multiobjective parameterization‐simulation‐optimization (MOPSO) approach. Results show that MOFQI provides a continuous approximation of the Pareto front with comparable accuracy as the reiterated use of FQI. MOFQI outperforms MOPSO when no a priori knowledge on the operating policy shape is available, while produces slightly less accurate solutions when MOPSO can exploit such knowledge.
Identifying and Understanding Patterns of Nonviolent Delinquency and Violence from Adolescence to Young Adulthood using Latent Class Analysis

From the group-based perspective and using Latent Class Analysis (LCA), the current research used data drawn from a nationally representative sample to identify developmental trajectories of antisocial behavior from adolescence to young adulthood. This research investigated the impact of separately considering nonviolent delinquency and violence on the identification of different trajectories using early family, neighborhood and school variables. Using only violence as response variable, previous studies found four common classes, namely never, desister, decliner, and chronic. Using both nonviolent delinquency and violence as response variable, I found one more distinct class, nonviolent desister. The new class was only found among females. The decliner and chronic class were only found among males. Discussion and implications were provided.
Simultaneous Optimization of Structure and Motion in Dynamic Scenes Using Unsynchronized Stereo Cameras

In this paper, we propose a simultaneous estimation method of structure and motion in dynamic scenes. Usual methods for obtaining structure and motion using stereo cameras require two kinds of operations: stereo correspondence and tracking. Therefore, we must separately determine the correspondence between stereo images and sequential images. This necessity complicates the algorithm and increases the possibility of mismatches because of the object's motion and visibility change in the images. Our proposed method makes two contributions. The first contribution is the method of corresponding all stereo images and sequential images at once. Therefore, we can obtain the structure and motion simultaneously and more accurately. On the other hand, most stereo correspondence algorithms are limited to use under a synchronized status. In a stereo rig using unsynchronized cameras, as are most commercially available cameras, the structure cannot be obtained by stereo correspondence and triangulation because of the unknown time offset between cameras. Therefore, our second contribution is a method of estimating structure, motion, and time offset simultaneously using unsynchronized stereo cameras. This latter task is accomplished by taking advantage of the first contribution scheme. Additionally, our method requires no preprocessing such as motion segmentation for separating identical-motion objects and advance calibration of the time offset. Finally, we present the experimental results using both synthetic and real images.
Involvement of Interleukin-17A-Induced Hypercontractility of Intestinal Smooth Muscle Cells in Persistent Gut Motor Dysfunction

Background and Aim The etiology of post-inflammatory gastrointestinal (GI) motility dysfunction, after resolution of acute symptoms of inflammatory bowel diseases (IBD) and intestinal infection, is largely unknown, however, a possible involvement of T cells is suggested. Methods Using the mouse model of T cell activation-induced enteritis, we investigated whether enhancement of smooth muscle cell (SMC) contraction by interleukin (IL)-17A is involved in postinflammatory GI hypermotility. Results Activation of CD3 induces temporal enteritis with GI hypomotility in the midst of, and hypermotility after resolution of, intestinal inflammation. Prolonged upregulation of IL-17A was prominent and IL-17A injection directly enhanced GI transit and contractility of intestinal strips. Postinflammatory hypermotility was not observed in IL-17A-deficient mice. Incubation of a muscle strip and SMCs with IL-17A in vitro resulted in enhanced contractility with increased phosphorylation of Ser19 in myosin light chain 2 (p-MLC), a surrogate marker as well as a critical mechanistic factor of SMC contractility. Using primary cultured murine and human intestinal SMCs, IκBζ- and p38 mitogen-activated protein kinase (p38MAPK)-mediated downregulation of the regulator of G protein signaling 4 (RGS4), which suppresses muscarinic signaling of contraction by promoting inactivation/desensitization of Gαq/11 protein, has been suggested to be involved in IL-17A-induced hypercontractility. The opposite effect of L-1β was mediated by IκBζ and c-jun N-terminal kinase (JNK) activation. Conclusions We propose and discuss the possible involvement of IL-17A and its downstream signaling cascade in SMCs in diarrheal hypermotility in various GI disorders.
A novel hybrid FC-Battery drive system for electric vehicles

The paper presents the dynamic modeling and coordinated control strategy for an integrated Electric Vehicle drive scheme using Fuel Cell (FC) and Battery back up. The integrated DC drive Scheme is fully stabilized using a novel FACTS based green filter compensator that ensures a fully stabilized Common-DC Bus voltage with reduced inrush current conditions under source and load excursions. The Fuel Cell and Battery are utilized efficiently using the error driven multi loop control strategy. The dynamic Filter Compensator Scheme is validated using the Modified Tri-loop PID controller developed by the Second Author to control the DC-DC converter and Green Power Filter Compensator (GPFC). The paper presents the validation of the multi loop Modified PID controller for the Hybrid FC and Battery E V powered four-wheel DC drive using Permanent Magnet DC (PMDC) motors. The model includes DC drive and load nonlinearities in inertia (J) and viscous friction (B). A Tri-Loop dynamic error controller is used to regulate the motor inrush current and overloading conditions, in addition to speed dynamic reference tracking. A type (B) DC-DC converter is employed to control the power transfer from the Fuel Cell and Battery to the PMDC motor load. All Matlab-Simulink Digital Simulation Functional Models for the Fuel Cell, battery, DC-DC converter, PMDC motor, interface LC-Filters, and Controller Loops are fully modeled using MATLAB/SIMULINK/Sim-power Software Environment.
3D Model Comparison through Kernel Density Matching

A novel 3D shape matching method is proposed in this paper. We first extract angular and distance feature pairs from pre-processed 3D models, then estimate their kernel densities after quantifying the feature pairs into a fixed number of bins. During 3D matching, we adopt the KL-divergence as a distance of 3D comparison. Experimental results show that our method is effective to match similar 3D shapes, and robust to model deformations or rotation transformations.
Iranian Feminist Activists and Critical Media Literacy

Abstract Considering the exponential growth of technology and media in Iranian society as well as the significant role of media culture in reproducing, reinforcing, and legitimizing dominant ideologies such as sexism, the central question posed by this paper is how Iranian feminist activists critically analyze media messages. Further, this paper explores the extent to which this analysis fits the critical media literacy framework. Using a critical media literacy framework underpinned by feminist standpoint theory, this paper presents results from qualitative interviews with 15 Iranian feminist activists. We find that Iranian feminist activists focus more on politics of representation and critique of gender ideology in the critical analysis of media products. Thus, critical analysis of media by Iranian feminist activists better fits the definition of critical media literacy than its core concepts. The findings also demonstrate that a transformative dimension of critical media literacy is ignored by the feminist activists despite using media in the struggle against dominant gender ideology.
Building peace through education

ABSTRACT Can we pave the way to world peace through education of the next generation? This paper focuses on how teaching social and emotional learning (SEL) skills in schools could promote the positive development of children and youth so that they can choose prosocial, nonviolent ways of building relationships with others. First, research on how belonging and fairness develop early in life is briefly reviewed. Then the authors introduce SEL and the research supporting its impact, and explain how it can be supported through various strategies in the classroom, including programs, teacher–student relationships, cooperative learning, approaches to discipline, and solving problems and dealing with conflict peacefully. Building peace by promoting social and emotional development through education offers hope for future generations.
Isoline Based Image Colorization

Colorization is a process of adding colors to black and white images and videos. Each scalar pixel value is replaced by three values which describe, depending on color mode, e.g. The luminance and the chrominance or red-green-blue components. By introducing colors, the resulting image benefit greatly. Not only that it is more attractive for a viewer, but also the perception of hardly noticeable features increases, which is crucial e.g. In medical image analysis. In our paper we present a novel algorithm for grayscale image colorization. The idea is based on the concept of isolines on geographical maps. For each color indicated by a user with scribbles, we create distance maps which present the measure of distance between a pixel and the colored region. The resulting color is calculated as a weighted average of all colors indicated by the user in a form of scribbles. The method is computationally efficient and can be adjusted by several parameters to obtain optimal colorization results. We present several multimedia and biomedical images, colorized using our approach.
microRNA as Repressors of Stress-Induced Anxiety: The Case of Amygdalar miR-34

The etiology and pathophysiology of anxiety and mood disorders is linked to inappropriate regulation of the central stress response. To determine whether microRNAs have a functional role in the regulation of the stress response, we inactivated microRNA processing by a lentiviral-induced local ablation of the Dicer gene in the central amygdala (CeA) of adult mice. CeA Dicer ablation induced a robust increase in anxiety-like behavior, whereas manipulated neurons survive and appear to exhibit normal gross morphology in the time period examined. We also observed that acute stress in wild-type mice induced a differential expression profile of microRNAs in the amygdala. Bioinformatic analysis identified putative gene targets for these stress-responsive microRNAs, some of which are known to be associated with stress. One of the prominent stress-induced microRNAs found in this screen, miR-34c, was further confirmed to be upregulated after acute and chronic stressful challenge and downregulated in Dicer ablated cells. Lentivirally mediated overexpression of miR34c specifically within the adult CeA induced anxiolytic behavior after challenge. Of particular interest, one of the miR-34c targets is the stress-related corticotropin releasing factor receptor type 1 (CRFR1) mRNA, regulated via a single evolutionary conserved seed complementary site on its 3′ UTR. Additional in vitro studies demonstrated that miR-34c reduces the responsiveness of cells to CRF in neuronal cells endogenously expressing CRFR1. Our results suggest a physiological role for microRNAs in regulating the central stress response and position them as potential targets for treatment of stress-related disorders.
Anti-Inflammatory Effects of Interleukin-19 in Vascular Disease

Despite aggressive dietary modification, lipid-lowering medications, and other interventional medical therapy, vascular disease continues to be a leading cause of mortality in the western world. It is a significant medical and socioeconomic problem contributing to mortality of multiple diseases including myocardial infarction, stroke, renal failure, and peripheral vascular disease. Morbidity and mortality of vascular disease are expected to worsen with the increasing number of patients with comorbid conditions such as obesity, metabolic syndrome, and diabetes mellitus type 2. Vascular diseases such as atherosclerosis, restenosis, and allograft vasculopathy are recognized to be driven by inflammation, and as such, cytokines which mediate inflammation not only represent important targets of rational therapy, but also can be considered as possible therapeutic modalities themselves. In this paper, we will examine the role of inflammatory cytokines and lymphocyte Th1/Th2 polarity in vascular inflammation, with a focus on atherosclerotic vascular disease. We will then introduce a recently described Th2 interleukin, interleukin-19 (IL-19), as a previously unrecognized mediator of vascular inflammatory disorders. We will review our current understanding of this interleukin in health and disease and present the possibility that IL-19 could represent a potential therapeutic to combat vascular inflammatory disease.
Design of optical devices using frequency domain solvers

This work deals with efficient frequency domain solvers specifically developed to design optical and plasmonic devices. Homogeneous and inhomogeneous objects embedded in multilayered media are analyzed using Method of Moment (MoM) and hybrid MoM-Finite Element Method (FEM), respectively. The capability of working with materials of complex permittivity makes these algorithms valid and useful for both microwave and optical regimes. Based on the good match between numerical results obtained with these algorithms and the ones found in the literature, we propose an optical antenna optimum for a semiconductor laser diode operating at a wavelength of 830 nm and an infrared sensor compatible with present silicon technology based optical devices.
Music auto-tagging with variable feature sets and probabilistic annotation

This paper proposes a music auto-tagging system based on probabilistic annotation of semantically meaningful tags with variable feature sets. The perception-related long-term features are extracted. The original features are selected by a combination algorithm of ReliefF and principle component analysis (PCA) to form a variable unique feature subset for each tag. The Gaussian mixture models (GMMs) are then trained for each tag. The test tracks are tagged by the output probability of GMMs. To evaluate the quality of the proposed music auto-tagging system, the per-tag precision and recall rates and F-score are measured. Experiment results indicate that the performance of the models trained with the original feature sets is comparable with those trained with MFCC. The reduced variable feature sets demonstrates 2% and 5% up than the original system in precision and recall rates.
Total Ionizing Dose and Single Event Latch-Up Characterization of a 16-Bit A-to-D Converter Fabricated in 0.18µm Triple-Well CMOS Process

A radiation-hardened 16-bit CMOS pipeline Analog-to-Digital Converter operated at 10 MSamples per second (MSps) has been tested with 60Cobalt for Total Ionizing Dose (TID) in five different operating modes - static and dynamic. The built-in bandgap reference showed <;130 mV output voltage shifts, and the ADC as a whole incurred minimal performance variations versus TID, maintaining up to 74.1 dBFS Signal-to-Noise Ratio (SNR) and 92 dBc Spurious-Free Dynamic Range (SFDR) up to 2 Mrad(Si). Selected converter blocks had previously exhibited Single Event Latch-up (SEL) immunity up to an LET level of 121 MeV·cm2/mg with Au ions. The SEL immunity of the whole ADC up to the same LET level was also confirmed after running a series of heavy-ion tests with the converter operating in all of its static and dynamic modes.
A Novel System Model to Recognition of Merged-Characters Under Linear, Nonlinear and Overlapped

This article introduces a traditional model for recognition of the merged-characters. It consists in modeling each sequence with the segmentation which is to cut the isolate items and the recognition which is to make the decision for accepting. In this paper we propose two algorithms based on this system model with one Kinematical Drop-Fall Algorithm for segmentation which could resolve different kinds of merged-relationship as Linear, Nonlinear and Overlapped summarized by Song et al. [1] and a revised Complex Moment Invariants for recognition of single object under the translation, scaling and rotation (TRS). These frame of model and two aspects of improvements in the model could provide two major benefits: Firstly, through the improvement in two aspects, the final effect in correctness and intactness is guaranteed. Moreover, the segmentation and recognition containing in the common set offset and help each other so that would be more effective to obtain the better performance. The experimental results show that our proposed revised algorithms for segmentation, recognition and whole system model are all receiving the better performances compared to former works from the Civil Aerial Meteorological Maps and UPS normalized characters dataset.
Modification of a novel angiogenic peptide, AG30, for the development of novel therapeutic agents

We previously identified a novel angiogenic peptide, AG30, with antibacterial effects that could serve as a foundation molecule for the design of wound‐healing drugs. Toward clinical application, in this study we have developed a modified version of the AG30 peptide characterized by improved antibacterial and angiogenic action, thus establishing a lead compound for a feasibility study. Because AG30 has an α‐helix structure with a number of hydrophobic and cationic amino acids, we designed a modified AG30 peptide by replacing several of the amino acids. The replacement of cationic amino acids (yielding a new molecule, AG30/5C), but not hydrophobic amino acids, increased both the angiogenic and the antimicrobial properties of the peptide. AG30/5C was also effective against methicillin‐resistant Staphylococcus aureus (MRSA) and antibiotic‐resistant Pseudomonas aeruginosa. In a diabetic mouse wound‐healing model, the topical application of AG30/5C accelerated wound healing with increased angiogenesis and attenuated MRSA infection. To facilitate the eventual clinical investigation/application of these compounds, we developed a large‐scale procedure for the synthesis of AG30/5C that employed the conventional solution method and met Good Manufacturing Practice guidelines. In the evaluation of stability of this peptide in saline solution, RP‐HPLC analysis revealed that AG30/5C was fairly stable under 5°C for 12 months. Therefore, we propose the use of AG30/5C as a wound‐healing drug with antibacterial and angiogenic actions.
The Decoupling Control of Tandem Cold Rolling Tension and Gauge

Tension and gauge control interact each other intensively in tandem cold rolling process, making control ineffective and unstable. In order to improve the thickness of the strip and tension control accuracy, control system of tandem cold rolling is equipped with the decoupling control of tension and gauge. Applying the method of Neural Network Simple Adaptive Control based on Genetic Algorithm, we design the Neural Network Simple Adaptive Control multi-variable decoupling control system of tension and gauge, The three layer neural network with genetic algorithm is used to decouple the tension and gauge system of cold rolling mill. The simulation result proves that the decoupling control of tension and gauge can be realized, furthermore, the dynamic and static characteristic of control system is better.