Miracle-dz/UniFAR-FTUs huggingface dataset:
mTORC1 Signaling Promotes Osteoblast Differentiation from Preosteoblasts

Preosteoblasts are precursor cells that are committed to the osteoblast lineage. Differentiation of these cells to mature osteoblasts is regulated by the extracellular factors and environmental cues. Recent studies have implicated mTOR signaling in the regulation of osteoblast differentiation. However, mTOR exists in two distinct protein complexes (mTORC1 and mTORC2), and the specific role of mTORC1 in regulating the progression of preosteoblasts to mature osteoblastis still unclear. In this study, we first deleted Raptor, a unique and essential component of mTORC1, in primary calvarial cells. Deletion of Raptor resulted in loss of mTORC1 but an increase in mTORC2 signaling without overtly affecting autophagy. Under the osteogenic culture condition, Raptor-deficient cells exhibited a decrease in matrix synthesis and mineralization. qPCR analyses revealed that deletion of Raptor reduced the expression of late-stage markers for osteoblast differentiation (Bglap, Ibsp, and Col1a), while slightly increasing early osteoblast markers (Runx2, Sp7, and Alpl). Consistent with the findings in vitro, genetic ablation of Raptor in osterix-expressing cells led to osteopenia in mice. Together, our findings have identified a specific role for mTORC1 in the transition from preosteoblasts to mature osteoblasts.
Visualizing Objects and Memory Usage

Most of the current garbage collector implementations work by reachability. This means they only take care of the objects that nobody else points to. As a consequence, there are objects which are not really used but are not garbage collected because they are still referenced. Such unused but reachable objects create memory leaks. This is a problem because applications use much more memory than what is actually needed. In addition, they may get slower and crash. It is important to understand which parts of the system are instantiated but also which are used or unused. There is a plethora of work on runtime information or class instantiation visualizations but none of them show whether instances are actually used. Such information is important to identify memory leaks. In this paper, we present some visualizations that show used/unused objects in object-oriented applications. For this, we use Distribution Map which is a visualization showing spread and focus of properties across systems. We extend Distribution Maps to represent the way classes are used or not, since we distinguish between a class that just has instances from one that has used instances. To identify unused objects, we modified the Pharo Virtual Machine.
Perceptions of Orthodontic Specialist Training in the United Kingdom: A National Survey of Postgraduate Orthodontic Student Opinion

Introduction Postgraduate orthodontic education is highly challenging. There is a lack of evidence regarding orthodontic postgraduates’ perceptions of their orthodontic education in the United Kingdom. In addition, no study has been conducted to compare the postgraduate orthodontic training experience between UK trainees and international postgraduates. Objectives (1) To investigate orthodontic UK trainees’ and international postgraduates’ perception and satisfaction of their orthodontic education. (2) To compare postgraduate orthodontic training experience between UK trainees and international postgraduates. (3) To compare orthodontic caseload between UK trainees and international postgraduates. Methods Utilizing a descriptive, cross-sectional survey, conducted through a web-based self-administered questionnaire, the sample population consisted of all 189 orthodontic postgraduates in the United Kingdom. Results One hundred and twenty-five questionnaires were completed resulting in a response rate of 66.1%. All three academic years were evenly represented. Ninety five (76%) respondents rated the quality of their postgraduate orthodontic education as excellent. Over 90% of third year postgraduates felt the quality of their education was excellent. The majority of orthodontic trainees (n = 98, 78.4%) were satisfied with the number of cases. Apart from the first year postgraduates, other postgraduates regardless of UK trainee or international postgraduate status reported a high number of cases in treatment, with 45 (36%) respondents treating more patients than the recommended range of 80–120. Postgraduates’ responses were less positive regarding their ability to influence the delivery of their programme, whether their programme represented value for money and the fairness of their training. Most of the respondents believed that UK trainees had an advantage in communicating with patients. Conclusion The majority of orthodontic postgraduates rated the quality of their postgraduate education as excellent. Apart from first year postgraduates, other postgraduates irrespective of UK trainee or international postgraduate status reported a high number of cases in treatment. There was no difference in caseload between UK trainee and international postgraduate groups.
Simplified DFT methods for consistent structures and energies of large systems

Kohn–Sham density functional theory (DFT) is routinely used for the fast electronic structure computation of large systems and will most likely continue to be the method of choice for the generation of reliable geometries in the foreseeable future. Here, we present a hierarchy of simplified DFT methods designed for consistent structures and non-covalent interactions of large systems with particular focus on molecular crystals. The covered methods are a minimal basis set Hartree–Fock (HF-3c), a small basis set screened exchange hybrid functional (HSE-3c), and a generalized gradient approximated functional evaluated in a medium-sized basis set (B97-3c), all augmented with semi-classical correction potentials. We give an overview on the methods design, a comprehensive evaluation on established benchmark sets for geometries and lattice energies of molecular crystals, and highlight some realistic applications on large organic crystals with several hundreds of atoms in the primitive unit cell.
Mse-Optimal 1-Bit Precoding for Multiuser Mimo Via Branch and Bound

In this paper, we solve the sum mean-squared error (MSE)-optimal 1-bit quantized precoding problem exactly for small-to-moderate sized multiuser multiple-input multiple-output (MU-MIMO) systems via branch and bound. To this end, we reformulate the original NP-hard precoding problem as a tree search and deploy a number of strategies that improve the pruning efficiency without sacrificing optimality. We evaluate the error-rate performance and the complexity of the resulting 1-bit branch-and-bound (BB-1) precoder, and compare its efficacy to that of existing, suboptimal algorithms for 1-bit precoding in MU-MIMO systems.
Coagulation factor and protease pathways in thrombosis and cardiovascular disease

Summary The biochemical characterisation of the proteolytic pathways that constitute blood coagulation was one of the most relevant achievements in biomedical research during the second half of the 20th century. Understanding these pathways was of crucial importance for improving global health through application in haemostasis and thrombosis pathologies. Immediately after the cloning of the genes corresponding to these proteins, mutations were discovered in them that were associated with imbalances in haemostasis. Later, the importance of coagulation pathways in other pathological processes was demonstrated, such as in atherosclerosis and inflammation, both essential processes involved in vascular disease. In the present review we evaluate the concepts that have allowed us to reach the integrated vision on coagulation that we have today. The thrombo-inflammation model encompassing these aspects includes a pivotal role for the proteases of the coagulation pathway as well as the regulatory proteins thereof. These concepts illustrate the importance of the coagulation cascade in cardiovascular pathology, not only in thrombotic processes, but also in atherosclerotic processes and in the response to ischaemia-reperfusion injury, making it a central mechanism in cardiovascular disease.
Stoichiometry and compositional plasticity of the yeast nuclear pore complex revealed by quantitative fluorescence microscopy

Significance The nuclear pore complex (NPC) is one of the largest protein assemblies in eukaryotes comprising over 500 nucleoporin subunits. The NPC is essential for the transport of biomolecules across the nuclear envelope; however, due to its enormous size, it has been a challenge to characterize its molecular architecture. Herein, we have developed a widely applicable imaging pipeline to determine the absolute nucleoporin abundances in native yeast NPCs. This reveals that the NPC composition dramatically differs between yeast and humans despite overall conservation of individual subunits. We also applied our imaging pipeline to examine yeast mutants revealing remarkable compositional plasticity of NPCs. Our stoichiometry analyses provide an important resource for the generation of high-resolution structure models of the NPC. The nuclear pore complex (NPC) is an eightfold symmetrical channel providing selective transport of biomolecules across the nuclear envelope. Each NPC consists of ∼30 different nuclear pore proteins (Nups) all present in multiple copies per NPC. Significant progress has recently been made in the characterization of the vertebrate NPC structure. However, because of the estimated size differences between the vertebrate and yeast NPC, it has been unclear whether the NPC architecture is conserved between species. Here, we have developed a quantitative image analysis pipeline, termed nuclear rim intensity measurement (NuRIM), to precisely determine copy numbers for almost all Nups within native NPCs of budding yeast cells. Our analysis demonstrates that the majority of yeast Nups are present at most in 16 copies per NPC. This reveals a dramatic difference to the stoichiometry determined for the human NPC, suggesting that despite a high degree of individual Nup conservation, the yeast and human NPC architecture is significantly different. Furthermore, using NuRIM, we examined the effects of mutations on NPC stoichiometry. We demonstrate for two paralog pairs of key scaffold Nups, Nup170/Nup157 and Nup192/Nup188, that their altered expression leads to significant changes in the NPC stoichiometry inducing either voids in the NPC structure or substitution of one paralog by the other. Thus, our results not only provide accurate stoichiometry information for the intact yeast NPC but also reveal an intriguing compositional plasticity of the NPC architecture, which may explain how differences in NPC composition could arise in the course of evolution.
Multi-modal Embedding for Main Product Detection in Fashion

We present an approach to detect the main product in fashion images by exploiting the textual metadata associated with each image. Our approach is based on a Convolutional Neural Network and learns a joint embedding of object proposals and textual metadata to predict the main product in the image. We additionally use several complementary classification and overlap losses in order to improve training stability and performance. Our tests on a large-scale dataset taken from eight e-commerce sites show that our approach outperforms strong baselines and is able to accurately detect the main product in a wide diversity of challenging fashion images.
Dynamic Probabilistic Relational Models

Intelligent agents must function in an uncertain world, containing multiple objects and relations that change over time. Unfortunately, no representation is currently available that can handle all these issues, while allowing for principled and efficient inference. This paper addresses this need by introducing dynamic probabilistic relational models (DPRMs). DPRMs are an extension of dynamic Bayesian networks (DBNs) where each time slice (and its dependences on previous slices) is represented by a probabilistic relational model (PRM). Particle filtering, the standard method for inference in DBNs, has severe limitations when applied to DPRMs, but we are able to greatly improve its performance through a form of relational Rao-Blackwellisation. Further gains in efficiency arc obtained through the use of abstraction trees, a novel data structure. We successfully apply DPRMs to execution monitoring and fault diagnosis of an assembly plan, in which a complex product is gradually constructed from subparts.
Multivalency in healable supramolecular polymers: the effect of supramolecular cross-link density on the mechanical properties and healing of non-covalent polymer networks

Polymers with the ability to heal themselves could provide access to materials with extended lifetimes in a wide range of applications such as surface coatings, automotive components and aerospace composites. Here we describe the synthesis and characterisation of two novel, stimuli-responsive, supramolecular polymer blends based on π-electron-rich pyrenyl residues and π-electron-deficient, chain-folding aromatic diimides that interact through complementary π–π stacking interactions. Different degrees of supramolecular “cross-linking” were achieved by use of divalent or trivalent poly(ethylene glycol)-based polymers featuring pyrenyl end-groups, blended with a known diimide–ether copolymer. The mechanical properties of the resulting polymer blends revealed that higher degrees of supramolecular “cross-link density” yield materials with enhanced mechanical properties, such as increased tensile modulus, modulus of toughness, elasticity and yield point. After a number of break/heal cycles, these materials were found to retain the characteristics of the pristine polymer blend, and this new approach thus offers a simple route to mechanically robust yet healable materials.
Advanced Glycation End-Products-, C-Type Lectin- and Cysteinyl/ Leukotriene-Receptors in Distinct Mesenchymal Stromal Cell Populations: Differential Transcriptional Profiles in Response to Inflammation

Objective We aimed at characterizing the transcription profiles of immunological receptors associated with the biology of mesenchymal stromal cells (MSCs). Materials and Methods In this experimental study, quantitative real time-polymerase chain reaction (qRT- PCR) was performed to establish the transcription profiles of advanced glycation end-products (RAGE) receptor, C-type lectin receptors (CLRs, including DECTIN-1, DECTIN-2 and MINCLE), leukotriene B4 (LTB4) receptors (BLT1 and BLT2) and cysteinyl leukotrienes (CysLTs) receptors (CYSLTR1 and CYSLTR2) in distinct populations of MSCs grown under basic or inflammatory conditions. Results MSCs derived from adipose tissue (AT), foreskin (FSK), Wharton’s jelly (WJ) and bone marrow (BM) exhibited significantly different transcription levels for these genes. Interestingly, these transcription profiles substantially changed following exposure of MSCs to inflammatory signals. Conclusion Collectively, for the first time, our data highlights that MSCs depending on their tissue-source, present several relevant receptors potentially involved in the regulation of inflammatory and immunological responses. Understanding the roles of these receptors within MSCs immunobiology will incontestably improve the efficiency of utilization of MSCs during cell-based therapies.
Gesture and Sign Language Recognition with Temporal Residual Networks

Gesture and sign language recognition in a continuous video stream is a challenging task, especially with a large vocabulary. In this work, we approach this as a framewise classification problem. We tackle it using temporal convolutions and recent advances in the deep learning field like residual networks, batch normalization and exponential linear units (ELUs). The models are evaluated on three different datasets: the Dutch Sign Language Corpus (Corpus NGT), the Flemish Sign Language Corpus (Corpus VGT) and the ChaLearn LAP RGB-D Continuous Gesture Dataset (ConGD). We achieve a 73.5% top-10 accuracy for 100 signs with the Corpus NGT, 56.4% with the Corpus VGT and a mean Jaccard index of 0.316 with the ChaLearn LAP ConGD without the usage of depth maps.
Effects and Mechanisms of Taurine as a Therapeutic Agent

Taurine is an abundant, β-amino acid with diverse cytoprotective activity. In some species, taurine is an essential nutrient but in man it is considered a semi-essential nutrient, although cells lacking taurine show major pathology. These findings have spurred interest in the potential use of taurine as a therapeutic agent. The discovery that taurine is an effective therapy against congestive heart failure led to the study of taurine as a therapeutic agent against other disease conditions. Today, taurine has been approved for the treatment of congestive heart failure in Japan and shows promise in the treatment of several other diseases. The present review summarizes studies supporting a role of taurine in the treatment of diseases of muscle, the central nervous system, and the cardiovascular system. In addition, taurine is extremely effective in the treatment of the mitochondrial disease, mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), and offers a new approach for the treatment of metabolic diseases, such as diabetes, and inflammatory diseases, such as arthritis. The review also addresses the functions of taurine (regulation of antioxidation, energy metabolism, gene expression, ER stress, neuromodulation, quality control and calcium homeostasis) underlying these therapeutic actions.
C-Type Lectin Receptors in Asthma

Asthma is a heterogeneous disease that affects approximately 300 million people worldwide, largely in developed countries. The etiology of the disease is poorly understood, but is likely to involve specific innate and adaptive responses to inhaled microbial components that are found in allergens. Fungal-derived allergens represent a major contributing factor in the initiation, persistence, exacerbation, and severity of allergic asthma. C-type lectin like receptors, such as dectin-1, dectin-2, DC-specific intercellular adhesion molecule 3-grabbing nonintegrin, and mannose receptor, recognize many fungal-derived allergens and other structurally similar allergens derived from house dust mites (HDM). In some cases, the fungal derived allergens have been structurally and functionally identified alongside their respective receptors in both humans and mice. In this review, we discuss recent understanding on how selected fungal and HDM derived allergens as well as their known or unknown receptors shape allergic airway diseases.
Molecular mimicry in Helicobacter pylori infections

Gram-negative bacteria Helicobacter pylori (H. pylori) colonize gastric mucosa in humans and increase the risk of serious diseases such as gastric and duodenal ulcers, stomach cancers and mucosa associated lymphoid tissue lymphoma. The role of H. pylori infection in the pathogenesis of several extragastric diseases has been suggested including immune thrombocytopenic purpura, iron deficiency anemia, vitamin D deficiency, cardiovascular diseases, diabetes mellitus and dermatological disorders. Also neurological diseases and even lung cancer have attracted researchers concern. The relation between H. pylori infection and a growth retardation in children has also been suggested. Many mechanisms of molecular mimicry between H. pylori and the host have been proposed as a pathogen strategy to manipulate the immune system of the host in order to remain unrecognized and avoid eradication. A lot of effort has been put into the demonstration of homologous sequences between H. pylori and host compounds. However, knowledge about how often autoantibodies or autoreactive T lymphocytes induced during H. pylori infections cause pathological disorders is insufficient. This review provides data on H. pylori antigenic mimicry and possible deleterious effects due to the induction of immune response to the components common to these bacteria and the host.
Design, analysis, and characterization of stress-engineered 3D microstructures comprised of PECVD silicon oxide and nitride

Microelectromechanical systems (MEMS) are typically 2D or quasi-3D structures fabricated using surface and bulk micromachining processes. In this work, an approach for 3D structure fabrication based on stress engineering is demonstrated. Specifically, sub-mm 3D spherical cage-like structures are realized through the deformation of bilayers of residually-stressed silicon oxide and silicon nitride with micrometer-scale thicknesses. Analytical and finite models to predict the shape of stress-engineered structures based on geometry and residual stress are described and used for structure design. A systematic experimental study was performed to quantify residual stresses in silicon nitride films made by plasma-enhanced chemical vapor deposition (PECVD). The measurements show that the residual stress of PECVD silicon nitride can be tuned over a wide range of tensile stresses through the control of deposition parameters, such as flow rate and power. Stress engineered 3D cage-like structures comprised of PECVD silicon nitride and oxide films were fabricated. 3D structures with a range of curvatures were demonstrated. The measured geometry of the fabricated structures are in good agreement with predictions from analytical and finite element models.
D-dimer level as a risk factor for postoperative venous thromboembolism in Japanese women with gynecologic cancer.

BACKGROUND The purpose of the present study was to evaluate whether early postoperative D-dimer levels and certain pre-, intra-, and postoperative parameters can be used to predict venous thromboembolism (VTE) in gynecologic cancer patients. MATERIALS AND METHODS We prospectively evaluated 267 gynecologic cancer patients who underwent surgery at our institution. The plasma D-dimer level was measured serially before the operation and on certain postoperative days. After the operation, primary screening for VTE was undertaken by meticulous examination for clinical signs and elevation of the plasma D-dimer level. Seventy-five patients underwent multidetector row computed tomography and were subjected to further investigations. RESULTS VTE was detected in 21 of the 75 patients. There were significant differences in the D-dimer value between VTE-positive and VTE-negative patients on postoperative days 3, 5, and 7. The optimal cut-off value for the postoperative D-dimer level was determined as 5 mug/ml on day 3. Logistic regression multivariate analysis revealed that high D-dimer values on postoperative day 3, the use of recombinant human erythropoietin (rHuEPO), and non-O blood group were independent risk factors for postoperative VTE. CONCLUSION High plasma D-dimer level on postoperative day 3, the use of rHuEPO, and non-O blood group were independent risk factors for postoperative VTE.