Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Sodium in Diabetic Cardiomyopathy

Cardiovascular complications are the major cause of mortality and morbidity in diabetic patients. The changes in myocardial structure and function associated with diabetes are collectively called diabetic cardiomyopathy. Numerous molecular mechanisms have been proposed that could contribute to the development of diabetic cardiomyopathy and have been studied in various animal models of type 1 or type 2 diabetes. The current review focuses on the role of sodium (Na+) in diabetic cardiomyopathy and provides unique data on the linkage between Na+ flux and energy metabolism, studied with non-invasive 23Na, and 31P-NMR spectroscopy, polarography, and mass spectroscopy. 23Na NMR studies allow determination of the intracellular and extracellular Na+ pools by splitting the total Na+ peak into two resonances after the addition of a shift reagent to the perfusate. Using this technology, we found that intracellular Na+ is approximately two times higher in diabetic cardiomyocytes than in control possibly due to combined changes in the activity of Na+–K+ pump, Na+/H+ exchanger 1 (NHE1) and Na+-glucose cotransporter. We hypothesized that the increase in Na+ activates the mitochondrial membrane Na+/Ca2+ exchanger, which leads to a loss of intramitochondrial Ca2+, with a subsequent alteration in mitochondrial bioenergetics and function. Using isolated mitochondria, we showed that the addition of Na+ (1–10 mM) led to a dose-dependent decrease in oxidative phosphorylation and that this effect was reversed by providing extramitochondrial Ca2+ or by inhibiting the mitochondrial Na+/Ca2+ exchanger with diltiazem. Similar experiments with 31P-NMR in isolated superfused mitochondria embedded in agarose beads showed that Na+ (3–30 mM) led to significantly decreased ATP levels and that this effect was stronger in diabetic rats. These data suggest that in diabetic cardiomyocytes, increased Na+ leads to abnormalities in oxidative phosphorylation and a subsequent decrease in ATP levels. In support of these data, using 31P-NMR, we showed that the baseline β-ATP and phosphocreatine (PCr) were lower in diabetic cardiomyocytes than in control, suggesting that diabetic cardiomyocytes have depressed bioenergetic function. Thus, both altered intracellular Na+ levels and bioenergetics and their interactions may significantly contribute to the pathology of diabetic cardiomyopathy.
Regulation of an Opioid Receptor Chaperone Protein, RTP4, by Morphine

Signaling by classic analgesics, such as morphine, is governed primarily by the relative abundance of opioid receptors at the cell surface, and this is regulated by receptor delivery to, and retrieval from, the plasma membrane. Although retrieval mechanisms, such as receptor endocytosis, have been extensively investigated, fewer studies have explored mechanisms of receptor maturation and delivery to the plasma membrane. A previous study implicated receptor transporter proteins (RTPs) in the latter process. Since not much is known about regulation of RTP expression, we initiated studies examining the effect of chronic morphine administration on the levels of RTPs in the brain. Among the four RTPs, we detected selective and region-specific changes in RTP4 expression; RTP4 mRNA is significantly upregulated in the hypothalamus compared with other brain regions. We examined whether increased RTP4 expression impacted receptor protein levels and found a significant increase in the abundance of mu opioid receptors (MOPrs) but not other related G protein–coupled receptors (GPCRs, such as delta opioid, CB1 cannabinoid, or D2 dopamine receptors) in hypothalamic membranes from animals chronically treated with morphine. Next, we used a cell culture system to show that RTP4 expression is necessary and sufficient for regulating opioid receptor abundance at the cell surface. Interestingly, selective MOPr-mediated increase in RTP4 expression leads to increases in cell surface levels of MOPr–delta opioid receptor heteromers, and this increase is significantly attenuated by RTP4 small interfering RNA. Together, these results suggest that RTP4 expression is regulated by chronic morphine administration, and this, in turn, regulates opioid receptor cell surface levels and function.
Inhibition of LINE-1 Retrotransposition by Capsaicin

Long interspersed nuclear element 1 (LINE-1 or L1) is a non-long terminal repeat (LTR) retrotransposon that constitutes approximately 17% of the human genome. Since approximately 100 copies are still competent for retrotransposition to other genomic loci, dysregulated retrotransposition of L1 is considered to be a major risk factor of endogenous mutagenesis in humans. Thus, it is important to find drugs to regulate this process. Although various chemicals are reportedly capable of affecting L1 retrotransposition, it is poorly understood whether phytochemicals modulate L1 retrotransposition. Here, we screened a library of compounds that were derived from phytochemicals for reverse transcriptase (RT) inhibition with an in vitro RT assay. We identified capsaicin as a novel RT inhibitor that also suppressed L1 retrotransposition. The inhibitory effect of capsaicin on L1 retrotransposition was mediated neither through its receptor, nor through its modulation of the L1 promoter and/or antisense promoter activity, excluding the possibility that capsaicin indirectly affected L1 retrotransposition. Collectively, capsaicin suppressed L1 retrotransposition most likely by inhibiting the RT activity of L1 ORF2p, which is the L1-encoded RT responsible for L1 retrotransposition. Given that L1-mediated mutagenesis can cause tumorigenesis, our findings suggest the potential of capsaicin for suppressing cancer development.
Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology

Online inter-camera trajectory association is a promising topic in intelligent video surveillance, which concentrates on associating trajectories belong to the same individual across different cameras according to time. It remains challenging due to the inconsistent appearance of a person in different cameras and the lack of spatio-temporal constraints between cameras. Besides, the orientation variations and the partial occlusions significantly increase the difficulty of inter-camera trajectory association. Targeting to solve these problems, this work proposes an orientation-driven person re-identification (ODPR) and an effective camera topology estimation based on appearance features for online inter-camera trajectory association. ODPR explicitly leverages the orientation cues and stable torso features to learn discriminative feature representations for identifying trajectories across cameras, which alleviates the pedestrian orientation variations by the designed orientation-driven loss function and orientation aware weights. The effective camera topology estimation introduces appearance features to generate the correct spatio-temporal constraints for narrowing the retrieval range, which improves the time efficiency and provides the possibility for intelligent inter-camera trajectory association in large-scale surveillance environments. Extensive experimental results demonstrate that our proposed approach significantly outperforms most state-of-the-art methods on the popular person re-identification datasets and the public multi-target, multi-camera tracking benchmark.
A lifetime of pulmonary gas exchange

Pulmonary gas exchange is the primary function of the lung, and during my lifetime, its measurement has passed through many stages. When I was born, many physiologists still believed that the lung secreted oxygen. When I was a medical student, the only way we had to recognize defective gas exchange was whether the patient was cyanosed. The advent of the oximeter soon showed that this sign could be very misleading. A breakthrough was the introduction of blood gas electrodes that could measure the PO2, PCO2, and pH of a small sample of arterial blood. It was soon recognized that the commonest cause of hypoxemia was ventilation‐perfusion inequality, and that this could also be responsible for CO2 retention. In the early days, the understanding of the mechanisms of pulmonary gas exchange relied on graphical analysis because the oxygen and carbon dioxide dissociation curves are nonlinear and interdependent which precluded algebraic methods. However, with the introduction of digital computing, problems that had hitherto been impossible to tackle became amenable to study. A key advance was the development of the Multiple Inert Gas Elimination Technique. Now, noninvasive methods for measuring gas exchange show promise, and the whole subject continues to develop.
Crowd Science in the Perspective of Economics

In this paper, we try to bridge two disciplines: crowd science and economics. We first re-expound five essential research questions in the perspective of economics and elaborate how to study crowd science in the framework of economics in each essential question. Then, we build a simple model to illustrate the importance of wisdom of crowd by investigating the incentive of product development in crowdfunding when the market demand is uncertain. We show that, that under certain conditions, crowdfunding can provide more accurate demand information and therefore can generate better incentives to entrepreneurs and help entrepreneurs make correct decisions.
Attacks and Defenses towards Machine Learning Based Systems

Recent research1 has shown that machine learning models are venerable to attacks by adversaries almost at all phases of machine learning pipeline, such as positioning attacks on training data, attacks on the learning algorithm, input attacks based on carefully crafted adversarial samples, model steal and model inversion attack etc. Input samples that are maliciously created can affect the learning process of a ML system by either slowing the learning process, or affecting the performance of the learned model or causing the system make error. So, understanding the security of machine learning algorithms and systems is emerging as an important research area among computer security and machine learning researchers and practitioners. We present a survey on this emerging area: firstly, we define the processing pipeline of a generic machine learning system, then, we identify the attacks in different points of the pipeline and its potential defense solution. Finally, the research work of this paper is summarized and the further research directions are proposed.
Qualifizieren statt Entlassen - Betriebliche Weiterbildung in der Krise

Unter dem programmatischen Slogan „Qualifizieren statt Entlassen“ wurde Ende 2008 ein arbeitsmarktpolitisches Maßnahmenpaket beschlossen. Es sollte in Reaktion auf die globale Wirtschaftskrise dazu beitragen, Beschäftigung zu sichern. Im Mittelpunkt der Aufmerksamkeit stand die Ausweitung des Kurzarbeitergeldes. Obwohl die Wirtschaftsleistung im Verlauf der Krise dramatisch einbrach, verlief die Entwicklung am Arbeitsmarkt deutlich moderater. Die Betriebe entließen Beschäftigte also nicht in dem zu erwartenden Maße, sondern nutzten die Kurzarbeit, aber auch den flexiblen Einsatz von Arbeitszeitkonten. Zu wenig ist bislang jedoch über die betrieblichen Qualifizierungsbemühungen bekannt.
Stacking of doxorubicin on folic acid-targeted multiwalled carbon nanotubes for in vivo chemotherapy of tumors

Abstract In this work, we developed a novel active targeting and pH-responsive system for delivering the drug doxorubicin (DOX) to tumor sites using folic acid (FA)-modified multiwalled carbon nanotubes (MWCNTs). Acid-treated MWCNTs with carboxyl groups were first covalently conjugated with polyethyleneimine (PEI). Subsequent sequential modification with FA (via a polyethylene glycol spacer), fluorescein isothiocyanate (FI), and acetic anhydride/triethylamine resulted in multifunctional FA-bound MWCNT (MWCNT-PEI.Ac-FI-PEG-FA) nanomaterials that possessed exceptional colloidal stability and good biocompatibility in a given concentration range. The FA-bound MWCNTs were characterized using various techniques and exhibited a high drug loading and an encapsulation efficiency as high as 70.4%. DOX/MWCNT-PEI.Ac-FI-PEG-FA nanocomplexes (DOX/MWCNT NCs) exhibited pH-responsive release in acidic environments. Importantly, the DOX/MWCNT NCs targeted tumor cells overexpressing FA receptors (FARs) and effectively inhibited their growth. In vivo anticancer experiments demonstrated that DOX/MWCNT NCs not only enhanced the suppression of tumor growth but also decreased the side effects of free DOX. The developed FA-modified MWCNTs with an unconventionally high DOX loading boosted in vivo anti-tumor efficacy, and the lower systemic toxicity may be utilized for tumor therapy upon clinical translation.
Competence-based recommender systems: a systematic literature review

ABSTRACT Competence-based learning is increasingly widespread in many institutions since it provides flexibility, facilitates the self-learning and brings the academic and professional worlds closer together. Thus, the competence-based recommender systems emerged taking the advantages of competences to offer suggestions (performance of a learning experience, assistance of an expert or recommendation of a learning resource) to the user (learner or instructor). The objective of this work is to conduct a new Systematic Literature Review (SLR) concerning competence-based recommender systems to analyse in relation to their nature and assessment of competences an others key factors that provide more flexible and exhaustive recommendations. To do so, a SLR research methodology was followed in which 25 competence-based recommender systems related to learning or instruction environments were classified according to multiple criteria. We evaluate the role of competences in these proposals and enumerate the emerging challenges. Also a critical analysis of current proposals is carried out to determine their strengths and weakness. Finally, future research paths to be explored are grouped around two main axes closely interlinked; first about the typical challenges related to recommender systems and second, concerning ambitious emerging challenges.
Augmented Reality Travel App Based on Image Retrieval Algorithm

In recent years, AR technology has attracted the attention of many researchers or groups, and has been applied in many ways, such as game, teaching, military, medicine and so on, bringing new life experience to people. But in the tourist attractions traveler may encounter such problem. As the traditional travel software cannot provide the real-time introduction of the tourist attractions for the users, we proposed a novel travel app based on AR to solve this problem. It can retrieve similar picture from the data sets according to the pictures taken by travelers, then show the relevant introduction of tourist attractions. Travelers can also choose to scan the photos on the tour book, and achieve the same effect. In order to retrieve the similar image quickly and accurately, this paper uses the Vector Of Locally Aggregated Descriptors (VLAD) combining the cosine distance to retrieve images. The experimental results show that the method meets the demands of real-time and accuracy in a large number of image retrieval.
Distance to care, care seeking and child mortality in rural Burkina Faso: findings from a population‐based cross‐sectional survey

Although distance has been identified as an important barrier to care, evidence for an effect of distance to care on child mortality is inconsistent. We investigated the association of distance to care with self‐reported care seeking behaviours, neonatal and post‐neonatal under‐five child mortality in rural areas of Burkina Faso.
Drosophila as a Model for Developmental Biology: Stem Cell-Fate Decisions in the Developing Nervous System

Stem cells face a diversity of choices throughout their lives. At specific times, they may decide to initiate cell division, terminal differentiation, or apoptosis, or they may enter a quiescent non-proliferative state. Neural stem cells in the Drosophila central nervous system do all of these, at stereotypical times and anatomical positions during development. Distinct populations of neural stem cells offer a unique system to investigate the regulation of a particular stem cell behavior, while comparisons between populations can lead us to a broader understanding of stem cell identity. Drosophila is a well-described and genetically tractable model for studying fundamental stem cell behavior and the mechanisms that underlie cell-fate decisions. This review will focus on recent advances in our understanding of the factors that contribute to distinct stem cell-fate decisions within the context of the Drosophila nervous system.
IcooBook: When the Picture Book for Children Encounters Aesthetics of Interaction

In this work, we propose a novel PCA (Perception & Cognition & Affection) model from the prospective of aesthetics in interaction. Based on PCA, we establish a new electronic interactive picture book for children, named IcooBook. At the first level of perception, the proposed IcooBook provides interfaces of multi-sensory interaction; at the second level of cognition, IcooBook builds immersive interactive scenes; at the third level of affection, IcooBook creates high-level interaction modes based on automatic emotion recognition. The research on user study had proved the effectiveness of IcooBook in helping children being focusing on reading, getting better understanding about the context, and further encouraging children to appreciate the beauty of deep affective interaction.
Compositional Dynamics of the Milk Fat Globule and Its Role in Infant Development

Human milk is uniquely optimized for the needs of the developing infant. Its composition is complex and dynamic, driven primarily by maternal genetics, and to a lesser extent by diet and environment. One important component that is gaining attention is the milk fat globule (MFG). The MFG is composed of a triglyceride-rich core surrounded by a tri-layer membrane, also known as the milk fat globule membrane (MFGM) that originates from mammary gland epithelia. The MFGM is enriched with glycerophospholipids, sphingolipids, cholesterol, and proteins, some of which are glycosylated, and are known to exert numerous biological roles. Mounting evidence suggests that the structure of the MFG and bioactive components of the MFGM may benefit the infant by aiding in the structural and functional maturation of the gut through the provision of essential nutrients and/or regulating various cellular events during infant growth and immune education. Further, antimicrobial peptides and surface carbohydrate moieties surrounding the MFG might have a pivotal role in shaping gut microbial populations, which in turn may promote protection against immune and inflammatory diseases early in life. This review seeks to: (1) understand the components of the MFG, as well as maternal factors including genetic and lifestyle factors that influence its characteristics; (2) examine the potential role of this milk component on the intestinal immune system; and (3) delineate the mechanistic roles of the MFG in infant intestinal maturation and establishment of the microbiota in the alimentary canal.
Green tea (Camellia sinensis) aqueous extract alleviates postmenopausal osteoporosis in ovariectomized rats and prevents RANKL-induced osteoclastogenesis in vitro

Background Green tea (Camelliasinensis [L.] Kuntze) belongs to the plant family Theaceae and is mainly distributed in East Asia, the Indian subcontinent and Southeast Asia. This plant has been proven to be beneficial to human health, and green tea is the second most consumed beverage in the world after water. However, until now, the effect of green tea aqueous extract (GTE) upon postmenopausal osteoporosis has remained unclear. In this study, we investigated the therapeutic effects of GTE on estrogen deficiency-induced osteoporosis and explored the possible mechanisms in vivo and in vitro. Materials and methods Ovariectomized (OVX) female rats were orally administered with GTE at doses of 60, 120, and 370 mg kg−1 for 13 consecutive weeks. The biochemical parameters, bone gla protein, alkaline phosphatase, acid phosphatase, estrogen, interleukin-1β, and interleukin-6 in blood samples were detected, and histological change in bones was analyzed by hematoxylin and eosin staining. Meanwhile, the mechanisms of GTE on osteoclast formation were explored in RAW 264.7 cells induced by receptor activation of the nuclear factor kappa B ligand (RANKL). Results The results showed that GTE could increase bone mass and inhibit trabecular bone loss in OVX rats. Furthermore, real-time quantitative reverse transcription polymerase chain reaction analysis from in vitro experiments also showed that GTE reduced the mRNA expression of osteoclast-associated genes such as cathepsin K (cath-K), c-Fos, matrix metalloproteinase 9, nuclear factor of activated T cells cytoplasmic 1 (NFATc1) and tartrate-resistant acid phosphatase. In addition, GTE caused a reduction in the protein levels of NFATc1, c-Fos, c-src and cath-K. Conclusion Evidence from both animal models and in vitro experiments suggested that GTE might effectively ameliorate the symptoms of osteoporosis in OVX rats and inhibit RANKL-induced osteoclast-specific gene and protein expression.
Wear Resistance Mechanism of Alumina Ceramics Containing Gd2O3

Excellent wear resistance of alumina ceramics is a desirable quality for many products. The purpose of this work was to improve the wear resistance of 99% alumina ceramics in an Al2O3–Gd2O3–SiO2–CaO–MgO (AGSCM) system. The content of Gd2O3 varied from 0.01% to 1%. A test of wear rate was performed in a ball milling apparatus in a water environment according to the Chinese industry standard. The compositions and microstructure of this material, as well as the effect of bulk density on wear rate, were studied. The effect of Gd2O3 on phases, grain growth mode, and grain boundary cohesion was investigated. It was found that Gd2O3 could refine grain size, form compressive stress of the grain boundary, and promote the crystallization of CaAl12O19. The wear rate of this material was as low as 0.00052‰ (the Chinese industry standard wear rate is ≤0.15‰). The mechanisms for wear resistance of AGSCM ceramics were also determined.
A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians.

Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models. The DFTB layer takes, as input, Hamiltonian matrix elements generated from earlier layers and produces, as output, electronic properties from self-consistent field solutions of the corresponding DFTB Hamiltonian. Backpropagation enables efficient training of the model to target electronic properties. Two types of input to the DFTB layer are explored, splines and feed-forward neural networks. Because overfitting can cause models trained on smaller molecules to perform poorly on larger molecules, regularizations are applied that penalize nonmonotonic behavior and deviation of the Hamiltonian matrix elements from those of the published DFTB model used to initialize the model. The approach is evaluated on 15 700 hydrocarbons by comparing the root-mean-square error in energy and dipole moment, on test molecules with eight heavy atoms, to the error from the initial DFTB model. When trained on molecules with up to seven heavy atoms, the spline model reduces the test error in energy by 60% and in dipole moments by 42%. The neural network model performs somewhat better, with error reductions of 67% and 59%, respectively. Training on molecules with up to four heavy atoms reduces performance, with both the spline and neural net models reducing the test error in energy by about 53% and in dipole by about 25%.