Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Concerns of Various Nanoparticles: A Review of Their in Vitro and in Vivo Toxicity

Nanoparticles (NPs) are currently used in diagnosis and treatment of many human diseases, including autoimmune diseases and cancer. However, cytotoxic effects of NPs on normal cells and living organs is a severe limiting factor that hinders their use in clinic. In addition, diversity of NPs and their physico-chemical properties, including particle size, shape, surface area, dispersity and protein corona effects are considered as key factors that have a crucial impact on their safe or toxicological behaviors. Current studies on toxic effects of NPs are aimed to identify the targets and mechanisms of their side effects, with a focus on elucidating the patterns of NP transport, accumulation, degradation, and elimination, in both in vitro and in vitro models. NPs can enter the body through inhalation, skin and digestive routes. Consequently, there is a need for reliable information about effects of NPs on various organs in order to reveal their efficacy and impact on health. This review covers the existing knowledge base on the subject that hopefully prepares us better to address these challenges.
The Sheep Cornea: Structural and Clinical Characteristics

ABSTRACT Purpose The aim of this study was to perform qualitative and quantitative analyses to characterize the corneas of young, healthy sheep. Materials and methods Eight healthy male sheep, 10 months to 1 year of age, were included as experimental subjects. Central corneal thickness was measured using a handheld pachymeter, and an Easygraph corneal topographer provided topographic maps. Microstructural imaging of corneal layers was achieved by using the Heidelberg Retina Tomograph III Rostock Corneal Module in vivo corneal microscope (IVCM). An Ocular Response Analyzer (ORA) provided quantitative measurements of intraocular pressure (IOP), corneal hysteresis (CH), and corneal resistance factor. Tissue histology and immunohistochemistry were carried out to obtain detail on the corneal layers. Results Light microscopy and immunohistochemical labeling revealed a stratified epithelium, a limbus with numerous limbal crypts, a thick basement membrane, a thin Bowman’s layer, a thick corneal stroma with a dense population of keratocytes, and a thick, hyper-reflective Descemet’s membrane. Using IVCM, the cell density of the basal layer was noted to be significantly higher than that of other epithelial cell types. The density of keratocytes was significantly higher (P value = 0.0223) in the anterior compared to the posterior stroma. The endothelial cells were organized in a characteristic honeycomb pattern. The mean and standard deviation values for central corneal pachymetry were 623.14 ± 19.5 μm and 616.37 ± 34.87 μm for the left and right eyes, respectively. ORA-derived mean values for IOPcc and CH for the left and right eyes were 14.93 ± 1.73 mm Hg and 15.16 ± 2.02 mm Hg and 3.56 ± 0.72 mm Hg and 3.73 ± 0.49 mm Hg, respectively. Conclusions The anatomical and clinical characteristics of the sheep cornea, as outlined in this study, make the sheep a suitable and relevant model for corneal research. This study provides researchers with important data on the suitability of sheep as a model for ophthalmic experiments.
SPECIFIC FEATURES OF FOREIGN ECONOMIC RELATIONS

Введение: статья посвящена анализу и выявлению особенностей внешнеэкономических правоотношений в России, исследованию тенденций определения направлений совершенствования внешнеэкономического законодательства регулирующего внешн , еэкономические правоотношения в условиях динамично изменяющихся политических , , экономических социальных и правовых процессов , . Цель: основная цель статьи состоит в попытке решения научной проблемы касающейся построения современной ко , нцепции внешнеэкономических правоотношений в России Достижение поставленной . цели имеет существенное теоретическое значение для науки гражданского и предп ринимательского права. Методы: историко-правовой логико , -юридический системно , структурный метод межотраслевых юридических исследований метод системного , , анализа. Результаты: внешнеэкономическое правоотношение обладает уникальным структурным типом правоотношения Это сложная система состоящая из основного . , синтетического внешнеэкономического правоотношения с динамической структурой и сопровождающих его дополнительных правоотношений которые напрямую влияют на , ВЕСТНИК ПЕРМСКОГО УНИВЕРСИТЕТА ЮРИДИЧЕСКИЕ НАУКИ .
Recent Developments in Recombinant Protein–Based Dengue Vaccines

Recombinant proteins are gaining enormous importance these days due to their wide application as biopharmaceutical products and proven safety record. Various recombinant proteins of therapeutic and prophylactic importance have been successfully produced in microbial and higher expression host systems. Since there is no specific antiviral therapy available against dengue, the prevention by vaccination is the mainstay in reducing the disease burden. Therefore, efficacious vaccines are needed to control the spread of dengue worldwide. Dengue is an emerging viral disease caused by any of dengue virus 1–4 serotypes that affects the human population around the globe. Dengue virus is a single stranded RNA virus encoding three structural proteins (capsid protein, pre-membrane protein, and envelope protein) and seven non-structural proteins (NS1, NS2a, NS2b, NS3, NS4a, NS4b, NS5). As the only licensed dengue vaccine (Dengvaxia) is unable to confer balanced protection against all the serotypes, therefore various approaches for development of dengue vaccines including tetravalent live attenuated, inactivated, plasmid DNA, virus-vectored, virus-like particles, and recombinant subunit vaccines are being explored. These candidates are at different stages of vaccine development and have their own merits and demerits. The promising subunit vaccines are mainly based on envelope or its domain and non-structural proteins of dengue virus. These proteins have been produced in different hosts and are being investigated for development of a successful dengue vaccine. Novel immunogens have been designed employing various strategies like protein engineering and fusion of antigen with various immunostimulatory motif to work as self-adjuvant. Moreover, recombinant proteins can be formulated with novel adjuvants to enhance the immunogenicity and thus conferring better protection to the vaccinees. With the advent of newer and safer host systems, these recombinant proteins can be produced in a cost effective manner at large scale for vaccine studies. In this review, we summarize recent developments in recombinant protein based dengue vaccines that could lead to a good number of efficacious vaccine candidates for future human use and ultimately alternative dengue vaccine candidates.
Multi-Source News Recommender System Based on Convolutional Neural Networks

The recommender system can help users solve the problem of information overload and find the item which the user requires efficiently. In this paper, we combine the text and image in a given user's browsing news article and classify the article through deep neural network, then we recommend the news article's tag to the user. In the process of extracting text eigenvector, we apply Convolutional Neural Network(CNN) method because of its good performance. On the other hand, VGG method is used to extract the image eigenvector. Meanwhile we imply the Autoencoder(AE) to reduce the dimension of the output vector from VGG and we regularize the output vector. Then we combine the obtained text eigenvector with the image eigenvector and send the spliced eigenvector to Multi-layer Perceptron(MLP) to classify the given news. Finally, we predict and recommend the tag of the article to the user. Experimental results show that our method has obtained good results on the Ifeng News dataset. Compared with traditional CNN, Long Short-Term Memory(LSTM) and other methods that only recommend news based on the text eigenvector, our method achieves good results. For example, our method's accuracy in the test set is 16.1% higher than CNN and 58.2% higher than LSTM.
The What, the Why, and the How of Artificial Explanations in Automated Decision-Making

The increasing incorporation of Artificial Intelligence in the form of automated systems into decision-making procedures highlights not only the importance of decision theory for automated systems but also the need for these decision procedures to be explainable to the people involved in them. Traditional realist accounts of explanation, wherein explanation is a relation that holds (or does not hold) eternally between an explanans and an explanandum, are not adequate to account for the notion of explanation required for artificial decision procedures. We offer an alternative account of explanation as used in the context of automated decision-making that makes explanation an epistemic phenomenon, and one that is dependent on context. This account of explanation better accounts for the way that we talk about, and use, explanations and derived concepts, such as `explanatory power', and also allows us to differentiate between reasons or causes on the one hand, which do not need to have an epistemic aspect, and explanations on the other, which do have such an aspect. Against this theoretical backdrop we then review existing approaches to explanation in Artificial Intelligence and Machine Learning, and suggest desiderata which truly explainable decision systems should fulfill.
Kv12.1 channels are not sensitive to GqPCR-triggered activation of phospholipase Cβ

ABSTRACT Kv12.1 K+ channels are expressed in several brain areas, but no physiological function could be attributed to these subunits so far. As genetically-modified animal models are not available, identification of native Kv12.1 currents must rely on characterization of distinct channel properties. Recently, it was shown in Xenopus laevis oocytes that Kv12.1 channels were modulated by membrane PI(4,5)P2. However, it is not known whether these channels are also sensitive to physiologically-relevant PI(4,5)P2 dynamics. We thus studied whether Kv12.1 channels were modulated by activation of phospholipase C β (PLCβ) and found that they were insensitive to receptor-triggered depletion of PI(4,5)P2. Thus, Kv12.1 channels add to the growing list of K+ channels that are insensitive to PLCβ signaling, although modulated by PI(4,5)P2 in Xenopus laevis oocytes.
Game-theoretic network bandwidth distribution for self-adaptive cameras

Devices sharing a network compete for bandwidth, being able to transmit only a limited amount of data. This is for example the case with a network of cameras, that should record and transmit video streams to a monitor node for video surveillance. Adaptive cameras can reduce the quality of their video, thereby increasing the frame compression, to limit network congestion. In this paper, we exploit our experience with computing capacity allocation to design and implement a network bandwidth allocation strategy based on game theory, that accommodates multiple adaptive streams with convergence guarantees. We conduct some experiments with our implementation and discuss the results, together with some conclusions and future challenges.
Strategies to manage obstructive sleep apnea to decrease the burden of atrial fibrillation

ABSTRACT Introduction: In the last decades, consistent data derived from experimental, epidemiological, and clinical studies pointed obstructive sleep apnea (OSA), the most common sleep disordered breathing worldwide, as a potential risk factor for incidence and recurrence of atrial fibrillation (AF). Areas covered: This review article describes the impact of OSA on AF and discusses potential strategies for managing OSA in the AF scenario. Expert commentary: Untreated OSA seems to be one important predictor of AF treatment failure after chemical or electrical cardioversion as well as after a successful AF ablation. There is biological plausibility for this association including the effects of intermittent hypoxia and sleep fragmentation promoting sympathetic activation, blood pressure instability, inflammation, and oxidative stress. The negative swings derived from the obstructive events also increases left ventricle afterload contributing to cardiac remodeling. Altogether, these factors provide a structural and electrical substrate for AF. Despite this evidence, however, OSA remains still poorly recognized and consequently undertreated in clinical practice. Therefore, active programs to incorporate valid screening for cardiologists to work in partnership to sleep medicine experts may ultimately contribute to decreasing the burden of AF.
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation

Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism. However, the conventional attention mechanism treats the decoding at each time step equally with the same matrix, which is problematic since the softness of the attention for different types of words (e.g. content words and function words) should differ. Therefore, we propose a new model with a mechanism called Self-Adaptive Control of Temperature (SACT) to control the softness of attention by means of an attention temperature. Experimental results on the Chinese-English translation and English-Vietnamese translation demonstrate that our model outperforms the baseline models, and the analysis and the case study show that our model can attend to the most relevant elements in the source-side contexts and generate the translation of high quality.
Nanoporous 3D-Printed Scaffolds for Local Doxorubicin Delivery in Bone Metastases Secondary to Prostate Cancer

The spine is the most common site of bone metastasis, often originating from prostate, lung, and breast cancers. High systemic doses of chemotherapeutics such as doxorubicin (DOX), cisplatin, or paclitaxel often have severe side effects. Surgical removal of spine metastases also leaves large defects which cannot spontaneously heal and require bone grafting. To circumvent these issues, we designed an approach for local chemotherapeutic delivery within 3D-printed scaffolds which could also potentially serve as a bone substitute. Direct treatment of prostate cancer cell line LAPC4 and patient derived spine metastases cells with 0.01 µM DOX significantly reduced metabolic activity, proliferation, migration, and spheroid growth. We then assessed uptake and release of DOX in a series of porous 3D-printed scaffolds on LAPC4 cells as well as patient-derived spine metastases cells. Over seven days, 60–75% of DOX loaded onto scaffolds could be released, which significantly reduced metabolic activity and proliferation of both LAPC4 and patient derived cells, while unloaded scaffolds had no effect. Porous 3D-printed scaffolds may provide a novel and inexpensive approach to locally deliver chemotherapeutics in a patient-specific manner at tumor resection sites. With a composite design to enhance strength and promote sustained drug release, the scaffolds could reduce systemic negative effects, enhance bone repair, and improve patient outcomes.
Yeast Probiotics Shape the Gut Microbiome and Improve the Health of Early-Weaned Piglets

Weaning is one of the most stressful challenges in the pig’s life, which contributes to dysfunctions of intestinal and immune system, disrupts the gut microbial ecosystem, and therefore compromises the growth performance and health of piglets. To mitigate the negative impact of the stress on early-weaned piglets, effective measures are needed to promote gut health. Toward this end, we tamed a Saccharomyces cerevisiae strain and developed a probiotic Duan-Nai-An, which is a yeast culture of the tamed S. cerevisiae on egg white. In this study, we tested the performance of Duan-Nai-An on growth and health of early-weaned piglets and analyzed its impact on fecal microbiota. The results showed that Duan-Nai-An significantly improved weight gain and feed intake, and reduced diarrhea and death of early-weaned piglets. Analysis of the gut microbiota showed that the bacterial community was shaped by Duan-Nai-An and maintained as a relatively stable structure, represented by a higher core OTU number and lower unweighted UniFrac distances across the early weaned period. However, fungal community was not significantly shaped by the yeast probiotics. Notably, 13 bacterial genera were found to be associated with Duan-Nai-An feeding, including Enterococcus, Succinivibrio, Ruminococcus, Sharpea, Desulfovibrio, RFN20, Sphaerochaeta, Peptococcus, Anaeroplasma, and four other undefined genera. These findings suggest that Duan-Nai-An has the potential to be used as a feed supplement in swine production.
Spatial global assessment of the pest Bagrada hilaris (Burmeister) (Heteroptera: Pentatomidae): current and future scenarios.

BACKGROUND The insect Bagrada hilaris (Burmeister) an important pest worldwide, mainly due to the serious economic losses incurred and the large number of zones invaded. However, current and future spatial distributions of this pest, and the total area of cropland potentially affected have not been estimated. Here, we aim to: (1) estimate the potential geographic distribution of B. hilaris; (2) quantify the total area of cropland potentially affected worldwide, and in two recently colonized zones (California and Chile); and (3) estimate future changes in distribution under different climate change scenarios. RESULTS We found that B. hilaris shows high environmental suitability in Mediterranean and arid regions, potentially affecting 1 108 184.1 km2 of cropland worldwide. The most affected continents were Asia and America, with 309 659.8 and 294 638.6 km2 of cropland at risk. More than 50% of cropland areas are at risk in seven countries. In California and central Chile, 43.7% and 50% of susceptible crops are at a high level of risk, respectively. Climate change scenarios predict an increase in the potential distribution of B. hilaris worldwide; America being the most affected continent. CONCLUSIONS Our results provide a spatially explicit baseline from which to focus efforts on the prevention, management and control of this pest worldwide. © 2018 Society of Chemical Industry.
Managing the exploration-exploitation paradox in healthcare: Three complementary paths to leverage on the digital transformation

Purpose The purpose of this paper is to understand how digital technologies can help healthcare organisations and improve the exploration-exploitation paradox over time. The authors explore inputs, processes and outcomes of implementing digital transformation programs and advance four testable propositions. Design/methodology/approach The authors conducted multiple case studies with embedded units of analysis: digital transformation processes; hospitals; and regional healthcare systems. Primary sources come from 107 semi-structured interviews with key informants within 14 Italian hospitals between 2009 through 2011. Findings Three complementary paths emerge as fundamental to balance exploratory and exploitatory efforts in healthcare: assets digitalisation within hospitals; digitally based process integration; and disruptive decision-making through analytics. Intra- and inter-path characteristics are discussed to show how digital transformation can both move hospital within the exploration-exploitation space. Research limitations/implications By its very nature, this study is exploratory. Notwithstanding the number of cases and interviews, its generalisability is limited. Practical implications Digital transformation programs are fundamental to resolve the tensions raised by the exploration-exploitation paradox. Their implementation leads to better performance (cost reductions, quality improvements). A framework is provided for practitioners to make better decisions. Originality/value This study sheds new light on how digital technologies are actually adopted and adapted in healthcare contexts. It does it by entailing a longitudinal perspective.
Is the Institutional Environment a Challenge for the Well-Being of Female Managers in Europe? The Mediating Effect of Work–Life Balance and Role Clarity Practices in the Workplace

The advancement of women to top management positions positively affects firm competitiveness. However, this advancement may also negatively affect individuals as women find themselves forced to overwork to match their male counterparts in organisations, which can cause a decrease in their professional well-being. Although the literature highlights that human resource practices (HRPs) have a positive impact on well-being, it also warns that national institutions may condition the adoption of HRPs by organisations. If that is true, institutions may become either a challenge to—or trigger for—female managers’ well-being. Accordingly, this study analyses the effects of institutions and the mediating effects of HRPs on the influence that is exerted by institutions on well-being. The empirical analysis, which was carried out on a sample of 575 female managers located in 27 European countries, confirms the direct and indirect effects (through HRPs for work–life balance and role clarity) of institutions on female managers’ well-being at work.
Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents

In this study, we focus on extracting knowledgeable snippets and annotating knowledgeable documents from Web corpus, consisting of the documents from social media and We-media. Informally, knowledgeable snippets refer to the text describing concepts, properties of entities, or relations among entities, while knowledgeable documents are the ones with enough knowledgeable snippets. These knowledgeable snippets and documents could be helpful in multiple applications, such as knowledge base construction and knowledge-oriented service. Previous studies extracted the knowledgeable snippets using the pattern-based method. Here, we propose the semantic-based method for this task. Specifically, a CNN based model is developed to extract knowledgeable snippets and annotate knowledgeable documents simultaneously. Additionally, a "low-level sharing, high-level splitting" structure of CNN is designed to handle the documents from different content domains. Compared with building multiple domain-specific CNNs, this joint model not only critically saves the training time, but also improves the prediction accuracy visibly. The superiority of the proposed method is demonstrated in a real dataset from Wechat public platform.