Miracle-dz/UniFAR-FTUs huggingface dataset:
Competitive adsorption of multiple proteins to nanoparticles: the Vroman effect revisited

ABSTRACT Proteins adsorbed from the blood plasma change nanoparticles interactions with the surrounding biological environment. In general, the adsorption of multiple proteins has a non-monotonic time dependence, that is, proteins adsorbed at first may slowly be replaced by others. This ‘Vroman effect’ leads to a highly dynamic protein corona on nanoparticles that profoundly influences the immune response of the body. Thus, the temporal evolution of the corona must be taken into account when considering applications of nanocarriers in, e.g., nanomedicine or drug delivery. Up to now, the Vroman effect is explained solely in terms of diffusion: Smaller proteins which diffuse faster are adsorbed first, while larger ones, having a stronger interaction with the surface, are preferred at equilibrium. Here we use dynamic density functional theory (DDFT) including steric and electrostatic interactions to provide a full model for the temporal evolution of the protein corona. In particular, we demonstrate that proper consideration of all interactions leads to Vroman-like adsorption signatures in widely different scenarios. Moreover, consideration of energetic terms predicts both competitive as well as cooperative adsorption. In this way, DDFT provides a reacher picture of the evolution of the dynamic protein corona. GRAPHICAL ABSTRACT
Therapeutic applications of Caryocar brasiliense: Systematic review

Animal Bioscience Graduate Program, Federal University of Jataí, BR 364, Km 195, 3800, CEP 75801.615, Jataí, Goiás, Brazil. Academic Unit of Biology, Federal University of Jataí, BR 364, Km 195, 3800, CEP 75801.615, Jataí, Goiás, Brazil. Academic Unit of Agrarian Sciences, Animal Bioscience Graduate Program, Federal University of Jataí, BR 364, Km 195, 3800, CEP 75801.615, Jataí, Goiás, Brazil. Chemistry Department,Chemistry Graduate Program, Federal University of Jataí, BR 364, Km 195, 3800, CEP 75801.615, Jataí, Goiás, Brazil.
A Constitutive Modeling and Experimental Effect of Shock Wave on the Microstructural Sub-strengthening of Granular Copper

Micro-sized copper powder (99.95%; O≤0.3) has been shock-processed with explosives of high detonation velocities of the order of 7.5km/s to observe the structural and microstructural sub-strengthening. Axisymmetric shock-consolidation technique has been used to obtain conglomerates of granular Cu. The technique involves the cylindrical compaction system wherein the explosive-charge is in direct proximity with the powder whereas the other uses indirect shock pressure with die-plunger geometry. Numeric simulations have been performed on with Eulerian code dynamics. The simulated results show a good agreement with the experimental observation of detonation parameters like detonation velocity, pressure, particle velocity and shock pressure in the reactive media. A pin contactor method has been utilized to calculate the detonation pressure experimentally. Wide angled x-ray diffraction studies reveal that the crystalline structure (FCC) of the shocked specimen matches with the un-shocked specimen. Field emissive scanning electron microscopic examination of the compacted specimens show a good sub-structural strengthening and complement the theoretical considerations. Laser diffraction based particle size analyzer also points towards the reduced particle size of the shock-processed specimen under high detonation velocities. Micro-hardness tests conducted under variable loads of 0.1kg, 0.05kg and 0.025kg force with diamond indenter optical micrographs indicate a high order of micro-hardness of the order of 159Hv. Nitrogen pycnometry used for the density measurement of the compacts shows that a compacted density of the order of 99.3% theoretical mean density has been achieved.
Native Language Identiﬁcation through Speech

The ubiquity of automatic speech systems in modern technology has garnered increasing interest in spoken Native Language Identiﬁcation (NLI) as a means to addressing clear shortcomings in existing commercial systems. While previous studies have thoroughly explored traditional NLI through simple classiﬁers and classical language processing features, we focus here on the identiﬁcation of a speaker’s native language on phonetic information only. The 2017 NLI shared task provides a dataset constructed solely for NLI improvement, containing speech transcripts and feature extracted i-vectors. Here, we compare baseline methods that have seen success in the past with baseline neural architectures. We report an F1 score of 0.76 for a linear one-versus-all SVM classiﬁer, where transcript derived word-features contribute only 0.01 to the ﬁnal score. With a deep neural model, we achieve an F1 score of 0.78 with the same data. Our results indicate that for this particular task, SVMs with i-vectors provide a fast, competitive baseline model that uses no lexical information, which we propose as a baseline for future NLI tasks.
The Effect of Internal and External Conflicts on the Country Trade and Economic Growth: Case from Emerging and Developed Countries

The current study investigates the impact of internal conflicts and external conflicts on the overall trade of a country, imports and exports in both developed and emerging markets. The study has used 128 countries for the estimation with data collected from the world bank for the period of 1996 and 2016 using the Hausman test. The results of the random effect showed that internal conflicts and external conflicts have negative impact on the imports and exports of countries in both developing and developed economies. The findings of the current study have several implications for both academicians and practitioners. The study has provided a deep insight in to the role of internal and external conflicts (a commonly emergent issue) for international trade and economic growth in both developing and developed countries. The current study has broadened the scope of literature on international finance and trade by providing a unique empirical examination on the role of conflicts in international trade and economic growth which is rarely been examined in the literature. Moreover, the study has some practical implications for the policymakers and government to make their international relations as such that to avoid internal and external conflicts if they want to increase international trade and economic growth. More specifically in developing countries there is more prevalence of internal and external conflicts which is the route cause of the lower level of international trade and is one of the important cause of lower level of economic growth.
Elevated Extracellular cGMP Produced after Exposure to Enterotoxigenic <named-content content-type='genus-species'>Escherichia coli</named-content> Heat-Stable Toxin Induces Epithelial IL-33 Release and Alters Intestinal Immunity

Enterotoxigenic Escherichia coli (ETEC) is a major diarrheal pathogen in children in lowto middle-income countries. Previous studies identified heat-stable enterotoxin (ST)-producing ETEC as a prevalent diarrheal pathogen in children younger than 5 years. While many studies have evaluated the interaction of ETEC heat-labile enterotoxin (LT) with host epithelium and immunity, few investigations have attempted similar studies with ST. To further understand ST pathogenesis, we examined the impact of ST on cGMP localization, epithelial cell cytokine production, and antibody development following immunization. In addition to robust intracellular cGMP in T84 cells in the presence of phosphodiesterase inhibitors (PDEis) that prevent the breakdown of cyclic nucleotides, we found that prolonged ST intoxication induced extracellular cGMP accumulation in the presence or absence of PDEis. Further, ST intoxication induced luminal cGMP in vivo in mice, suggesting that secreted cGMP may have other cellular functions. Using transcriptome sequencing (RNA-seq) and quantitative PCR (qPCR), we demonstrated that ST intoxication, or treatment with the clinically used ST mimic linaclotide, altered inflammatory cytokine gene expression, including the interleukin 1 (IL-1) family member IL-33, which could also be induced by cell-permeative 8-Br-cGMP. Finally, when present during immunization, ST suppressed induction of antibodies to specific antigens. In conclusion, our studies indicate that ST modulates epithelial cell physiology and the interplay between the epithelial and immune compartments.
Tree-to-graph transductions

High-level natural language processing requires formal languages to represent semantic information. A recent addition of this kind is Abstract Meaning Representations (AMRs). These are directed graphs, in which nodes encode concepts, and edges relations. We show that AMRs can be modelled through the combination of (i) a regular tree grammar, (ii) a sequence of top-down tree transducers, and (iii) an operator that merges selected nodes, folding the generated tree into a graph. We discuss the viability of the approach and analyse the computational complexity of the associated membership problem.
A FISTA-type average curvature accelerated composite gradient method for nonconvex optimization problems

This paper presents an accelerated composite gradient (ACG) variant of the FISTA type, referred to as AC-FISTA, for solving nonconvex smooth composite minimization problems. As opposed to well-known ACG variants that are either based on a known Lipschitz gradient constant or a line search procedure, AC-FISTA uses the average of all observed functional curvatures and never backtracks. This paper also provides the convergence rate result of AC-FISTA in terms of the aforementioned average curvatures. Finally, computational results are presented to illustrate the efﬁciency of AC-FISTA on real-world problem instances
Efficiency and Safety of Chinese Herbal Medicine in the TreatmentofPrediabetes:ASystemicReviewandMeta-Analysis of Randomized Controlled Trials

1 e School of Life Sciences, Beijing University of Chinese Medicine, Beijing 100029, China Beijing International Technology Cooperation Base for Prevention and Treatment of Diabetes Mellitus with Chinese Medicine, Beijing 100029, China Health-Cultivation Laboratory of the Ministry of Education, Beijing University of Chinese Medicine, Beijing 100029, China Physical Examination Laboratory, Linyi Lanshan District Center for Disease Control and Prevention, Linyi, Shandong 276000, China
Obstructive Sleep Apnea in Infants and Children in Review

Background: Obstructive sleep apnea is described as a breathing problem during sleep associated with partial or full airway obstruction that disturbs normal sleeping breathing resulting in oxygen desaturation and hypercapnia, which causes increase in respiratory activity and other changes in intrathoracic pressure, which eventually lead to culminating in subcortical or cortical arousal then the infant wanders back to sleep and the cycle continues to happen all night resulting in disturbed sleep. Aim: In this review, we will look into the pathophysiology, symptoms, risk factors, diagnosis and treatment of OSA in children and infants. Conclusion: OSA is prevalent among children and infants and underdiagnosed by both parents and clinician until now. Junior physicians should be educated to be familiar with symptoms of OSA because children have various clinical symptoms and signs according to their developmental stages. Risk factors of OSA should be further studied to prevent increased incidence and further complication of untreated OSA. Untreated OSA in children is correlated with neuropsychiatric impairment, hyperactivity and cardiovascular impacts and sometimes can be correlated with sudden infant death syndrome.
Semi-Compatible and Weakly Compatible Self Maps in an Intuitionistic Fuzzy Metric Space

The present paper is aimed to obtain a common fixed point theorem in intuitionistic fuzzy metric spaces under using the ideas of weak compatibility and semi compatibility for six self mappings. The results presented in this paper extended and generalized various known fixed point theorems in the literature in the setting of fuzzy and intuitionistic fuzzy metric spaces. As a consequence, the main results in this paper extend and unify many results in the topic of fixed points such as C. Alaca, D. Turkoglu and C. Yildiz, Fixed points in intuitionistic fuzzy metric spaces (Choas Solitons & Fractals, 29(5)(2006), 1073-1078). Also, some corollaries of the main results are given.
Tight Risk Bounds for Multi-class Learning

We develop minimal test-set risk bounds of any given classiﬁer in the multi-class setting. Unlike most previous works, which typically reduce multi-class classiﬁcation to a greedy series of dichotomizations, we consider a simultaneous risk bounds with valued asymmetric loss function reﬂecting unequal gravity of misclassiﬁcation. We ﬁrst observe that on an i.i. d test set, the observed losses follow a multinomial distribution which makes it possible to represent the multi-class classiﬁcation in a compact form. We then formulate a mathematical program that yields the tightest possible bound. Due to a pseudo-convex constraint, a special method of centers is used to solve this problem.
Machine Control Using Hand Gesture Recognition

Human to machine collaboration is an important game changer in industries as well as in daily lives. Hand gesture recognition assists this interaction by automating the process. This collaboration is supported by proposing system which can recognize human hand gestures and perform tasks based on those gestures. This paper presents the system that uses hand gesture recognition to control machine. The machine executes programmed task according to gestures recognized. The hand gestures are recognized using vision-based approach. The defined gestures are static and carried out by bare hands. The recognition of the system involves hand segmentation, feature extraction, and classification. The hand is segmented using bounding box in webcam preview, and features are extracted by determining signature of hand and histogram of signature. The classification process uses supervised feed forward neural network system to train some samples of different gestures. Once gesture is recognized, the programmed tasks start to execute. The proposed system has found to have an average accuracy of 92.6% for the classification of gestures.
Modeling and daily operation optimization of a distributed energy system considering economic and energy aspects

This paper presents a distributed energy system (DES) for a local district and formulates a constrained nonlinear multiobjective optimization model for the daily operation of the system. The main objective of the study is to increase the efficiency by minimizing energy cost, energy consumption, and energy losses. It is implemented through the integration and complementation of renewable energies and fossil fuels as well as the recycling utilization of waste heat in the DES. The consideration of network topology and energy losses of water heating network could also contribute to the improvement of energy efficiency. To solve the optimization problem, a novel Whale Optimization Algorithm is employed. Furthermore, the economic and energy performance of the DES are evaluated and compared with that of conventional centralized energy systems, ie, the EG and MG energy‐supply modes. After simulation studies, the hourly optimal energy (both natural gas and electricity) purchasing schedule as well as the hourly optimal set points of mass water flow rates and supply/return water temperatures could be determined. The results show that the DES saves more than 50% of energy costs/energy consumption than the MG mode and over 22% than the EG mode for a whole day, verifying the competitive advantage and great potential of both energy saving and cost reduction of the DES.
T HE F EATURES OF D EEP L EARNING A LGORITHMS

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains
Accelerating the energy transition through intermediary management of high-growth energy

This systematic literature review is conducted prior to the master thesis project aiming to improve the understanding of intermediaries in the Dutch renewable energy market organized to stimulate high-growth firms. The literature review delivers an overview of the scientific enhancements regarding three fundamental concepts central to this research: energy transition, high-growth firms and intermediaries. Chapter 1 gives an introduction of these research themes is given followed by a problem statement. Chapter 2 describes the methodology of this systematic review which is based on the methodology proposed by Tranfield, Denyer, & Smart (2003). Chapter 3 presents a descriptive analysis on the general characteristics of the 32 included studies. Chapter 4 presents a thematic analysis on the theoretical findings. Here, all the relevant findings are synthesized and combined into one complete collection of information. Chapter 5 draws a conclusion on the literature study by identifying the research gap and defining a challenging but feasible research question for the master thesis project: How are intermediaries in the Dutch renewable energy market organized to stimulate high-growth firms?
Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility

Mobility-on-demand (MoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of vehicles. Crucially, the efficiency of an MoD system highly depends on how well supply and demand distributions are aligned in spatio-temporal space (i.e., to satisfy user demand, cars have to be available in the correct place and at the desired time). To do so, we argue that predictive models should aim to explicitly disentangle between temporal} and spatial variability in the evolution of urban mobility demand. However, current approaches typically ignore this distinction by either treating both sources of variability jointly, or completely ignoring their presence in the first place. In this paper, we propose recurrent flow networks (RFN), where we explore the inclusion of (i) latent random variables in the hidden state of recurrent neural networks to model temporal variability, and (ii) normalizing flows to model the spatial distribution of mobility demand. We demonstrate how predictive models explicitly disentangling between spatial and temporal variability exhibit several desirable properties, and empirically show how this enables the generation of distributions matching potentially complex urban topologies.
An approximate gradient based hyper-parameter optimization in a neural network architecture

As we know, most of the learning algorithms arising from scientiﬁc applications involve hyper-parameters which control model structure in order to minimize testing error (i.e. to achieve minimum loss or maximum accuracy of the objective network). The objective and constraint are available only as the output of a black-box or simulation oracle that does not provide derivative information. In this paper, to ﬁnd the best architecture of a neural network architecture to classify cat and dog images, we purpose an approximate gradient based method for optimal hyper-parameters setting which is efﬁcacious than both grid search and random search. Unlike grid search which is exhaustive, the proposed method only searches in its nearest neighbourhood thus reduces computational time. Comparing with random search which does not guarantee convergence, the proposed method converges. Several hyper-parameter settings using CNN architecture with reference to LeNet-5 and a MNIST datasets are performed for cat and dog classiﬁcation. And the numerical results demonstrated that random search takes 5 hour to give 82% accuracy, but our method takes 2 hour to give the same accuracy.
Uncertainty-aware sensitivity analysis using Rényi divergences

For nonlinear supervised learning models, assessing the importance of predictor variables or their interactions is not straightforward because it can vary in the domain of the variables. Importance can be assessed locally with sensitivity analysis using general methods that rely on the model's predictions or their derivatives. In this work, we extend derivative based sensitivity analysis to a Bayesian setting by differentiating the R\'enyi divergence of a model's predictive distribution. By utilising the predictive distribution instead of a point prediction, the model uncertainty is taken into account in a principled way. Our empirical results on simulated and real data sets demonstrate accurate and reliable identification of important variables and interaction effects compared to alternative methods.
GC-MSEvaluation, AntioxidantContent, andCytotoxicActivity of Propolis Extract from Peninsular Malaysian Stingless Bees,

Introduction. Propolis has been used traditionally in several countries for treating various diseases as it possessed healing properties including antioxidant and anticancer qualities. In Peninsular Malaysia, Tetrigona apicalis is one of the species of stingless bees mainly found in virgin jungle reserves which largely contribute to propolis production. 'erefore, this study is designed to evaluate the phytochemical contents, antioxidant properties, and the cytotoxic effect of ethanolic crude of propolis extract against MCF7 and MCF 10A cell lines.Method. 'e ethanolic extract of propolis (EEP) was extracted using 80% ethanol. Identification of phytochemical contents and antioxidant properties of EEP was analysed by gas chromatography-mass spectrometry (GC-MS) and using 2, 2′-azinobis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) method, respectively. 'e EEP cytotoxic activity was evaluated on MCF7 and MCF 10A using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay. Results. Phytochemical contents of EEP demonstrated 28 compounds in which caryophyllene (99%), β-amyrin (96%), α-amyrin (93%), and caryophyllene oxide (93%) were the main compounds. 'e percentage of ABTS scavenging activity of EEP showed an inhibition of 9.5% with half-inhibitory concentration (IC50) value of 1.68mg/mL. 'e EEP reduced MCF7 cells viability at IC50 value of 62.24 μg/mL, 44.15 μg/mL, and 32.70 μg/mL at 24, 48, and 72 hours, respectively. 'e IC50 value of MCF 10A was 49.55 μg/mL, 56.05 μg/mL, and 72.10 μg/mL at 24, 48, and 72 hours, respectively. 'e EEP cytotoxic effect of T. apicalis was more selective towards MCF7 at 72-hour incubation with a selectivity index (SI) of 2.20. Conclusion. 'e EEP has been shown to have antioxidants and potential bioactive compounds and inhibited proliferation of the MCF7 cells. Further studies on the EEP role in the apoptosis pathway and its screening towards other cell lines will be evaluated.