Miracle-dz/UniFAR-FTUs huggingface dataset:
Castanea sativa Mill. Bark Extract Protects U‐373 MG Cells and Rat Brain Slices Against Ischemia and Reperfusion Injury

Ischemic brain injury is one of the most important causes of death worldwide. The use of one‐drug‐multi‐target agents based on natural compounds is a promising therapeutic option for cerebral ischemia due to their pleiotropic properties. This study assessed the neuroprotective properties of Castanea sativa Mill. bark extract (ENC) in human astrocytoma U‐373 MG cells subjected to oxygen‐glucose deprivation and reperfusion and rat cortical slices subjected to ischemia‐like conditions or treated with glutamate or hydrogen peroxide. Neuroprotective effects were determined by assessing cells or slices viability (MTT assay), ROS formation (DCFH‐DA assay), apoptosis (sub G0/G1 peak), nuclear fragmentation and chromatin condensation (DAPI staining) as well as changes in lysosomes and mitochondria morphology (Acridine Orange and Rhodamine123 staining, respectively). ENC treatment before injury on U‐373 MG cells (5–50 μg/ml) and cortical slices (50–100 μg/ml) provided neuroprotection, while lower or higher concentrations (100 μg/ml U‐373 MG cells, 200 μg/ml brain slices) were ineffective. ENC addition during reperfusion or after the injury was not found to be effective. The results suggest that ENC might hold potential as preventive neuroprotective agent, and indicate the importance of further studies exploring its mechanism of action. J. Cell. Biochem. 118: 839–850, 2017. © 2016 Wiley Periodicals, Inc.
Effect of pretreatment and geometry on the thermophysical properties of raw ubi (Dioscorea alata L.)

in the Philippines that can be processed into powder for use in manufacturing several food products such as ice cream, ubi jam, among others. The thermophysical properties of ubi need to be determined and compared to prediction equations to understand and optimize the heat transfer in processing ubi. This study was conducted to determine the effect of two pretreatments (sulfite dipping and steaming) and two sample geometries (parallel and perpendicular) on the thermophysical properties (apparent density, specific heat, thermal conductivity, and thermal diffusivity) of raw ubi (Hinaligi variety). Apparent density (ρ) was measured by liquid displacement method. Results (944.0 – 1092.5 kg m) showed that pretreatment had U negligible effect. Specific heat (Cp) was determined using the method of mixtures. Cp values (3.44 – 4.07 kJ kg oC) varied minimally with pretreatment and approximated theoretical values. The heated probe method was used to measure thermal conductivity (k). Thermal conductivity of steamed samples (0.426 W m°C) approximated predictive equations using perpendicular geometry, while that of raw (0.382 W m°C) and sulfite dipped samples (0.379 W m°C) correlated better with equations based on parallel geometry. Results of this study can be used to design efficient heat processes for ubi.
Convergence analysis for rigid formation control with unrealizable shapes: The 3 agent case

We study the outcome of using a gradient descent control law for a minimally rigid formation consisting of N agents, in which each agent is modeled by a single integrator and the desired interagent distances are specified though they are not realizable. We first formulate the problem for formations of N ≥ 3 agents and derive a condition in terms of the rigidity matrix which the final formation must satisfy. Special attention will be given to the triangular formation for which the desired distances fail to satisfy the triangle inequality. In this case, we show the formation converges to a straight line. Detailed analysis is provided to describe the stability properties in the unrealizable triangle shape control problem.
Transgenic over‐expression of growth differentiation factor 11 propeptide in skeleton results in transformation of the seventh cervical vertebra into a thoracic vertebra

Growth differentiation factor 11 (GDF11) is one of the significant genes that control skeletal formation. Knockout of GDF11 function causes abnormal patterning of the anterior/posterior axial skeleton. The mRNA of GDF11 is initially translated to a precursor protein that undergoes a proteolytic cleavage to generate the C‐terminal peptide or mature GDF11, and the N‐terminal peptide named GDF11 propeptide. The propeptide can antagonize GDF11 activity in vitro. To investigate the effects of GDF11 propeptide on GDF11 function in vivo, we generated transgenic mice that over‐express the propeptide cDNA in skeletal tissue. The transgenic mice showed formation of extra ribs on the seventh cervical vertebra (C7) as a result of transformation of the C7 vertebra into a thoracic vertebra. The GDF11 propeptide transgene mRNA was detected in tail tissue in embryos and was highly expressed in tail and calvaria bones after birth. A high frequency of C7 rib formation was noticed in the transgenic mouse line with a high level of transgene expression. The anterior boundaries of Hoxa‐4 and Hoxa‐5 mRNA in situ expressions showed cranial shifts from their normal prevertebra locations in transgenic embryos. These results demonstrated significant effects of GDF11 propeptide transgene on vertebral formation, which are likely occurring through depressing GDF11 function and altered locations of Hoxa‐4 and Hoxa‐5 expression. Mol. Reprod. Dev. 77:990–997, 2010. © 2010 Wiley‐Liss, Inc.
Mining Techniques in Social Media : A Survey

Today, the use of social networks is growing ceaselessly and rapidly. More alarming is the fact that these networks have become a substantial pool for unstructured data that belong to a host of domains, including business, governments and health. The increasing reliance on social networks calls for data mining techniques that is likely to facilitate reforming the unstructured data and place them within a systematic pattern. The goal of the present survey is to analyze the data mining techniques that were utilized by social media networks between 2003 and 2015. Espousing criterion-based research strategies, 66 articles were identified to constitute the source of the present paper. After a careful review of these articles, we found that 19 data mining techniques have been used with social media data to address 9 different research objectives in 6 different industrial and services domains. However, the data mining applications in the social media are still raw and require more effort by academia and industry to adequately perform the job. We suggest that more research be conducted by both the academia and the industry since the studies done so far are not sufficiently exhaustive of data mining techniques.
Lying Upside-Down: Alibis Reverse Cognitive Burdens of Dishonesty

The cognitive processes underlying dishonesty, especially the inhibition of automatic honest response tendencies, are reflected in response times and other behavioral measures. Here we suggest that explicit false alibis might have a considerable impact on these cognitive operations. We tested this hypothesis in a controlled experimental setup. Participants first performed several tasks in a preexperimental mission (akin to common mock crime procedures) and received a false alibi afterward. The false alibi stated alternative actions that the participants had to pretend to have performed instead of the actually performed actions. In a computer-based inquiry, the false alibi did not only reduce, but it even reversed the typical behavioral effects of dishonesty on response initiation (Experiment1) and response execution (Experiment 2). Follow-up investigations of response activation via distractor stimuli suggest that false alibis automatize either dishonest response retrieval, the inhibition of the honest response, or both (Experiments 3 and 4). This profound impact suggests that false alibis can override actually performed activities entirely and, thus, documents a severe limitation for cognitive approaches to lie detection.
Robust Text Detection in Natural Scene Images by Generalized Color-Enhanced Contrasting Extremal Region and Neural Networks

This paper presents a robust text detection approach based on generalized color-enhanced contrasting extremal region (CER) and neural networks. Given a color natural scene image, six component-trees are built from its gray scale image, hue and saturation channel images in a perception-based illumination invariant color space, and their inverted images, respectively. From each component-tree, generalized color-enhanced CERs are extracted as character candidates. By using a "divide-and-conquer" strategy, each candidate image patch is labeled reliably by rules as one of five types, namely, Long, Thin, Fill, Square-large and Square-small, and classified as text or non-text by a corresponding neural network, which is trained by an ambiguity-free learning strategy. After pruning non-text components, repeating components in each component-tree are pruned by using color and area information to obtain a component graph, from which candidate text-lines are formed and verified by another set of neural networks. Finally, results from six component-trees are combined, and a post-processing step is used to recover lost characters and split text lines into words as appropriate. Our proposed method achieves 85.72% recall, 87.03% precision, and 86.37% F-score on ICDAR-2013 "Reading Text in Scene Images" test set.
DeepSpeedometer: Vehicle speed estimation from accelerometer and gyroscope using LSTM model

In recent years, as the development of Mobility On Demand (MOD) services, smartphone-based car-sharing applications became more and more popular. The accurate estimation on waiting-time for a reserved car is a significant aspect to evaluate the car-sharing applications. Waiting-time prediction can be achieved by estimating the traffic situation from the probe information, such as speed and position, which are provided by a huge number of shared cars. Thus, developing a smartphone-based vehicle speed estimation system could improve the performance of the MOD services, and an independent smartphone-based application without any connection with the car also satisfies the convenience demand of MOD service applications. In order to achieve this aim, we proposed the DeepSpeedometer system, which estimates vehicle speed from the accelerometer and gyroscope in the smartphone using a two-layer Long Short-Term Memory (LSTM). For evaluating the performance of the proposed system, we performed a series of experiments, including the analysis on the rationality of the proposed algorithm itself and the comparison with other methods as well. The experimental results show the robustness of our system in different traffic scenarios.
1 RELATIONS AMONG PROPOSED PREDICTORS AND OUTCOMES OF SOCIAL SELF-EFFICACY IN TURKISH LATE ADOLESCENTS

The goal of this study was to examine both the direct and indirect relations among late adolescents’ social self-efficacy and their perceived parents’ attitudes, attachment to parents and peers, problem-solving skills and learned resourcefulness. Two hundred and fifty five volunteer students (average age=18.5) were the participants. The Social Self-Efficacy Inventory, the Inventory of Peer Attachment and Parent Attachment, the Parents’ Attitudes Inventory, the Problem-Solving Skills Inventory and the Learned Resourcefulness Inventory were administered in order to collect data. Structural equation modelling revealed that parental attachment had direct effects on social self-efficacy. In addition, authoritative parenting had indirect effects on social self-efficacy through peer attachment. The links between social selfefficacy and problem-solving skills and, between problem-solving skills and learned resourcefulness were also observed. Different model to be tested and the results showed links between parental attachment and problem-solving skills and, between social self-efficacy and learned resourcefulness.
The Impact of Cardiovascular Diseases on Cardiovascular Regulation During Exercise in Humans: Studies on Metaboreflex Activation Elicited by the Post-exercise Muscle Ischemia Method

Background: Hemodynamics during dynamic exercise is finely regulated by some neural mechanisms. One of these mechanisms is the metabolic part of the exercise pressor reflex, i.e. the muscle metaboreflex. Hemodynamic response during the metaboreflex is characterised by the re-cruitment of the reserves in cardiac inotropism, pre-load, after-load and chronotropism. If one of these reserves is exhausted, then the cardiovascular response is achieved by recruiting one of the other re-serves, thereby indicating a remarkable plasticity of the control of circulation. Conclusion: In this review, the effects of a number of cardiovascular diseases – such as heart failure, heart failure with preserved ejection fraction, hypertension, type 1 and type 2 diabetes mellitus, obesi-ty and metabolic syndrome - on hemodynamics during the metaboreflex are reviewed.
Minority Rule Applied to Multiple Agents Linked into a Social Network with Communication and Memory

In our work we simulate a community of agents, linked into a simple social network based on communication among the nodes, who must take a binary decision at every step. This resembles the original Minority Game (MG), which is a simple, generalized framework, belonging to the Game Theory field, which represents the collective behaviour of agents in an idealized situation where they have to compete through adaptation for some finite resource. It generalizes the study of how many individuals may reach a collective solution to a problem under adaptation of each one’s expectations about the future. The main differences between this work and the original MG are the introduction of communication among the agents, which are now grouped basing on the common choices, and the number of players, that can also be an even number, while in the MG must be an odd number. This is done in order to generalize as much as possible the study of the choices made by agents trying to be in the minority group. Two communication protocols are implemented in the model: the asynchronous one, in which the agents act sequentially. So the first agents which act take their decision, and from then on they reply to the other agents with the new decision taken. The synchronous protocol states that the agents always communicate to the others their original opinion: they broadcast their opinion to all the agents which are linked to them. Finally, after having collected all the opinions of their friends, they reconsider their choice. After examining some random choosing agents, we embed a sort of memory into them, so that they can reason on which has been the best choice by looking at the past n results.
Liposomes as a Putative Tool to Investigate NAADP Signaling in Vasculogenesis

Nicotinic acid adenine dinucleotide phosphate (NAADP) is the newest discovered intracellular second messengers, which is able to release Ca2+ stored within endolysosomal (EL) vesicles. NAADP‐induced Ca2+ signals mediate a growing number of cellular functions, ranging from proliferation to muscle contraction and differentiation. Recently, NAADP has recently been shown to regulate angiogenesis by promoting endothelial cell growth. It is, however, still unknown whether NAADP stimulates proliferation also in endothelial progenitor cells, which are mobilized in circulation after an ischemic insult to induce tissue revascularization. Herein, we described a novel approach to prepare NAADP‐containing liposomes, which are highly cell membrane permeable and are therefore amenable for stimulating cell activity. Accordingly, NAADP‐containing liposomes evoked an increase in intracellular Ca2+ concentration, which was inhibited by NED‐19, a selective inhibitor of NAADP‐induced Ca2+ release. Furthermore, NAADP‐containing liposomes promoted EPC proliferation, a process which was inhibited by NED‐19 and BAPTA, a membrane permeable intracellular Ca2+ buffer. Therefore, NAADP‐containing liposomes stand out as a promising tool to promote revascularization of hypoxic/ischemic tissues by favoring EPC proliferation. J. Cell. Biochem. 118: 3722–3729, 2017. © 2017 Wiley Periodicals, Inc.
Cooperative Content Dissemination and Offloading in Heterogeneous Mobile Networks

To cope with an exponentially increasing demand on mobile data traffic in cellular networks, high-capacity WiFi and device-to-device networks can be exploited as a complementary means to offload and reduce the traffic load of cellular networks. This paper proposes a novel cooperative content dissemination strategy for a heterogeneous network consisting of different types of devices, such as WiFi access points (APs), smartphones, and intelligent vehicles. The proposed strategy offloads a significant proportion of data traffic from cellular networks to WiFi or device-to-device networks using ad hoc connections that opportunistically emerge when mobile devices move and meet one another. The strategy is particularly suited for dissemination of delay-tolerant content. Detailed analysis is provided for the content dissemination process in heterogeneous mobile networks adopting the proposed strategy. On this basis, the optimal parameter settings for the content dissemination strategy are discussed. Simulation and numerical results show that the proposed strategy significantly reduces the amount of data traffic for cellular networks while guaranteeing the successful delivery of content.
SF1 and spleen development: new heterozygous mutation, literature review and consequences for NR5A1‐mutated patient's management

Steroidogenic factor 1 (encoded by SF1/NR5A1) is a transcription factor with multiple target genes involved in the development and function of multiple steroidogenic and non‐steroidogenic tissues. NR5A1 mutations lead to several phenotypes, including sex reversal, spermatogenesis failure, premature ovarian failure and adrenocortical insufficiency. The implication of NR5A1 mutations in spleen development anomalies was recently highlighted. We provide new evidence of this involvement, describing a novel heterozygous non‐sense NR5A1 mutation in a 46,XY‐DSD with polysplenia female proband and her father, who had hypospadias and asplenia.
Efficacy and safety of the human anti-IL-1 beta monoclonal antibody canakinumab in rheumatoid arthritis : results of a 12-week , phase II , dose-finding study

Background: Canakinumab is a fully human anti-interleukin IL-1beta monoclonal antibody, being investigated for the treatment of rheumatoid arthritis (RA). This multicenter, phase II, randomized, double-blind, placebo-controlled, parallel-group, dose-finding study investigated the efficacy and safety of canakinumab in patients with active RA despite ongoing therapy at stable doses of methotrexate. Methods: Patients were randomized to receive one of four regimens, in addition to methotrexate, for 12 weeks: canakinumab 150 mg subcutaneously (SC) every 4 weeks (q4wk), canakinumab 300 mg SC (2 injections of 150 mg SC) every 2 weeks, a 600 mg intravenous loading dose of canakinumab followed by 300 mg SC every 2 weeks’, or placebo SC every 2 weeks. Results: Among 274 patients with evaluable efficacy data, the percentage of responders according to American College of Rheumatology 50 criteria (the primary endpoint, based on a 28-joint count) was significantly higher with canakinumab 150 mg SC q4wk than with placebo (26.5% vs. 11.4%, respectively; p = 0.028). Compared to placebo, this dosage of canakinumab was also associated with significantly more favorable responses at week 12 with respect to secondary endpoints including the Disease Activity Score 28, scores on the Health Assessment Questionnaire and Functional Assessment of Chronic Illness Therapy-Fatigue, swollen 28-joint count, and patient’s and physician’s global assessments of disease activity. No safety concerns were raised with canakinumab therapy, particularly with regard to infections. Few injection-site reactions occurred. Conclusion: The addition of canakinumab 150 mg SC q4wk improves therapeutic responses among patients who have active RA despite stable treatment with methotrexate. Trial Registration: (ClinicalTrials.gov identifier: NCT00784628)
Predicting Neighbor Distribution in Heterogeneous Information Networks

Recently, considerable attention has been devoted to the prediction problems arising from heterogeneous information networks. In this paper, we present a new prediction task, Neighbor Distribution Prediction (NDP), which aims at predicting the distribution of the labels on neighbors of a given node and is valuable for many different applications in heterogeneous information networks. The challenges of NDP mainly come from three aspects: the infinity of the state space of a neighbor distribution, the sparsity of available data, and how to fairly evaluate the predictions. To address these challenges, we first propose an Evolution Factor Model (EFM) for NDP, which utilizes two new structures proposed in this paper, i.e. Neighbor Distribution Vector (NDV) to represent the state of a given node's neighbors, and Neighbor Label Evolution Matrix (NLEM) to capture the dynamics of a neighbor distribution, respectively. We further propose a learning algorithm for Evolution Factor Model. To overcome the problem of data sparsity, the learning algorithm first clusters all the nodes and learns an NLEM for each cluster instead of for each node. For fairly evaluating the predicting results, we propose a new metric: Virtual Accuracy (VA), which takes into consideration both the absolute accuracy and the predictability of a node. Extensive experiments conducted on three real datasets from different domains validate the effectiveness of our proposed model EFM and metric VA.
Discriminative Part Selection for Human Action Recognition

Semantic parts have shown a powerful discriminative capacity for action recognition. However, many existing methods select parts according to predefined heuristic rules, which may cause the correlation among parts to be lost, or do not appropriately consider the cluttered candidate part space, which may result in weak generalizability of the resulting action labels. Therefore, better consideration of the correlation among parts and refinement of the candidate space will lead to a more discriminative action representation. This paper achieves improved performance by more elegantly addressing these two factors. First, considering the cluttered nature of the candidate space, we propose a recursive part elimination strategy for iterative refinement of the candidate parts. In each iteration, we eliminate the parts with the lowest weights, which are deemed to be noise. Second, we measure the discriminative capabilities of the candidates and select the top-ranked parts by applying a maximum margin model, which can alleviate overfitting while simultaneously improving generalizability and correlation extraction. Finally, using the selected parts, we extract mid-level features. We report experiments conducted on four datasets (KTH, Olympic Sports, UCF50, and HMDB51). The proposed method can achieve significant improvements compared with other recent methods, including a lower computational cost, a faster speed, and higher accuracy.