Miracle-dz/UniFAR-FTUs huggingface dataset:
miR-760 mediates hypoxia-induced proliferation and apoptosis of human pulmonary artery smooth muscle cells via targeting TLR4

MicroRNAs (miRNAs) have a key role in the pathogenesis of pulmonary arterial hypertension (PAH), a disease characterized by enhanced proliferation and reduced apoptosis of pulmonary artery smooth muscle cells. In the present study, miR-760 was demonstrated to be downregulated in PAH lung tissues compared with normal lung tissues, an effect that may be associated with the development of PAH. Hypoxia is an important stimulus for human pulmonary artery smooth muscle cell (hPASMC) proliferation and the occurrence of PAH. Therefore, the effect of miR-760 in hypoxia-treated and normal hPASMCs was investigated. Expression of exogenous miR-760 decreased cell proliferation in hypoxia-induced hPASMCs, and promoted cell apoptosis with an increase in the BCL2 associated X/BCL2 ratio and the expression levels of Caspase-3 and Caspase-9. In addition, overexpression of miR-760 suppressed the migration of hPASMCs under hypoxic conditions. Furthermore, miR-760 was demonstrated to mediate its anti-proliferation effect via the regulation of toll-like receptor 4 (TLR4), a direct target of miR-760. The results revealed that knockdown of TLR4 restrained the hypoxia-induced hPASMC proliferation and induced cell apoptosis. The present study uncovered a novel regulatory pathway involving miR-760 and suggested that miR-760 may be explored as a potential therapy for PAH in the future.
Improving Superquadric Modeling and Grasping with Prior on Object Shapes

This paper proposes an object modeling and grasping pipeline for humanoid robots. This work improves our previous approach based on superquadric functions. In particular, we speed up and refine the modeling process by using prior information on the object shape provided by an object classifier. We use our previous method for the computation of grasping pose to obtain pose candidates for both the robot hands and, then, we automatically choose the best candidate for grasping the object according to a given quality index. The performance of our pipeline has been assessed on a real robotic system, the iCub humanoid robot. The robot can grasp 18 objects of the YCB and iCub World datasets considerably different in terms of shape and dimensions with a high success rate.
Metacognitive judgements of perceptual-motor steering performance

Control of skilled actions requires rapid information sampling and processing, which may largely be carried out subconsciously. However, individuals often need to make conscious strategic decisions that ideally would be based upon accurate knowledge of performance. Here, we determined the extent to which individuals have explicit awareness of their steering performance (conceptualised as “metacognition”). Participants steered in a virtual environment along a bending road while attempting to keep within a central demarcated target zone. Task demands were altered by manipulating locomotor speed (fast/slow) and the target zone (narrow/wide). All participants received continuous visual feedback about position in zone, and one sub-group was given additional auditory warnings when exiting/entering the zone. At the end of each trial, participants made a metacognitive evaluation: the proportion of the trial they believed was spent in the zone. Overall, although evaluations broadly shifted in line with task demands, participants showed limited calibration to performance. Regression analysis showed that evaluations were influenced by two components: (a) direct monitoring of performance and (b) indirect task heuristics estimating performance based on salient cues (e.g., speed). Evaluations often weighted indirect task heuristics inappropriately, but the additional auditory feedback improved evaluations seemingly by reducing this weighting. These results have important implications for all motor tasks where conscious cognitive control can be used to influence action selection.
Evaluating Effectiveness of Concept Maps for Ontology Conceptualization: A Quantitative Study

over the passage of time different techniques for conceptualizing ontologies have been presented. Conceptualization comprises a simplified vision of the objects, concepts, and other entities that are assumed to exist in some area of interest and the relations among them. The use of graphical knowledge representation formalisms with a representational vocabulary agreement of terms of conceptualization of the universe of discourse is a new high potential approach in the ontology engineering and knowledge management context. Initially, concept maps were used in the field of education and learning but soon it got popular in other areas because of its flexible and intuitive nature. It has also been proven as an effective tool to increase communication in corporate environments. In the field of ontologies, concept maps have been explored and used for facilitating different aspects of ontology development. An important reason behind this motivation is the structural resemblance of concept maps with the hierarchical structure of ontologies. This research intends to demonstrate quantitative evaluation of 2 different hypotheses related with the effectiveness of using concept maps for ontology conceptualization. Results demonstrate that concept maps instill the element of learning in the conceptualization process. Furthermore, they provide a platform for participants to resolve conflicting opinions and term ambiguities on immediate basis.
A Quality Improvement Initiative to Decrease Time to Antibiotics for Children with Intestinal Failure, Fever, and a Central Line

Introduction: Pediatric intestinal failure (IF) patients experience significant morbidity, including sepsis related to central line–associated bloodstream infections. Adult studies of sepsis demonstrate an association between time to antibiotic administration (TTA) and mortality. To overcome challenges in treating pediatric IF patients in an emergency department (ED), we appropriated an existing, reliable system for febrile immunocompromised oncology/bone marrow transplant children. We describe the translation of this process to febrile IF patients in the ED and steps toward sustained improvement. Methods: We formed a multidisciplinary team and used the Model for Improvement to define aims and identify key drivers. The goal was to use an existing improvement process to increase the percentage of patients with IF who receive antibiotics within 60 minutes of arrival to the ED from 46% to 90%. Key drivers included pre- and postarrival processes, staff and family engagement, and a preoccupation with failure. We performed Plan-Do-Study-Act cycles targeting family engagement, prearrival efficiency, and postarrival consistency. Results: Two hundred seventy-six encounters involving febrile IF patients between November 2012 and March 2017 were evaluated. There was a sustained reduction in the median time from arrival to antibiotic administration (71–45 minutes). We decreased TTA to less than 60 minutes for 77% of febrile IF patients. Conclusions: The basic tenets of process improvement for 1 high-risk population can be translated to another high-risk population but must be adjusted for variability in characteristics.
Identity-based Adversarial Training of Deep CNNs for Facial Action Unit Recognition

Automatic detection of facial action units (AU) is a key towards fully understanding spontaneous facial expressions in human emotion analysis. The most recent development shows encouraging results in utilizing deep learning models to recognize facial action units. However, existing AU-labeled spontaneous facial expression datasets are either in a small-scale due to labor-intensive annotations, or lack of sufﬁcient variety in terms of amount, ethical background, age ranges, and facial appearance variations of subjects, thus limiting the learning effectiveness. To mitigate the issue of high redundancy and low level of variants existing among image frames of facial video sequences with respect to both subject identities (ID) and facial action units (AU), we propose a novel learning process with convolutional neural networks (CNNs), named Adversarial Training Framework (ATF). Architecture wise, ATF takes the form of a multi-task learning CNN, where a deep stack of convolutional layers, pooling layers and fully connected layers act as feature learners, and an AU loss together with an ID loss is computed with the shared deep representations. The crucial property of ATF is that during training, the underlying feature layers are trained to minimize the AU loss while maximizing the ID loss. This adversarial training w.r. t. the ID loss essentially makes the learned features effective for AU detection, while invariant to subject identities, thus alleviating the affection of personal identity to a great extent. We have conducted experiments on the public databases BP4D and DISFA, showing signiﬁcant performance improvements over the peer methods.
Explore the Effects of Opinion Leader's Characteristics and Information on Consumer's Purchase Intention: Weibo Case

With the development of the technology and social media, the micro-blog began to enter people's lives. We have a new information environment and more competitive market. Weibo opinion leaders become a hot issue for e-commerce industry and academics. In this paper, we want to explore the effects of micro-blog opinion leaders on consumer's purchase intention. This study uses a modified TRA model and constructs an online questionnaire for data collection. Then, structural equation model is adopted for fitting and path analysis. We found that: In all 16 hypotheses, 9 of them have significant positive correlation, which verified that variants including professional knowledge, product involvement, interaction and consistency influence trust. And at the same time, professional knowledge, product involvement and visual cue impact perceived value directly. The results of this study provide principles for the enterprises to operate the micro-blog marketing.
Identification of Multiple-Input Multiple-Output Channels Under Linear Side Constraints

We investigate the impact of having additional information in a form of linear constraints in the channel identification problem. With those constraints taken into account, the problem turns into solving a linear system that is associated with a block matrix where each submatrix is either a Gabor system matrix or a matrix prescribed by the linear constraints. So, the identifiability hinges on whether one can find some generating windows of the Gabor systems for which the full linear system is solvable. We show that in single-input single-output (SISO) settings as well as in multiple-input multiple-output (MIMO) settings, linear constraints consisting of a single equation are beneficial for channel identification, as there always exist windows for which the corresponding full linear system is solvable. Concerning multiple linear constraints, however, there exists a set of linear constraints with two equations for which the full linear system is singular for all choices of windows. In the SISO case, we also provide some sufficient conditions on the linear side constraints under which the full linear system is solvable.
Learning on a Budget for User Authentication on Mobile Devices

Since the amount of sensitive information stored on smart-phones expands with the growth of their popularity, the need for reliable and usable authentication on these devices increases. Constant reauthentication requests of standard methods, such as PINs, passwords, and swipe patterns, annoy many users who prefer to sacrifice the security of their mobile devices for the quest for maximum usability. An alternative to this approach is sensor-based authentication, where we fingerprint the user interaction by processing signals from sensors such as touchscreen or accelerometer. In this paper, we construct a budgeted online version of One-Class Support Vector Machine (OC-SVM) to maintain authentication performance while limiting the model size and evaluate the performance compared to an unconstrained model. The results of our experiments reveal that it is possible to correctly detect invalid smartphone users in a constrained environment using our budgeted learning methodology.
Targeting the Substrate in Ablation of Persistent Atrial Fibrillation: Recent Lessons and Future Directions

While isolation of the pulmonary veins is firmly established as effective treatment for the majority of paroxysmal atrial fibrillation (AF) patients, there is recognition that patients with persistent AF have substrate for perpetuation of arrhythmia existing outside of the pulmonary veins. Various computational approaches have been used to identify targets for effective ablation of persistent AF. This paper aims to discuss the clinical aspects of computational approaches that aim to identify critical sites for treatment. Various analyses of electrogram characteristics have been performed with this aim. Leading techniques for electrogram analysis are Complex Fractionated Atrial Electrograms (CFAE) and Dominant Frequency (DF). These techniques have been the subject of clinical trials of which the results are discussed. Evaluation of the activation patterns of atria in AF has been another avenue of research. Focal Impulse and Rotor Modulation (FIRM) mapping and forms of Body Surface Mapping aim to characterize multiple atrial wavelets, macro-reentry and focal sources which have been proposed as basic mechanisms perpetuating AF. Both invasive and non-invasive activation mapping techniques are reviewed. The presence of atrial fibrosis causes non-uniform anisotropic impulse propagation. Therefore, identification of fibrosis by imaging techniques is an avenue of potential research. The leading contender for imaging-based techniques is Cardiac Magnetic Resonance (CMR). As this technology advances, improvements in resolution and scar identification have positioned CMR as the mode of choice for analysis of atrial structure. AF has been demonstrated to be associated with obesity, inactivity and diseases of modern life. An opportunity exists for detailed computational analysis of the impact of risk factor modification on atrial substrate. This ranges from microstructural investigation through to examination at a population level via registries and public health interventions. Computational analysis of atrial substrate has moved from basic science toward clinical application. Future directions and potential limitations of such analyses are examined in this review.
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations

In social networks, by removing some target-sensitive links, privacy protection might be achieved. However, some hidden links can still be re-observed by link prediction methods on observable networks. In this paper, the conventional link prediction method named Resource Allocation Index (RA) is adopted for privacy attacks. Several defense methods are proposed, including heuristic and evolutionary approaches, to protect targeted links from RA attack. In particular, incremental computation is proposed for accelerating the calculation of fitness in evolutionary approaches. This is the first time to study privacy protection for targeted links against similarity based link prediction attacks. Some links are randomly selected from original network as targeted links for experimentation. The experimental results on nine real-world networks demonstrate the superiority of the evolutionary perturbations, especially EDA, for defending against RA attack. Moreover, experimental results show that the proposed perturbation generated by EDA is transferable and can even defend against other link prediction attacks which are based on high order similarity between pairwise nodes, although it is designed to prevent RA attack.
Design of a Serial-Parallel Hybrid Leg for a Humanoid Robot

This paper presents a 6 DOF leg mechanism for a humanoid robot. The proposed Hybrid Leg is designed to combine serial and parallel mechanisms and consists of a pair of twin 3 DOF serial chains in parallel. A 5-bar-linkage mechanism is implemented to the serial mechanism to generate 2 DOF motion regarding hip and knee pitch rotation. The hardware prototype is designed by matching the kinematic specification of a commercial robot's leg to compare the proposed mechanism with a conventional serial leg. We derive the analytical expressions of its forward and inverse kinematics. End-effector workspaces are shown with plots and inverse dynamics analysis of Hybrid Leg and serial leg with a given walking gait trajectory is presented. Hardware experiment is conducted with a prototype to verify the simulated workspace and trajectory tracking performance.
Blockchain-Based UDDI Data Replication and Sharing

Universal Description, Discovery and Integration (UDDI), as the main technical support of Web service, plays an increasingly important role in SOA (Service Oriented Architecture). Smart contract technology based on blockchain is used to solve the problem of data replication and sharing between UDDI registries. The UDDI registry only stores the information of simple service description and reference to the blockchain, and provides identity management, authority management, and other functions. The detailed information of the service provider and the services it provides is stored in the blockchain. The cost of the UDDI registry is greatly reduced, and the sharing and storage of data can be easily realized. Meanwhile, the security and auditability of data are guaranteed by the characteristics of the blockchain. This paper innovatively introduces the blockchain into the field of service computing, which provides a new idea for future research.
A Hierarchical Approach for Devising Area Efficient Concurrent Online Checkers

The shrinking feature size in semiconductor technology beyond the sub-micron domain negatively affects the reliability of digital circuits and makes them more susceptible to run-time faults (such as wear-out and aging) and transient faults during systems life time. This motivates investigation of online faults detection approaches, which would react instantaneously at run-time, concurrent with the system operation. Concurrent online checkers have been one of the approaches introduced in the literature for handling run-time faults online in control part of digital systems. An ideal set of checkers provides high fault detection and localization with minimal area overhead. To reach such optimal set, a diverse initial set of checkers are required which provide a trade-off between the above mentioned parameters. This work presents a methodology to generate (1) high-level functional checkers based on abstract design specification, and (2) structural checkers, which are devised from Register Transfer Level (RTL) description of the circuit. The functional checkers are fewer in number with lower area overhead and provide high fault coverage, however they lead to lower fault localization accuracy and cannot cover all the Single Event Upsets (SEUs). On the other hand, structural checkers provide higher localization accuracy and guarantee 100% SEU coverage, but at the price of higher area overhead. The proposed methodology provides the designer with trade-offs between the parameters mentioned above, for further optimization. The proposed methodology has been applied to the control part of routing logic of a NoC router.
A Batch Normalization Autoencoder Model for Breast Cancer Multidimensional Follow-up Data

The traditional follow-up model is insufficient in terms of processing speed and accuracy, which results in poor analysis. In addition, a unidimensional follow-up data limits the reliability of analysis. Aimed at the problems, a deep neural network model is constructed, that is based on stacked denoising sparse autoencoder with batch normalization algorithm to deal with data of ten dimensions. It not only improves the learning ability of the autoencoder, but also enhances the prediction ability of the follow-up model. The follow-up data sets are derived from Peking University First Hospital, the Second Hospital of Shandong University and so on. ROC curve and AUC are used as evaluation indicators. The experiments indicate the model has advanced performance in both accuracy and stability for prediction than traditional machine learning algorithms.
Neonatal Blood Analysis and Generating Disorder Estimation Using Image Processing

The analysis of blood cells in microscopic images can provide useful information concerning the health of patients; however, manual classification of blood cells is time-consuming. Classification is susceptible to error due to the different morphological features of the cells. Therefore, a fast and automated method for identifying the different shape of blood cells is required. Neonatal blood cell diseases have grown in a large amount in the recent past. Acute pneumonia, leukemia and anemia being some of such diseases, they do have an impact on the blood cell size and its properties. Such diseases tend to change the size and shape of the RBCs, WBCs and Platelet, and also sometimes leading to change in their gross counts. The propose system help to work for analysis the changes by shape analysis & size detection of blood cell using image processing. The image is converted to gray scale image from further processing. The use of image processing discards the need of the expert pathologists as well as reduces the volume of blood required to be withdrawn from the neonatal baby. A novel shape detection base algorithm will be developed which can classify the neonatal diseases depending upon morphological changes in the blood cells. Also, this algorithm causes the reduction of noise in the final image that might completely discards the possibility of error, leading to accurate detection of blood cell problems.
Dictionary Learning for High Dimensional Graph Signals

In recent years there is a growing interest in operating on graph signals. One systematic and productive such line of work is incorporating sparsity-inspired models to this data type, offering these signals a description as sparse linear combinations of atoms from a given dictionary. In this paper, we propose a dictionary learning algorithm for this task that is capable of handling high dimensional data. We incorporate the underlying graph topology by forcing the learned dictionary atoms to be sparse combinations of graph wavelet functions. The resulting atoms thus adhere to the underlying graph structure and possess a desired multi-scale property, yet they capture the prominent features of the data of interest. This results in both adaptive representations and an efficient implementation. Experimental results on different datasets, representing both synthetic and real network data, demonstrate the effectiveness of the proposed algorithm for graph signal processing.
A Dreaming Approach to Perceptual Class Delimitation Within the DREAM Architecture

Perceptual classes allow cognitive systems to organize the continuous perceptual space they are usually faced with into discrete categories so that planning and long-term prospection can take place. However, for the correct operation of the system it is quite important that the perceptual classes are precisely delimited in order to prevent classification errors that would lead to planning and prediction errors. In this paper we propose a simple but effective mechanism that makes use of the prospective capabilities of a cognitive architecture during a dreaming phase in order to allow it to generate hypotheses on the delimitation of perceptual classes. These hypotheses are later tested as the system interacts with the world when in an awake state.