Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of ischemic postconditioning on cell apoptosis and expression of relevant genes in non-culprit coronary arteries

This study was performed to determine the effect of ischemic postconditioning on cell apoptosis and angiotensin II receptor type 1 (AT1), connexin 43 (Cx43), and β-tubulin mRNA expression in non-culprit arteries. Non-culprit arterial tissues were isolated from a rabbit myocardial ischemia-reperfusion model and randomly divided into sham, ischemia-reperfusion, and ischemic postconditioning groups. Cell apoptosis was detected by terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) staining. Expression of angiotensin II, AT1, Cx43, and β-tubulin mRNA was evaluated by quantitative real-time polymerase chain reaction (qRT-PCR). TUNEL analysis indicated significantly higher ratios of apoptotic cells in the ischemia-reperfusion group than in the sham group. However, significantly fewer apoptotic cells were observed in the ischemic postconditioning group than in the ischemia-reperfusion group. The qRT-PCR results indicated significantly higher expression of AT1, Cx43, and β-tubulin mRNA in the ischemia-reperfusion group than in the sham group. However, expression of AT1, Cx43, and β-tubulin was lower in the ischemic postconditioning group than in the ischemia-reperfusion group. The ratios of apoptotic cells and mRNA expression of AT1, Cx43, and β-tubulin in non-culprit arteries were increased after ischemia-reperfusion. Ischemic postconditioning may decrease these features and inhibit the progression of non-culprit arteries.
Ambidextrous Leadership and Employee Work Outcomes: A Paradox Theory Perspective

Numerous studies have examined the influence of ambidextrous leadership on employee work outcomes, but few have explored the issue through the congruence or incongruence of two seemingly conflicting leadership styles. Based on paradox theory, we adopted polynomial regression and surface analysis methods to investigate the congruent/incongruent effects of loose and tight leadership techniques. In order to reduce common method bias, we used a two-wave design with a two-month time interval. By using two-wave surveys of 301 employees, this study posited that ambidextrous leadership congruence creates higher leader–member exchange quality, and that loose and tight leadership with a high strength plays a more positive role in enhancing LMX quality. This study demonstrated that LMX quality mediates the relationship between ambidextrous leadership congruence/incongruence and employee work outcomes (i.e., job performance and creativity).
Is the minimal clinically important difference (MCID) in acute pain a good measure of analgesic efficacy in regional anesthesia?

In the field of acute pain medicine research, we believe there is an unmet need to incorporate patient related outcome measures that move beyond reporting pain scores and opioid consumption. The term “minimal clinically important difference” (MCID) defines the clinical benefit of an intervention as perceived by the patient, as opposed to a mathematically determined statistically significant difference that may not necessarily be clinically significant. The present article reviews the concept of MCID in acute postoperative pain research, addresses potential pitfalls in MCID determination and questions the clinical validity of extrapolating MCID determined from chronic pain and non-surgical pain studies to the acute postoperative pain setting. We further suggest the concepts of minimal clinically important improvement, substantial clinical benefit and patient acceptable symptom state should also represent aspirational outcomes for future research in acute postoperative pain management.
MIT COVID-19 Datathon: data without boundaries

The COVID-19 virus is a formidable global threat, impacting all aspects of society and exacerbating the existing inequities of our current social systems.1 2 As we battle the virus across multiple fronts, data are critical for understanding this disease and for coordinating an effective global response. Given the current digitisation of so many aspects of life, we are amassing data that can be extrapolated and analysed for the effective forecasting, prevention and treatment of COVID-19. With responsible stewardship, the tools and data-driven solutions currently in development for the COVID-19 pandemic will serve in the present while providing a much-needed foundation for a data-based response to future outbreaks and disasters. In response to COVID-19, and using data generated thus far, groups at the Massachusetts Institute of Technology (MIT) in partnership with the American Civil Liberties Union (ACLU) of Massachusetts, Google Cloud, Beth Israel Deaconess Medical Center (BIDMC) Innovations Group and Harvard Medical Faculty Physicians at BIDMC came together to host the MIT Challenge COVID-19 Datathon (COVID-19 Datathon) from 10–16 May 2020. A ‘datathon’ adopts the ‘hackathon’ model, with a focus on data and data science methodologies, which promotes collaboration, design thinking and problem solving.3 In a typical hackathon, participants with disparate but complementary backgrounds work together in small groups for a prescribed and intensive ‘sprint’, typically over the course of one weekend, to develop a new concept, product or business idea. Subject matter expert ‘mentors’' oversee and advise the teams. At the conclusion of the event, the teams present to a panel of judges. Winners are selected and are typically awarded seed funding. …
Sialadenitis of the Major Salivary Glands: A Tumor-Like Lesion of Various Etiologies and Morphological Appearances

Abstract Inflammatory conditions of salivary glands—so-called sialadenitis—may clinically and histologically resemble a true neoplasm and result in a surgical resection. This review summarizes distinctive morphological patterns of inflammation that affect major salivary glands, and within this context, an unusual case of sialadenitis is also presented.
Silybum marianum (Carduus marianus)

S. marianum is a medicinal plant which has been widely used in traditional European medicine. Nowadays silymarin, the purified extract of the fruits of S. marianum, and its main constituent, silybin, are used in the treatment of diseases of the liver. This review deals with the botany, chemistry, pharmacology and the clinical efficacy of silymarin, silybin and of a recently synthesized silybin derivative, Silipide. The cosmetical applications of silymarin are also shortly described
Robust Reasoning Over Heterogeneous Textual Information for Fact Verification

Automatic fact verification (FV) based on artificial intelligence is considered as a promising approach which can be used to identify misinformation distributed on the web. Even though previous FV using deep learning have made great achievements in single dataset (e.g., FEVER), the trained systems are unlikely to be capable of extracting evidence from heterogeneous web-sources and validating claims in accordance with evidence found on the Internet. Nevertheless, the heterogeneity covers abundant semantic information, which will help FV system identify misinformation in a more accurate way. The current work is the first attempt to make the combination of knowledge graph (KG) and graph neural network (GNN) to enhance the robustness of FV systems for heterogeneous information. As a result, it can be generalized to multi-domain datasets after training on a sufficient single one. To make information update and aggregate well on the collaborative graph, the present study proposes a double graph attention network (DGAT) framework which recursively propagates the embeddings from a node’s neighbors to refine the node’s embedding as well as applies an attention mechanism to classify the importance of the neighbors. We train and evaluate our system on FEVER, a single and benchmark dataset for FV, and then re-evaluate our system on UKP Snopes Corpus, a new richly annotated corpus for FV tasks on the basis of heterogeneous web sources. According to experimental results, although DGAT has no excellent advantages in a single dataset, it shows outstanding performance in more realistic and multi-domain datasets. Moreover, the current study also provides a feasible method for deep learning to have the ability to infer heterogeneous information robustly.
Evolutionary Design of Hash Functions for IPv6 Network Flow Hashing

Fast and high-quality network flow hashing is an essential operation in many high-speed network systems such as network monitoring probes. We propose a multi-objective evolutionary design method capable of evolving hash functions for IPv4 and IPv6 flow hashing. Our approach combines Cartesian genetic programming (CGP) with Non-dominated sorting genetic algorithm II (NSGA-II) and aims to optimize not only the quality of hashing, but also the execution time of the hash function. The evolved hash functions are evaluated on real data sets collected in computer network and compared against other evolved and conventionally created hash functions.
Thermo-Optic Property Measurement Using Surface Plasmon Resonance-Based Fiber Optic Sensor

The temperature disturbances greatly influence the performances of microfluidic chips during the working process. It is therefore important to evaluate the thermo-optic properties of microfluidic materials. Here, we measure the thermo-optic property by employing a fiber optic surface plasmon resonance (SPR) sensor combined with a thermocouple. This setup can be applied for embedding in microfluidic materials or directly detecting fluidic samples. Three fiber optic SPR sensors were fabricated with a <inline-formula> <tex-math notation="LaTeX">$400~\mu m$ </tex-math></inline-formula>-bare-core, <inline-formula> <tex-math notation="LaTeX">$600~\mu m$ </tex-math></inline-formula>-bare-core and mismatched-core structures. The thermo-optic responses of the three sensors were compared to determine the optimum sensor structure. The thermo-optic property of polydimethylsiloxane was then investigated over the full temperature detection range of 25–200 °C. The thermo-optic coefficient of polydimethylsiloxane was determine to be <inline-formula> <tex-math notation="LaTeX">$- 4.19\times 10^{-4}$ </tex-math></inline-formula> RIU/°C, which agrees well with previous reports. The thermo-optic properties of ethanol and brine were also evaluated to confirm the universality of the proposed measurement method. To our knowledge, this is the first reported detection of thermo-optic properties using a fiber optic SPR sensor.
Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)

Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either way generated by humans or machines. In the era of social networks, the quick rotation of news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issue, a hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square. This work proposed to employ the dimensionality reduction techniques to reduce the dimensionality of the feature vectors before passing them to the classifier. To develop the reasoning, this work acquired a dataset from the Fake News Challenges (FNC) website which has four types of stances: agree, disagree, discuss, and unrelated. The nonlinear features are fed to PCA and chi-square which provides more contextual features for fake news detection. The motivation of this research is to determine the relative stance of a news article towards its headline. The proposed model improves results by ~4% and ~20% in terms of $Accuracy$ and $F1-score$ . The experimental results show that PCA outperforms than Chi-square and state-of-the-art methods with 97.8% accuracy.
Multiobjective Neuromodulated Controllers for Efficient Autonomous Vehicles with Mass and Drag in the Pursuit-Evasion Game

Autonomous vehicles in the pursuit-evasion game, subject to the effects of mass and drag, are controlled using an evolutionary multiobjective neuromodulated controller with unsupervised learning. Multiobjective evolution of network weights and topologies (NEAT-MODS) is extended with Lamarckian-inherited neuromodulated learning. NEAT-MODS is an NSGA-II augmented multiobjective neurocontroller that uses two conflicting objectives. By evolving pursuit agents optimized with the separate and conflicting objectives of ‘capturing evaders’ and ‘minimizing energy consumption’, efficient neurocontrollers can be evolved. NEAT-MODS uses a selection process that aims to ensure Pareto-optimal genotypic diversity and elitism. Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. Lamarckian inheritance allows behaviours learned during parent generations to be passed on to their offspring. The capability of the design is demonstrated in a series of experiments with a simulated evolved vehicle pursuing a basic evader vehicle. It is shown that compact and efficient neurocontrollers for pursuer agents with nonzero mass and drag, capable of capturing an optimal evader while simultaneously minimizing energy consumption, are evolved.
An Inclusion Boundary and Conductivity Simultaneous Estimation Method for Ultrasound Reflection Guided Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a promising technology to visualize the conductivity distribution within a closed domain by injecting electric currents and measuring the corresponding voltages at its boundary. Due to the advantages of non-radiative, high-speed and low-cost, EIT has been widely used in industrial and biomedical fields. However, the image reconstruction of EIT is a nonlinear ill-posed inverse problem, which greatly limits its spatial resolution and noise robustness. Although many efforts have been devoted to developing the EIT imaging method, the improvement of EIT spatial resolution and noise robustness is still a focus of current research. Inspired by multimodal information fusion, an ultrasound reflection guided EIT imaging method is proposed to simultaneously reconstruct the inclusion boundary and conductivity. The local structural information obtained from ultrasound reflection measurements will provide a good initial guess and prior constraint on inclusion boundary reconstruction of EIT under the Bayesian inversion framework. The Maximum A Posterior (MAP) estimation combining with alternative optimization strategy is used to solve the inclusion boundary and conductivity simultaneous estimation problem. The numerical and phantom experimental tests are carried out and show that the proposed method has better imaging accuracy and noise robustness than traditional single-modality EIT method.
Simultaneous Wireless Power and Data Transmission Over One Pair of Coils for Sensor-Integrated Rotating Cutter

Tool condition monitoring is becoming particularly important in the cutting process. More and more researchers start to integrate the sensors into the tool holder due to its convenient use and wide applicability. However, it is difficult to transmit out the condition data and supply stable power to the sensor-integrated systems through the traditional wired way as the tool holder is under high-speed rotation in milling or drilling process. In this paper, a novel simultaneous wireless power and data transfer system over one pair of coils for sensor-integrated rotating cutter is presented, and the eddy effect caused by the metal handle that impacts the system performance is solved with ferrite shielding. To transmit out the cutting condition data wirelessly, the synchronous detection and envelope detection technology are employed. Meanwhile, the synchronous rectification technology is adopted to improve the performance of the wireless power transfer. Eventually, the experimental platform has been set up and the cutting tests have also been carried out. The results show that the designed new wireless power and data transfer system over one pair of coils is stable and practical to track and transmit out the condition data of the rotating cutter.
A Hybrid Methodology for the Reverse Engineering of Gene Regulatory Networks

In this work, a computational approach has been proposed based on the hybridisation of two modelling formalisms, recurrent neural networks and half-systems, for the reconstruction of gene regulatory networks from time-series gene expression datasets. To the best of our knowledge, the proposed hybridisation has not been attempted previously in this domain. Here, recurrent neural networks and half-systems have been hybridised to capture the underlying dynamics present in the temporal gene expression profiles. The motivation behind this work is to integrate the advantages of both the techniques in the proposed model such that the problem of reverse engineering of gene regulatory networks can be resolved more efficiently. Artificial bee colony optimisation has been used for the estimation of the model parameters. We have implemented the proposed hybrid methodology on the real-world experimental datasets (in vivo) of the SOS DNA Repair network of Escherichia coli. The obtained results are comparable to or better than that of other reverse engineering methodologies present in contemporary literature.
A Genetic Programming-Based Multi-Objective Optimization Approach to Data Replication Strategies for Distributed Systems

Data replication is the core of distributed systems to enhance their fault tolerance and make services highly available to the end-users. Data replication masks run-time failures and hence, makes the system more reliable. There are many contemporary data replication strategies for this purpose, but the decision to choose an appropriate strategy for a certain environment and a specific scenario is a challenge and full of compromises. There exists a potentially indefinite number of scenarios that cannot be covered entirely by contemporary strategies. It demands designing new data replication strategies optimized for the given scenarios. The constraints of such scenarios are often conflicting in a sense that an increase in one objective could be sacrificial to the others, which implies there is no best solution to the problem but what serves the purpose. In this regard, this research provides a genetic programming-based multi-objective optimization approach that endeavors to not only identify, but also design new data replication strategies and optimize their conflicting objectives as a single-valued metric. The research provides an intelligent, automatic mechanism to generate new replication strategies as well as easing up the decision making so that relevant strategies with satisfactory trade-offs of constraints can easily be picked and used from the generated solutions at run-time. Moreover, it makes the notion of hybrid strategies easier to accomplish which otherwise would have been very cumbersome to achieve, therefore, to optimize.
Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge

The proliferation of fake news on social media sites is a serious problem with documented negative impacts on individuals and organizations. A fake news item is usually created by manipulating photos, text, or videos that indicate the need for multimodal detection. Researchers are building detection algorithms with an aim for high accuracy as this will have a massive impact on the prevailing social and political issues. A shortcoming of existing strategies for identifying fake news is their inability to learn a feature representation of multimodal (textual+visual) information. In this paper, we present a novel approach using a Cultural Algorithm with situational and normative knowledge to detect fake news using both text and images. An extensive set of experiments have been carried out on real-world multimedia datasets collected from Weibo and Twitter. The proposed method outperforms the state-of-the-art methods for identifying fake news in terms of accuracy by 9% on average.
An Overlapping Community Detection Based Multi-Objective Evolutionary Algorithm for Diversified Social Influence Maximization

Influence maximization refers to selecting a group of nodes from a social network, which obtains the largest influence spread under a cascade model. However, most of the existing works only focused on the influence and ignored the diversity of influenced crowd. Thus, scholars have raised the issue of diversified social influence maximization recently, using the category information of nodes to design diversity indicator and introducing a trade-off parameter to balance the two objectives influence and diversity as one single objective for optimization. In fact, the category information of nodes in the network is usually difficult to be collected, thus the definition of diversity based on nodes’ categories is not very general and accurate. In addition, it is very difficult to set the trade-off parameter, especially when there is no prior knowledge in real applications. To this end, we employ overlapping community structure information to design the diversity of nodes without any node’s additional (e.g. category) information. Due to the two objectives of influence and diversity may be conflicting, a multi-objective evolutionary algorithm named MOEA-DIM is proposed to optimize the two objectives simultaneously, which does not need to set the tradeoff parameter between the two objectives. In MOEA-DIM, a network reduction strategy based on overlapping community structure is suggested to greatly reduce the search space. In addition, a population initialization strategy based on random walk is designed to accelerate the convergence of the algorithm. Experiments on six real-world datasets show that the proposed algorithm MOEA-DIM has promising performance in terms of both effectiveness and efficiency.
Multi-View Gene Clustering using Gene Ontology and Expression-based Similarities

Gene is a fundamental, physical and functional unit of hereditary. A proper grouping of genes is needed for a better understanding of the natural patterns amongst them. Most of the existing approaches utilize gene expression values for the clustering of genes. They mostly ignore the semantic similarity between genes obtained from the global database like Gene Ontology(GO). We present a multi-view multi-objective clustering approach where Euclidean distances between the gene expression values and GO-based multi-factored gene-gene semantic similarity are considered as two complementary views and a consensus partitioning is obtained that satisfies both the aspects or views. Two real-life gene expression datasets are used to demonstrate the effectiveness of the proposed multi-view clustering technique. We have compared our approach with various standard single-view, multi-view and multi-objective optimization based clustering approaches. The results show that better co-expressed and biologically significant gene partitions are obtained by our proposed approach. Also, to validate the significance of the obtained clusters statistically and biologically, various biological, statistical and visual significance tests were conducted, and the corresponding results are reported.