Miracle-dz/UniFAR-FTUs huggingface dataset:
Dopamine D1 Receptors Contribute Critically to the Apomorphine-Induced Inhibition of Form-Deprivation Myopia in Mice.

Purpose To determine the roles of dopamine D2 receptors (D2Rs) and dopamine D1 receptors (D1Rs) in the inhibition of form-deprivation myopia (FDM) by the nonselective dopamine agonist apomorphine (APO) in D2R-knockout (D2R-KO) and D1R-KO mice. Methods Retinal layer thicknesses and electroretinograms (ERGs) were analyzed in KO mice and in D2R and D1R antagonist-treated mice. D2R-KO or D1R-KO mice and wild-type (WT) littermates were subjected to form deprivation during postnatal weeks 5 to 8. Both groups were intraperitoneally injected daily with either APO (5 μg/g body weight) dissolved in 1 μg/μL ascorbic acid or vehicle alone. Refraction, vitreous chamber depth (VCD), and axial length (AL), among other parameters, were measured prior to and at the end of the treatment period. Results The retinal layer thicknesses and ERGs in KO mice were similar to those treated with D2R and D1R antagonists. APO administration in WT mice inhibited the development of FDM by approximately 80%. FDM in D2R-KO mice was inhibited approximately 50% compared with WT mice and was further inhibited by APO to a level similar to that in APO-treated WT mice. FDM development in D1R-KO mice was similar to that in WT mice and was not affected by APO administration. The changes in VCD and AL were consistent with refraction data. Conclusions In mice, APO-mediated FDM inhibition was abolished by D1R KO but not D2R KO. This indicates the specificity of D1Rs for the pharmacologic inhibitory effect of APO on FDM and a nonessential role of D2Rs in this process in mice.
Learning Video Summarization Using Unpaired Data

We consider the problem of video summarization. Given an input raw video, the goal is to select a small subset of key frames from the input video to create a shorter summary video that best describes the content of the original video. Most of the current state-of-the-art video summarization approaches use supervised learning and require training data in the form of paired videos. Each pair consists of a raw input video and a ground-truth summary video curated by human annotators. However, it is difficult to create such paired training data. To address this limitation, we propose a novel formulation to learn video summarization from unpaired data. Our method only requires training data in the form of two sets of videos: a set of raw videos ($V$) and another set of summary videos ($S$). We do not require the correspondence between these two sets of videos. We argue that this type of data is much easier to collect. We present an approach that learns to generate summary videos from such unpaired data. We achieve this by learning a mapping function $F : V \rightarrow S $ which tries to make the distribution of generated summary videos from $F(V)$ similar to the distribution of $S$ while enforcing a high visual diversity constraint on $F(V)$. Experimental results on two benchmark datasets show that our proposed approach significantly outperforms other alternative methods.
Prediction for the Newsroom: Which Articles Will Get the Most Comments?

The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80% in comparison to state-of-the-art approaches.
Fast Entropy Attribute Value Frequency Algorithm to Detect Outliers for Categorical Data

Outliers are extreme observations which is far away from other observations. Outlier detection becomes a significant procedure for many applications such as detecting insurance fraud or industrial damage. Most outlier detection techniques work on numerical data, that is, continuous attributes. However, there are few research works on outlier detection for categorical data. AEVF(Automated Entropy Value Frequency) is a measure to detect outliers for categorical data. AEVF has complexity, and it cannot be applied to large number of observations and the number of attributes. We propose a fast entropy attribute value frequency(FEAVF) having complexity. Furthermore, we propose a fast algorithm for multiple records deletion as well as single record deletion. The performance of FEAVF can be effectively illustrated for UCI machine learning datasets.
α-Chaconine and α-Solanine Inhibit RL95-2 Endometrium Cancer Cell Proliferation by Reducing Expression of Akt (Ser473) and ERα (Ser167)

The aim of this study is to investigate the potential inhibitory effect of α-chaconine and α-solanine on RL95-2 estrogen receptor (ER) positive human endometrial cancer cell line and to identify the effect of these glycoalkaloids on the Akt signaling and ERα. The cell proliferation profiles and the cytotoxicity studies were performed by Real-Time Cell Analyzer (xCELLigence) and compared with Sulphorhodamine B (SRB) assay. The effects of α-chaconine (2.5, 5, 10 µM), α-solanine (20, 30, 50 µM), API-1 (25 µM) and MPP (20 µM) effects on Akt (Ser473) and ERα (Ser167) expressions evaluated by Western blot and qPCR method. Their IC50 values were as α-chaconine (4.72 µM) < MPP (20.01 µM) < α-solanine (26.27 µM) < API-1 (56.67 µM). 10 μM α-chaconine and 20, 30 and 50 μM α-solanine were effective in decreasing p-Akt(Ser473)/Akt ratio compared to positive control API-1. When the p-ERα/ERα ratios were evaluated, it was observed that α-chaconine (2.5, 5, 10 μM) and α-solanine (50 μM) were as effective as the specific ERα inhibitor MPP in reducing the ratio of p-ERα/ERα compared to the control group. In conclusion, it has been shown that the proliferation of α-chaconine and α-solanine in human endometrial carcinoma cells reduces the expression and activity of the Akt and ERα signaling pathway.
A Multi-Layer Neural Network Model Integrating BiLSTM and CNN for Chinese Sentiment Recognition

Technology of artificial intelligent has become research focus. Natural language understanding (NLU) is regarded as core technology of AI. Sentiment recognition is a difficult task in NLU; however it is advantageous to business market and public opinion analysis. We proposed a multi-layer neural network model through integrating LSTM and CNN to improve the performance of sentiment recognition. The structure of LSTM is appropriate to storage text sequence information, and CNN has ability to extract salient features for sentiment recognition task. We implemented models of LSTM-CNN and BiLSTM-CNN, and conduct experiments on different dataset. In the end, we contrast our proposed method with certain baseline methods. The result shows that the proposed method outperforms single layer model and other statistic learnint method.
Information overload and coping strategies in the big data context: Evidence from the hospitality sector

With the advent of technology, a greater amount of information is available in a greater variety of formats that are accessible through a greater variety of media and communication channels, resulting in a much more complex and rich information environment for business managers. Many businesses are seeing the development of big data as unique opportunity and also experience it as demanding in terms of managerial skills and organisational capability to deal with it. Existing literature provides managerial prescriptions and systemic guidelines to make use of this information, but does not provide empirical evidence on how practising managers actually deal with information overload and make sense of the available data. This article discusses the findings from an interpretive case study of five organisations from the hotel industry within the hospitality sector. It was found that the volume and pace coupled with the qualitative and unsolicited nature of information caused information overload to managers. To cope with this phenomenon at personal level, managers used a combination of filtering, withdrawal and summarising strategies. At organisational level, the practice of summarising evolved into development and use of interactive dashboards.
A stacked deep autoencoder model for biomedical figure classification

Figures in biomedical research papers are a significant source of vital information surrounding the experimental settings and results. One of the most important steps towards mining biomedical figures and reducing the vast search space is figure classification. A challenge that presents itself when classifying biomedical figures is the huge imbalance between the devised classes. This paper presents a novel method to overcome this imbalance in the available biomedical figures, by improving the prediction accuracy on the small classes, hence improving the overall accuracy. A stacked deep autoencoder model is trained to reconstruct the figures, then trimmed, by removing its decoder, and used to automatically extract visual features. The remaining encoder is fine-tuned to classify each class against the rest, with each encoder generating features that feed into a one-vs-all support vector machine (SVM). The ensemble of SVMs determines the class of the figure by taking the highest probability. The obtained results show that the proposed method improves not only the accuracy on the smaller classes in the taxonomy, but also the overall accuracy.
Special Issue on Numerical Modelling of Low-Temperature Plasmas for Various Applications – Part I: Review and Tutorial Papers on Numerical Modelling Approaches

Low-temperature plasmas are used for a growing number of applications. To improve these applications, a good insight in the underlying plasma processes is indispensable. This insight can be obtained by experimental research, but also by computer modelling. Several different modelling approaches exist in literature for describing low-temperature plasmas, and the type of modelling approach that is most suitable for a certain application will depend on the plasma operating conditions (e.g., pressure, power, dimensions, gas temperature) and the type of information requested (e.g., information on the breakdown and maintenance of the discharge, on the non-equilibrium behaviour of electrons / ions or on the detailed chemical kinetics of the heavy species). This special issue of Plasma Processes and Polymers aims to give an overview of the variousmodelling approaches that can be used to describe the plasma behaviour, as well as of closely related topics, such as model verification and validation, modelling of plasma-surface interactions and input data for the models. Furthermore, several examples of modelling efforts in various application fields are demonstrated. The special issue contains 31 papers in total, divided in two parts,with each part organized as a double issue. Part I, presented here, contains all reviewand tutorial papers. It was indeed our intention to cover the different modelling approaches in tutorial-like review papersthatprovideagoodintroduction to the field for starting PhD students. Roughly speaking, twomajor groups of numerical modelling approaches to describe the plasma behaviour can be
A Hybrid Algorithm to Address Ambiguities in Deformable Image Registration for Radiation Therapy

We proposed the use of a hybrid deformable image registration approach that combines compact-support radial basis functions (CSRBF) spline registration with intensity-based image registration. The proposed method first uses the pre-viously developed image intensity-based method to achieve voxel-by-voxel correspondences over the entire image re-gion. Next, for those areas of inaccurate registration, a sparse set of landmark correspondences was defined for local deformable image registration using a multi-step CSRBF approach. This hybrid registration takes advantage of both intensity-based method for automatic processing of entire images and the CSRBF spline method for fine adjustment over specific regions. The goal of using this hybrid registration is to locally control the quality of registration results in specific regions of interest with minimal human intervention. The major applications of this approach in radiation ther-apy are for the corrections of registration failures caused by various imaging artifacts resulting in, low image contrast, and non-correspondence situations where an object may not be imaged in both target and source images. Both synthetic and real patient data have been used to evaluate this hybrid method. We used contours mapping to validate the accuracy of this method on real patient image. Our studies demonstrated that this hybrid method could improve overall registra-tion accuracy with moderate overhead. In addition, we have also shown that the multi-step CSRBF registration proved to be more effective in handling large deformations while maintaining the smoothness of the transformation than origi-nal CSRBF.
Semiautomatic bone removal technique from CT angiography data

Cortical bone is the major barrier in visualizing the 3-D blood vessel tree from CT Angiography [CTA] data. Thus, we have developed a novel semi-automatic technique that removes the cortical bone and retains the clinical diagnostic information such as blood vessels, aneurysms, and calcifications. The technique is based on a methodical composite set of filters that use region-growing, adaptive, and morphological filtering algorithms. While using only voxel intensity value and region size information, this technique retains most of the CTA data untouched. We have implemented this method on 10 CTA abdomen and head data sets. The accuracy of the method was tested and proved successful by visual inspection of all segmented slices. The segmented CTA data were also visualized in 3-D with different Ray Casting Volume Rendering techniques (e.g. Maximum Intensity Projection). The blood vessels along with other diagnostic information were clearly visualized in 3-D without the obstruction of bone. The segmentation technique ran under one second per slice (image size is 512x512x2 bytes) on a PC with 550 MHz processor.
Semantic Schema Matching Using DBpedia

In semantic computing, Match is an operator that takes as an input two graph-like structures; it can be database schemas or XML schemas and generates a mapping between the corresponding nodes of the two graphs. In semantic schema matching, we attempt to explore the mappings between the two schemas; based on their semantics by employing any semantic similarity measure. In this study, we have defined taxonomy of all possible semantic similarity measures; moreover we also proposed an approach that exploits semantic relations stored in the DBpedia dataset while utilizing a hybrid ranking system to dig out the similarity between nodes of the two graphs.
Longitudinal Sparse Regression for Neuroimage Based Consciousness Assessing and Tracking of Hydrocephalus Patients

Hydrocephalus is a condition that causes ventricle enlargement by pathological accumulation of cerebrospinal fluid (CSF) and ends with different levels of disorders of consciousness (DOCs). Assessment of the consciousness level will help for the planning of the treatment of the patients. In this paper, a longitudinal sparse regression model featured by the temporal constraint is proposed to assess the levels of consciousness for hydrocephalus patients and to track their temporal alterations based on magnetic resonance (MR) images. Specifically, for the time points before and after neurosurgeries, we extract features from the corresponding MR scans and then regress out the clinical scores that reflect the respective consciousness levels. The longitudinal regression model can thus be applied to automatically track and evaluate the consciousness level change for individual patients, while the reading of the regression can act as an important indicator for the planning of subsequent treatment in clinical practice.
Sequential Monte Carlo smoothing for general state space hidden Markov models

Computing smoothing distributions, the distributions of one or more states conditional on past, present, and future observations is a recurring problem when operating on general hidden Markov models. The aim of this paper is to provide a foundation of particle-based approximation of such distributions and to analyze, in a common unifying framework, different schemes producing such approximations. In this setting, general convergence results, including exponential deviation inequalities and central limit theorems, are established. In particular, time uniform bounds on the marginal smoothing error are obtained under appropriate mixing conditions on the transition kernel of the latent chain. In addition, we propose an algorithm approximating the joint smoothing distribution at a cost that grows only linearly with the number of particles.
Towards Smart City Platform Intelligence: PI Decoupling Math Model for Temperature and Humidity Control

Providing a comfortable indoor climate for people, using heating, ventilation and air conditioning (HVAC) systems, in commercial or residential buildings is an extremely important problem. This is directly related to the design of the multi-channel controllers for temperature, humidity, and air quality. First, the analysis and building of an interactive nonlinear mathematical model of the required comfort temperature, humidity, and air quality are presented in this paper. Then, the article refers to methods of design of traditional PID controllers, PID controllers combined with decoupled controllers, and PID controllers with self-tuning parameters based on fuzzy logic principles, combined with decoupled controllers for heating and humidifying processes. Finally, we present the simulation results of a proposed fuzzy-PID controller in Matlab, and analyze experimental results for a 6-month period by applying the proposed system.
Ameliorating Impact of Prophylactic Intranasal Oxytocin on Signs of Fear in a Rat Model of Traumatic Stress

Oxytocin treatment reduces signs of long-term emotional stress after exposure to trauma; however, little is known about the potential protective effects of oxytocin treatment on behavioral and physiological changes associated with extreme stress exposure. The objective of this study was to investigate oxytocin treatment as a prophylactic measure against rat signs of fear. Two separate experiments were conducted in which the time of intranasal oxytocin administration differed. Intranasal oxytocin (1.0 μg/kg) was administered 5 min after daily exposure to foot shock in Experiment #1 and 1 h before foot shock in Experiment #2. In Experiment #1, possible massage-evoked oxytocin release (5 min after foot shock) was also investigated. In both experiments, a contextual fear conditioning procedure was employed in which stress was induced via inescapable foot shock (3 days, 40 shocks/day, 8 mA/shock) in a fear conditioning chamber. Male Sprague-Dawley rats (n = 24) were divided into four groups (n = 6, per group) for each experiment. Experiment #1 groups: Control Exp#1 (intranasal saline and no foot shock); Stress Exp#1 (intranasal saline 5 min after foot shock); Massage+Stress Exp#1 (massage-like stroking and intranasal saline 5 min after foot shock); Oxytocin+Stress Exp#1 (intranasal oxytocin 5 min after foot shock). Experiment #2 groups: Control Exp#2 (intranasal saline and no foot shock); Stress Exp#2 (intranasal saline 1 h before foot shock); Oxytocin Exp#2 (intranasal oxytocin and no foot shock); Oxytocin+Stress Exp#2 (intranasal oxytocin 1 h before foot shock). One week after fear conditioning (and other treatments), rats were independently evaluated for behavioral signs of fear. Two weeks after conditioning, physiological signs of fear were also assessed (Experiment #1). Relative to controls, rats treated with intranasal oxytocin 5 min after daily foot shock sessions exhibited significantly less immobility upon re-exposure to the shock chamber and attenuated physiological responses related to fear (e.g., elevated heart rate and blood pressure). Furthermore, intranasal oxytocin treatment given 1 h before daily foot shock sessions significantly decreased immobility and defecation upon re-exposure to the shock chamber, relative to controls. The results of this study suggest that prophylactic intranasal oxytocin, administered contemporaneously with aversive stimuli, mitigates behavioral and physiological responses associated with traumatic stress.
Systems AI: A Declarative Learning Based Programming Perspective

Data-driven approaches are becoming dominant problem-solving techniques in many areas of research and industry. Unfortunately, current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas on real-world data and need to evaluate those as a part of an end-to-end system. We review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques as well as the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.
Monitoring Tissue Engineering and Regeneration by Magnetic Resonance Imaging and Spectroscopy

In this article, based on the invited talk at the “Tissue Science 2012” meeting in Chicago on October 1-3, 2012, we describe some examples of characterization of engineered cartilage and bone tissue using magnetic resonance spectroscopy and imaging. Two different models of engineered cartilage and engineered bone tissue constructs were used for these studies: 1) chondrocyte based cartilage tissue engineering constructs: human and bovine chondrocytes seeded in alginate beads (Hydrogel scaffold model) or bovine chondrocytes grown as pellets (scaffold free model); 2) mesenchymal stem cell (MSC) based cartilage and bone tissue engineering constructs: human mesenchymal stem cell (HMSCs) seeded in cartilage biomimetic scaffolds (collagen/chitosan scaffold integrated with extracellular matrix of cartilage) or HMSCs seeded in collagen/chitosan scaffolds. Magnetic resonance spectroscopy and imaging experiments using 9.4 T (400 MHz proton frequency), 11.7 T (500 MHz proton frequency) or 14.1 T (600 MHz proton frequency) MR spectrometers/Imager were performed on these constructs over two to four weeks of tissue culture time. Specifically, water suppressed proton NMR spectroscopy; proton and sodium multi-quantum coherence spectroscopy and proton T1, T2 and ADC parametric MRI were used to study the chondrogenesis and osteogenesis of these tissues. We found that the change in MR relaxation and diffusion coefficient parameters correlate well with the growth of engineered tissues. We found that the MR parameters and the change in these parameters in growing tissue are strongly influenced by the choice of scaffolds. We also found as expected that the tissue-engineered cartilage lacked order or preference in collagen orientation. Further work is underway to elucidate these findings. We anticipate that in future, MRI will augment histological and immunohistochemical techniques by providing a complimentary and real time quantitative assessment of engineered tissue growth at all growth stages: (i) cell seeding to pre implantation; (ii) preclinical validation studies post implantation in small and large animal models; (iii) clinical studies of performance of engineered tissues.