Miracle-dz/UniFAR-FTUs huggingface dataset:
YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter

This paper describe the system we proposed to participate the first year of Irony detection in English tweets competition. Previous works demonstrate that LSTMs models have achieved remarkable performance in natural language processing; besides, combining multiple classification from various individual classifiers in general is more powerful than a single classification. In order to obtain more precision classification of irony detection, our system trained several individual neural network classifiers and combined their results according to the ensemble-learning algorithm.
Egyptian propolis compared to nanohydroxyapatite graft in the treatment of Class II furcation defects in dogs.

BACKGROUND Complementary and alternative medicine approaches are gaining ground in everyday practice. Propolis is a bee product that has been suggested to have bone regenerative effects. This study was conducted to compare the effectiveness of Egyptian propolis with nanohydroxyapatite graft on furcation defect regeneration in mongrel dogs. METHODS A split mouth design was utilized in six clinically healthy mongrel dogs. A total of 24 Class II furcation defects were surgically created in mandibular third and fourth premolars. The defects on the right side were filled with nanohydroxyapatite graft and covered with collagen membrane, while on the left side the defects were filled with propolis and also covered with collagen membrane. The dogs were sacrificed after 1 and 3 months. Segments containing the defects were prepared for histological evaluation. RESULTS The histological evaluation after one month revealed newly formed bone in both treatment groups. However, after 3 months, the bone trabeculae appeared thinner in the collagen/nanohydroxyapatite group than in the collagen/propolis group. The histomorphometric evaluation showed a significant increase in bone height as well as bone surface area for the collagen/propolis group compared to collagen/nanohydroxyapatite group. CONCLUSIONS Both Egyptian propolis and nanohydroxyapatite graft material showed favorable periodontal regenerative effect. Propolis showed increased cellular proliferative ability that could be beneficial in reducing the healing period needed after periodontal therapy.
Potential cost savings owing to the route of administration of oncology drugs: a microcosting study of intravenous and subcutaneous administration of trastuzumab and rituximab in the Netherlands

There is little evidence on the costs associated with the route of administration of oncology drugs. We investigated time and resource use for hospitals and patients and compared healthcare and societal costs for intravenous (IV) and subcutaneous (SC) administration of trastuzumab and rituximab. Data for the preparation and administration of both drugs were collected at the hospital pharmacy and at the oncology day care unit. Patients completed a questionnaire for obtaining information on societal costs (productivity losses, informal care and traveling expenses). A total of 126 patients were recruited in six hospitals; 82 received trastuzumab (37 IV and 45 SC) and 44 received rituximab (23 IV and 21 SC). The costs per administration (including societal cost but excluding drug costs) were &OV0556;167 and &OV0556;264 for IV and &OV0556;76 and &OV0556;146 for SC trastuzumab and rituximab, respectively. The costs for SC administration were lower in all categories. The largest cost component was related to time spent at the day care unit (overhead costs). This resulted in savings of &OV0556;47 for SC trastuzumab and &OV0556;69 for SC rituximab. The costs related to time of healthcare professionals was &OV0556;9 lower for both drugs. The costs for consumables resulted in another &OV0556;12 savings. Societal costs were &OV0556;22 lower for SC trastuzumab and &OV0556;28 lower for SC rituximab. Although administration costs are relatively a small part of the total costs, important savings can be generated by switching to an SC route of administration especially because a large number of patients receive oncology drugs and patients receive more than one administration.
Effect of resveratrol on glucose control and insulin sensitivity: a meta-analysis of 11 randomized controlled trials.

BACKGROUND The results of human clinical trials investigating the effects of resveratrol on glucose control and insulin sensitivity are inconsistent. OBJECTIVE We aimed to quantitatively evaluate the effects of resveratrol on glucose control and insulin sensitivity. DESIGN We performed a strategic literature search of PubMed, Embase, MEDLINE, and the Cochrane Library (updated to March 2014) for randomized controlled trials that estimated the effects of resveratrol on glucose control and insulin sensitivity. Study quality was assessed by using the Jadad scale. Weighted mean differences were calculated for net changes in glycemic measures by using fixed-effects or random-effects models. We performed prespecified subgroup and sensitivity analyses to evaluate potential heterogeneity. Meta-regression analyses were conducted to investigate dose effects of resveratrol on fasting glucose and insulin concentrations in nondiabetic subjects. RESULTS Eleven studies comprising a total of 388 subjects were included in this meta-analysis. Resveratrol consumption significantly reduced fasting glucose, insulin, glycated hemoglobin, and insulin resistance (measured by using the homeostatic model assessment) levels in participants with diabetes. No significant effect of resveratrol on glycemic measures of nondiabetic participants was found in the meta-analysis. Subgroup and sensitivity analyses indicated that the pooled effects of resveratrol on fasting glucose and insulin concentrations in nondiabetic participants were not affected by body mass index, study design, resveratrol dose, study duration, or Jadad score. CONCLUSIONS Resveratrol significantly improves glucose control and insulin sensitivity in persons with diabetes but does not affect glycemic measures in nondiabetic persons. Additional high-quality studies are needed to further evaluate the potential benefits of resveratrol in humans.
Training needs of dairy farmers

The training can be more valuable and significant when analysis of training needs prior to beginning of training programmes and imparting knowledge according to the needs of the farmers. A list of seven major dairy practices/components was prepared to know training need index (TNI) of the dairy farmers of Jammu district. Under each major component, specific and relevant training need items were collected and systematically incorporated in to the interview schedule and administered. The results revealed that even in the most popular areas of training, there was an inadequacy. According to livestock owners perceived training needs, animal health (I), followed by marketing and financial management (II), breeding and general management (III), milking hygiene practices (IV), animal nutrition (V), animal welfare management (VI) and housing and environment management (VII) respectively. Therefore, the extension agencies and training imparting agencies in border belts of Jammu district has to give special emphasis on health care, feeding and breeding during dairy farmers training for increasing the livestock productivity and improving the living standards of the farmers. The various dairy extension agencies have to re-orient their trainings based on these findings to reduce the existing technological and adoption gap among the dairy farmers.
Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

The incoming era of the fifth-generation fog computing-supported radio access networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of virtual machines (VMs) toward nearby fog data centers. For this purpose, the bandwidths of the multiple wireless network interface cards of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena, the energy consumptions of the current state-of-the-art VM migration techniques may still offset their expected benefits. Motivated by these considerations, in this paper, we analytically characterize and implement in software and numerically test the optimal minimum-energy settable-complexity bandwidth manager (SCBM) for the live migration of VMs over 5G FOGRAN MPTCP connections. The key features of the proposed SCBM are that: 1) its implementation complexity is settable on-line on the basis of the target energy consumption versus implementation complexity tradeoff; 2) it minimizes the network energy consumed by the wireless device for sustaining the migration process under hard constraints on the tolerated migration times and downtimes; and 3) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the wireless environment without requiring forecasting. The actual effectiveness of the proposed SCBM is supported by extensive energy versus delay performance comparisons that cover: 1) a number of heterogeneous 3G/4G/WiFi FOGRAN scenarios; 2) synthetic and real-world workloads; and, 3) MPTCP and wireless connections.
Resting-state functional brain networks in first-episode psychosis: A 12-month follow-up study

Introduction: Schizophrenia is increasingly conceived as a disorder of brain network connectivity and organization. However, reports of network abnormalities during the early illness stage of psychosis are mixed. This study adopted a data-driven whole-brain approach to investigate functional connectivity and network architecture in a first-episode psychosis cohort relative to healthy controls and whether functional network properties changed abnormally over a 12-month period in first-episode psychosis. Methods: Resting-state functional connectivity was performed at two time points. At baseline, 29 first-episode psychosis individuals and 30 healthy controls were assessed, and at 12 months, 14 first-episode psychosis individuals and 20 healthy controls completed follow-up. Whole-brain resting-state functional connectivity networks were mapped for each individual and analyzed using graph theory to investigate whether network abnormalities associated with first-episode psychosis were evident and whether functional network properties changed abnormally over 12 months relative to controls. Results: This study found no evidence of abnormal resting-state functional connectivity or topology in first-episode psychosis individuals relative to healthy controls at baseline or at 12-months follow-up. Furthermore, longitudinal changes in network properties over a 12-month period did not significantly differ between first-episode psychosis individuals and healthy control. Network measures did not significantly correlate with symptomatology, duration of illness or antipsychotic medication. Conclusions: This is the first study to show unaffected resting-state functional connectivity and topology in the early psychosis stage of illness. In light of previous literature, this suggests that a subgroup of first-episode psychosis individuals who have a neurotypical resting-state functional connectivity and topology may exist. Our preliminary longitudinal analyses indicate that there also does not appear to be deterioration in these network properties over a 12-month period. Future research in a larger sample is necessary to confirm our longitudinal findings.
Application of Word Embedding to Drug Repositioning

As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embedding algorithm, senses of over 1.7 million words were well represented in sufficiently short feature vectors. Through various analysis including clustering and classification, feasibility of our approach was tested. Finally, our trained classification model achieved 87.6% accuracy in the prediction of drug-disease relation in cancer treatment and succeeded in discovering novel drug-disease relations that were actually reported in recent studies.
Image de-noising algorithm based on Gaussian mixture model and adaptive threshold modeling

In this paper, we conduct research on the image de-noising algorithm based on Gaussian mixture model and adaptive threshold modeling. Minimum spanning tree based on the local threshold value method, can make full use of the image information change itself, by defining a new local threshold method to calculate the regional difference and extracting, gives image a good segmentation and de-noising effect. Gaussian mixture model is a kind of important background modeling method. The method using multiple Gaussian background model is established through the background update constantly adjust background model of the components of the Gaussian distribution, and therefore has a certain ability to adapt. Under this assistance, we integrate the corresponding mode to propose the new image de-noising algorithm that obtains better performance.
Initial validation and results of the Symptoms in Persons At Risk of Rheumatoid Arthritis (SPARRA) questionnaire: a EULAR project

Objectives To describe the development and assess the psychometric properties of the novel ‘Symptoms in Persons At Risk of Rheumatoid Arthritis’ (SPARRA) questionnaire in individuals at risk of rheumatoid arthritis (RA) and to quantify their symptoms. Methods The questionnaire items were derived from a qualitative study in patients with seropositive arthralgia. The questionnaire was administered to 219 individuals at risk of RA on the basis of symptoms or autoantibody positivity: 74% rheumatoid factor and/or anticitrullinated protein antibodies positive, 26% seronegative. Validity, reliability and responsiveness were assessed. Eighteen first degree relatives (FDR) of patients with RA were used for comparison. Results Face and content validity were high. The test-retest showed good agreement and reliability (1 week and 6 months). Overall, construct validity was low to moderate, with higher values for concurrent validity, suggesting that some questions reflect symptom content not captured with regular Visual Analogue Scale pain/well-being. Responsiveness was low (small subgroup). Finally, the burden of symptoms in both seronegative and seropositive at risk individuals was high, with pain, stiffness and fatigue being the most common ones with a major impact on daily functioning. The FDR cohort (mostly healthy individuals) showed a lower burden of symptoms; however, the distribution of symptoms was similar. Conclusions The SPARRA questionnaire has good psychometric properties and can add information to currently available clinical measures in individuals at risk of RA. The studied group had a high burden and impact of symptoms. Future studies should evaluate whether SPARRA data can improve the prediction of RA in at risk individuals.
Immunohistochemically detectable metallothionein expression in malignant pleural mesotheliomas is strongly associated with early failure to platin-based chemotherapy

Background Malignant pleural mesothelioma (MPM) is a biologically highly aggressive tumor arising from the pleura with a dismal prognosis. Cisplatin is the drug of choice for the treatment of MPM, and carboplatin seems to have comparable efficacy. Nevertheless, cisplatin treatment results in a response rate of merely 14% and a median survival of less than seven months. Due to their role in many cellular processes, methallothioneins (MTs) have been widely studied in various cancers. The known heavy metal detoxifying effect of MT-I and MT-II may be the reason for heavy metal drug resistance of various cancers including MPM. Methods 105 patients were retrospectively analyzed immunohistochemically for their MT expression levels. Survival analysis was done by Cox-regression, and statistical significance determined using likelihood ratio, Wald test and Score (logrank) tests. Results Cox-regression analyses were done in a linear and logarithmic scale revealing a significant association between expression of MT and shortened overall survival (OS) in a linear (p=0.0009) and logarithmic scale (p=0.0003). Reduced progression free survival (PFS) was also observed for MT expressing tumors (linear: p=0.0134, log: p=0.0152). Conclusion Since both, overall survival and progression-free survival are negatively correlated with detectable MT expression in MPM, our results indicate a possible resistance to platin-based chemotherapy associated with MT expression upregulation, found exclusively in progressive MPM samples. Initial cell culture studies suggest promoter DNA hypomethylation and expression of miRNA-566 a direct regulator of copper transporter SLC31A1 and a putative regulator of MT1A and MT2A gene expression, to be responsible for the drug resistance.
Chinese Relation Classification via Convolutional Neural Networks

Relation classification is an important task in natural language processing. Traditional relation classification techniques suffer from extensive use of linguistic features and external toolkits. In recent years, deep learning models that can automatically learn features from text are playing a more essential role in this area. In this paper we present a novel convolutional neural network (CNN) approach along shortest dependency paths (SDP) for Chinese relation classification. We first propose a baseline end-to-end model that only takes sentence-level features, and then improve its performance by joint use of pre-extracted linguistic features. The performance of the system is evaluated on the ACE 2005 Multilingual Training Corpus Chinese dataset. The baseline model achieved a 74.93% F-score on six general type relations and a 66.29% F-score on eighteen subtype relations, and the performance improved 10.71% and 13.60% respectively by incorporating linguistic features into the baseline system.
Enhanced Signal Recovery via Sparsity Inducing Image Priors

Parsimony in signal representation is a topic of active research. Sparse signal processing and representation is the outcome of this line of research which has many applications in information processing and has shown significant improvement in real-world applications such as recovery, classification, clustering, super resolution, etc. This vast influence of sparse signal processing in real-world problems raises a significant need in developing novel sparse signal representation algorithms to obtain more robust systems. In such algorithms, a few open challenges remain in (a) efficiently posing sparsity on signals that can capture the structure of underlying signal and (b) the design of tractable algorithms that can recover signals under aforementioned sparse models.
Energy distribution and pricing based on load demand taxonomy in a smart grid tree network

The energy demand is growing drastically due to the technological developments in the world. It requires a smart energy distribution, scheduling and pricing based on the dynamic load demand. In this paper, a new Load Demand Taxonomy (LDT) based Energy Distribution and Pricing on Smart Grid is proposed. Based on the load demand and load request, the load is classified into different categories. Further, based load Taxonomy, Fuzzy pricing strategies is implemented. An Intelligent Block based Load Shifting Algorithm (IBLS) is proposed to maintain the load balancing and avoids demand request over the queue. From the simulation results it is observed that, the proposed algorithm controls the load demand and the dynamic pricing using load demand taxonomy controls the unit energy charge and provides benefit to the user. The IBLS with low category users to high category users satisfies a number of users and improves system fairness.
Thiourea bridged periodic mesoporous organosilica with ultra-small Pd nanoparticles for coupling reactions

A synthesis of thiourea-bridged mesoporous organosilica is reported. Using transmission electron microscopy, low-angle XRD measurements and N2 adsorption–desorption isotherms the formation of a highly ordered mesoporous structure with a high surface area (>400 m2 g−1) has been confirmed. Deposition of Pd nanoparticles inside the mesopores resulted in a material that showed excellent catalytic activity in a Suzuki-coupling reaction and retained this activity over several consecutive runs.
Survey of density based clustering algorithms and its variants

Clustering technique is a unsupervised machine learning technique in the domain of data mining. Many of the clustering techniques are inherently sensitive to the input parameters. Different clustering techniques works differently for different types of the input datasets. Among all different varieties of clustering techniques, DBSCAN is one of the most important clustering technique whose working principle based on the density estimation while forming the clusters of the input dataset points which is basically used for spatial datasets of random shapes and sizes. It also eliminates the noise during the clustering formation process with a worst case run-time complexity of O(nA2). DBSCAN technique also produces a bad result for varied density datasets. In this paper we have discussed about different density based clustering techniques along with DBSCAN, its variants and some of its modified algorithms with respect to their input parameters and running time complexities. Also we have presented the comparison analysis of all the different variants of DBSCAN algorithms over different benchmark datasets for computing various measures.
On the need of machine learning as a service for the internet of things

In recent years we are witnessing a rapid increase in the diffusion of the Internet of Things (IoT) technology, with a large scale adoption of interconnected heterogeneous devices that are pervasively collecting information through the interaction with humans in their environment. The adoption of Machine Learning (ML) methodologies can play a fundamental role, allowing smarter IoT applications to continuously adapt to evolving environmental conditions and user's needs. In this context, the time is now ripe for a decisive step forward in the direction of a systematic integration of ML functionalities within the IoT platform. In this paper, we outline the principles that should guide the realization of a ML service for the IoT, proposing a conceptual architecture of such a learning service, integrated within the IoT reference model. Our proposal leverages on the experience of recent successful European initiatives that led to the realization of intelligent sensor networks built on the synergy between resource efficient ML models for temporal data processing and wireless sensor networks. The relevant impact of ML in applicative domains of interest for the IoT is also enucleated through a brief summary of recent results.
Relating connectivity changes in brain networks to genetic information in Alzheimer patients

To better understand brain network disruption in Alzheimer disease (AD), advanced descriptors from genetics and neuroimaging can be useful. Combining those can help to shed new lights on neurodegeneration related to AD, since predicting brain changes is critically important for early treatment. We investigate correlation between global brain metrics changes and known gene biomarkers for AD. In practice, we construct connectivity networks using whole-brain tractography and examine the longitudinal changes. In a case-control setting, we compute the difference in time of global metrics of integration and segregation. Then, we observe that these longitudinal changes in metrics are correlated to the gene expression of known genetic biomarkers, such as the APOE and SPON1 genes, showing the relevance of these genes also in the pace of neurodegenerations.