Miracle-dz/UniFAR-FTUs huggingface dataset:
Modification of some denture cleaning agents employed with mechanical cleansing methods

The present study was carried out to investigate the effect of 5 cleaning agents(dettol soap, whitening tooth paste, sodium bicarbonate powder, and ethanol handsanitizer gel and siwak powder) on color alteration of heat-polymerized acrylic resinand to compare it with that of distilled water. 60 specimen strips of heat-polymerizedacrylic resin material were prepared measuring 5mm width, 40mm length and 2mmthickness; five specimens were used for each group. After two weeks of immersion intea solution, the light transmission percent value for each specimen was determined at400nm and 700nm wave lengths by a spectrophotometer device.Then the half of the stained specimens were cleaned by the cleaning agents bybrushing with a hand brush , and rest specimens were cleaned by the same cleaningagents by an electrical dental brush and again measure the light transmission percentvalue for each specimen by the spectrophotometer device.The result obtained in this study showed that ethanol gel specimen group has thehighest mean value for light transmission among all the specimen groups of hand andmotor brush at the two wave lengths, followed by tooth paste, siwak powder, sodiumbicarbonate powder & dettol soap groups but distal water group has the lowest one.Furthermore, there were no measurable differences between the hand brush and theelectric tooth brush with respect to staining removal from the acrylic resin specimensfor all cleaning agents groups used in this study.
Research on the Influencing Factors of Patent Commercialization in Chinese Enterprises under the Background of Association of Southeast Asian Nations

With the implementation of the national "One Belt And One Road" strategy, ASEAN has become an essential partner for Chinese enterprises to "go global". The purpose of this paper is to help Chinese enterprises to develop appropriate patent commercialization strategies. On the basis of combing relevant documents, this paper adopts the research method of induction and summary. In the context of ASEAN, Chinese enterprises' patent commercialization strategy is mainly influenced by policy factors, market, technology, service and other factors. This paper argues that companies should strengthen cross-border cooperation in patent protection in order to seek policy support from ASEAN; secondly, in order to strengthen patent warning work, enterprises should improve the intermediary service system; third, companies should improve their independent innovation capabilities and form their own technological competitive advantages; on the whole, Chinese companies should strengthen strategic cooperation with ASEAN to achieve mutual benefit and win-win results. Keywords—Association of southeast Asian nations (ASEAN); Patent Commercialization; Influence factor; Patent strategy
Biocultural Diversity and Satoyama. Emotions and the fun-factor in nature conservation – A lesson from Japan

“Biodiversity virtually has become a cult concept” (COUNCIL OF EUROPE, 1996), and is a common topic of conversation among people concerned with environmental topics. Many conservation policies, programs and projects, laws and re gulations have been designed for the conservation of biodiversity, but not much success can be reported; publications are still dominated by reports on the continual losses of biodiversity worldwide. And, if one considers that the diversity of the living world in space and time is a meta-concept with practically indefinite attributes (NORTON, 1994), it is questionable whether science can be helpful here at all. The bio-scientific approach to conservation issues will have to be amended or supported by economic and social concerns, which consider attitudes, emotions and values as important factors in the system. The conclusion of an international group of experts discussing the possibilities of achieving the goals of the Convention on Biological Diversity (Rio 92) was: “Successful implementation of the CBD depends on a complex interplay of ecological processes, culture, economic and social concerns. Be-
Clinicopathological and prognostic values of ALDH1 in epithelial ovarian cancer: a meta-analysis

Background: Previous studies have confirmed the prognostic value of positive aldehyde dehydrogenase 1 (ALDH1) expression for ovarian cancer, but its predictive value remains controversial. Methods: Meta-analysis was conducted to clarify the correlation of positive ALDH1 expression with the clinicopathological characteristics, types, overall survival and progression-free survival of epithelial ovarian cancer. PubMed, EMBASE and Cochrane were comprehensively searched through July 2017. Nine studies were included. Results: High ALDH1 expression was significantly correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage of ovarian cancer in both Chinese and Caucasians, and with tumor grade in Chinese people (pooled OR and 95% CI = 2.10 (1.20, 3.67)), but not with lymph node metastasis (2.47 (0.77, 7.87)) or tumor location in the ovary (1.10 (0.62, 1.96)). ALDH1 expression was correlated with subtypes of epithelial ovarian cancer (e.g. serous carcinoma, mucinous carcinoma and endometrioid carcinoma) in Caucasians and with the overall survival (1.59 (1.11, 2.29)), but not with progression-free survival (1.14 (0.72, 1.82)). Conclusions: Positive ALDH1 expression is significantly correlated with the clinicopathological characteristics and prognosis of ovarian cancer.
Great Barrier Reef Source modelling: Assessing hillslope erosion modelling performance at paddock scale experimental sites

Catchment modelling is used as one of multiple lines of evidence to report on progress towards Great Barrier Reef (GBR) Water Quality Protection Plan (Reef Plan) water quality targets. Progress is monitored via the Paddock to Reef (P2R) Integrated Monitoring, Modelling and Reporting Program. Within the P2R program, the Source modelling framework is used to construct GBR specific models (McCloskey 2017) and the overarching purpose of the modelling is to assess the impact of land management changes across the GBR against Reef Plan targets. To optimise predictive capability and thereby help facilitate better targeting, the modeller is faced with two common problems. Firstly, assessing the quality of model inputs and outputs and secondly, where possible, improving modelling performance. While observation datasets such as flow are often adequate, water quality data are scarcer, sporadically sampled, and frequently well downstream of their source. To further improve the water quality model validation data pool, the modelling team have attempted to tabulate all relevant datasets that can be used in model evaluation. However, utilising all available datasets to assess and improve performance can be an arduous task. As such, small catchment studies can initially be overlooked as a validation source in large “lumped” catchment scale modelling studies due to scale and the inherent spatial and temporal mismatch issues. As a small illustrative case study, we firstly designed a simple methodology to disaggregate the lumped modelled hillslope loads for a grazing landuse in a subcatchment. We then discuss the benefit of paddock scale research sites for informing the GBR Source modelling. In the GBR Source framework, spatially variable Revised Universal Soil Loss Equation (RUSLE) was used to generate the hillslope sediment loads for rangelands. Sediment loads are lumped and reported for a single landuse (typically1 km2 – 30km2) for a given sub-catchment at a daily timestep. However the modelled daily loads are initially generated at a finer scale, typically, 30m by 30m 900 m2. As the individual grid calculations are not recorded at model runtime, they needed to be recorded prior to aggregation in the model. This enabled evaluations between GBR Source modelled loads and the much smaller catchment study data to be made at appropriate scales. The disaggregation and assessment of modelled loads outlined in this paper facilitated small to large scale hillslope erosion comparisons. For the two case study sites, erosion and cover are shown to be well predicted at the average annual scale and areas for further improvement and investigation have been highlighted. We recommend further sites across the GBR be assessed. Validating disaggregated modelled data against plot scale experimental data, provides an alternative data set for validation previously not utilised. The approach shows a lot of potential to improve confidence in modelled sediment generation predictions.
Performance and microbial status of turkeys fed diets containing different levels of inulin*

* Supported by the State Committee for Scientific Research, Grant No 3 P06 Z0 21 24. 1Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Division of Food Science, Olsztyn, Poland 2Department of Poultry Science, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland 3Dept. of Poultry Disease, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland Manuskript eingegangen am 5. März 2004, angenommen am 17. Juni 2004
RALIGRAPH at HASOC 2019: VGCN-BERT: Augmenting BERT with Graph Embedding for Offensive Language Detection

Hate speech and oﬀensive language detection are receiving more and more attention in recent years. The RALIGRAPH team participated in the Shared Task on the Identiﬁcation of Oﬀensive content for Indo-European languages within the FIRE conference. This paper describes our approach VGCN-BERT model for all three sub-tasks of hate language and oﬀensive language detection in English. VGCN-BERT takes into account both local and global information, by combining the Graph Convolutional Networks (GCN) and the Self-Attention Encoder (BERT). Our approach produced good results in the experiments.
Effects of Summer Rainfall on the Soil Thermal Properties and Surface Energy Balances in the Badain Jaran Desert

Based on observational data collected during the summer of 2009 from the southern Badain Jaran Desert, the surface sensible and latent heat fluxes and shallow soil thermal storage were obtained through corrections and quality control measures. The soil thermal properties and characteristics of the land surface energy budget before and after rainfall episodes were systematically analyzed. Short-term precipitation had a greater influence than systematic precipitation on the soil temperature (ST) and soil volumetric water content (VWC). After rainfall, the VWC rapidly increased, showing a decreasing growth rate trend with depth and time in all layers; the soil temperature change rate (TCR) exhibited the opposite tendency. The surface albedo, which was affected little by the solar elevation angle and short-term precipitation, fluctuated from low to high during short-term rainfall. The soil thermal parameters, including the volumetric heat capacity, thermal conductivity, and diffusivity, all increased after rainfall. The diurnal soil heat flux variations in each layer manifested as quasisinusoids, and the amplitude gradually decreased with depth. The energy balance ratio (EBR) without and with soil heat storage (S) varied differently; after incorporating S, the EBR increased by approximately 5-6% regardless of rainfall but remained lower afterward. Throughout the observation period, the maximum daytime EBR appeared approximately 1-2 days before or after rainfall and gradually declined otherwise. These findings are fundamental for understanding the influences of cloud and precipitation disturbances on radiation budgets and energy distributions and improving the parameterization of surface radiation budgets and energy balances for numerical models of semiarid areas.
Study concerning evidence-based nursing and clinical efficacy of interventional therapy in patients with coronary heart disease

Objective: The aim of the current study was to explore the effects of evidence-based nursing on interventional therapy for patients with coronary atherosclerotic heart disease. Methods: A total of 108 patients with coronary heart disease were randomly divided into the control group and experimental group, with 54 cases in each group. The control group was given routine nursing, while the experimental group was given evidence-based nursing. Physical function measurements (Physical Function Scale, CM-PPT), 30-second arm flexion tests (30-ACT), 10 sitting position test measurements (STS10), 6-minute walking tests (6MWT), depression scores (Self-Rating Depression Scale, SDS), anxiety scores (Self-Rating Anxiety Scale, SAS), pain scores (Numerical Rating Scale, NRS), daily living abilities (Barthel index), quality of life levels (China questionnaire of quality of life in patients with cardiovascular diseases, CQQC), and nursing satisfaction scores (Seattle Scale, SAQ) of the two groups, before and after three months of nursing, were recorded. Results: Compared with routine nursing, evidence-based nursing improved physical function levels of patients more effectively (P < 0.05), reduced anxiety and depression (P < 0.05), improved daily living abilities and quality of life (P < 0.05), and improved nursing satisfaction scores (P < 0.05). Conclusion: Application of evidence-based nursing intervention model in patients with coronary heart disease can alleviate bad moods and pain, as well as improve physical function, daily living abilities, quality of life, and nursing satisfaction scores.
Causal Inference and Data Fusion in Econometrics

Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected and the substantive knowledge that is available about the phenomenon under investigation. For instance, unobserved confounding factors threaten the internal validity of estimates; data availability is often limited to non-random, selection-biased samples; causal effects need to be learned from surrogate experiments with imperfect compliance; and causal knowledge has to be extrapolated across structurally heterogeneous populations. A powerful and flexible causal inference framework is required in order to tackle all of these challenges, which plague essentially any data analysis to varying degrees. Building on the structural perspective on causality introduced by Haavelmo (1943) and the graph-theoretic approach proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal inference that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we review recent advances made in this literature that have the potential to contribute to econometric methodology along three broad dimensions. First, they provide a unified and comprehensive framework for causal learning, in which the above-mentioned problems can be addressed in generality. Second, due to their origin in AI, they come together with sound, efficient, and complete (to be formally defined) algorithmic criteria for automation of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the analytical rigor of structural econometrics with the flexibility of the potential outcomes framework, and thus offer a valuable complement to these two literature streams.
KCE DALab-APDA@FIRE2019: Author Profiling and Deception Detection in Arabic using Weighted Embedding

This paper explaining the work submitted on Author Pro-ﬁling and Deception Detection in Arabic Tweets shared task organized at the Forum for Information Retrieval Evaluation (FIRE) 2019. The ﬁrst task Author proﬁling illustrates identifying the categories of authors based on the Arabic tweets. In the second task, the aim is to Detect deception in Arabic for two genres such as Twitter and News. Deception detection means that the automatic way of identifying false messages in the text content on social network or news. For each task, we have submitted three diﬀerent systems. For submission 1, we have used the Term Frequency and Inverse Document Frequency (TFIDF) based Support Vector Machine classiﬁcation and in submission 2, we have used fastText classiﬁer. For submission 3, we have proposed a low dimensional weighted document embedding (TFIDF + Word embedding) with SVM classiﬁcation. We have attained second place in the Deception detection and third in Author proﬁling. The performance diﬀerence between the top team results and the submitted runs are only 3.34% for Author pro-ﬁling and 1.16% for Deception detection.
Adding Text Document to Cluster Based on the Similarity Measures

Information retrieval systems retrieves a large set of text documents. By using the clustering techniques, text documents in huge voluminous arranged in a systematic compact relevant group of documents. By this, way users allowed to find information quickly and easily in response. This paper analyzes the measure of similarity between clusters of the same dataset produced using the K-means algorithm. The similarity measure thus used to identify the best match cluster in terms of most similar to given document. Experimental results of known datasets webkb and AustLII along with one real-world text dataset built by us using deep crawling discussed. Here, we compare clusters with four most-accepted similarity measures (Cosine, Pearson correlation, Euclidean, and Extended Jaccard) by using vector space representation (term frequency and inverse document frequency) of text documents.
The antitumor effects and mechanisms of the novel compound Alocasia B against gastric cancer cells

Purpose: Alocasia cucullata (Lour.) G. Don is an antitumor herb, and Alocasia B is a novel molecule from it, indicating signs of antitumor activity. In this study, Alocasia B was analyzed to evaluate its effects and mechanisms against gastric cancer and was identified as the key chemical component of A. cucullata. Methods: A cell line panel with 12 gastric cancer cells was screened to determine the cytotoxic properties of Alocasia B using an MTT assay. MGC-803 was selected for further evaluation of the cell cycle and apoptosis by flow cytometry. The protein expressions of p-Akt, Akt, Bax, Bcl-2, p-p53, p53 and caspase-3 were estimated by western blotting. Results: Alocasia B was found to be cytotoxic towards 12 gastric cancer cell lines presenting IC50 values from 12.8 to 93.4 μg/ml. MGC-803 was the most sensitive cell line and was inhibited by Alocasia B in both doseand time-dependent manners. Apoptosis and G0/G1 phase arrest were induced by Alocasia B. Alocasia B could trigger multiple signaling pathways, including the upregulation of Bax, p-p53, caspase-3 and the downregulation of p-Akt, Bcl-2. In an in vivo study, Alocasia B also significantly impeded tumor growth in the xenograft model. Conclusion: Taken together, Alocasia B exhibited cytotoxic activity against gastric cancer both in vitro and in vivo through multiple cancer-related signaling pathways. This study provides evidence that Alocasia B is a chemotherapeutic candidate for antitumor agent development.
The involvement of miR-375 in the adriamycin resistance of pancreatic carcinoma cells

Background: Yes-associated protein 1 (YAP1) is correlated with the onset, progression, drug resistance and prognosis of pancreatic carcinoma. MicroRNA (miR)-375 is down-regulated in pancreatic cancer tissues and may target the 3’-untranslated region (3’-UTR) of the YAP1 gene. We thus investigated whether miR-375 mediates the YAP1 expression, proliferation, apoptosis, and Adriamycin (ADM) resistance of pancreatic carcinoma cells. Methods: A dual luciferase reporter assay confirmed the targeted relationship between miR-375 and YAP1. The ADM-resistant cell lines SW1990/ADM, BxPC-3/ADM, the non-resistant cell lines SW1990 and BxPC-3, and the normal pancreatic ductal cell line HPDE6-C7 were cultured and followed up by an analysis of the cell apoptosis using flow cytometry. Cultured ADM-resistant cells were transfected with an miR-375 mimic followed by measuring the YAP1 expression and the ADM-induced apoptosis by flow cytometry and the cell proliferation by EdU staining. Results: A targeted relationship exists between miR-375 and YAP1 mRNA. The SW1990/ADM and BxPC-3/ADM cells had significantly lower miR-375 than the non-resistant cells, which had a lower miR-375 expression compared to the HPDE6-C7 cells. Drug resistant cells had a remarkably higher YAP1 expression than the non-resistant cells, which had a higher YAP1 level than HPDE6-C7. Under ADM concentration (IC50), the drug resistant cells showed remarkably decreased apoptosis compared to the non-resistant cells. In addition, the transfection of the miR-375 mimic significantly suppressed the YAP1 protein expression in the resistant cells, enhanced apoptosis, and inhibited proliferation. Conclusion: The down-regulation of miR-375 is correlated with the ADM resistance of pancreatic carcinoma cells. The up-regulation of miR-375 inhibits cancer cell proliferation, facilitates apoptosis, and decreases ADM resistance possibly by suppressing YAP1 expression.
A novel system for remote sensing measurements of evapotranspiration and CWSI of agricultural crops

-Remotely sensed plant canopy temperature has long been recognized as having potential as a tool for irrigation management. Changes of actual evapotranspiration (ET)as a function of water availability were measured with infrared temperature radiometers (IRT). A a novel deterministic algorithm was developed and solved numerically to compare ground-based remote sensing method to ET from a weighing lysimeter. The algorithm is based on a closed form of Penman Monteith (P-M) model and its thermal interpretation by Jackson et al. this algorithm can automatically provide timely valuable information on plant and soil water status (Crop Water Stress Index CWSI) , which can improve the management of irrigated crops. Four treatments with 3 replications each were trickle irrigating soilless culture. They were used to test response of Lettuce (Lactuca sativa var 936 ) to water applications amounts by yield weighing and IRT evapotranspiration monitoring. Results of comparison between lysimeter and IRT measurements were satisfactory for field measurements with a correlation slope close to a unit and r 2 = 0.77. The t-test accepted the null hypothesis that there was no difference between populations of IRT measurements and lysimeter evapotranspiration.
Running Title: CYP1B1 Expression in Breast Tissue Key Words: CYP1B1, CYP1A1, expression, breast, polycyclic aromatic hydrocarbons Footnotes

Polycyclic aromatic hydrocarbons (PAHs) are metabolically activated to ultimate carcinogens by the cytochrome P450 isozymes CYP1A1 and CYP1B1. High levels of these enzymes may increase DNA adduct formation and initiation of cancer. We investigated whether expression of CYP1B1 in breast tissue compared with CYP1A1 is a risk factor for breast cancer. Expression of CYP1B1 and CYP1A1 was measured in a collection of histologically normal breast tissue specimens from breast cancer patients and from cancer-free individuals. Using a semiquantitative RT-PCR assay, CYP1B1 and CYP1A1 expression levels relative to the constantly expressed β-actin gene were determined. In the study sample of 61 non-tumor epithelial breast specimens, we found about 300-fold and 400-fold interindividual variation in expression for CYP1B1 and CYP1A1, respectively. For most samples, CYP1B1 transcript levels were 2-7 times higher than CYP1A1, suggesting that CYP1B1 plays a greater role than CYP1A1 in activation of PAHs in the breast. The mean CYP1B1 transcript level in normal breast tissue was 60% higher in mastectomy patients compared with cancer-free individuals (p = 0.0517). Differences in CYP1B1 expression between cases and controls was greater among subject aged > 50 years. These data suggest that CYP1B1 plays a role in breast cancer etiology, particularly among women > 50 years of age.