A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements

Crystal size and shape distribution systematics of plagioclase and the determination of crystal residence times in the micromonzogabbros of Qisir Dagh, SE of Sabalan volcano (NW Iran)

The Qisir Dagh igneous complex occurs as a combination of volcanic and intrusive rocks to the south-east of the Sabalan volcano, north-western Iran. Micromonzogabbroic rocks in the region consist of plagioclase, alkaline feldspar and clinopyroxene as the major mineral phases and orthopyroxene, olivine, apatite and opaque minerals as the accessory minerals. Microgranular and microporphyritic textures are well developed in these rocks. Considering the importance of plagioclase in reconstructing magma cooling processes, the size and shape distribution and chemical composition of this mineral were investigated. Based on microscopic studies, it is shown that the 2-dimensional size average of plagioclase in the micromonzogabbros is 538 micrometers and its 3-dimensional shape varies between tabular to prolate. Crystal size distribution diagrams point to the presence of at least two populations of plagioclase, indicating the occurrence of magma mixing and/or fractional crystallization during magma cooling. The chemical composition of plagioclase shows a wide variation in abundances of Anorthite-Albite-Orthoclase (An = 0.31—64.58, Ab = 29.26—72.13, Or = 0.9—66.97), suggesting a complex process during the crystal growth. This is also supported by the formation of antiperthite lamellae, which formed as the result of alkali feldspar exsolution in plagioclase. The calculated residence time of magma in Qisir Dagh, based on 3D crystal size distribution data, and using growth rate G = 10—10 mm/s, varies between 457 and 685 years, which indicates a shallow depth (near surface) magma crystallization and subvolcanic nature of the studied samples

Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals

Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especially if they have been discharged with a different initial diagnosis. Machine learning approaches have been devised to expedite the process and detect the cases that demand instant follow up. However, these approaches require a large amount of labeled data to train reliable predictive models. Preparing such a large dataset, which needs to be manually annotated by health professionals, is costly and time-consuming. This paper investigates a semi-supervised learning framework for radiology report classification across three hospitals. The main goal is to leverage clinical unlabeled data in order to augment the learning process where limited labeled data is available. To further improve the classification performance, we also integrate a transfer learning technique into the semi-supervised learning pipeline . Our experimental findings show that (1) convolutional neural networks (CNNs), while being independent of any problem-specific feature engineering, achieve significantly higher effectiveness compared to conventional supervised learning approaches, (2) leveraging unlabeled data in training a CNN-based classifier reduces the dependency on labeled data by more than 50% to reach the same performance of a fully supervised CNN, and (3) transferring the knowledge gained from available labeled data in an external source hospital significantly improves the performance of a semi-supervised CNN model over their fully supervised counterparts in a target hospital

Investigation of antimicrobial, antioxidant and physicochemical properties of active film based on whey protein containing pomegranate and red grape anthocyanins and zinc oxide nanoparticles

Introductio: Active anti-microbial packages, metal nanocomposites are a new generation of packages with nano structure, which are produced by direct combination of metal nanoparticles with base resin. This research was conducted with the aim of investigating the physicochemical, antioxidant and antimicrobial properties of whey protein concentrate smart film containing pomegranate and red grape anthocyanins and zinc oxide nanoparticles. Materials and methods: permeability to water vapor, antioxidant properties, antimicrobial properties by disk diffusion method and differential scanning calorimeter test were performed on the prepared films. Agar diffusion method was used to determine the antimicrobial effects of the film. Results and Discussion: By increasing the percentage of zinc oxide nanoparticles and anthocyanin in the film, the percentage of solids in the film increased. The antioxidant activity of active films increased significantly with the increase of anthocyanin content of the films. The highest antioxidant activity with a significant difference (p<0.05) was attributed to the film sample with 2.6 cc extract. According to the data obtained from the DSC test, with the addition of anthocyanins, the temperature of 290 and the glass transition have changed to some extent, and on the other hand, it can be concluded that the addition of nanoparticles to the film can reduce the glass transition temperature. Conclusion: In general, this study showed that anthocyanins and zinc oxide nanoparticles have the potential to be used to prepare films based on bioactive whey concentrate with improved physicochemical properties and biological properties such as antioxidant properties when used in appropriate concentrations

Epidemiological features of irritable bowel syndrome and its subtypes among Iranian adults

Abstract Background The epidemiological features of irritable bowel syndrome (IBS) have not been properly investigated in Iran. Also, worldwide there is limited knowledge about the characteristics of IBS subtypes. The aim of the study was to explore the epidemiological features of IBS and its subtypes among Iranian adults

Role of Long Non-Coding RNAs in Conferring Resistance in Tumors of the Nervous System

Tumors of the nervous system can be originated from several locations. They mostly have high mortality and morbidity rate. The emergence of resistance to chemotherapeutic agents is a hurdle in the treatment of patients. Long non-coding RNAs (lncRNAs) have been shown to influence the response of glioblastoma/glioma and neuroblastoma to chemotherapeutic agents. MALAT1, NEAT1, and H19 are among lncRNAs that affect the response of glioma/glioblastoma to chemotherapy. As well as that, NORAD, SNHG7, and SNHG16 have been shown to be involved in conferring this phenotype in neuroblastoma. Prior identification of expression amounts of certain lncRNAs would help in the better design of therapeutic regimens. In the current manuscript, we summarize the impact of lncRNAs on chemoresistance in glioma/glioblastoma and neuroblastoma

Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts

The rapid shrinkage of Lake Urmia, one of the world\u27s largest saline lakes located in northwestern Iran, is a tragic wake-up call to revisit the principles of water resources management based on the socio-economic and environmental dimensions of sustainable development. The overarching goal of this paper is to set a framework for deriving dynamic, climate-informed environmental inflows for drying lakes considering both meteorological/climatic and anthropogenic conditions. We report on the compounding effects of meteorological drought and unsustainable water resource management that contributed to Lake Urmia\u27s contemporary environmental catastrophe. Using rich datasets of hydrologic attributes, water demands and withdrawals, as well as water management infrastructure (i.e. reservoir capacity and operating policies), we provide a quantitative assessment of the basin\u27s water resources, demonstrating that Lake Urmia reached a tipping point in the early 2000s. The lake level failed to rebound to its designated ecological threshold (1274 m above sea level) during a relatively normal hydro-period immediately after the drought of record (1998–2002). The collapse was caused by a marked overshoot of the basin\u27s hydrologic capacity due to growing anthropogenic drought in the face of extreme climatological stressors. We offer a dynamic environmental inflow plan for different climate conditions (dry, wet and near normal), combined with three representative water withdrawal scenarios. Assuming effective implementation of the proposed 40% reduction in the current water withdrawals, the required environmental inflows range from 2900 million cubic meters per year (mcm yr−1) during dry conditions to 5400 mcm yr−1 during wet periods with the average being 4100 mcm yr−1. Finally, for different environmental inflow scenarios, we estimate the expected recovery time for re-establishing the ecological level of Lake Urmia

The physicochemical properties of the spirulina‐wheat germ‐enriched high‐protein functional beverage based on pear‐cantaloupe juice

Abstract The formulation of a novel functional juice, enriched with wheat germ powder and spirulina algae and based on cantaloupe and pear juice, was optimized by D‐optimal combined design. Firstly, sensory evaluation was performed by hedonic test to evaluate the organoleptic properties, and organoleptically desirable samples were screened for further experiments. Various chemical experiments including PH, acidity, formalin index, total phenol, flavonoids, antioxidant capacity, mineral contents (Fe, Zn, Ca, P, K, Mg, and Cu), and fatty acids profile were evaluated. The steady shear flow rheological test also was performed on the screened samples. The results of sensory evaluation showed that the samples containing 1% spirulina and wheat germ had the highest organoleptic score. The results of physicochemical tests on the selected samples showed that the addition of spirulina and wheat germ powder had little effect on pH, acidity, and formalin index but they affected brix, dry matter, and protein content. Also, the addition of spirulina and wheat germ powder, changed the amounts of antioxidant capacity (from 90 to 98%), total phenol (from 4 to 22 mg GAE/g), and flavonoid content (from 5 to 15 mg/L) in the functional beverages. Furthermore, the results of rheological tests showed that the addition of wheat germ powder in the functional fruit juices increased apparent viscosity however; spirulina did not affect important change in rheological properties. The GC‐Mass analysis presented fatty acid profiles of the functional beverages and confirmed the presence of polyunsaturated fatty acids (for example decanoic acid and heptadecanoic acid) in the samples