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Heterogeneous Information Networks (HINs) consist of multiple categories of nodes and edges and encompass rich semantic information. Representing HINs in a low-dimensional feature space is challenging due to its complex structure and rich semantics. In this paper, we focus on link prediction and node classification by learning efficient low-dimensional feature representations of HINs. Metapath-guided walkers have been extensively studied in the literature for learning feature representations. However, the metapath walker does not control the length of random walks, resulting in weak structural and semantic information embeddings. In this work, we present an influence propagation controlled metapath-guided random walk model (called IPCMetapath2Vec) for representation learning in HINs. The model works in three phases: first, we perform node transition to generate a metapath-guided random walk, which is conditioned on two factors: (i) type mapping of the next node according to the metapath, and (ii) compute influence propagation score for each node and detect potential influencers on the walk by a threshold based filter. Next, we provide the collected random walks as input to the skip-gram model to learn each node's feature representation. Lastly, we employ an attention mechanism that aggregates the learned feature representations of each node from various semantic metapath-guided walks, preserving the importance of different semantics. We use these network representation features to address link prediction and multi-label node classification tasks. Experimental results on two public HIN datasets, namely DBLP and IMDB, show that our model outperforms the state-of-the-art representation learning models such as DeepWalk, Node2vec, Metapath2Vec, and HIN2Vec by 4.5% to 17.2% in terms of micro-F1 score for multi-label node classification and 4% to 14.50% in terms of AUC-ROC score for link prediction. [ABSTRACT FROM AUTHOR]

Background: The present study was carried out to compare macro-microscopy, powder microscopy and HPTLC analysis of six species such as Senna auriculata (L.) Roxb., Senna alata (L.) Roxb., Senna alexandrina Mill., Senna occidentalis (L.) Link., S. uniflora (Mill.) Irwin and Barneby, S. tora (L.) Roxb. of the genus Senna Mill. belongs to the family Caesalpiniaceae. Materials and Methods: Six selected Senna species were collected, shade dried and pharmacognostic study were performed used techniques such as microscopy and HPTLC analysis. Results: Seed macro-morphology of the selected six species shows significant variations in size, shape, colour, ornamentation and number of seeds per pod. The basic cellular structure of six Senna species was anatomically similar but variation was observed in number of layers. Microscopic characters of the selected species revealed key diagnostic features that can help to determine the relationship between the closely related species; macrosclerieds and osteosclerdies were observed in Senna auriculata and S. occidentalis. Powder microscopy of selected six Senna species varied in colour, texture and odour. Numerous rosette crystals in cotyledonary cells were found only in Senna tora which is a significant variation from other five Senna species. HPTLC finger print profiling was performed with ethanol extract at 254 nm, 366 nm and 575 nm derivatization using vannilin-sulphuric acid and the results were documented. In 254 nm 14 bands were separated but the major peak at Rf 0.31 (22.24%) appeared in S. alexandrina. Conclusion: A detailed morphological and pharmacognostical evaluations play an important role to avoid misidentification and delimitation of selected six Senna species and HPTLC profiling is an additional analytical tool for species identification. [ABSTRACT FROM AUTHOR]

Novel inorganic phosphor luminescent materials are widely used in various optoelectronic devices such as field emission displays, plasma display panels, solid state lasers, cathode‐ray tubes, and tricolor white light emitting diodes. Structural and luminescence properties of calcium borophosphate phosphors with different Fe3+ concentrations using solid state reaction technique have been investigated. X‐ray powder diffraction reveals that all samples exhibited a triclinic system of CaBP and lattice parameter analysis indicated the dopant ions may be substituted into the lattice position of the parent atom. The surface morphology of prepared nanophosphors is identified by SEM with EDS and TEM, which shows spherical‐like chains in an irregular spatial distribution with less agglomeration. Optical studies give the information about the site symmetry of dopant ions with host ligands. [ABSTRACT FROM AUTHOR]

The classification of recurrence and non recurrence of Hepato Cellular carcinoma (HCC) outcome after Radio Frequency Ablation therapy is a critical task. Multiple time series clinical liver cancer dataset is collected from different dataset and time interval. A merging algorithm is used to merge all attributes collected from different sources in multiple time periods. In order to preserve the originality of information, statistical measures of each attribute is calculated and considered them as additional attributes for accurate prediction. However the merged dataset is unbalanced, in which, the number of samples from HCC recurrence class is much smaller than from HCC non recurrence. The feature weighting scheme select optimal features and parameter of classifiers are sequentially obtained from multiple iterations which causes higher computation time. In this paper, an efficient sampling approach is proposed using Inverse Random under Sampling (IRUS) to overcome class imbalance issue. IRUS under sample the majority class which creates a number of distinct partitions with a boundary separated minority and majority class samples. Additionally an optimization approach is proposed using Artificial Plant Optimization (APO) algorithm to select optimal features and parameters of classifiers to improve the effectiveness and efficiency of classification. The optimization approach reduces the number of iteration and computation time for feature selection and parameter selection for classifiers which classify the recurrence and non recurrence of HCC. Classify patients with HCC and without HCC based on optimal features and parameters by Support Vector Machine (SVM) and Random Forest(RF) classifiers. Finally, the experimental results are conducted to prove the effectiveness of the proposed method over existing method in terms of accuracy, specificity, sensitivity and balanced accuracy. [ABSTRACT FROM AUTHOR]

Background: During pregnancy, thyroid issues are common. A straightforward pregnancy and good foetal growth and development depend on the availability of thyroid hormone. A wide range of obstetric and child development outcomes are linked to overt hypothyroidism. More research is now showing a link between thyroid dysfunction, even in milder forms, and poor pregnancy outcomes. The risk of difficulties for both the mother and the foetus is reduced with adequate, early treatment and maintenance of a normal level of thyroid hormones, making it possible to carry the pregnancy to term without serious complications. Methods: The current study is a case-control investigation carried out on 100 pregnant patients of the obstetric outpatient department of the government general hospital. Kurnool. Thyroid-stimulating hormone (TSH) testing was carried out in addition to regular obstetrical examinations at the first antenatal visit. Patients were followed up till delivery after levothyroxine treatment. Results: Among cases, 46% had hypothyroidism prior to pregnancy and 44% were newly diagnosed. 32% of cases had subclinical hypothyroidism. Common adverse maternal outcomes were anemia (58%), Pregnancy induced hypertension, preterm labour, (24%), and an increased need for a caesarian section (36%). Conclusions: Overall, maternal and foetal complications were higher in patients with hypothyroidism. Levothyroxine treatment decreased the risk of maternal and foetal outcomes. [ABSTRACT FROM AUTHOR]

In a traditional ER model, once we specify a subclass or superclass relationship, any changes to that relationship are treated as schema evolution. Further, ER models are rigid in the sense that once a relationship type is specified across a set of entity types, an instance of relationship type occur when one instance of all participating entity types are specified. Therefore, it is difficult to introduce in a simplified manner all relationship types across subsets of given set of entity types. In this paper, we provide mechanisms to model in our extended ER model: (i) specification of dynamic relationship types across subsets of instances of entity types, (ii) a simplified specification of relationships across subsets of given set of entity types, and (iii) mapping our extended ER model to relational database schema. We also show through an e-contract example the utility of our extended ER model. [ABSTRACT FROM AUTHOR]

Extant sequential wrapper-based feature subset selection (FSS) algorithms are not scalable and yield poor performance when applied to big datasets. Hence, to circumvent these challenges, we propose parallel and distributed hybrid evolutionary algorithms (EAs) based wrappers under Apache Spark. We propose two hybrid EAs based on the Binary Differential Evolution (BDE), and Binary Threshold Accepting (BTA), namely, (i) Parallel Binary Differential Evolution and Threshold Accepting (PB-DETA), where BDE and BTA work in tandem in every iteration, and (ii) its ablation variant, Parallel Binary Threshold Accepting and Differential Evolution (PB-TADE). Here, BTA is invoked to enhance the search capability and avoid premature convergence of BDE. For comparison purposes, we also parallelized two state-of-the-art algorithms: adaptive DE (ADE) and permutation based DE (DE-FSPM), and named them PB-ADE and P-DE-FSPM respectively. Throughout, logistic regression (LR) is employed to compute the fitness function, namely, area under the receiver operator characteristic curve (AUC). The effectiveness of the proposed algorithms is tested over the five big datasets of varying dimensions. It is noteworthy that the PB-TADE turned out to be statistically significant than the rest. All the algorithms have shown the repeatability property. The proposed parallel model attained a speedup of 2.2–2.9. We also reported feature subset with high AUC and least cardinality. [ABSTRACT FROM AUTHOR]

V. Slavik [8] introduced the concept of ideal(filter) in a skew lattice. In this paper we introduce the concept of a filter in a skew lattice which includes the class of all filters in the sense of Slavik. Also we introduce the concept of prime filter in a skew lattice and study the distributive skew lattices and the space of prime filters of a distributive skew lattice with hull-kernel topology. [ABSTRACT FROM AUTHOR]

In this paper characterization for non-symmetric skew lattices, with number of elements less than or equal to ten, is obtained. Non-Symmetric skew lattices with least number of elements are described, which are at the same time the examples to show that the converse of the Spink's theorem ([1, Corollary 4]) however is not true. [ABSTRACT FROM AUTHOR]

In this paper we introduce several concepts which are useful in the development of the theory of Generalised lattices, including the notions of Strong ideals, Strong filters, R--Strong ideals, R--Strong filters, Homomorphisms, Strong homomorphisms and Strong congruences in Generalised lattices. We also introduce the concepts of Congruence and Modularity in Posets. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation. Subsequent clusters are formed using the concept of constrained-similarity upper approximation wherein a condition of relative similarity is used as a merging criterion. We report results of experimentation on msnbc web navigation dataset that are intrinsically sequential in nature. We have compared the results of the proposed approach with that of the traditional hierarchical clustering algorithm using vector coding of sequences. The results establish the viability of the proposed approach. The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group. Such descriptions can help web miners to identify potential and meaningful groups of users. [Copyright &y& Elsevier]

Data mining is widely used to solve real-world problems in engineering, science and business. Usually the results from data mining obtained through the traditional approaches are not interpretable in the real life scenario. This paper suggests an approach for logical interpretations of the clustered data. Our approach involves using fuzzy ART technique for clustering the data and then applying the soft regression technique for interpreting the results of the clustering. The proposed model provides better analysis of data for describing overlapping clusters. We used our model to analyze patterns in the advances data of a public sector bank. Analyses and experiments show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Web usage mining is the application of data mining techniques to web log data repositories. Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user''s behavior. In general, the discovered knowledge or any unexpected rules are likely to be imprecise or incomplete, which requires a framework with soft computing techniques like rough sets. In this paper, we present a rough approximation-based clustering to cluster web transactions from web access logs. Using this approach, users can effectively mine web log records to discover web page access patterns. [Copyright &y& Elsevier]

Data warehousing is emerging as one of the hottest areas of growth in global business and the critical component and tool for business intelligence. In the data warehouse environment, the level of summarization plays an important role. Some Data Warehouse applications, directly account for the uncertainty and vagueness often associated with time-series and enterprise-wide decision making. In this paper, we enhance the concept of summarization using fuzzy technology, keeping in view the time-series and enterprise-wide data. This enhancement, which extends the aggregation process used in data warehouse, provides insightful information intelligently and allows greater facility for modeling human decision making. [ABSTRACT FROM AUTHOR]

There are two types of vitamin D dependent rickets (VDDR) that cause rickets in children. VDDR type 1 (VDDR-I) is caused by an inborn error of vitamin D metabolism, which interferes with renal conversion of calcidiol (25OHD) to calcitriol (1,25(OH)2 D) by the enzyme 1-α-hydroxylase. Patients with VDDR-I have mutations of chromosome 12 that affect the gene for the enzyme 1-α-hydroxylase, resulting in decreased levels of 1,25(OH) vitamin D. Clinical features include growth failure, hypotonia, weakness, rachitic rosary, convulsions, tetany, open fontanels and pathologic fractures. We report a case of VDDR-I in 14-month-old male child. Establishing an early diagnosis of these genetic forms of rickets is challenging, especially in developing countries where nutritional rickets is the most common variety of the disease where genetic diagnosis is not always possible because of financial constraints. A prompt diagnosis is necessary to initiate adequate treatment, resolve biochemical features and prevent complications, such as severe deformities that may require surgical intervention. [ABSTRACT FROM AUTHOR]

Introduction: Empty sella is usually an incidental finding. Empty sella is an anatomical condition characterized by the presence of cerebrospinal fluid within the sella with a small pituitary gland compressed above the pituitary floor causing endocrine abnormalities. Aim: The aim of this study is to evaluate hormonal abnormalities associated with empty sella in tertiary care center. Materials and Methods: This ongoing study was carried out in patients attending to endocrine out-patient departments from August 2012 to July 2013. A detailed history and examination was done. Hormonal evaluation including free thyroid hormones, thyroid stimulating hormone, growth hormone (GH), follicular stimulating hormone, luteinizing hormone, cortisol and prolactin was done. Results: A total of 16 patients diagnosed clinically and biochemically of hormonal abnormalities were found to have empty sella on magnetic resonance imaging. Hypocortisolemia in 62.5% of cases, hypothyroidism in 50% of cases, in 18.75% hypogonadism, hyper prolactinemia in 18.7.5%, GH deficiency in 12.5% of cases and in 12.5% cases posterior pituitary involvement is seen. Conclusion: The high incidence of endocrine abnormalities associated with empty sella necessitates the need for prompt evaluation and early replacement of hormones for better quality-of-life. [ABSTRACT FROM AUTHOR]

A new and efficient method for the synthesis of 2-substituted 1,3-benzazoles under environmentally friendly conditions is developed. [ABSTRACT FROM AUTHOR]

In recent years, biodiesel synthesis and production demands have increased because of its high degradability, cleaner emissions, non-toxicity, and an alternative to petroleum diesel. In this context, Single Cell Oil (SCO) has been identified as an alternative feedstock, having the advantage of accumulating high intracellular lipid. SCO/microbial lipids are potential alternatives for sustainable biodiesel production. The traditional technique for biodiesel production from the oils obtained from microbes generally requires two steps: lipid extraction and transesterification. In-situ transesterification is an innovative and renewable process for biodiesel production. It rules out the need to isolate and refine the feedstock lipid, as it directly uses biomass in a single step, i.e., the pretreated biomass will be subjected to in-situ transesterification in the presence of catalysts. Hence, the production cost can be reduced by eliminating the lipid extraction procedure. The current review focuses on the basic features and advantages of in-situ transesterification of SCO for biodiesel production with the aid of short-chain alcohols along with different acid, base, and enzyme catalysts. In addition, a comparative study was carried out to highlight the merits of in-situ transesterification over conventional transesterification. [ABSTRACT FROM AUTHOR]

In this work, we report the covalent immobilization of lipase enzyme on the surface of chitosan coated magnetic nanoparticles (MNPs) using N -(3-dimethylaminoproyl)- N -ethylcarbodiimide (EDC) and N -hydroxysuccinimide (NHS) as coupling agents. Surface functional groups of chitosan coated MNPs, lipase enzyme, and immobilized lipase enzyme were characterized by Fourier transform infrared spectroscopic (FT-IR) analysis. The structural characteristics of the chitosan coated magnetic nanoparticles were analyzed by X-ray Diffraction (XRD) studies. The statistical methodology with central composite design (CCD) was applied to evaluate the effects of immobilization, including the magnetic nanoparticles, lipase enzyme, pH and immobilization time on the enzyme activity. Based on the statistical analysis, the optimum immobilization conditions of magnetic nanoparticles (40 mg), lipase enzyme (40 mg), pH (9), and immobilization time (6 h), the maximum enzyme activity obtained was 16.94 U/mL. The optimum pH and temperature for maximum enzyme activity of immobilized lipase are found to be pH 9 and 40 °C respectively. The immobilized lipase exhibited excellent catalytic activity over eight successive cycles and retain 64% original stability. The Michaelis–Menten enzyme kinetic studies of immobilized lipase on chitosan coated MNPs showed maximum activity ( V max ) and Michaelis–Menten constant ( K m ) of 33.7 (μmol/mL min) and 0.89 mM respectively. The thermal stability of lipase was significantly improved after immobilization. The thermal deactivation rate of immobilized lipase was studied to follow the Arrhenius law with deactivation energy of 73 kJ/mol. Glucose ester yield of 80.8% was achieved in this study revealed that immobilized lipase on MNPs have promising application in industrial scale for sugar ester production. [ABSTRACT FROM AUTHOR]

Introduction: Say Meyer syndrome is rare X linked condition characterized by developmental delay, short stature and metopic suture synostosis. We are reporting a case of Say Meyer syndrome presented to our hospital for short stature and developmental delay at age 3 1/2 years. Case Report: A 3 1/2-year-old boy presented to our hospital for decreased growth velocity from the age of 1 year. History revealed the boy had a birth weight of 2.3 kg, had an episode of seizures in the neonatal period. He was born to non-consanguineous marriage. He had global developmental delay and there was a lack of bowel and bladder control. History did not reveal any hearing or visual impairment. No history of any chronic systemic illnesses. Magnetic resonance imaging (MRI) brain revealed mild diffuse frontotemporal atrophy with multiple irregular gliotic areas in bilateral frontal lobes. Diffuse white matter volume loss in bilateral cerebral hemispheres. Diffuse thinning of corpus callosum. Diffuse periventricular hyper intensity on T2W and fluid attenuated inversion recovery sequences. Conclusion: Say Meyer syndrome is rare X linked condition characterized by developmental delay, short stature and metopic suture synostosis. Characteristic MRI brain findings include diffuse frontotemporal atrophy with multiple gliotic areas in frontal lobes. Diffuse white matter volume loss in bilateral cerebral hemispheres. [ABSTRACT FROM AUTHOR]

Multimedia transmission over Mobile Ad-hoc Networks (MANETs) is crucial to many applications. However, MANETs possess several challenges including transmitting large size packets, minimizing delay, loss-tolerant and buffer size estimation. For effective multimedia transmission, delay should be minimized and packets should be received in the defined order. The existing standards such as 802.11b and 802.11e perform well in wireless networks, but exhibit poor response in MANETs for multimedia traffic, especially in multi-hop networks. In this paper, we first establish the dependency of delay on buffer size and packet size, and then present a delay optimization approach for multimedia traffic in MANETs. We use Knapsack algorithm for buffer management to maximize the in-order packets and minimize the out-of-order packets simultaneously. Our approach exploits the buffer internals and dynamically adjusts the buffer usage so that a node transmits the packets in the desired order to its successive nodes. Careful estimation of packet size and buffer size helps in minimizing the delay, improving the capability of receiving packets in the correct order and reducing out-of-order packets in the buffer at intermediate nodes. Our approach also controls the loss of multimedia data packets during transmission. We validate our approach with real-world examples using network simulator. [ABSTRACT FROM AUTHOR]

Persulfated derivatives of natural polyhydroxy compounds, such as lignans secoisolariciresinol, and isolariciresinol, flavonoid dihydroquercetin, and myo-inositol, have been synthesized. The ability of these compounds to inhibit the intrinsic pathway of blood coagulation (APTT-test) and to reduce the activity of coagulation factor Xa in the presence of antithrombin(III) has been studied. [ABSTRACT FROM AUTHOR]

Obesity in children and adolescents predispose to the development of obesity in adulthood and subsequent cardiovascular disease. High-sensitivity C-reactive protein (hsCRP) is a marker of low grade inflammatory state, which characterizes an atherosclerotic process. The aim of this study was to assess the metabolic abnormalities and its association with hsCRP in obese children and adolescents. A total of 62 obese children and adolescents and 24 healthy children and adolescents with a normal weight were recruited. In all subjects, anthropometric and biochemical parameters were measured. Body mass index (BMI) and blood pressure were significantly higher in the obese children and adolescents than the control. Obese children had significantly higher hsCRP levels (P < 0.001), total cholesterol, triglyceride, low-density lipoprotein-cholesterol (LDL-C) and lower high-density lipoprotein-cholesterol than the control group. Furthermore, homeostatic model assessment-insulin resistance (HOMA-IR) was significantly higher in obese children compared with the normal weight children. Furthermore, hsCRP showed a positive correlation with BMI (r = 0.357; P = 0.028), total cholesterol (r = 0.367; P = 0.008) and LDL-C (r = 0.356; P = 0.01), insulin (r = 0.311; P = 0.026) and not with HOMA-IR (r = 0.244; P = 0.084)). In conclusion, obese children and adolescents have significantly increased hsCRP compared with a normal weight group. Early intervention and prevention of obesity in children and adolescents decreases cardiovascular disease in later life. [ABSTRACT FROM AUTHOR]