Evidence-based understanding of introductions of research articles

Scientometrics - Achieving publications in high-impact journals is a major cornerstone for academic careers in the US and elsewhere in the world. However, apart from novel insights and relevant...     

Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

Recommendation services become an essential and hot research topic for researchers nowadays. Social data such as Reviews play an important role in the recommendation of the products. Improvement was achieved by deep learning approaches for capturing user and product information from a short text. However, such previously used approaches do not fairly and efficiently incorporate users’ preferences and product characteristics. The proposed novel Hybrid Deep Collaborative Filtering (HDCF) model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations. To overcome the cold start problem, the new overall rating is generated by aggregating the Deep Multivariate Rating DMR (Votes, Likes, Stars, and Sentiment scores of reviews) from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision, either product is truly popular or not. The proposed novel HDCF model consists of four major modules such as User Product Attention, Deep Collaborative Filtering, Neural Sentiment Classifier, and Deep Multivariate Rating (UPA-DCF + NSC + DMR) to solve the addressed problems. Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb, Yelp2013, and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy, confidence, and trust of recommendation services.     

Rapamycin Alternatively Modifies Mitochondrial Dynamics in Dendritic Cells to Reduce Kidney Ischemic Reperfusion Injury

Dendritic cells (DCs) are unique immune cells that can link innate and adaptive immune responses and Immunometabolism greatly impacts their phenotype. Rapamycin is a macrolide compound that has immunosuppressant functions and is used to prevent graft loss in kidney transplantation. The current study evaluated the therapeutic potential of ex-vivo rapamycin treated DCs to protect kidneys in a mouse model of acute kidney injury (AKI). For the rapamycin single (S) treatment (Rapa-S-DC), Veh-DCs were treated with rapamycin (10 ng/mL) for 1 h before LPS. In contrast, rapamycin multiple (M) treatment (Rapa-M-DC) were exposed to 3 treatments over 7 days. Only multiple ex-vivo rapamycin treatments of DCs induced a persistent reprogramming of mitochondrial metabolism. These DCs had 18-fold more mitochondria, had almost 4-fold higher oxygen consumption rates, and produced more ATP compared to Veh-DCs (Veh treated control DCs). Pathway analysis showed IL10 signaling as a major contributing pathway to the altered immunophenotype after Rapamycin treatment compared to vehicle with significantly lower cytokines Tnfa, Il1b, and Il6, while regulators of mitochondrial content Pgc1a, Tfam, and Ho1 remained elevated. Critically, adoptive transfer of rapamycin-treated DCs to WT recipients 24 h before bilateral kidney ischemia significantly protected the kidneys from injury with a significant 3-fold improvement in kidney function. Last, the infusion of DCs containing higher mitochondria numbers (treated ex-vivo with healthy isolated mitochondria (10 µg/mL) one day before) also partially protected the kidneys from IRI. These studies demonstrate that pre-emptive infusion of ex-vivo reprogrammed DCs that have higher mitochondria content has therapeutic capacity to induce an anti-inflammatory regulatory phenotype to protect kidneys from injury.     

Reconstruction of scene using corneal reflection

Multimedia Tools and Applications - Corneal reflection extracted from an eye image identifies the relationship between the subject of the image and the scene in front of the subject. The...     

Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

In computational and clinical environments, autoclassification of brain magnetic resonance image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to investigate the computer vision and machine learning methods for classification of brain magnetic resonance (MR) slices. In routine health-care units, MR scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. It is almost impossible to analyze each slice manually due to the large amount of data produced by MRI devices at each moment. Irrefutably, if an automated protocol performing this task is executed, not only the radiologist will be assisted, but a better pathological assessment process can also be expected. Numerous schemes have been reported to address the issue of autoclassification of brain MRI slices as normal and abnormal, but accuracy, robustness and optimization are still an open issue. The proposed method, using Gabor filter and support vector machines, classifies brain MRI slices as normal or abnormal. Accuracy, sensitivity, specificity and ROC-curve have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on Whole Brain Atlas-Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and ROC-curve as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and ROC-curve (0.97) were achieved. Comparison with state-of-the-art methods proved the overall efficacy of the proposed method.     

A scientometric assessment of research output in nanoscience and nanotechnology: Pakistan perspective

In this study we present an analysis of the research trends in Pakistan in the field of nanoscience and nanotechnology. Starting with just seven publications in the year 2000, this number has steadily increased to 542 for the year 2011. Among the top 15 institutions with publications in nanotechnology 13 are universities and only two are R&D organizations. Almost 35 % of the research publications are in the field of material sciences followed by chemistry and physics in that order. The growth in the publications for period 2000–2011 is studied through relative growth rate and doubling time. The authorship pattern is measured by different collaboration parameters, like collaborative index, degree of collaboration, collaboration coefficient and modified collaboration coefficient. Finally the quality of papers is assessed by means of the h-index, g-index, hg-index and p-index.     

Bibliometric analysis of biotechnology research in Pakistan

In this study we present an analysis of the research trends in Pakistan in the field of biotechnology for the period 1980–2011. Starting with just 15 publications in 1980 with a negligible annual growth rate for the initial 15 years, the number of publications reached 3,273 in 2011 with an annual growth rate of 22 % for the last 15 years. This growth in publications is studied through factors such as Relative Growth Rate and Doubling Time. A comparison of organizations actively engaged in research in biotechnology is made through factors such as their total publications, total citations, and average citations per paper and indices that determine the quality of publications like h-index, g-index, hg-index and p-index. University of Karachi shows the highest number of publications (2,698), while National Institute of Biotechnology and Genetic Engineering with fewer publications shows the highest average citation per paper (8.07). Agha Khan University however, shows the highest h, g, hg and p indices.     

Removal of lead and cobalt using lignocellulosic fiber derived from <Emphasis Type="Italic">Citrus reticulata</Emphasis> waste biomass

The present investigation explored the use of Citrus reticulata waste biomass (CWB) for the removal of Pb(II) and Co(II) from the aqueous solutions. The Pb(II) and Co(II) biosorption was found to be dependent on pH of the solution, biosorbent dose, biosorbent particle size, temperature, shaking speed, contact time and initial concentration of metal ions. A metal uptake capacity of 41.16 and 52.64 mg/g was observed at pH 5 and 7 for Pb(II) and Co(II), respectively. The biosorption data followed the Freundlich model for both metals. The overall biosorption process was best described by pseudo-second order kinetics. The effect of several pretreatments on the biosorption efficiency of CWB was also investigated. The results demonstrated that pretreatments influenced the biosorption capacity of the biomass for the both metals significantly. Maximum biosorption capacity of 83.77 and 95.55 mg/g was observed for Pb(II) and Co(II) with sodium hydroxide treated and simply heated biomass, respectively. FTIR spectrum indicated the presence of -OH, -NH, -COOH groups in the biomass. The surface structure of CWB was analyzed by JEOL JMT 300 scanning electron microscope (SEM), and the existence of metal ions on the surface of biosorbent was determined by energy dispersive X-ray (EDX) spectroscopy.     

Automatic brain tumor segmentation from magnetic resonance images using superpixel-based approach

Multimedia Tools and Applications - Cancer is the second leading cause of deaths worldwide, reported by World Health Organization (WHO). The abnormal growth of cells, which should die at the time...