455 research outputs found
DbKB a knowledge graph dataset for diabetes: A system biology approach
No full textDiabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions.The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships.Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes.These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions
Data-driven multiscale modeling and robust optimization of composite structure with uncertainty quantification
No full textIt is important to accurately model materials’ properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can significantly reduce the amount of experimentation required to develop new technologies. Robustness analysis of fuel and structural performance for harsh environments (such as power uprated reactor systems or aerospace applications) using machine learning-based multiscale modeling and robust optimization under uncertainties are required. The fiber and matrix material characteristics are potential sources of uncertainty at the microscale. The stacking sequence (angles of stacking and thickness of layers) of composite layers causes mesoscale uncertainties. It is also possible for macroscale uncertainties to arise from system properties, like the load or the initial conditions. This chapter demonstrates advanced data-driven methods and outlines the specific capability that must be developed/added for multiscale modeling of advanced composite materials. This chapter proposes a multiscale modeling method for composite structures based on a finite element method (FEM) simulation driven by surrogate models/emulators based on microstructurally informed mesoscale materials models to study the impact of operational parameters/uncertainties using machine learning approaches. To ensure optimal composite materials, composite properties are optimized with respect to initial materials volume fraction using data-driven numerical algorithms
Design of evolutionary computational intelligent solver for nonlinear corneal shape model by Mexican Hat and Gaussian wavelet neural networks
No full textIn this study, an integrated computational intelligence algorithm is implemented for the numerical treatment of the two-point boundary value problems that arise in the nonlinear corneal shape (NCS) model through the exploitation of wavelet neural networks including Mexican-Hat (MHWNNs) and Gaussian-wavelet (GWNNs)
through global genetic algorithms (GAs) then hybridization with local sequential quadratic programming (SQP) solvers, i.e. MHWNNsGAs, GWNNs-GAs, MHWNNs-GA-SQP, and GWNNs-GA-SQP respectively. The GWNNs and MHWNNs are applied to calculate the mean squared error of mathematical modeling of the proposed problem through objective functions while optimization of the fitness functions is initially conducted with an efficiency of global search GAs and then the efficacy of local search technique SQP for fine-tuning. A comparison of the proposed solutions of MHWNNs-GAs, GWNNsGAs, MHWNNs-GA-SQP, and GWNNs-GA-SQP solvers with a reference solution of Adam’s method shows that the proposed schemes have better accuracy, stability, efficiency consistency on an independent number of runs analyzed through complexity analysis and different statistical operators
Dengue infection severity score – improvised disease management
No full textSyed Uzair Mahmood,1 Maryam Jamil Syed,1 Aisha Jamal,1 Maria Shoaib2 1Sindh Medical College, Jinnah Sindh Medical University, Karachi, Pakistan; 2Dow Medical College, Dow University of Health Sciences, Karachi, PakistanWe would like to add our views regarding the paper “Validation of Dengue infection severity score” by Pongpan et al.1 As the paper outlines, the purpose of the Dengue Severity Score is to classify individuals with dengue infection into three levels of severity with clinically acceptable underestimation or overestimation. View the original paper by Pongpan and colleagues. 
Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding-Based Deep Learning Techniques
No full textDictionaries not only are the source of getting meanings of the word but also serve the purpose of comprehending the context in which the words are used. For such purpose, we see a small sentence as an example for the very word in comprehensive book-dictionaries and more recently in online dictionaries. The lexicographers perform a very meticulous activity for the elicitation of Good Dictionary EXamples (GDEX)—a sentence that is best fit in a dictionary for the word’s definition. The rules for the elicitation of GDEX are very strenuous and require a lot of time for committing the manual process. In this regard, this paper focuses on two major tasks, i.e., the development of labelled corpora for top 3K English words through the usage of distant supervision approach and devising a state-of-the-art artificial intelligence-based automated procedure for discriminating Good Dictionary EXamples from the bad ones. The proposed methodology involves a suite of five machine learning (ML) and five word embedding-based deep learning (DL) architectures. A thorough analysis of the results shows that GDEX elicitation can be done by both ML and DL models; however, DL-based models show a trivial improvement of 3.5% over the conventional ML models. We find that the random forests with parts-of-speech information and word2vec-based bidirectional LSTM are the most optimal ML and DL combinations for automated GDEX elicitation; on the test set, these models, respectively, secured a balanced accuracy of 73% and 77%
Development of a non-living model system for cell membranes and investigation of its mechanical and tribological properties
No full textWhile our exposure to nanomaterials (NMs) has increased with advancements in nanotechnology, understanding harmful effects of such materials on humans is still wanting. Here we have proposed and developed a non-living model system for cell membranes which is suitable for elucidating interactions between NMs and living cells. In contrast to existing model systems for cell membranes, PAAm hydrogel was used as soft support for the lipid. Grafting of lipid with PAAm was achieved through layer by layer deposition of alternating poly(allylamine hydrochloride) (PAH)and poly(sodium 4-styrenesulfonate) (PSS) polyelectrolyte multilayers (PEM). Single step bilayer formation was observed under QCM on the PAAm-PEM support owing to high electrostatic interactions between the PEM and lipid vesicles with frequency and dissipation changes of ~-30 Hz and ~0.8x10-6, respectively. It is also shown that the PEM architecture is robust and reproducible on gels of different elastic modulus. AFM images confirm bilayer formation on top of PAAm-PEM supports with uniform bilayer patches of ~ 0.5 μm. AFM indentation experiments show significant differences in the elastic modulus and adhesion forces for systems with soft underlying supports compared to systems having a hard substrate. The physiological relevance of the developed system is clear from its mechanical characterization via AFM, where the system undergoes considerable deformation before and after bilayer rupture. This behavior is similar to behavior of real cells, in which deformation of cytoskeleton is dominant over that of the cell membrane. The model cell membrane system was also used to study shear forces at the interface of the lipid bilayer on hydrogel, which gave insights into the frictional behavior of the system and its mechanical interactions with nanoprobes.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-12-01The student, Tooba Shoaib, accepted the attached license on 2016-07-22 at 08:53.The student, Tooba Shoaib, submitted this Thesis for approval on 2016-07-22 at 10:40.This Thesis was approved for publication on 2016-07-22 at 15:06.DSpace SAF Submission Ingestion Package generated from Vireo submission #10071 on 2017-02-28 at 14:35:29Made available in DSpace on 2017-03-01T16:36:31Z (GMT). No. of bitstreams: 2
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Cerebral malaria: insight into pathogenesis, complications and molecular biomarkers
No full textFarah Hafiz Yusuf,1 Muhammad Yusuf Hafiz,1 Maria Shoaib,1 Syed Ahsanuddin Ahmed2 1Department of Medicine, Dow Medical College, Dow University of Health Sciences, 2Department of Medicine, Sindh Medical College, Jinnah Sindh Medical University, Karachi, Pakistan Abstract: Cerebral malaria is a medical emergency. All patients with Plasmodium falciparum malaria with neurologic manifestations of any degree should be urgently treated as cases of cerebral malaria. Pathogenesis of cerebral malaria is due to damaged vascular endothelium by parasite sequestration, inflammatory cytokine production and vascular leakage, which result in brain hypoxia, as indicated by increased lactate and alanine concentrations. The levels of the biomarkers’ histidine-rich protein II, angiopoietin-Tie-2 system and plasma osteoprotegrin serve as diagnostic and prognostic markers. Brain imaging may show neuropathology around the caudate and putamen. Mortality is high and patients who survive sustain brain injury which manifests as long-term neurocognitive impairments. Keywords: cerebral malaria, neurologic manifestations, mortality, biomarkers, brain imagin
Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.
No full textA novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences
Effectiveness of a School-Based Medicine Safety Program for Children in Karachi: A Pre-Post Quasi-Experimental Study
No full textNot Availabl
A stabilized discontinuous Galerkin method for variational embedding of physics-based data
No full textA stabilized variational framework that admits overlapping as well as non overlapping coupling of domains for a variety of Partial Differential Equations (PDEs) is employed in this work. This method accommodates non-matching meshes across the interfaces between the subdomain boundaries and allows for sharp changes in mechanical material properties. Interface coupling operators that emanate via embedding of Discontinuous Galerkin ideas in the continuous Galerkin framework provide a unique avenue to embed physics-based data in the modeling and analysis of the system. Physics-based data, either in discrete or in distributed form can be embedded via the interface operators that are otherwise devised to enforce continuity of the fields across internal discontinuities. The least-squares form of the interface coupling operators is exploited for its inherent linear regression type structure, and it is shown that it helps improve the overall accuracy of the numerical solution. Method is applicable to multi-PDE class of problems wherein different PDEs are operational on adjacent domains across the common interface. The method also comes equipped with a residual based error estimation method which is shown to be applicable to test problems employed. Different test cases are employed to investigate the mathematical attributes of the method.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-08-01The student, Shoaib Ahmad Goraya, accepted the attached license on 2019-07-12 at 16:53.The student, Shoaib Ahmad Goraya, submitted this Thesis for approval on 2019-07-12 at 16:54.This Thesis was approved for publication on 2019-07-15 at 14:47.DSpace SAF Submission Ingestion Package generated from Vireo submission #14260 on 2019-11-26 at 14:03:50Made available in DSpace on 2019-11-26T20:59:45Z (GMT). No. of bitstreams: 3
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