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Protein-protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein-protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.

The coronavirus disease 2019 (COVID‐19) pandemic has spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID‐19 causative organism severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS‐CoV‐2 and related β coronaviruses Middle East respiratory syndrome coronavirus (MERS‐CoV), SARS‐CoV, bat coronavirus (BAT‐CoV) were comprehensively analyzed using a custom‐built pipeline that employed phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole‐genome sequence‐based phylogeny revealed that the strains of SARS‐CoV‐2 are more similar to the BAT‐CoV strains. The mutational analysis showed that on average MERS‐CoV and BAT‐CoV genomes differed at 134.21 and 136.72 sites, respectively, whereas the SARS‐CoV genome differed at 26.64 sites from the reference genome of SARS‐CoV‐2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS‐CoV and SARS‐CoV‐2 (106.8 and 107, respectively), and a lower number for SARS‐CoV and BAT‐CoV (95.8 and 98.5, respectively). Collectively, the analysis of multiple genetic markers of selected β viral genomes revealed that the newly born SARS‐COV‐2 is closely related to BAT‐CoV, whereas, MERS‐CoV is more distinct from the SARS‐CoV‐2 than BAT‐CoV and SARS‐CoV.

Image domain prior models have been shown to improve the quality of reconstructed images, especially when data are limited. Pre-processing of raw data, through the implicit or explicit inclusion of data domain priors have separately also shown utility in improving reconstructions. In this work, a principled approach is presented allowing the unified integration of both data and image domain priors for improved image reconstruction. The consensus equilibrium framework is extended to integrate physical sensor models, data models, and image models. In order to achieve this integration, the conventional image variables used in consensus equilibrium are augmented with variables representing data domain quantities. The overall result produces combined estimates of both the data and the reconstructed image that is consistent with the physical models and prior models being utilized. The prior models used in both domains in this work are created using deep neural networks. The superior quality allowed by incorporating both data and image domain prior models is demonstrated for two applications: limited-angle CT and accelerated MRI. The prior data model in both these applications is focused on recovering missing data. Experimental results are presented for a $90^0$ limited-angle tomography problem from a real checked-bagged CT dataset and a $4\times$ accelerated MRI problem on a simulated dataset. The new framework is very flexible and can be easily applied to other computational imaging problems with imperfect data.

INTRODUCTION Various genome wide association studies have manifested that Major Histocompatibility Complex (MHC) region on chromosome 6p21 houses many potential candidate genes for asthma. OBJECTIVE This Case-Control association study was planned to determine the association of 10 Single Nucleotide Polymorphisms (SNPs), residing within and around MHC genes' region on chromosome 6p21, with Asthma in Punjabi population of Lahore, Pakistan. METHODS A total of 161 subjects, 61 physician-diagnosed asthma patients and 100 age-matched healthy controls, were recruited from Lahore, a city in Punjab. Ten single nucleotide polymorphisms (SNPs) (rs9378249, rs2070600, rs404860, rs6689, rs1049124, rs1063355, rs1049225, rs1049219, rs7773955 and rs928976) located within or near AGER, NOTCH and HLA genes in MHC region, were genotyped in both patients and controls using single base extension reaction and capillary electrophoresis-based genetic analyser. Statistical models were applied using SHEsis Plus. Results were adjusted for various cofactors (age, gender and environment) and by applying multiple corrections. Haplotype and linkage disequilibrium analyses were performed on Haploview software v4.1. RESULTS Three of the studied SNPs rs1049124, rs1049219 and rs7773955 show independent significant association with asthma under allelic and genotypic models. Two of the haplotypes, H7 and H13, "CTAATTT" and "CCACTAT", respectively, for rs2070600, rs404860, rs6689, rs1049124, rs1063355, rs1049219 and rs7773955, are found to be significantly associated with the disease. CONCLUSION This study reports association of SNP variants residing on HLA-DQB1 and HLA-DQA2 genes and haplotypes H7 and H13 on genomic region 6p21 with Asthma in the Punjabi population of Lahore, Pakistan.

Background:Exploring various functional aspects of a biological cell system has been a focused research trend for last many decades. Biologists, scientists and researchers are continuously striving for unveiling the mysteries of these functional aspects to improve the health standards of life. For getting such understanding, astronomically growing, heterogeneous and geographically dispersed omics data needs to be critically analyzed. Currently, omics data is available in different types and formats through various data access interfaces. Applications which require offline and integrated data encounter a lot of data heterogeneity and global dispersion issues.Objective:For facilitating especially such applications, heterogeneous data must be collected, integrated and warehoused in such a loosely coupled way so that each molecular entity can computationally be understood independently or in association with other entities within or across the various cellular aspects.Methods:In this paper, we propose an omics data integration schema and its corresponding data warehouse system for integrating, warehousing and presenting heterogeneous and geographically dispersed omics entities according to the cellular functional aspects.Results & Conclusion:Such aspect-oriented data integration, warehousing and data access interfacing through graphical search, web services and application programing interfaces make our proposed integrated data schema and warehouse system better and useful than other contemporary ones.

Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.

Filtered back projection (FBP) is the most widely used method for image reconstruction in X-ray computed tomography (CT) scanners, and can produce excellent images in many cases. However, the presence of dense materials, such as metals, can strongly attenuate or even completely block X-rays, producing severe streaking artifacts in the FBP reconstruction. These metal artifacts can greatly limit subsequent object delineation and information extraction from the images, restricting their diagnostic value. This problem is particularly acute in the security domain, where there is great heterogeneity in the objects that can appear in a scene, highly accurate decisions must be made quickly, and processing time is highly constrained. The standard practical approaches to reducing metal artifacts in CT imagery are either simplistic nonadaptive interpolation-based projection data completion methods or direct image post-processing methods. These standard approaches have had limited success. Motivated primarily by security applications, we present a new deep-learning-based metal artifact reduction approach that tackles the problem in the projection data domain. We treat the projection data corresponding to dense, metal objects as missing data and train an adversarial deep network to complete the missing data directly in the projection domain. The subsequent complete projection data is then used with conventional FBP to reconstruct an image intended to be free of artifacts. This new approach results in an end-to-end metal artifact reduction algorithm that is computationally efficient textcolorredand therefore practical and fits well into existing CT workflows allowing easy adoption in existing scanners. Training deep networks can be challenging, and another contribution of our work is to demonstrate that training data generated using an accurate X-ray simulation can be used to successfully train the deep network, when combined with transfer learning using limited real data sets. We demonstrate the effectiveness and potential of our algorithm on simulated and real examples.

SARS-CoV2 has led to a global pandemic affecting almost 3 million people in almost over 3 months. Various clinical presentations have been reported so far and no definite therapy is established. Anticoagulation is recommended by several experts to address the potential prothrombotic complications from COVID-19, but its safety and regimen need further clinical trials and safety and efficacy profile. Here, we present three cases of intracranial hemorrhage in three critically ill patients with COVID-19 and discuss their course in relation to various regimens of anticoagulation used.

Hepatocellular carcinoma (HCC) has been increasing among Pakistani males. Aim: The main objective of this study was to evaluate the role of TGF-β-1 gene polymorphism as a risk factor in chronic hepatitis C virus (HCV) infected HCC patients in Pakistan. Patients & methods: A total of 286 subjects were recruited into three different groups (Group I: 96 healthy controls, Group II: 96 HCV, Group III: 94 HCC). Results: A significant increase in genotype and allele frequencies of TGF-β-1 gene was observed in HCC, HCV with OR = 1.9; 95% CI: 1.275–2.871; p > 0.05, OR = 1.6; 95% CI: 1.12–2.51; p > 0.05 and OR = 1.7; 95% CI: 1.036–1.923; p > 0.05. Conclusion: A higher frequency of the TT genotype and T allele of the TGF-β-1 gene is observed in Pakistani HCV and HCC patients.

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages.

Objectives: To investigate the relationship of 3 single nucleotide polymorphism (SNP) variants of ADAM33 with asthma susceptibility in patients from Northern and Central Punjab, Punjab, Pakistan. Methods : In this case-control study, healthy and asthmatic participants were recruited between 2015 and 2017. The SNPs of ADAM33 gene, rs2280089, rs2280090, and rs2280091 were analyzed in 296 asthma patients and 343 healthy controls, as well as linkage disequilibrium and haplotype analysis. Results : The non-significant differences were observed in allele and genotype frequencies of the SNPs in asthmatic and healthy persons even after population stratification based on age, caste, gender, family history, and environment. Although these SNPs were non-significant for disease susceptibility among children and adults, a fixed unique pattern of inheritance was nevertheless observed for the studied SNPs. Linkage disequilibrium analysis presented a very strong linkage between the SNP variants to predict their co-inheritance in study population. However, none of the haplotypes were found to be associated with asthma disease development. Conclusion : The studied SNPs of ADAM33 appeared to be non-significant for asthma susceptibility in Northern and Central Punjabi population. The fixed allele combination inheritance pattern was a unique observation contrary to findings in other global populations.

Zinc deficiency is a global public health problem, affecting ~17% of the world's population, with the greatest burden in low‐ and middle‐income countries. An increasing body of evidence suggests that biofortification may be a cost‐effective and sustainable approach to reducing zinc and other micronutrient deficiencies. Biofortification enhances the nutritional quality of food crops through conventional plant breeding techniques and agronomic practices. This paper presents ongoing research on biofortification in Pakistan, where over 40% of women are zinc deficient. The Biofortified Zinc Flour to Eliminate Deficiency (BiZiFED) project aims to investigate the impact of biofortification as a strategy to alleviate zinc deficiency in Pakistan. The project is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Global Challenges Research Fund from May 2017 to April 2019. This paper outlines the four objectives and work packages within the BiZiFED project: (1) a double‐blind, randomised controlled trial to examine the effect of consuming flour made from a high zinc variety of biofortified wheat (Zincol‐2016/NR‐421) on dietary zinc intake and status; (2) a cost‐effectiveness study to assess the health and economic impact of agronomic biofortification of wheat; (3) a mixed methods study to explore the cultural acceptability and sustainability of biofortification in Pakistan; (4) capacity building and development of long‐term research partnerships in Pakistan. The findings will contribute to the evidence base for the potential impact of biofortification to alleviate zinc deficiency among the poorest communities.

The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount of anatomical and physiological information, but it is sometimes difficult even for the expert radiologist to derive the related information they contain. Automatic classification using deep learning models can help in better assessing these infections swiftly. Deep CNN models, namely, MobileNet, ResNet50, and InceptionV3, were applied with different variations, including training the model from the start, fine-tuning along with adjusting learned weights of all layers, and fine-tuning with learned weights along with augmentation. Fine-tuning with augmentation produced the best results in pretrained models. Out of these, two best-performing models (MobileNet and InceptionV3) selected for ensemble learning produced accuracy and FScore of 95.18% and 90.34%, and 95.75% and 91.47%, respectively. The proposed hybrid ensemble model generated with the merger of these deep models produced a classification accuracy and FScore of 96.49% and 92.97%. For test dataset, which was separately kept, the model generated accuracy and FScore of 94.19% and 88.64%. Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays. Consequently, this swift and computer-aided diagnosis can help in saving precious human lives and minimizing the social and economic impact on society.

In vertebrate animals, the molecules encoded by major histocompatibility complex (MHC) genes play an essential role in the adaptive immunity. MHC class I deals with intracellular pathogens (virus) in birds. MHC class I diversity depends on the consequence of local and global environment selective pressure and gene flow. Here, we evaluated the MHC class I gene in four species of the Turdidae family from a broad geographical area of northeast China. We isolated 77 MHC class I sequences, including 47 putatively functional sequences and 30 pseudosequences from 80 individuals. Using the method based on analysis of cloned amplicons ( n = 25 ) for each species, we found two and seven MHC I sequences per individual indicating more than one MHC I locus identified in all sampled species. Results revealed an overall elevated genetic diversity at MHC class I, evidence of different selection patterns among the domains of PBR and non-PBR. Alleles are found to be divergent with overall polymorphic sites per species ranging between 58 and 70 (out of 291 sites). Moreover, transspecies alleles were evident due to convergent evolution or recent speciation for the genus. Phylogenetic relationships among MHC I show an intermingling of alleles clustering among the Turdidae family rather than between other passerines. Pronounced MHC I gene diversity is essential for the existence of species. Our study signifies a valuable tool for the characterization of evolutionary relevant difference across a population of birds with high conservational concerns.

Introduction Enhanced recovery after surgery (ERAS) protocols have been widely studied in elective abdominal surgeries with promising outcomes. However, the use of these protocols in emergency abdominal surgeries has not been widely investigated. This study aimed to evaluate ERAS application outcomes via early oral feeding compared to regular postoperative care in patients undergoing perforated duodenal ulcer repairs in emergency abdominal surgeries. Materials and methods We conducted a randomized controlled trial at the Surgical Unit 1 Benazir Bhutto Hospital from August 2018 to December 2019. A total of 42 patients presenting to the emergency department with peritonitis secondary to suspected perforated duodenal ulcer were included in the study. Patients were randomly assigned into two groups. Group A patients followed an ERAS protocol for early oral feeding, and Group B received regular postoperative care (i.e., delayed oral feeding). Our primary outcomes were the length of hospital stay, duodenal repair site leak, the severity of pain (via the visual analog scale), and postoperative ileus duration. Results were analyzed via IBM Statistical Product and Service Solutions (SPSS) Statistics for Windows, Version 20.0 (Armonk, NY: IBM Corp.). and chi-square and independent t-tests were applied. Results Patients who received early oral feeding (Group A) showed a shorter length of hospital stay, lower pain scores, and shorter postoperative ileus duration than patients in the traditional postoperative care group. Also, we noted no duodenal repair site leak in the early oral feeding group. The differences between the two groups were statistically significant (P

Novel Coronavirus is deadly for humans and animals. The ease of its disper-sion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furthermore, we analyze deep learning models, namely, AlexNet, SqueezeNet, DenseNet201, MobileNetV2 and InceptionV3 with numerous variations such as training the models from scratch, fine-tuning without pre-trained weights, fine-tuning along with updating pre-trained weights of all layers and fine-tuning with pre-trained weights along with applying augmentation. Our results show that fine-tuning with augmentation generates best results in pre-trained models. Finally, we have made architectural adjustments in MobileNetV2 and InceptionV3 models to learn more intricate features, which are then merged in our proposed ensemble model. The performance of our model is statistically analyzed against other models using four different performance metrics with paired two-sided t-test on 5 different splits of training and test sets of our dataset. We find that it is statistically better than its competing methods for the four metrics. Thus, the computer-aided classification based on the proposed model can assist radi-ologists in identifying coronavirus from other related infections in chest X-rays with higher accuracy. This can help in a reliable and speedy diagnosis, thereby saving valuable lives and mitigating the adverse impact on the socioeconomics of our community.

Hepatitis C virus (HCV) is endemic in Pakistan, infecting approximately 12 million people and expected to increase in the coming decades. IFIH1 is a viral RNA sensor gene essential for the activation of innate immunity against RNA viruses. Aim: The aim of the study was to analyze the association of the polymorphism (rs1990760) in IFIH1 gene among HCV infected Pakistani patients and healthy controls. Materials & methods: Blood samples from 400 chronic HCV patients (including 323 responders and 77 nonresponders) and 100 healthy individuals were collected. Results: Frequencies of heterozygous computed tomography genotype significantly associated with decreased HCV risk (χ2 = 0.072; 95% Cl: 1.06; 0.68−1.65; p ≤ 0.788). Conclusion: No significant differences were observed in alleles and genotype frequencies of IFIH1 (rs1990760) in Pakistani HCV infected patients.

Polymorphisms in Toll-like receptor 4 (TLR4) gene may exaggerate the chances and pathogenesis of HCV. Aim: This study intends to examine the relationship of genetic polymorphisms of TLR4 with HCV disease in Pakistani patients. Methodology: For this purpose, 500 blood samples were collected to confirm the genetic profile of all the subjects. Results: Single nucleotide polymorphism (SNP) in the TLR4 gene, rs4986790 (A>G), was genotyped in 400 HCV infected and 100 healthy individuals. The TLR4 gene was associated with a significantly increased risk of HCV-related diseases in comparing genotype assessments in patients and controls (pooled OR [95%CI]: 1.671 [0.946–2.949] for dominant; 0.503 [0.221–1.145] for recessive; 2.33 [1.518–3.576] for allele frequency). Subgroup analyses showed an increased risk of HCV infection and effect on interferon therapy associated with AA+AG/GG genotypes of rs4986790 (2.033 [1.094–3.778]). Conclusion: TLR4 SNP rs4986790 was found to be related to HCV-infected patients in the Pakistani population.

rs12603332, an important regulatory site variant, is known to alter the regulatory motif E2A that is involved in the maturation of B-lymphocytes. The study was designed to check whether different environmental exposures alter its risk allele association with asthma or not.200 Physician-diagnosed asthma patients and 108 healthy individuals were enrolled from hospitals of Lahore. After quantitation of DNA extracted from peripheral blood, amplification of genomic region with rs12603332, followed by single base extension (SBE), was performed. Allele and genotype frequencies were calculated by SHEsis and Haploview software packages. Statistical analyses on PLINK were also performed, taking different factors as covariates. HaploReg analysis was done to predict the effect of risk allele on different regulatory motifs.Risk allele for rs12603332 i.e., "C" allele was found to be significantly associated with male patients residing in urban localities.The finding suggests that on exposure with urban environment, risk allele carriers tend to develop asthma symptoms via epigenetic regulation of motif associated with maturation of B-lymphocytes.

Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.

Question classification is considered one of the most significant phases of a typical Question Answering (QA) system. It assigns certain answer types to each question which leads to narrow down the search space of possible answers for factoid and list type questions. The process of assigning certain answer types to each question is also known as Lexical Answer Type (LAT) Prediction. Although much work has been done to enhance the performance of question classification into coarse and fine classes in diverse domains, it is still considered a challenging task in the biomedical field. The difficulty in biomedical question classification stems from the fact that one question might have more than one label or expected answer types associated with it (also, referred to as a multi-label classification). In the biomedical domain, only preliminary work is done to classify multi-label questions by transforming them into a single label through copy transformation technique. In this paper, we have generated a multi-labeled corpus (MLBioMedLAT) by exploring the process of Open Advancement of Question Answering (OAQA) system for the task of biomedical question classification. We use 780 biomedical questions from BioASQ challenge and assign them appropriate labels. To annotate these labels, we use the answers for each question and assign the question semantic type labels by leveraging an existing corpus and utilizing OAQA system. The paper introduces a data transformation approach namely Label Power Set with logistic regression (LPLR) for the task of multi-label biomedical question classification and compares its performance with Structured SVM (SSVM), Restricted Boltzmann Machine (RBM), and copy transformation based logistic regression (CLR) (previously used for a similar task in the OAQA system). To evaluate the integrity of the introduced data transformation technique, we use three prominent evaluation measures namely Micro F 1 , Accuracy, and Hamming Loss. Regarding MicroF 1 , our introduced technique coupled with a new feature set surpasses CLR, SSVM, and RBM with a margin of 7%, 8%, and 22% respectively.

Patient radiation dose associated with $X$ -ray CT is a significant concern in the medical community. One of the ways to reduce patient dose is to acquire projection data at fewer angles. Using conventional reconstruction methods with such sparsely sampled data introduces severe streaking artifacts in the reconstructions, that reduces their diagnostic utility. Conventional methods in this domain have focused on postprocessing the artifact-filled images or the use of model-based inversion techniques with image-domain priors. In this work, we examine the potential of a deep-learning-based method to construct a mapping from the observed, sparsely sampled, CT projection data to a set of densely sampled projection estimates directly in the original projection domain. We cast the problem as one of sinogram in-painting and focus on completing the projection data prior to reconstructing the images. As compared to existing work, we focus on “correcting” the data rather than the subsequent images and avoid costly iterative tomographic inversion. Our initial results on a simulated dataset demonstrate the potential effectiveness of this new approach in suppressing artifacts.

Meniscal surgery is considered the most general orthopedic process that deals with the treatment of meniscus tears for human health care. It leads to a communal contusion to the cartilage that stabilizes and cushions the knee joints of human beings. Such tears can be classified into different categories based on age group, region, and occupation. Further, a large number of sportsmen and heavy weightlifters even in developed countries are affected by meniscus injuries. These patients are subjected to arthroscopic surgery, and during surgical treatment, the perseverance of meniscus is a very crucial task. Current research provides a significant ratio of meniscal tear patients around the globe, the critical expanse is considered as having strikingly risen with a mean annual of 0.066% due to surgery failure. To decumbent this ratio, an innovative training mechanism is proposed through video retrieval system in this research. This research work is focussed on developing a corpus and video retrieval system for meniscus surgery. Using the proposed system, surgeons can access guidance by watching the videos of surgeries performed by an expert and their seniors. The proposed system is comprised of four approaches to the spatiotemporal methodology to improve health care services. It entails key point, statistical modeling, PCA-scale invariant feature transform (SIFT), and PCA-Gaussian mixture model (GMM) with a combination of sparse-optical flow. The real meniscal surgery dataset is used for testing purposes and evaluation. The results conclude that using PCA-SIFT approach improves the results with an average precision of 0.78.

Development of Hepatocellular carcinoma (HCC) after Hepatitis C virus (HCV) infection is common cause of poor long-term survival. This study aimed to evaluate the effect of Vascular Endothelial growth factor (VEGF) genes polymorphism as a risk factor of Hepatocellular carcinoma (HCC) in chronic hepatitis C virus in Pakistani population. VEGF regulates angiogenesis so it is mainly involved in progression and development of solid tumor. The results showed that HCC patients had a higher frequency of the VEGF-634 GG genotype. In this study results revealed that VEGF Genotype GG is not associated with the risk of HCC in HCV infected patients in Punjab region of Pakistan. Keywords: HCV; HCC; VEGF Polymorphism http://dx.doi.org/10.19045/bspab.2017.60077

In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.

Background Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity. New method We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features. Results Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines. Comparison with existing methods Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem. Conclusions Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.

Medical images contain precious anatomical information for clinical procedures. Improved understanding of medical modality may contribute significantly in arena of medical image analysis. This paper investigates enhancement of monochromatic medical modality into colorized images. Improving the contrast of anatomical structures facilitates precise segmentation. The proposed framework starts with pre-processing to remove noise and improve edge information. Then colour information is embedded to each pixel of a subject image. A resulting image has a potential to portray better anatomical information than a conventional monochromatic image. To evaluate the performance of colorized medical modality, the structural similarity index and the peak signal to noise ratio are computed. Supremacy of proposed colorization is validated by segmentation experiments and compared with greyscale monochromatic images.

Accelerated thermal stability of vaccines during shelf life has vital impact on its potency. Temperature directly affects the stability of the vaccines and degradation of immunogen depends upon the storage and environmental conditions. The current study was undertaken to evaluate the potency of three different inactivated oil based commercial vaccines under artificially induced thermal conditions such as 4°C, 24°C, 36°C and 45°C. The vaccines were analyzed for their viscosity, density, pH, stability, particle distribution and serological potency after 10, 20 and 30 days. It was revealed that vaccines stored for 30 days at 4°C upto 24°C showed significantly better physiochemical response and in vivo serological potency (p>0.05) as compared to vaccines stored for 30 days at 36°C and 45°C (p0.05) as compared to Gallimune and Medivac (p0.05) as compare to Otto ND and Gallimune vaccine (p30°C when stored for 30 days whereas, immunogenic response was protective with minimal uniformity. However, above vaccine could be used for effective immune-prophylaxis in poultry.

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types., Accepted for BioImageComputing workshop at ECCV 2016

Dendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape variations to understand their relation with neuronal activity. Currently this analysis performed manually, the availability of reliable automated tools would assist neuroscientists and accelerate this research. Previously, morphological features based spine analysis has been performed and reported in the literature. In this paper, we explore the idea of using and comparing manifold learning techniques for classifying spine shapes. We start with automatically segmented data and construct our feature vector by stacking and concatenating the columns of images. Further, we apply unsupervised manifold learning algorithms and compare their performance in the context of dendritic spine classification. We achieved 85.95% accuracy on a dataset of 242 automatically segmented mushroom and stubby spines. We also observed that ISOMAP implicitly computes prominent features suitable for classification purposes.