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© 2022 The Author(s)Background: The selection of genotypes in breeding programs relies on accurate estimates of heritability for variables relevant to yield. The better genotypes may be chosen rapidly using statistical estimate. Seventeen advanced breeding lines and three commercial bread wheat (Triticum aestivum L.) genotypes (used as checks) were assessed regarding their genetic characteristics for yield-related traits in the current study. Methods: Grain yield's correlation with several possible qualities was investigated and compared to regional controls. Results: The results showed that the peduncle length was positively correlated with the spike length, plant height, and total number of spikes. There was a favorable correlation between grain yield and spike count. The number of tillers per square meter was favorably connected with days to maturity but negatively correlated with grain weight, moisture percentage, and gluten content. The percentage of protein and gluten also declined with the increasing quantity of grains in each spike. Weight per 1000 grains was favorably connected with moisture percent and negatively correlated with days to maturity. Day of maturity was positively correlated with yield, whereas day of blooming was negatively correlated. The number of tillers m−2 had strong heritability and rapid genetic improvement. The number of grains per spike, plant height, 1000-grain weight, peduncle length, days to maturity, and protein percentage all demonstrated high broad sense heritability and moderate genetic advance, whereas spike length and starch percentage both displayed low heritability and poor genetic advance. The number of spikes per plant, plot yield, days to flowering, moisture%, and gluten% all showed moderate heritability with modest genetic progress. Conclusion: Following from the above, it is recommended that plant features with high heritability and genetic advancement be given preference in selection, while qualities with a negative link to grain output be subjected to cautious selection.

About 334 million people worldwide have been affected by asthma and additionally 100 million expected to have asthma by the year 2025. Being the sixth most populous and economically developing country and with the increasing trend of urbanization and due to poor health facilities, lacking the knowledge of the causes of the disease, increased pollutant exposures and consanguineous marriages, Pakistani population has greater chances of asthma and allergic diseases. Almost 20% of overall pediatric population of Pakistan is affected by this disease. In the past, only environmental factors were considered responsible for asthma but now it is a well-known fact that genetic factors have major contribution in the development of the disease. Poor knowledge of the possibility of genetics involved in this disease has been very common in both uneducated as well as educated population of this region. There is no doubt today that genetics have a major role in the exhibition of asthma but unlike other Mendelian disorders it cannot easily be identified by a single or a simple set of associated genes, rather small contributions of multiple genes and environmental exposure to asthmatic patients may be found. Our focus in this article is genetic factors of asthma in Pakistan. More than 100 genes associated with asthma have already been reported in different populations worldwide but generally, due to an ignored disease in this region, possible genetic causes, impacts, management and treatment of the disease has rarely been studied in Pakistan. This article was intended to explore some latest developments in asthma genetics, diagnosis, prognosis and management and suggest some recommendations to control this deadly disease.

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 degree limited-angle tomography problem from a real checked-bagged CT dataset and a 4x 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., Submitted to IEEE Transactions on Computational Imaging

In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this paper for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove. The integrity of generated neural word embeddings is evaluated using intrinsic and extrinsic evaluation approaches. Considering the lack of publicly available benchmark datasets, it provides a first-ever Roman Urdu dataset which consists of 3241 sentiments annotated against positive, negative and neutral classes. To provide benchmark baseline performance over the presented dataset, we adapt diverse machine learning (Support Vector Machine Logistic Regression, Naive Bayes), deep learning (convolutional neural network, recurrent neural network), and hybrid approaches. Effectiveness of generated neural word embeddings is evaluated by comparing the performance of machine and deep learning based methodologies using 7, and 5 distinct feature representation approaches respectively. Finally, it proposes a novel precisely extreme multi-channel hybrid methodology which outperforms state-of-the-art adapted machine and deep learning approaches by the figure of 9%, and 4% in terms of F1-score. Roman Urdu Sentiment Analysis, Pretrain word embeddings for Roman Urdu, Word2Vec, Glove, Fast-Text

In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it pro-vides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages. Third, for the very first time, it as-sesses the performance of various deep learning based methodologies for Urdu text document classification. In this regard, for experimentation, we adapt 10 deep learning classification methodologies which have pro-duced best performance figures for English text classification. Fourth, it also investigates the performance impact of transfer learning by utiliz-ing Bidirectional Encoder Representations from Transformers approach for Urdu language. Fifth, it evaluates the integrity of a hybrid approach which combines traditional machine learning based feature engineering and deep learning based automated feature engineering. Experimental results show that feature selection approach named as Normalised Dif-ference Measure along with Support Vector Machine outshines state-of-the-art performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and CLE Urdu Digest 1Million with a significant margin of 32%, and 13% respectively. Across all three datasets, Normalised Differ-ence Measure outperforms other filter based feature selection algorithms as it significantly uplifts the performance of all adopted machine learning, deep learning, and hybrid approaches. The source code and presented dataset are available at Github repository.

Pakistan is globally sixth most populous, economically developing south Asian country with tremendously increasing trend of urbanization. This increase in urbanization along with consanguineous marriages trend in Pakistani nationals might contribute as most important factors of increasing asthma prevalence. Up-till now, a few studies related to asthma genetics have been conducted in Pakistan. These studies suggest that the SNP variants like (rs2569190, rs2569191), (rs2243250, rs2227284), (rs1800896), (rs1881457, rs20541, rs1800925), (rs2280091, rs2787094, rs528557), (rs1131882) in CD14, IL-4, IL10, IL-13, ADAM33 and TBXA2R genes respectively and the SNPs (rs3816470, rs6503525, rs3859192, rs12603332) on chromosomal region 17q21 are significantly associated in Pakistani population whereas the haplotype “CCTCAG" of SNPs (rs12936231, rs7216389, rs7216558, rs9894164, rs1007654 and rs7212938) in 17q21 and ‘AAGTCG’ of SNPs (rs2280089, rs2280090, rs2280091, rs44707, rs528557, and rs612709) in ADAM-33 region are protective factor against asthma susceptibility. These studies will definitely contribute in understanding genetic basis of asthmatic complications in Pakistan and large population cohort size and sub-ethnic studies in future will give more meaningful conclusions to predict possible asthma susceptible genomic variants in sub-ethnic and general population of Pakistan.

Computed Tomography (CT) is a non-invasive imaging modality with applications ranging from healthcare to security. It reconstructs cross-sectional images of an object using a collection of projection data collected at different angles. Conventional methods, such as FBP, require that the projection data be uniformly acquired over the complete angular range. In some applications, it is not possible to acquire such data. Security is one such domain where non-rotational scanning configurations are being developed which violate the complete data assumption. Conventional methods produce images from such data that are filled with artifacts. The recent success of deep learning (DL) methods has inspired researchers to post-process these artifact laden images using deep neural networks (DNNs). This approach has seen limited success on real CT problems. Another approach has been to pre-process the incomplete data using DNNs aiming to avoid the creation of artifacts altogether. Due to imperfections in the learning process, this approach can still leave perceptible residual artifacts. In this work, we aim to combine the power of deep learning in both the data and image domains through a two-step process based on the consensus equilibrium (CE) framework. Specifically, we use conditional generative adversarial networks (cGANs) in both the data and the image domain for enhanced performance and efficient computation and combine them through a consensus process. We demonstrate the effectiveness of our approach on a real security CT dataset for a challenging 90 degree limited-angle problem. The same framework can be applied to other limited data problems arising in applications such as electron microscopy, non-destructive evaluation, and medical imaging., Comment: Accepted for publication in proceedings of IEEE ICCV Workshop on Learning for Computational Imaging (ICCVW-LCI)

Filtered back projection (FBP) is the most widely used method for image reconstruction in X-ray computed tomography (CT) scanners. The presence of hyper-dense materials in a scene, such as metals, can strongly attenuate X-rays, producing severe streaking artifacts in the 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. The standard practical approaches to reducing metal artifacts in CT imagery are either simplistic non-adaptive 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 (MAR) approach that tackles the problem in the projection data domain. We treat the projection data corresponding to metal objects as missing data and train an adversarial deep network to complete the missing data in the projection domain. The subsequent complete projection data is then used with FBP to reconstruct image intended to be free of artifacts. This new approach results in an end-to-end MAR algorithm that is computationally efficient so 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., Comment: Accepted for publication in IEEE Transactions on Computational Imaging

Sinirsel morfoloji ve fonksiyon birbiriyle oldukça ilintilidir. Özellikle dendritik diken morfolojisi güçlü bir şekilde gelen sinirsel aktivite ile yönetilir. Önceki çalışmalarda dendritik dikenlerin hacminin diken morfolojisini incelemek ve yapı-fonksiyon ilişkisini anlamak için temel parametre olduğu düşünülüyordu. Fakat bu indirgemeci yaklaşım dikenlerin kapsamlı yapı dağarcığını içermemektedir. Zengin diken morfoloji bilgisini fonksiyonel eşleşmeyle bütünleştirmenin ilk adımı, diken şekillerinin literatürde önerilen temel şekil sınıflarına göre sınıflandırılmasıdır. Yeterli seviyede güvenilir otomatik analiz araçlarının olmaması nedeniyle sınıflandırma işlemi elle yapılmaktadır. Bu da analizin öznel ve zaman isteyen bir işlem olmasına yol açmaktadır. Otomatik diken şekil analiz araçları bu işlemi hızlandırarak sinirbilimcilerin altta yatan yapı ve fonksiyon ilişkisini anlamasına yardımcı olacaktır. Literatürde diken şekil sınıflandırması ile ilgili birçok çalışma yer almaktadır. Fakat diken şekillerinin ayrı sınıflar halinde mi yoksa bir şekil değişim süreci olarak mı ele alınması gerektiği konusunda bir fikir birliğine varılmamıştır. Bu problemde karşımıza çıkan bir diğer güçlük sınıflandırma yaklaşımlarının güdümlü yapısının getirdiği öznellik ve yanlılıktır. Bu tez, hem kümeleme hem de sınıflandırma yaklaşımlarını morfolojik, şekil ve görüntü öznitelikleriyle kullanarak dendritik diken şekil analizi gerçekleştirme üzerine kurulmuştur. Dendritik diken sınıflandırma problemine çok katlı (manifold) öğrenme yöntemlerini uyguladığımızda ISOMAP'in dolaylı olarak sınıflandırma için önemli öznitelikleri hesapladığını gözlemledik. Sınıflandırma amacıyla doğrusal temsil yaklaşımına başvurduğumuzda seyrek temsilin kısmen daha iyi bir sınıflandırma performansı sağladığını gördük. 2 boyutlu ve 3 boyutlu morfolojik özniteliklere dayalı diken şekil analizi yaklaşımında 3 boyutlu morfolojik özniteliklerin sağladığı avantajları gösterdik. Derin öğrenmeye dayanan sınıflandırma yaklaşımında Konvolüsyonel Sinir Ağlarından (CNNs) çıkarılan orta seviye özniteliklerin özel çıkarılmış öznitelikler kadar iyi performans gösterdiğine şahit olduk. Dendritik diken sınıflandırması için çekirdek yoğunluk tahminine (KDE) bağlı bir çerçeve tasarladık. Önerdiğimiz yaklaşımları sinirbilimci bir uzmanın belirlediği etiketlerle karşılaştırdık. Çekirdek yoğunluk tahminine bağlı çerçeve sinirbilimcilerin dikenlerin şekil sınıflarının ayrılabilirliğini olabilirlik oranı uzayında incelemelerine olanak vererek şekil analiz problemine daha derinden bakabilmelerini sağlayabilir. Bunlara ek olarak diken şekillerini güdümsüz öğrenme ve kümeleme yöntemleri üzerinde çalışmalar yaptık. Bayes bilgi kıstasını kullanarak küme sayısını veriden otomatik seçen x-ortalama (x-means) tekniğini kullandık. Bu bağlamda kümelemenin iki farklı amaçla kullanılmasından söz edilebilir: ayrı şekil sınıflarının varlığına dair hipotezi doğrulamak ve yeni gruplar keşfetmek. Elimizdeki veride çok sayıda diken standart şekil sınıfları içinde değerlendirilebilse de, önemli sayıda dikenin bu sınıflara uymadığını ve ara niteliklere sahip olduğunu gözlemledik. Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. Previously, volumes of dendritic spines have been considered as a primary parameter to study spine morphology and gain insight into structure-function coupling. However, this reductionist approach fails to incorporate the broad spine structure repertoire. First step towards integrating the rich spine morphology information into functional coupling is to classify spine shapes into main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is currently performed manually, which is a time-intensive task and prone to subjectivity. Availability of automated spine shape analysis tools can accelerate this process and help neuroscientists understand underlying structure and function relationship. 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. This thesis focuses on morphological, shape, and appearance features based methods to perform dendritic spine shape analysis using both clustering and classification approaches. We apply manifold learning methods for dendritic spine classification and observe that ISOMAP implicitly computes prominent features suitable for classification purposes. We also apply linear representation based approach for spine classification and conclude that sparse representation provides slightly better classification performance. We propose 2D and 3D morphological features based approach for spine shape analysis and demonstrate the advantage of 3D morphological features. We also use a deep learning based approach for spine classification and show that mid-level features extracted from Convolutional Neural Networks (CNNs) perform as well as hand-crafted features. We propose a kernel density estimation (KDE) based framework for dendritic spine classification. We evaluate our proposed approaches by comparing labels assigned by a neuroscience expert. Our KDE based framework also enables neuroscientists to analyze separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the spine shape analysis problem. Furthermore, we also propose a methodology for unsupervised learning and clustering of spine shapes. In particular, we use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). The objective of clustering in this context is two-fold: confirm the hypothesis of some distinct shape classes and discover new natural groups. We observe that although there are many spines which easily fit into the definition of standard shape types (confirming the hypothesis), there are also a significant number of others which do not comply with standard shape types and demonstrate intermediate properties. 92

Functional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly.

Objective: Single nucleotide polymorphisms (SNPs) on 17q21 are known to be associated with asthma disease in multiple populations. This study was designed to know whether this region is associated with asthma in Lahore region population or not. Methods: A total of 200 asthma patients and 100 healthy controls were enrolled from different hospitals of Lahore, Pakistan. Twelve SNPs from chromosomal region 17q21 were analyzed in cases and controls by single base extension method and capillary-based genetic analyzers. Associations with asthma were checked using basic allelic model, genotypic model, and results were adjusted by logistic regression analysis using PLINK v1.9. Pair-wise linkage disequilibrium among the SNPs was analyzed by using Haploview software. Results: SNP rs3816470 showed a strong association (p = 8.89 × 10−5, Odd Ratio = 3.082 [1.755-5.41]) with asthma, whereas rs3859192 and rs6503525 also showed a significant association with the development of asthma, especially in the case of positive family history. In LD block1 (93 kb) consisting of six SNPs (rs12936231, rs7216389, rs7216558, rs9894164, rs1007654 and rs7212938), none of the haplotypes show any significant association with asthma except the haplotype “CCTCAG”, which is a significant protective factor against asthma having frequency 0.051 in controls while 0.017 in cases (p = 3.56 × 10−2, χ2 = 4.415). Conclusion: The present study reports that the polymorphic genomic variant rs3816470 is significantly and independently associated with asthma in the studied population, while the variants, rs6503525 and rs3859192, also indicate a significant association with asthma in this population when family history of the disease is taken as a covariate.