Bacteria associated with Comamonadaceae are key arsenite oxidizer associated with Pteris vittata root

Pteris vittata (P. vittata), an arsenic (As) hyperaccumulator commonly used in the phytoremediation of As-contaminated soils, contains root-associated bacteria (RAB) including those that colonize the root rhizosphere and endosphere, which can adapt to As contamination and improve plant health. As(III)-oxidizing RAB can convert the more toxic arsenite (As(III)) to less toxic arsenate (As(V)) under As-rich conditions, which may promote plant survial. Previous studies have shown that microbial As(III) oxidation occurs in the rhizospheres and endospheres of P. vittata. However, knowledge of RAB of P. vittata responsible for As(III) oxidation remained limited. In this study, members of the Comamonadaceae family were identified as putative As(III) oxidizers, and the core microbiome associated with P. vittata roots using DNA-stable isotope probing (SIP), amplicon sequencing and metagenomic analysis. Metagenomic binning revealed that metagenome assembled genomes (MAGs) associated with Comamonadaceae contained several functional genes related to carbon fixation, arsenic resistance, plant growth promotion and bacterial colonization. As(III) oxidation and plant growth promotion may be key features of RAB in promoting P. vittata growth. These results extend the current knowledge of the diversity of As(III)-oxidizing RAB and provide new insights into improving the efficiency of arsenic phytoremediation.

Biological role and regulation of circular RNA as an emerging biomarker and potential therapeutic target for cancer

Circular RNAs (circRNAs) are a unique family of endogenous RNAs devoid of 3' poly-A tails and 5' end caps. These single-stranded circRNAs, found in the cytoplasm, are synthesized via back-splicing mechanisms, merging introns, exons, or both, resulting in covalently closed circular loops. They are profusely expressed across the eukaryotic transcriptome and offer heightened stability against exonuclease RNase R compared to linear RNA counterparts. This review endeavors to provide a comprehensive overview of circRNAs' characteristics, biogenesis, and mechanisms of action. Furthermore, aimed to shed light on the potential of circRNAs as significant biomarkers in various cancer types. It has been performed an exhaustive literature review, drawing on recent studies and findings related to circRNA characteristics, synthesis, function, evaluation techniques, and their associations with oncogenesis. CircRNAs are intricately associated with tumor progression and development. Their multifaceted roles encompass gene regulation through the sponging of proteins and microRNAs, controlling transcription and splicing, interacting with RNA binding proteins (RBPs), and facilitating gene translation. Due to these varied roles, circRNAs have become a focal point in tumor pathology investigations, given their promising potential as both biomarkers and therapeutic agents. CircRNAs, due to their unique biogenesis and multifunctionality, hold immense promise in the realm of oncology. Their stability, widespread expression, and intricate involvement in gene regulation underscore their prospective utility as reliable biomarkers and therapeutic targets in cancer. As our understanding of circRNAs deepens, advanced techniques for their detection, evaluation, and manipulation will likely emerge. These advancements might catalyze the translation of circRNA-based diagnostics and therapeutics into clinical practice, potentially revolutionizing cancer care and prognosis.

QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment

Tuberculosis (TB) is one of the most contagious diseases that has a greater mortality rate than HIV/AIDS and the cases of TB are feared to rise as a repercussion of the COVID-19 pandemic. The pharmaceutical industry is constantly looking for ways to improve drug design processes in order to combat the growth of infections and cure newly identified syndromes or genetically based dysfunctions with the help of QSPR models. QSPR models are mathematical tools that establish relationships between a molecular structure and its physicochemical attributes using structural properties. Topological indices are such properties that are generated from the molecular graph without any empirically derived measurements. This work focuses on developing a QSPR model using distance-based topological indices for anti-tuberculosis medications and their diverse physicochemical features.

Exploring the diversity of CFTR gene mutations in cystic fibrosis individuals of South Asia

OBJECTIVE

Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CFTR gene. This study aimed to identify the spectrum of CFTR variants reported in individuals with CF from South Asia (ISA).

DATA SOURCES AND STUDY SELECTION

We conducted a PubMed search for CFTR variants reported in ISA. Full text of original articles and case reports was read to compile data on reported variants. To gather additional data, we independently cross-referenced each variant with the CFTR Mutation Database and ClinVar.

RESULTS

Our investigation identified a total of 92 CFTR variants reported across 30 articles. The most frequently tested, and reported variant was ΔF508 with a global frequency of 69.74%. Notably, we found 14 pathogenic CFTR mutations shared among ISA, originating from more than one South Asian country: ΔF508, 1525-1 G > A, G542X, S549N, R117H, S549R, R709X, V456A, Y569D, L1077P, 1161delC, 1898 + 1 G > T, G551D, and 2184insA.

CONCLUSION

In summary, the higher prevalence of consanguinity and the limited availability of CF diagnostic resources in South Asia considerably contribute to the prevalence of genetic disorders like CF. The spectrum of CFTR mutations exhibits noticeable variations within South Asian and other populations. The inclusion of current study-enlisted CFTR gene variants is highly recommended for CF disease genetic testing in South Asia which may aid in achieving a precise diagnosis, enhancing disease management, and discovering drugs for currently untreatable genetic variants. It is also imperative to conduct a comprehensive study in this region, especially in previously unexplored countries such as Nepal, Bhutan, Maldives, and Bangladesh.

Interleukins-6 -174G/C (rs1800795) and -572C/G (rs1800796) polymorphisms and prostate cancer risk

Prostate cancer (PCa) is an aggressive cancer influenced by a complex interplay of genetic and environmental factors. Amongst these risk factors, the impact of Interleukin6 (IL6) gene polymorphisms in PCa risk has received a lot of attention. IL-6 is a cytokine that has been implicated in the pathogenesis of several malignancies, including PCa. Two IL-6 gene polymorphisms, - 174 G/C (rs1800795) and - 572 C/G (rs1800796), have received intellectual attention due to their potential role as modulators of prostate cancer risk. The main objective of this research was to comprehensively explore the potential associations between IL-6 rs1800795 and rs1800796 polymorphisms, and their impact on the occurrence of PCa. A case-control study was carried out with a well-defined cohort comprising 110 PCa cases and 110 controls (total n = 220). The genotyping of rs1800795 and rs1800796 was carefully performed using the highly sensitive and accurate Polymerase Chain Reaction-High Resolution Melting Curve (PCR-HRM) technique. The assessment of genetic associations was evaluated using various R packages, such as Haplo-Stats, SNP stat, pheatmap, and LD heatmap. The present study applied odds ratio (OR) analysis to reveal significant evidence of strong associations between the genotypes of rs1800795 and rs1800796 and the susceptibility to PCa. The findings of this study underscore the noteworthy impact of genetic variations in the IL-6 gene on the development of prostate cancer. Specifically, the C/G and G/G genotypes of rs1800795 demonstrated increased PCa risk, with odds ratios (OR) of 1.650 (95% CI = 1.068-2.549, p = 0.032) and 2.475 (95% CI = 1.215-5.043, p < 0.001), respectively. Similarly, the G/C genotype of rs1800796 exhibited an OR of 2.374 (95% CI = 1.363-4.130, p = 0.012) for elevated prostate cancer risk, while the C/C genotype had an OR of 1.81 (95% CI = 1.02-3.22, p = 0.7). Furthermore, our haplotype analysis have revealed an association between haplotype 4 (C-G) and increased risk of PCa (OR = 1.69, 95% CI = 1.05-2.73, p = 0.032). In conclusion, this case-control analysis presents compelling evidence for a significant association between IL-6 variants (rs1800795 and rs1800796) and increased susceptibility to prostate cancer.

A paradigmatic approach to the molecular descriptor computation for some antiviral drugs

In theoretical chemistry, topological indices are commonly employed to model the physico-chemical properties of chemical compounds. Mathematicians frequently use Zagreb indices to calculate a chemical compound's strain energy, melting point, boiling temperature, distortion, and stability. The current global pandemic caused by the new SARS-CoV-2, also known as COVID-19, is a significant public health concern. Various therapy modalities are advised. The issue has become worse since there hasn't been enough counseling. Researchers are looking at compounds that might be used as SARS and MERS therapies based on earlier studies. In several quantitative structure-property-activity relationships (QSPR and QSAR) studies, a variety of physiochemical properties are successfully represented by topological indices, a sort of molecular descriptor that just specifies numerical values connected to a substance's molecular structure. This study investigates several irregularity-based topological indices for various antiviral medicines, depending on the degree of irregularity. In order to evaluate the effectiveness of the generated topological indices, a QSPR was also carried out using the indicated pharmaceuticals, the various topological indices, and the various physiochemical features of these antiviral medicines. The acquired results show a substantial association between the topological indices being studied by the curve-fitting approach and the physiochemical properties of possible antiviral medicines.

Computational Analysis of Some More Rectangular Tessellations of Kekulenes and Their Molecular Characterizations

Cycloarene molecules are benzene-ring-based polycyclic aromatic hydrocarbons that have been fused in a circular manner and are surrounded by carbon-hydrogen bonds that point inward. Due to their magnetic, geometric, and electronic characteristics and superaromaticity, these polycyclic aromatics have received attention in a number of studies. The kekulene molecule is a cyclically organized benzene ring in the shape of a doughnut and is the very first example of such a conjugated macrocyclic compound. Due to its structural characteristics and molecular characterizations, it serves as a great model for theoretical research involving the investigation of π electron conjugation circuits. Therefore, in order to unravel their novel electrical and molecular characteristics and foresee potential applications, the characterization of such components is crucial. In our current research, we describe two unique series of enormous polycyclic molecules made from the extensively studied base kekulene molecule, utilizing the essential graph-theoretical tools to identify their structural characterization via topological quantities. Rectangular kekulene Type-I and rectangular kekulene Type-II structures were obtained from base kekulene molecules arranged in a rectangular fashion. We also employ two subcases for each Type and, for all of these, we derived ten topological indices. We can investigate the physiochemical characteristics of rectangular kekulenes using these topological indices.

A novel perspective for M-polynomials to compute molecular descriptors of borophene nanosheet

Nanomaterials feature exceptional, one-of-a-kind qualities that might be used in electronics, medicine, and other industries. Two-dimensional nanomaterials called borophene have a variety of intriguing characteristics, which helped them to leave an indelible impression in the fields of chemistry, material science, nanotechnology, and condensed matter physics. The concept of modelling the structure of a molecule or chemical network to a chemical graph and then quantitatively analysing them with the aid of topological descriptors was a major advance in the fields of mathematics and chemistry, with a wide range of applications. M-polynomial approach is a very versatile and quick method for computing the degree-based descriptors of chemical graphs or networks. The degree-based descriptors of the [Formula: see text]-Borophene nanosheet are established in this study utilising the M-polynomial technique. A program code that enables to generate the M-polynomial of any chemical structure was developed in Java platform and the same is displayed. At the conclusion, the numerical and graphical comparison based on the identified analytic expressions is also provided. Additionally, the QSPR analysis was also carried out and the outcoms are presented therein.

Entropy structural characterization of zeolites BCT and DFT with bond-wise scaled comparison

Entropy of a connected network is a quantitative measure from information theory that has triggered a plethora of research domains in molecular chemistry, biological sciences and computer programming due to its inherent capacity to explore the structural characteristics of complex molecular frameworks that have low structural symmetry as well as high diversity. The analysis of the structural order is greatly simplified through the topological indices based graph entropy metrics, which are then utilized to predict the structural features of molecular frameworks. This predictability has not only revolutionized the study of zeolitic frameworks but has also given rise to new generations of frameworks. We make a comparative study of two versatile framework topologies namely zeolites BCT and DFT, which have been widely utilized to create a new generation of frameworks known as metal organic frameworks. We discuss bond-additive topological indices and compute entropy measure descriptors for zeolites BCT and DFT using degree and degree-sum parameters. In addition, we perform bond-wise scaled comparative analysis between BCT and DFT which shows that zeolite BCT has greater entropy values compared to zeolite DFT.

Computation of Entropy Measures for Metal-Organic Frameworks

Entropy is a thermodynamic function used in chemistry to determine the disorder and irregularities of molecules in a specific system or process. It does this by calculating the possible configurations for each molecule. It is applicable to numerous issues in biology, inorganic and organic chemistry, and other relevant fields. Metal-organic frameworks (MOFs) are a family of molecules that have piqued the curiosity of scientists in recent years. They are extensively researched due to their prospective applications and the increasing amount of information about them. Scientists are constantly discovering novel MOFs, which results in an increasing number of representations every year. Furthermore, new applications for MOFs continue to arise, illustrating the materials' adaptability. This article investigates the characterisation of the metal-organic framework of iron(III) tetra-p-tolyl porphyrin (FeTPyP) and CoBHT (CO) lattice. By constructing these structures with degree-based indices such as the K-Banhatti, redefined Zagreb, and the atom-bond sum connectivity indices, we also employ the information function to compute entropies.

Fundamental Aspects of Skin Cancer Drugs via Degree-Based Chemical Bonding Topological Descriptors

Due to significant advancements being made in the field of drug design, the use of topological descriptors remains the primary approach. When combined with QSPR models, descriptors illustrate a molecule's chemical properties numerically. Numbers relating to chemical composition topological indices are structures that link chemical composition to physical characteristics. This research concentrates on the analysis of curvilinear regression models and degree-based topological descriptors for thirteen skin cancer drugs. The physicochemical characteristics of the skin cancer drugs are examined while regression models are built for computed index values. An analysis is performed for several significant results based on the acquired data.

Identification of NS2B-NS3 Protease Inhibitors for Therapeutic Application in ZIKV Infection: A Pharmacophore-Based High-Throughput Virtual Screening and MD Simulations Approaches

Zika virus (ZIKV) pandemic and its implication in congenital malformations and severe neurological disorders had created serious threats to global health. ZIKV is a mosquito-borne flavivirus which spread rapidly and infect a large number of people in a shorter time-span. Due to the lack of effective therapeutics, this had become paramount urgency to discover effective drug molecules to encounter the viral infection. Various anti-ZIKV drug discovery efforts during the past several years had been unsuccessful to develop an effective cure. The NS2B-NS3 protein was reported as an attractive therapeutic target for inhibiting viral proliferation, due to its central role in viral replication and maturation of non-structural viral proteins. Therefore, the current in silico drug exploration aimed to identify the novel inhibitors of Zika NS2B-NS3 protease by implementing an e-pharmacophore-based high-throughput virtual screening. A 3D e-pharmacophore model was generated based on the five-featured (ADPRR) pharmacophore hypothesis. Subsequently, the predicted model is further subjected to the high-throughput virtual screening to reveal top hit molecules from the various small molecule databases. Initial hits were examined in terms of binding free energies and ADME properties to identify the candidate hit exhibiting a favourable pharmacokinetic profile. Eventually, molecular dynamic (MD) simulations studies were conducted to evaluate the binding stability of the hit molecule inside the receptor cavity. The findings of the in silico analysis manifested affirmative evidence for three hit molecules with -64.28, -55.15 and -50.16 kcal/mol binding free energies, as potent inhibitors of Zika NS2B-NS3 protease. Hence, these molecules holds the promising potential to serve as a prospective candidates to design effective drugs against ZIKV and related viral infections.

Entropy Related to K-Banhatti Indices via Valency Based on the Presence of C6H6 in Various Molecules

Entropy is a measure of a system's molecular disorder or unpredictability since work is produced by organized molecular motion. Shannon's entropy metric is applied to represent a random graph's variability. Entropy is a thermodynamic function in physics that, based on the variety of possible configurations for molecules to take, describes the randomness and disorder of molecules in a given system or process. Numerous issues in the fields of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines are resolved using distance-based entropy. These applications cover quantifying molecules' chemical and electrical structures, signal processing, structural investigations on crystals, and molecular ensembles. In this paper, we look at K-Banhatti entropies using K-Banhatti indices for C6H6 embedded in different chemical networks. Our goal is to investigate the valency-based molecular invariants and K-Banhatti entropies for three chemical networks: the circumnaphthalene (CNBn), the honeycomb (HBn), and the pyrene (PYn). In order to reach conclusions, we apply the method of atom-bond partitioning based on valences, which is an application of spectral graph theory. We obtain the precise values of the first K-Banhatti entropy, the second K-Banhatti entropy, the first hyper K-Banhatti entropy, and the second hyper K-Banhatti entropy for the three chemical networks in the main results and conclusion.

An empirical method for geometric calibration of a photon counting detector-based cone beam CT system

BACKGROUND

Geometric calibration is essential in developing a reliable computed tomography (CT) system. It involves estimating the geometry under which the angular projections are acquired. Geometric calibration of cone beam CTs employing small area detectors, such as currently available photon counting detectors (PCDs), is challenging when using traditional-based methods due to detectors' limited areas.

OBJECTIVE

This study presented an empirical method for the geometric calibration of small area PCD-based cone beam CT systems.

METHODS

Unlike the traditional methods, we developed an iterative optimization procedure to determine geometric parameters using the reconstructed images of small metal ball bearings (BBs) embedded in a custom-built phantom. An objective function incorporating the sphericities and symmetries of the embedded BBs was defined to assess performance of the reconstruction algorithm with the given initial estimated set of geometric parameters. The optimal parameter values were those which minimized the objective function. The TIGRE toolbox was employed for fast tomographic reconstruction. To evaluate the proposed method, computer simulations were carried out using various numbers of spheres placed in various locations. Furthermore, efficacy of the method was experimentally assessed using a custom-made benchtop PCD-based cone beam CT.

RESULTS

Computer simulations validated the accuracy and reproducibility of the proposed method. The precise estimation of the geometric parameters of the benchtop revealed high-quality imaging in CT reconstruction of a breast phantom. Within the phantom, the cylindrical holes, fibers, and speck groups were imaged in high fidelity. The CNR analysis further revealed the quantitative improvements of the reconstruction performed with the estimated parameters using the proposed method.

CONCLUSION

Apart from the computational cost, we concluded that the method was easy to implement and robust.

Alfalfa-grass mixtures reduce greenhouse gas emissions and net global warming potential while maintaining yield advantages over monocultures

Improving forage productivity with lower greenhouse gas (GHG) emissions from limited grassland has been a hotspot of interest in global agricultural production. In this study, we analyzed the effects of grasses (tall fescue, smooth bromegrass), legume (alfalfa), and alfalfa-grass (alfalfa + smooth bromegrass and alfalfa + tall fescue) mixtures on GHG emissions, net global warming potential (Net GWP), yield-based greenhouse gas intensity (GHGI), soil chemical properties and forage productivity in cultivated grassland in northwest China during 2020-2021. Our results demonstrated that alfalfa-grass mixtures significantly improved forage productivity. The highest total dry matter yield (DMY) during 2020 and 2021 was obtained from alfalfa-tall fescue (11,311 and 13,338 kg ha^-1) and alfalfa-smooth bromegrass mixtures (10,781 and 12,467 kg ha^-1). The annual cumulative GHG emissions from mixtures were lower than alfalfa monoculture. Alfalfa-grass mixtures significantly reduced GHGI compared with the grass or alfalfa monocultures. Furthermore, results indicated that grass, alfalfa and alfalfa-grass mixtures differentially affected soil chemical properties. Lower soil pH and C/N ratio were recorded in alfalfa monoculture. Alfalfa and mixtures increased soil organic carbon (SOC) and soil total nitrogen (STN) contents. Importantly, alfalfa-grass mixtures are necessary for improving forage productivity and mitigating the GHG emissions in this region. In conclusion, the alfalfa-tall fescue mixture lowered net GWP and GHGI in cultivated grassland while maintaining high forage productivity. These advanced agricultural practices could contribute to the development of climate-sustainable grassland production in China.

Connecting SiO4 in Silicate and Silicate Chain Networks to Compute Kulli Temperature Indices

A topological index is a numerical parameter that is derived mathematically from a graph structure. In chemical graph theory, these indices are used to quantify the chemical properties of chemical compounds. We compute the first and second temperature, hyper temperature indices, the sum connectivity temperature index, the product connectivity temperature index, the reciprocal product connectivity temperature index and the F temperature index of a molecular graph silicate network and silicate chain network. Furthermore, a QSPR study of the key topological indices is provided, and it is demonstrated that these topological indices are substantially linked with the physicochemical features of COVID-19 medicines. This theoretical method to find the temperature indices may help chemists and others in the pharmaceutical industry forecast the properties of silicate networks and silicate chain networks before trying.

A Paradigmatic Approach to Find the Valency-Based K-Banhatti and Redefined Zagreb Entropy for Niobium Oxide and a Metal-Organic Framework

Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal-organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index.

An interactive time series image analysis software for dendritic spines

Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.

Post Covid-19 Sino-Orbital Mucormycosis: A Therapeutic Challange

Objective: To determine the frequency of sino-orbital mucormycosis outcomes in diagnosed patients of COVID-19 and its association with the duration of the disease. Study Design: Case series. Place and Duration of Study: Armed Forces Institute of Ophthalmology, Pak Emirates Military Hospital, Rawalpindi, from Feb to Jul 2021. Methodology: A sample size of 20 was calculated using WHO calculator. Diagnosed patients of COVID-19 who fulfilled the inclusion criteria were selected through consecutive sampling technique. The mode of presentation, in-depth imaging findings, associated co-morbidities and management/follow up details were collected. Results: Total 20 patients were included in study. There were 12 (60%) male and 8 (40%) female. Mean age of patients was 60.4 ± 9.8 years. Outcomes of study were treatment, still of follow up, mortality and mortality with sepsis, survival and exenteration (15%, 15%, 25%, 5%, 25% 15% respectively). Among all the patients, 2(10%) patients showed intraconal and extraconal orbital involvement, 9 (45%) showed left, 8 (40%) showed right and 1(5%) showed both orbits involvement. Outcomes of sino-orbital mucormycosis showed significant association with COVID-19 duration (p=0.03). Conclusion: Sino-Orbital mucormycosis is a common and potentially life-threatening complication of COVID-19. Early initiation of anti-fungal treatment as well as surgical intervention are key to decrease both the mortality and morbidity associated with the disease.

Acral melanoma detection using dermoscopic images and convolutional neural networks

Acral melanoma (AM) is a rare and lethal type of skin cancer. It can be diagnosed by expert dermatologists, using dermoscopic imaging. It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers. Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma. However, to date, limited research has been conducted on the classification of melanoma subtypes. The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes, such as, AM. In this study, we present a novel deep learning model, developed to classify skin cancer. We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions. Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection. Our custom-built model is a seven-layered deep convolutional network that was trained from scratch. Additionally, transfer learning was utilized to compare the performance of our model, where AlexNet and ResNet-18 were modified, fine-tuned, and trained on the same dataset. We achieved improved results from our proposed model with an accuracy of more than 90 % for AM and benign nevus, respectively. Additionally, using the transfer learning approach, we achieved an average accuracy of nearly 97 %, which is comparable to that of state-of-the-art methods. From our analysis and results, we found that our model performed well and was able to effectively classify skin cancer. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

Autoregression and Structured Low-Rank Modeling of Sinogram Neighborhoods

Sinograms are commonly used to represent the raw data from tomographic imaging experiments. Although it is already well-known that sinograms posess some amount of redundancy, in this work, we present novel theory suggesting that sinograms will often possess substantial additional redundancies that have not been explicitly exploited by previous methods. Specifically, we derive that sinograms will often satisfy multiple simple data-dependent autoregression relationships. This kind of autoregressive structure enables missing/degraded sinogram samples to be linearly predicted using a simple shift-invariant linear combination of neighboring samples. Our theory also further implies that if sinogram samples are assembled into a structured Hankel/Toeplitz matrix, then the matrix will be expected to have low-rank characteristics. As a result, sinogram restoration problems can be formulated as structured low-rank matrix recovery problems. Illustrations of this approach are provided using several different (real and simulated) X-ray imaging datasets, including comparisons against a state-of-the-art deep learning approach. Results suggest that structured low-rank matrix methods for sinogram recovery can have comparable performance to state-of-the-art approaches. Although our evaluation focuses on competitive comparisons against other approaches, we believe that autoregressive constraints are actually complementary to existing approaches with strong potential synergies.

Efficacy of rhizobacteria for degradation of profenofos and improvement in tomato growth

Pesticides are widely used for managing pathogens and pests for sustainable agricultural output to feed around seven billion people worldwide. After their targeted role, residues of these compounds may build up and persist in soils and in the food chain. This study evaluated the efficiency of bacterial strains capable of plant growth promotion and biodegradation of profenofos. To execute this, bacteria were isolated from an agricultural area with a history of repeated application of profenofos. The profenofos degrading bacterial strains with growth-promoting characteristics were identified based on biochemical and molecular approaches through partial 16S ribosomal rRNA gene sequencing. The results revealed that one strain, Enterobacter cloacae MUG75, degraded over 90% profenofos after 9 days of incubation. Similarly, plant growth was significantly increased in plants grown in profenofos (100 mg L^-1) contaminated soil inoculated with the same strain. The study demonstrated that inoculation of profenofos degrading bacterial strains increased plant growth and profenofos degradation. Novelty statementPesticides are extensively applied in the agriculture sector to overcome pest attacks and to increase food production to fulfill the needs of the growing world population. Residues of these pesticides can persist in the environment for long periods, may enter the groundwater reservoirs and cause harmful effects on living systems highlighting the need for bioremediation of pesticide-contaminated environments. Microbes can use pesticides as a source of carbon and energy and convert them into less toxic and non-toxic products. Application of profenofos degrading rhizobacteria in interaction with the plants in the rhizosphere can remediate the pesticide-contaminated soils and minimize their uptake into the food chain. Hence, this approach can improve soil health and food quality without compromising the environment.

Molecular Characterization of MHC Class I Genes in Four Species of the Turdidae Family to Assess Genetic Diversity and Selection

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.

Major histocompatibility complex class II polymorphic variants are associated with asthma predisposition in the Punjabi population of Lahore, Pakistan

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.

Early Oral Feeding Compared With Traditional Postoperative Care in Patients Undergoing Emergency Abdominal Surgery for Perforated Duodenal Ulcer

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<0.05).

Conclusions

Based on our results, ERAS protocols that promote early oral feeding can be applied in patients undergoing emergency abdominal surgery for perforated duodenal repair. Early oral feeding in emergency surgery patients can reduce the patient burden on hospitals. In addition, early oral feeding can promote better outcomes and reduced economic burden for patients. Keywords: Perforated duodenal ulcer, ERAS protocol, randomized controlled trial, duodenal repair site leak, length of hospital stay, VAS score, postoperative ileus.

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images

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.

Quantifying climate-induced drought risk to livelihood and mitigation actions in Balochistan

Climate change-induced disasters show the highest risk for agriculture and livelihoods in rural areas of developing countries. Due to changing rainfall pattern, the arid and semiarid region of Pakistan faces frequent droughts. Farming communities affected by drought disasters are causing serious threats to livelihood, global food crises, environmental migration, and sustainable development. The existing study was designed to quantify two key components through (1) analysis of agrometeorological data (1981-2017) with exploratory data analysis and Mann-Kendall trend analysis; (2) extensive field survey (200 households). The multivariate probit model has been run to detect determinants of coping and adaptive strategies by farmers. Our results showed that the farmers supposed that temperature and rainfall were highly fluctuating in recent years equally. Farmers adopted different coping and adaptive measures that include crop diversification, input adjustment, water management, asset depletion, income diversification, and migration to sustain their livelihood during stress periods. The agrometeorological data analysis revealed that the agricultural vulnerability to drought risks increased significantly, and the survey results projected that 64.7% of the population is exposed to drought directly or indirectly. Sen's slope quantification resulted in (0.025 °C) rise in temperature, (- 2.936 mm) decline in rainfall year^-1. Modeling future scenarios resulted in an increase in temperature up to 0.7 °C, 1.2 °C, and a decrease in precipitation up to 161.48 mm, 103.5 mm in 2040 and 2060. The study evaluated a huge gap in the provision of drought risk resilience services, crop insurance, and climate-smart training practices to build capacities among farmers to cope with the impacts of extreme weather conditions. Our research might provide the groundwork to upgrade actions to drought prevention and early warning in water scarcity areas.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11069-021-04913-4.

The Potential of Utilizing Mid-Energy X-Rays for In-Line Phase Sensitive Breast Cancer Imaging

OBJECTIVE

The objective of this study is to demonstrate the potential of utilizing mid-energy x-rays for in-line phase-sensitive breast cancer imaging by phantom studies.

METHODS

The midenergy (50-80kV) in-line phase sensitive imaging prototype was used to acquire images of the contrast-detail mammography (CDMAM) phantom, an ACR accreditation phantom, and an acrylic edge phantom. The low-dose mid-energy phase-sensitive images were acquired at 60 kV with a radiation dose of 0.9 mGy, while the high-energy phase-sensitive images were acquired at 90 kV with a radiation dose of 1.2 mGy. The Phase-Attenuation Duality (PAD) principle for soft tissue was used for the phase retrieval. A blind observer study was conducted and paired-sample T-test were performed to compare the mean differences in the two imaging systems.

RESULTS

The correct detection ratio for the CDMAM phantom for phase-contrast images acquired by the low-dose mid-energy system was 56.91%, whereas images acquired by the high-energy system correctly revealed only 40.97% of discs. The correct detection ratios were 57.88% and 43.41% for phase-retrieved images acquired by the low-dose mid-energy and high-energy imaging systems, respectively. The reading scores for all three groups of objects in the ACR phantom were higher for the mid energy imaging system as compared to the high-energy system for both phase-contrast and phase- retrieved images. The calculated edge enhancement index (EEI) from the acrylic edge phantom image for the mid-energy system was higher than that calculated for the high-energy imaging system. The quantitative analyses showed a higher Contrast to Noise Ratio (CNR) as well as a higher Figure of Merit (FOM) in images acquired by the low-dose mid-energy imaging system.

CONCLUSION

The PAD based retrieval method can be applied in mid-energy system without remarkably affecting the image quality, and in fact, it improves the lesion detectability with a patient dose saving of 25%.

TGF-β1 rs1800469 gene polymorphism in the development of cirrhosis & hepatocellular carcinoma in Pakistani HCV patients

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.

Evaluation of resistance and morphological parameters of different sunflower cultivars against charcoal rot

Charcoal rot is one of the major threats to sunflower which causes complete crop loss in epidemic conditions. The genetic resistance of sunflower germplasm could be more economical and durable approach for the management of charcoal rot. In the current experiment, six genotypes obtained from Ayub Agricultural Research Institute, Faisalabad were evaluated for their resistance and morphological parameters against Macrophomina phaseolina. Under field conditions, none of the cultivars gave immune or resistant response against the disease. FH-337 was found to be susceptible with 54.87% infection while FH-331, FH-106 and Hycanth-33 appeared moderately susceptible with 25-49% infections. The least affected varieties were FH-259 and DK-40 with moderately resistant status in the range of 10-24%. Yield parameters of resistant varieties were better than susceptible ones when compared under inoculated conditions. Moderately resistant genotypes might have the better genetic makeup and could be selected in breeding programs to find out resistant sunflower germplasm to fulfill the needs of growing population. Moreover, these cultivars could be used for better yield of sunflower.

Analysis of toll-like receptors-9 (TLR9) gene polymorphism (rs5743836) in Pakistani patients with HCV

Toll-like receptors (TLRs) are innate immune receptors that mediate the inflammatory response during HCV infections. The goal of this study was to evaluate the association of TLR9 gene polymorphism (rs5743836) in Pakistani patients infected with genotype 3a of HCV. Total 500 subjects were recruited, 400 HCV patients and 100 healthy individuals. Genotyping of TLR9 (-1237T/C, rs5743836) was carried out in 400 HCV patients (323 interferon responders and 77 interferon non responder) and control group by applying High resolution melting (HRM) curve assay. No remarkable differences in distribution of genotype between HCV (p<0.0001; OR= 3.21, 95% CI= (2.514.12) and control groups (p<0.0001; OR=0.092, 95%CI= (0.0580.14) were observed. In conclusion TLR9-1237T/C gene polymorphism may not be considered as a molecular risk for patients with HCV in Pakistan.

Analysis of dengue virus burden and serotypes pattern in Faisalabad, 2016–2017

Dengue is the fastest growing mosquito-borne viral infection and has become a public epidemic in Pakistan. This cross-sectional study was carried out to explore the prevalence and characterization of dengue virus serotypes in Faisalabad, Pakistan during 2016–2017. Aim: To determine most prevalent dengue serotype in Faisalabad, Pakistan. Methodology: Cross-sectional study of stored serum specimens for dengue virus genotype by multiplex real time (RT)-PCR, antidengue IgM, IgG and NS1.Results: Viral RNA detection of 42 patients revealed positive results in 125 (34.4%) samples. DENV-3 (76%) was the predominant genotype followed by DENV-2 (17%) and 7% of DENV1. None of the samples were positive for DENV-4. Conclusion: DENV-3 is the most prevalent serotype of dengue virus observed in Faisalabad, Pakistan.

Analysis of polymorphism rs1990760 of IFIH1 gene and treatment outcomes in HCV infection

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.

Toll-like receptor 4 polymorphism as pretreatment predictor of response to HCV genotype 3a interferon-based treatment

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 is associated with male asthma patients specifically in urban areas of Lahore, Pakistan

OBJECTIVE

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.

METHODS

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.

RESULTS

Risk allele for rs12603332 i.e., "C" allele was found to be significantly associated with male patients residing in urban localities.

CONCLUSION

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.

Nonparametric Joint Shape and Feature Priors for Image Segmentation

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.