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Abstract Background Dirigent (DIR) genes play a key role in the development of organic products in plants. They confer conformational influence on processes that lack stereoselectivity and regioselectivity through processes that are mostly understood. They are required to produce lignans, which are a unique and widely distributed family of plant secondary metabolites with intriguing pharmacological characteristics and potential role in plant development. DIR genes are implicated in the process of lignification and protect plants from environmental stresses, including biotic and abiotic stresses. Nevertheless, no research has been performed on the DIR gene family in Solanum lycopersicum. This study provides detailed information on the DIR gene family in S. lycopersicum. Methods and results The conserved domain analysis, phylogenetic analysis, evolutionary adaptation, cis-acting elements, proteomic analysis, signal peptide detection, transmembrane potential analysis, sequence identity and similarity analysis, gene assembly, genomic localization, duplication of gene analysis, and evolutionary linkage of 31 potential DIR genes were studied. All these analyses provide a deep understanding of DIR genes in the S. lycopersicum genome that will provide a useful reference for further functional analysis of the DIR genes in S. lycopersicum. Conclusion This research provides an in-depth and comprehensive explanation of the detailed process and structural characterization of DIR genes in the genome of S. lycopersicum, laying the groundwork for future plant genetic engineering and crop development exploration. This work will provide valuable information for identifying DIR genes in higher plants and support future research on the DIR gene family.

Abstract Background Acute promyelocytic leukemia (APL) is the sub-type of Acute myeloid leukemia (AML) which is described by differentiation block at promyelocytic stage and t(15; 17) translocation with All trans retinoic acid (ATRA) and arsenic trioxide (ATO) as standard treatments. Chronic myeloid leukemia (CML) translocation t (19; 22) causes a rise in granulocytes and their immature precursors in the blood. Different mutations cause resistance to first-line tyrosine kinase therapies in CML. Beside drug resistance, leukemia stem cells (LSC) are critical resources for relapse and resistance in APL and CML. The drug toxicity and resistant profile associated with LSC and current therapeutics of APL and CML necessitate the development of new therapies. Imidazoles are heterocyclic nitrogen compounds with diverse cellular actions. The purpose of this research was to assess the anti-leukemic properties of four novel imidazole derivatives including L-4, L-7, R-35, and R-NIM04. Methods and results Pharmacological and biochemical approaches were used which showed that all four imidazole derivatives interfere with the NB4 cells proliferation, an APL cell line, while only L-7 exhibit anti-proliferative activity against K562 cells, a CML cell line. The anti-proliferative effect of imidazole derivatives was linked to apoptosis induction. Further real-time polymerase chain reaction (RT-PCR) analysis revealed downregulation of AXL-Receptor Tyrosine Kinase (AXL-RTK) and target genes of Wnt/beta-catenin pathway like c-Myc, Axin2 and EYA3. An additive effect was observed after combinatorial treatment of L-7 with standard drugs ATRA or Imatinib on the proliferation of NB4 and K562 cells respectively which was related to further downregulation of target genes of Wnt/beta catenin pathway. Conclusion Imidazole derivatives significantly reduce proliferation of NB4 and K562 cells by inducing apoptosis, down regulating of AXL-RTK and Wnt/β-catenin target genes.

This article presents a time-delayed SIR epidemiological model that has been quantitatively examined. The model incorporates a logistic growth function for the susceptible population, a Crowley-Martin type incidence, and Holling type II treatment rates. We investigated two separate time delays. The first delay refers to the rate at which new infections occur, allowing us to evaluate the impact of the latent period. The second delay relates to the rate of treatment for those who have contracted the infection, which allows us to examine the consequences of postponed access to therapy. The investigation of the steady-state behavior of the model emphasizes two equilibria, namely, the infection-free equilibrium and the endemic equilibrium. The determination of critical values involves the use of the fundamental reproduction number, denoted R0, which serves as a predictive measure to determine the potential elimination of a disease within a specific population. Using the fundamental reproduction number, it can be shown that infection-free equilibrium exhibits local asymptotic stability when the value of R0 is less than 1. In contrast, when R0 exceeds 1, the infection-free equilibrium becomes unstable in the context of the time-delayed system. Furthermore, an analysis of the steady-state dynamics of the endemic equilibrium indicates the appearance of oscillations and periodic solutions with the Hopf bifurcation for all feasible combinations of two-time delays as the bifurcated parameter. In sensitivity analysis, a sensitivity index is utilized to evaluate the relative modification in the fundamental reproduction number caused by each parameter. In summary, numerical simulations are employed to offer empirical evidence for the theoretical findings.

Rapid urbanization attributed to population growth is affecting the built environment's thermal and landscape dynamics. Using Landsat satellite datasets, this study investigated the complex interplay between urban Land Cover (LC) modification, fluctuation in Land Surface Temperature (LST) and severity of Urban Heat Island (UHI) from 1990 to 2020 in Peshawar City, Pakistan. Thermal bands were used to calculate LST and severity of UHI using the Urban Thermal Field Variance Index (UTFVI). Furthermore, through Cellular Automata (CA), Logistic Regression (LR), and Artificial Neural Network (ANN), future predictions on thermal characteristics associated with land use changes were made. The results showed that the urban areas expanded by ∼25 % from 1990 to 2020, while a ∼10 % decrease occurred in urban vegetation. The city is projected to expand by ∼45 % and ∼56 % in 2035 and 2050, respectively. Notably, the results also demonstrated that urban hotspots were found the warmest with the strongest UHI severity (∼34 °C), followed by the barren land (∼32 °C), and vegetation. The results further predicted an increase of LST (∼55 % and ∼82 %) and UTFVI (∼62 % and ∼83 %) in 2035 and 2050, respectively. These findings provide useful insights for policymakers and city planners to mitigate heat stress and create a sustainable urban environment through the development of effective urban land use policies and urban greening.

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses the pressing need for early detection through a novel diagnostic approach that leverages innovative image processing techniques. The urgency of early lung cancer detection is emphasized by its alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, a comprehensive diagnosis requires a combination of imaging techniques. This research introduces an advanced diagnostic tool implemented through image processing methodologies. The methodology commences with histogram equalization, a crucial step in artifact removal from CT images sourced from a medical database. Accurate lung CT image segmentation, which is vital for cancer diagnosis, follows. The Otsu thresholding method and optimization, employing Colliding Bodies Optimization (CBO), enhance the precision of the segmentation process. A local binary pattern (LBP) is deployed for feature extraction, enabling the identification of nodule sizes and precise locations. The resulting image underwent classification using the densely connected CNN (DenseNet) deep learning algorithm, which effectively distinguished between benign and malignant tumors. The proposed CBO+DenseNet CNN exhibits remarkable performance improvements over traditional methods. Notable enhancements in accuracy (98.17%), specificity (97.32%), precision (97.46%), and recall (97.89%) are observed, as evidenced by the results from the fractional randomized voting model (FRVM). These findings highlight the potential of the proposed model as an advanced diagnostic tool. Its improved metrics promise heightened accuracy in tumor classification and localization. The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for lung cancer detection, setting it apart from traditional methods with superior performance metrics.

Globally, cardiovascular diseases (CVDs) rank among the leading causes of mortality. One out of every three deaths is attributed to cardiovascular disease, according to new World Heart Federation research. Cardiovascular disease can be caused by a number of factors, including stress, alcohol, smoking, a poor diet, inactivity, and other medical disorders like high blood pressure or diabetes. In contrast, for the vast majority of heart disorders, early diagnosis of associated ailments results in permanent recovery. Using newly developed data analysis technology, examining a patient's medical record could aid in the early detection of cardiovascular disease. Recent work has employed machine learning algorithms to predict cardiovascular illness on clinical datasets. However, because of their enormous dimension and class imbalance, clinical datasets present serious issues. An inventive model is offered in this work for addressing these problems.An efficient decision support system, also known as an assistive system, is proposed in this paper for the diagnosis and classification of cardiovascular disorders. It makes use of an optimisation technique and a deep learning classifier. The efficacy of traditional techniques for predicting cardiovascular disease using medical data is anticipated to advance with the combination of the two methodologies. Deep learning systems can reduce mortality rates by predicting cardiovascular illness based on clinical data and the patient's severity level. For an adequate sample size of synthesized samples, the optimisation process chooses the right parameters to yield the best prediction from an enhanced classifier. The 99.58% accuracy was obtained by the proposed method. Also, PSNR, sensitivity, specificity, and other metrics were calculated in this work and compared with systems that are currently in use.

Rapid urban sprawl adversely impacts the local climate and the ecosystem components. Islamabad, one of South Asia's green and environment-friendly capitals, has experienced major Land Use Land Cover (LULC) changes over the past three decades consequently, elevating the seasonal and annual Land Surface Temperature (LST) in planned and unplanned urban areas. The focus of this study was to quantify the fluctuations in LULC and LST in planned and unplanned urban areas using Landsat data and Machine Learning algorithms involving the Support Vector Machine (SVM) over the 1990–2020 data period. Moreover, hybrid Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were employed to project the future changes in LULC and annual LST, respectively, for the years 2035 and 2050. The findings of the study reveal a distinct difference in seasonal and annual LST in planned and unplanned areas. Results showed an increase of ∼22 % in the built-up area but vegetation and bare soil decreased by ∼10 % and ∼12 %, respectively. Built-up land showed a maximum annual mean LST followed by bare-soil and vegetative surfaces. Seasonal analysis showed that summer months experience the highest LST, followed by spring, autumn and winter. Future projections revealed that the built-up areas (∼27 % in 2020) are likely to increase to ∼37 % and ∼50 %, and the areas under the highest annual mean LST class i.e., ≥28 °C are likely to increase to ∼19 % and ∼21 % in planned, and ∼38 % and ∼42 % in unplanned urban areas for the years 2035 and 2050, respectively. Planned areas have better temperature control with urban green spaces, and controlled infrastructure. The Capital Development Authority of Islamabad may be advised to control the expansion of built-up areas, grow urban forests, and thus mitigate the possible Urban Heat Island (UHI) effect.

Abstract:Introduction: Bezoars are non-dissolved hard masses of undigested or partially digested food or foreign material that are trapped in the gastrointestinal tract. Bezoars are broadly classified into four groups, viz; phytobezoars, trichobezoar, lactobezoars, and pharmacobezoars. One of the common sub-types of phytobezoars is amlookobezoar (diospyrobezoar). Aims: To describe the clinical profile and treatment outcome of a series of patients of amlookobezoar presenting with small bowel obstruction.Methods: This prospective observational case-series study includes five patients of both sexes presenting with small bowel obstruction (SBO) caused due to amlookobezoar. All patients were evaluated with respect to detailed history, physical examination and radiological assessment to confirm the diagnosis. Radiological assessment included X-ray abdomen, USG and CECT abdomen. Results: We had total of five cases of SBO caused due to amlookobezoars. The age of these patients ranged from 8-years to 22-years with male-female ratio of 4:1. All the 5 patients in this series presented with the features of SBO in the autumn months. None of our patients had any other significant predisposing factor for bezoar formation except that all of these patients had taken significant amount of amlooks (persimmon fruit) 3-10days earlier. Four patients eventually required the exploratory laparotomy. All the four patients had soft to firm bezoars stuck up in distal or mid-ileum. Fortunately in all these 4 patients, we were able to manually breakdown the amlookobezoars and milk them down beyond the ileocaecal level without the need for any enterotomy, gut resection or creation of stoma. In all of the 4 operated patients, the postoperative period was almost uneventful. Conclusion: Though uncommon, but amlookobezoars should be considered as differential diagnosis in a case of small bowel obstruction. Amlookobezoars commonly affects young adolescent males in the autumn months and often needs a laparotomy with fragmentation and milking down of amlookobezoars.

The journal retracts the article “Continual Learning Approach for Continuous Data Stream Analysis in Dynamic Environments” [...]

One of the challenging problems in mobile robotics is mapping a dynamic environment for navigating robots. In order to disambiguate multiple moving obstacles, state-of-art techniques often solve some form of dynamic SLAM (Simultaneous Localization and Mapping) problem. Unfortunately, their higher computational complexity press the need for simpler and more efficient approaches suitable for real-time embedded systems. In this paper, we present a ROS-based efficient algorithm for constructing dynamic maps, which exploits the spatial-temporal locality for detecting and tracking moving objects without relying on prior knowledge of their geometrical features. A two-prong contribution of this work is as follows: first, an efficient scheme for decoding sensory data into an estimated time-varying object boundary that ultimately decides its orientation and trajectory based on the iteratively updated robot Field of View (FoV); second, lower time-complexity of updating the dynamic environment through manipulating spatial-temporal locality available in the object motion profile. Unlike existing approaches, the snapshots of the environment remain constant in the number of moving objects. We validate the efficacy of our algorithm on both V-Rep simulations and real-life experiments with a wide array of dynamic environments. We show that the algorithm accurately detects and tracks objects with a high probability as long as sensor noise is low and the speed of moving objects remains within acceptable limits.

Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A drift detection scenario is set forth to develop methods and strategies for detecting, interpreting, and adapting to conceptual changes over data streams. Machine learning approaches can produce poor learning outcomes in the conceptual change environment if the sudden change is not addressed. Furthermore, due to developments in concept drift, learning methodologies have been significantly systematic in recent years. The research introduces a novel approach using the fully connected committee machine (FCM) and different activation functions to address conceptual changes in continuous data streams. It explores scenarios of continual learning and investigates the effects of over-learning and weight decay on concept drift. The findings demonstrate the effectiveness of the FCM framework and provide insights into improving machine learning approaches for continuous data stream analysis. We used a layered neural network framework to experiment with different scenarios of continual learning on continuous data streams in the presence of change in the data distribution using a fully connected committee machine (FCM). In this research, we conduct experiments in various scenarios using a layered neural network framework, specifically the fully connected committee machine (FCM), to address conceptual changes in continuous data streams for continual learning under a conceptual change in the data distribution. Sigmoidal and ReLU (Rectified Linear Unit) activation functions are considered for learning regression in layered neural networks. When the layered framework is trained from the input data stream, the regression scheme changes consciously in all scenarios. A fully connected committee machine (FCM) is trained to perform the tasks described in continual learning with M hidden units on dynamically generated inputs. In this method, we run Monte Carlo simulations with the same number of units on both sides, K and M, to define the advancement of intersections between several hidden units and the calculation of generalization error. This is applied to over-learnability as a method of over-forgetting, integrating weight decay, and examining its effects when a concept drift is presented.

The concept of ghost/Khatith ileostomy is a bridge between covering ileostomy and no-ileostomy (‘Khatith’ meaning ‘hidden’ in Kashmiri language). We performed the pre-stage ghost ileostomy (GI) without parietal wall split. The technique of GI is that after the completion of resection-anastomosis of rectal cancer, a terminal ileal loop at about 20cm from ileocecal junction is identified. Small (10-12F) Ryle’s tube (RT) is passed through a small opening in the mesentery of the identified ileal loop. A small 4-5mm incision is given on abdominal wall at pre-operatively marked proposed stoma site in right iliac fossa region. Haemostatic Kelly’s forceps is introduced through this small incision to get out the two limbs of the RT that has been already looped around the identified ileal loop. These two limbs of the RT are cut short and fixed to each other and to the skin around it with 2-0 silk sutures, taking care to keep the tubing loop loose enough to avoid any tension to the vascular supply of the ileal loop and without causing any luminal compression of the loop to avoid bowel obstruction. In case of AL, the pre-stage GI can be converted into a formal covering stoma under local or spinal anesthesia by gentle pull of the two limbs of the looped RT to extract the isolated ileal loop through an adequate circular incision around the site of GI. In case of uncomplicated postoperative course, the fixing RT is pulled out gently from the abdominal cavity to release down the GI.

Healthcare comprises the largest revenue and data boom markets. Sharing knowledge about healthcare is crucial for research that can help healthcare providers and patients. Several cloud-based applications have been suggested for data sharing in healthcare. However, the trustworthiness of third-party cloud providers remains unclear. The third-party dependency problem was resolved using blockchain technology. The primary objective of this growth was to replace the distributed system with a centralized one. Therefore, security is a critical requirement for protecting health records. Efforts have been made to implement blockchain technology to improve the security of this sensitive material. However, existing methods depend primarily on information obtained from medical examinations. Furthermore, they are ineffective for sharing continuously produced data streams from sensors and other monitoring devices. We propose a trustworthy access control system that uses smart contracts to achieve greater security while sharing electronic health records among various patients and healthcare providers. Our concept offers an active resolution for secure data sharing in mobility computing while protecting personal health information from potential risks. In assessing existing data sharing models, the framework valuation and protection approach recognizes increases in the practicality of lightweight access control architecture, low network expectancy, and significant levels of security and data concealment.

Abstract Miscible CO2-based enhanced oil recovery (CO2-EOR) flooding has been used, on many occasions, to maximize the recovery by mobilizing the residual oil. The success of this method, however, depends highly on the minimum miscibility pressure (MMP), the compatibility of CO2 with the reservoir rock and fluids. Although there have been several studies on the application of CO2-EOR, the impact of oil compositions associated with MMP on the feasibility of this technique has not been addressed elsewhere. In this study, a simulation study was carried out to investigate the effect of three different oil compositions on the miscibility and ultimate oil recovery of miscible CO2-EOR method. The results obtained indicated the oil type does not pose a significant impact on the success of miscible CO2-EOR. It was also found that the ultimate recovery of oil increases in a short period of time by injecting CO2 at the miscible conditions at the early stage of natural production. Comparatively, the reservoir with the fluid sample S2 seems to be a suitable choice for the miscible CO2 flooding once the recovery factor was considered together with the total amount of the fluid injected and produced.

Wireless sensor networks (WSNs) are used for recording the information from the physical surroundings and transmitting the gathered records to a principal location via extensively disbursed sensor nodes. The proliferation of sensor devices and advances in size, deployment costs, and user-friendly interfaces have spawned numerous WSN applications. The WSN should use a routing protocol to send information to the sink over a low-cost link. One of the foremost vital problems is the restricted energy of the sensing element and, therefore, the high energy is consumed throughout the time. An energy-efficient routing may increase the lifetime by consuming less energy. Taking this into consideration, this paper provides a multi-criteria clustering and optimal bio-inspired routing algorithmic rule to reinforce network lifetime, to increase the operational time of WSN-based applications and make robust clusters. Clustering is a good methodology of information aggregation that increases the lifetime by group formation. Multi-criteria clustering is used to select the optimal cluster head (CH). After proper selection of the CH, moth flame and salp swarm optimization algorithms are combined to analyze the quality route for transmitting information from the CH to the sink and expand the steadiness of the network. The proposed method is analyzed and contrasted with previous techniques, with parameters such as energy consumption, throughput, end-to-end delay, latency, lifetime, and packet delivery rate. Consumption of energy is minimized by up to 18.6% and network life is increased up to 6% longer compared to other routing protocols.

The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network with fish swarm optimization (GG-Conv_Clus-FSO) in energy optimization of the network is compared with existing state-of-the-art protocols, and GG-Conv_Clus-FSO outperforms the existing approaches, whereby the packet delivery ratio (PDR) is enhanced by 95.14%.

Hepatitis B Virus is partially double standard DNA virus. It is a species of the genus Orthohepadnavirus and a member of the viral family Hepadnaviridae. This virus induces chronic liver infection known as hepatitis B which can finally leads to liver damage. Viral hepatitis is contagious infection it may transmit via blood transfusion, parental routes, and contaminated body fluids like saliva and semen. The current study was designed to determine the burden of HBV infection among people belongs to different areas of district Buner Khyber Pakhtunkhwa. A total of 6137 samples were collected from January 2017 to March 2018 in different Tehsils of district Buner. All the samples were tested for Anti-HBV antibodies using ICT and positive samples were then subjected to PCR to confirm active cases. About 139 (2.26%) individuals were found positive for antibodies in their sera and 107 (1.74%) were reconfirmed as active infection via PCR. On gender-based study, highest prevalence of HBV was found in male population 1.66%, while maximum incidence of infection was found in age group 1 month to 15 years 2.83%. According to the current study, the prevalence rate of HBV infection was low in Buner as compare to other districts of KP. The decreasing rate of HBV infection in Buner indicates awareness among the population about HBV infection.

Abstract The Clustered Regularly Interspaced Short Palindromic Repeats–Cas-9 (CRISPR-Cas9) system has been a revolutionising tool in the field of molecular genetics, which provides a versatile range of editing potentials. Researchers can produce breaks or alter genomes with ease using the system. Cancer is one of the multi-gene diseases whose genes need to be studied in detail. The CRISPR-Cas9 technology may also provide a promising potential in the field of cancer genetics. The current narrative review comprised 50 research articles which were keenly analysed and the applications and outcomes of CRISPR-Cas9 system in cancer genetics were comprehensively and critically discussed. It was concluded that application of the system had great potential to help understand cancer biology of various types and could be used for its genetic modelling. However, much work is still needed to be done to apply the technology for understanding...Continuous...

Aims: Current study was aimed to produce nitrogen fixing Azotobacter sp. (SR-4) and phosphorus solubilizers Aspergillus niger (A. niger) and to evaluate their efficiency as biofertilizers for agricultural practices. Methods: Two biofertilizer including nitrogen fixing and phosphorus solubilizing were grown. The nitrogen fixing efficiency of Azotobacter (SR-4) was determined by Kjeldahl method. Similarly, Vanadomoybdate method was used to measure the soluble phosphorus while Heinonen method was used to analyze concentration of phytase and phosphatase in the cultures. Furthermore, both biofertilizers were tested in a field trail on Lagenaria siceraria (bottle gourd) and Abelmoschus esculentus (okra). Results: The Azotobacter (SR-4) strain was found efficient nitrogen fixer as 35.08 mg of nitrogen per gram of carbon was produced after 72 h of fermentation. Similarly, A. niger strain excrete extracellular phosphate solubilizing enzymes such as phytase (133UI in 48 h of fermentation) and phosphatase (170UI in 48 h of fermentation) which can solubilize the rock phosphate and make it available to plants. In field trials on selected plants (L. siceraria and A. esculentus), both biofertilizers showed significant increase in plant height, leaf length/width, fruit size and number of fruits per plant when compared with controls/untreated plants. Furthermore, plants co-inoculated with both the N fixing Azotobacter and phosphorus solubilizing A. niger have enhanced performance than those treated with each biofertilizer alone. Conclusion: The inoculation of seeds with A. niger and Azotobacter may replace costly and environment toxic chemical fertilizers with environment friendly and cost effective biofertilizers. Keywords: Biofertilizers, Azotobacter, A. niger, Bottle gourd and okra