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Background. Biomarkers that predict posttransplant alloimmunity could lead to improved long-term graft survival. Evaluation of the number of mismatched epitopes between donor and recipient HLA proteins, termed molecular mismatch analysis, has emerged as an approach to classify transplant recipients as having high, intermediate, or low risk of graft rejection. When high-resolution genotypes are unavailable, molecular mismatch analysis requires algorithmic assignment, or imputation, of a high-resolution genotyping. Although imputation introduces inaccuracies in molecular mismatch analyses, it is unclear whether these inaccuracies would impact the clinical risk assessment for graft rejection. Methods. Using renal transplant patients and donors from our center, we constructed cohorts of surrogate donor-recipient pairs with high-resolution and low-resolution HLA genotyping that were racially concordant or discordant. We systemically assessed the impact of imputation on molecular mismatch analysis for cohorts of 180–200 donor-recipient pairs for each of 4 major racial groups. We also evaluated the effect of imputation for a racially diverse validation cohort of 35 real-world renal transplant pairs. Results. In the surrogate donor-recipient cohorts, imputation preserved the molecular mismatch risk category for 90.5%–99.6% of racially concordant donor-recipient pairs and 92.5%–100% of racially discordant pairs. In the validation cohort, which comprised 72% racially discordant pairs, we found that imputation preserved the molecular mismatch risk category for 97.1% of pairs. Conclusions. Overall, these data demonstrate that imputation preserves the molecular mismatch risk assessment in the vast majority of cases and provides evidence supporting imputation in the performance of molecular mismatch analysis for clinical assessment.

Competing Interests: Declaration of competing interest The authors declare no competing interests.

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches' challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.

Background and Objectives: The impact of neoadjuvant immunotherapy (NIT) on overall survival (OS) in patients with resectable stage III melanoma remains unknown. We sought to identify factors associated with receipt of NIT and survival outcomes in patients with clinical stage III melanoma undergoing surgery., Methods: The National Cancer Database (2016-2020) was used to identify patients with clinical stage III melanoma who underwent surgery and received either NIT or adjuvant immunotherapy (AIT) only. Multivariable regression, Kaplan-Meier, and Cox proportional hazard methods were used to analyze variables of interest., Results: Patients with clinical N3 disease had 2.5 times the odds of NIT compared to those with N1 disease (95% CI 1.74-3.49). There was no difference in 3-year OS between the two cohorts: 79% (95% CI 73%-85%) for NIT patients and 75% (95% CI 73%-76%) for AIT patients (p = 0.078). Patients with N2/N3 disease had improved 3-year OS of 79% with NIT versus 71% for AIT-only (HR 0.61, 95% CI 0.38-0.97, p = 0.037)., Conclusions: NIT is given more selectively to clinical stage III patients with more advanced N category disease. Despite significant differences in N category between groups, there was no difference in OS observed at 3 years, and NIT was associated with a survival advantage among N2/N3 patients., (© 2024 The Author(s). Journal of Surgical Oncology published by Wiley Periodicals LLC.)

In the rice-based system of mid-latitudes, mineral nitrogen (N) fertilizer serves as the largest source of the N cycle due to an insufficient supply of N from organic sources causing higher N losses due to varying soil and environmental factors. However, aiming to improve soil organic matter (OM) and nutrients availability using the best environmentally, socially, and economically sustainable cultural and agronomic management practices are necessary. This study aimed to enhance nitrogen use efficiency (NUE) and grain yield in rice-based systems of mid-latitudes by partially replacing inorganic N fertilizer with organic inputs. A randomized complete block design (RCBD) was employed to evaluate the effects of sole mineral N fertilizer (urea) and its combinations with organic sources-farmyard manure (FYM) and poultry compost-on different elite green super rice (GSR) genotypes and were named as NUYT-1, NUYT-2, NUYT-3, NUYT-4, NUYT-5, and NUYT-6. The study was conducted during the 2022 and 2023 rice growing seasons at the Rice Research Program, Crop Sciences Institute (CSI), National Agricultural Research Centre (NARC), Islamabad, one of the mid-latitudes of Pakistan. The key objective was to determine the most effective N management strategy for optimizing plant growth, N content in soil and plants, and overall crop productivity. The results revealed that the combined application of poultry compost and mineral urea significantly enhanced soil and leaf N content (1.36 g kg - 1 and 3.06 mg cm - 2 , respectively) and plant morphophysiological traits compared to sole urea application. Maximum shoot dry weight (SDW) and root dry weight (RDW) were observed in compost-applied treatment with the values of 77.62 g hill - 1 and 8.36 g hill - 1 , respectively. The two-year mean data indicated that applying 150 kg N ha⁻ 1 , with half provided by organic sources (10 tons ha⁻ 1 FYM or poultry compost) and the remainder by mineral urea, resulted in the highest N uptake, utilization, and plant productivity. Thus, integrated management of organic carbon sources and inorganic fertilizers may sustain the productivity of rice-based systems more eco-efficiently. Further research is recommended to explore root and shoot morphophysiological, molecular, and biochemical responses under varying N regimes, aiming to develop N-efficient rice varieties through advanced breeding programs., (© 2024. The Author(s).)

In alkaline calcareous soils, ammonia volatilization is the primary nitrogen (N) loss process, resulting in the reduced N use efficiency of crops. This study aimed at assessing the impact of different N sources for top dressing on ammonia volatilization, as well as their effects on wheat growth and yield over two years. In each year, half of the recommended N was applied as a basal dose using diammonium phosphate (DAP) and urea. The remaining half was top-dressed 35 days after sowing with various sources: prilled urea (PU), granular urea (GU), ammonium sulfate (AS), and calcium ammonium nitrate (CAN) in the first year; PU, urea coated with a urease inhibitor from 20 g (VnU-20) and 40 g (VnU-40) leaves of Vachellia nilotica , biochar-coated urea (BU), and urease inhibitor paraphenylenediamine-coated urea (PPDU) in the second year. Ammonia volatilization losses were tracked for up to 12 weeks from sowing. Ammonia losses from basal-applied N remained consistent in both years, comprising around 4% of the applied N. In the first year, top-dressed AS resulted in the highest losses, followed by GU, while losses from urea and CAN were statistically similar. In the second year, coated fertilizers showed lower ammonia losses compared to PU, with VnU-40 displaying the least losses, 48% less than PU. Nitrogen concentration in wheat grain and straw exhibited a negative correlation with ammonia losses. The choice of top-dressed N source influenced tillering, biological, straw, and grain yields of wheat. In the first year, CAN provided maximum yield benefits, and in the second year, VnU-20 exhibited 27% more grain yield than PU. These findings suggest that top dressing with coated urea, especially VnU-20, has the potential to reduce ammonia losses, improve crop nitrogen status, and enhance economic yield compared to other nitrogen sources., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2024 Muhammad Sajid Farooq et al.)

Saffron is the world's most expensive and legendary crop that is widely used in cuisine, drugs, and cosmetics. Therefore, the demand for saffron is increasing globally day by day. Despite its massive demand the cultivation of saffron has dramatically decreased and grown in only a few countries. Saffron is an environment-sensitive crop that is affected by various factors including rapid change in climate, light intensity, pH level, soil moisture, salinity level, and inappropriate cultivation techniques. It is not possible to control many of these environmental factors in traditional farming. Although, many innovative technologies like Artificial Intelligence and Internet of Things (IoT) have been used to enhance the growth of saffron still, there is a dire need for a system that can overcome primary issues related to saffron growth. In this research, we have proposed an IoT-based system for the greenhouse to control the numerous agronomical variables such as corm size, temperature, humidity, pH level, soil moisture, salinity, and water availability. The proposed architecture monitors and controls environmental factors automatically and sends real-time data from the greenhouse to the microcontroller. The sensed values of various agronomical variables are compared with threshold values and saved at cloud for sending to the farm owner for efficient management. The experiment results reveal that the proposed system is capable to maximize saffron production in the greenhouse by controlling environmental factors as per crop needs., (© 2024. The Author(s).)

In today's fast-moving world of information technology (IT), software professionals are crucial for a company's success. However, they frequently experience low motivation as a result of competitive pressures, unclear incentives, and communication gaps. This underscores the critical need to handle these internal marketing challenges such as employee motivation, development, and engagement in IT organizations. Internal marketing practices aiming at attracting, engaging, and inspiring employees to use excellent services have become increasingly important. Internal marketing is attracting, engaging, and motivating employees as internal customers to utilize their quality services. Gamification has emerged as a significant trend over recent years. Despite the expanding use of gamification in the workplace, there is still a lack of focus on internal marketing tactics that incorporate gamification approaches. Thus, addressing the challenges related to employee motivation, development, and engagement is crucial. Therefore, as a principal contribution, this research presents a comprehensive framework designed to implement gamified solutions for software teams of IT organizations. This framework has been tailored to effectively address the challenges posed by internal marketing by optimizing motivation, development, and engagement. Moreover, the framework is applied to design and implement a gamified work portal (GWP) through a systematic process, including the design of low-fidelity and high-fidelity prototypes. Additionally, the GWP is validated through a quasi-experiment involving IT professionals from different IT organizations to authenticate the effectiveness of framework. Finally, the outclass results obtained by the gamification-based GWP highlight the effectiveness of the proposed gamification approach in enhancing development, motivation, and engagement while fostering ongoing knowledge of the employees., Competing Interests: The authors declare that they have no competing interests., (© 2024 Obaid and Farooq.)

Mountain landscapes can be fragmented due to various human activities such as tourism, road construction, urbanization, and agriculture. It can also be due to natural factors such as flash floods, glacial lake outbursts, land sliding, and climate change such as rising temperatures, heavy rains, or drought.The study's objective was to analyze the mountain landscape ecology of Pir Chinasi National Park under anthropogenic influence and investigate the impact of anthropogenic activities on the vegetation. This study observed spatiotemporal changes in vegetation due to human activities and associated climate change for the past 25 years (1995-2020) around Pir Chinasi National Park, Muzaffrabad, Pakistan. A structured questionnaire was distributed to 200 residents to evaluate their perceptions of land use and its effects on local vegetation. The findings reveal that 60% of respondents perceived spatiotemporal pressure on the park. On the other hand, the Landsat-oriented Normalized Difference Vegetation Index (NDVI) was utilized for the less than 10% cloud-covered images of Landsat 5, 7, and 8 to investigate the vegetation degradation trends of the study area. During the entire study period, the mean maximum NDVI was approximately 0.28 in 1995, whereas the mean minimum NDVI was - 2.8 in 2010. QGIS 3.8.2 was used for the data presentation. The impact of temperature on vegetation was also investigated for the study period and increasing temperature trends were observed. The study found that 10.81% (1469.08 km 2 ) of the area experienced substantial deterioration, while 23.57% (3202.39 km 2 ) experienced minor degradation. The total area of degraded lands was 34.38% (or 4671.47 km 2 ). A marginal improvement in plant cover was observed in 24.88% of the regions, while 9.69% of the regions experienced a major improvement. According to the NDVI-Rainfall relationships, the area was found to be significantly impacted by human pressures and activities (r ≤ 0.50) driving vegetation changes covering 24.67% of the total area (3352.03 km 2 ). The area under the influence of climatic variability and change (r ≥ 0.50 ≥ 0.90) accounted for 55.84% (7587.26 km 2 ), and the area under both climatic and human stressors (r ≥ 0.50 < 0.70) was 64%. Sustainable land management practices of conservation tillage, integrated pest management, and agroforestry help preserve soil health, water quality, and biodiversity while reducing erosion, pollution, and the degradation of natural resources. landscape restoration projects of reforestation, wetland restoration, soil erosion control, and the removal of invasive species are essential to achieve land degradation neutrality at the watershed scale., (© 2024. The Author(s).)

Melanoma is the fifth most common cancer in the United States and accounts for the majority of all skin cancer-related deaths, making it the most lethal cutaneous malignancy. Systemic adjuvant therapy for stage IIB-IV melanoma is now approved for patients who have undergone surgical resection, given the appreciable risk of recurrence and mortality in this patient population. Despite the lower stage, high-risk stage II melanoma (stage IIB/IIC) can often exhibit an even more aggressive course when compared to stage IIIA/IIIB disease, thus justifying consideration of adjuvant therapy in these patients. In this review, we highlight the current standard of practice for the treatment of stage IIB/C melanoma, with a focus on adjuvant therapies supported by published landmark clinical trials, including anti-PD-1 therapy. Notably, adjuvant therapies approved thus far in this patient population have demonstrated an improvement in recurrence-free survival, while their impact on overall survival is pending. Finally, this review highlights currently ongoing trials and future directions for research and treatment possibilities for high-risk clinical stage II melanoma.

Leukemia is a type of blood cell cancer that is in the bone marrow's blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy., (© 2024. The Author(s).)

Soilless agriculture is acknowledged worldwide because it uses organic leftovers as a means of supporting intensive and efficient plant production. However, the quality of potting media deteriorates because of lower nutrient content and excessive shrinkage of most organic materials. A current study was undertaken to identify the optimal blend of locally available organic materials with desirable qualities for use as potting media. Therefore, different ingredients, viz., Pinus roxburghii needles, sugarcane bagasse, and farmyard manure were used alone or in combination as potting media to test their suitability by growing spinach as a test crop. Results showed that an increase in Pinus roxburghii needles and sugarcane bagasse decreased medium pH and electrical conductivity. Higher pH and electrical conductivity were recorded for the treatments having a higher farmyard manure ratio (≥50%) in combination. Except for pine needles 100%, pH and electrical conductivity were in the recommended range. The growth attributes include, leaves plant-1, shoot length, fresh- and dry shoot weight along with plant macronutrients (nitrogen, phosphorous, and potassium) and micronutrients (iron, copper, manganese, and zinc) content were higher in treatment pine needles 50%+farmyard manure 50% followed by pine needles 25%+farmyard manure 50%+sugarcane bagasse 25%. Moreover, the particular treatment of pine needles 50%+farmyard manure 50% exhibited the highest concentrations of macro- (nitrogen, phosphorus, and potassium) as well as micronutrients (iron, copper, manganese, and zinc) in the potting media following the harvest. This study highlights the potential of utilizing agro-industrial litter/waste as a soilless growing medium for spinach production under greenhouse conditions. When employed in appropriate proportions, this approach not only addresses disposal concerns but also proves effective for sustainable cultivation. Further research is needed to investigate the use of these wastes as potting media by mixing various particle-size ingredients., Competing Interests: The authors declare they have no known conflict or competing interest., (Copyright: © 2024 Alam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Thyroid dysfunction is common in older people, with females at higher risk. Evidence suggests that thyroid-stimulating hormone (TSH) levels naturally increase with age. However, as uniform serum TSH reference ranges are applied across the adult lifespan, subclinical hypothyroidism (SCH) diagnosis is more likely in older people, with some individuals also being commenced treatment with levothyroxine (LT4). It is unclear whether LT4 treatment in older people with SCH is associated with adverse cardiovascular or bone health outcomes., Methods: A systematic review and meta-analysis were performed to synthesise previous studies evaluating cardiovascular and bone health outcomes in older people with SCH, comparing LT4 treatment with no treatment. PubMed, Embase, Cochrane Library, MEDLINE, and Web of Science databases were searched from inception until March 13, 2023, and studies that evaluated cardiovascular and bone health events in people with SCH over 50 years old were selected., Results: Six articles that recruited 3853 participants were found, ranging from 185 to 1642 participants, with the proportion of females ranging from 45 to 80%. The paucity of data resulted in analysis for those aged over 65 years only. Additionally, a study with 12,212 participants aged 18 years and older was identified; however, only data relevant to patients aged 65 years and older were considered for inclusion in the systematic review. Of these 7 studies, 4 assessed cardiovascular outcomes, 1 assessed bone health outcomes, and 2 assessed both. A meta-analysis of cardiovascular outcomes revealed a pooled hazard ratio of 0.89 (95% CI 0.71-1.12), indicating no significant difference in cardiovascular risk between older individuals with SCH treated with LT4 compared to those without treatment. Due to overlapping sub-studies, meta-analysis for bone health outcomes was not possible., Conclusions: This systematic review and meta-analysis found no significant association between LT4 use and cardiovascular and bone health outcomes in SCH participants over 65 years., Systematic Review Registration: PROSPERO CRD42022308006., (© 2024. The Author(s).)

Rice (Oryza sativa L.) is an important member of the family Poaceae and more than half of world population depend for their dietary nutrition on rice. Rice cultivars with higher yield, resilience to stress and wider adaptability are essential to ensure production stability and food security. The fundamental objective of this study was to identify higher-yielding rice genotypes with stable performance and wider adaptability in a rice growing areas of Pakistan. A triplicate RCBD design experiment with 20 Green Super Rice (GSR) advanced lines was conducted at 12 rice growing ecologies in four Provinces of Pakistan. Grain yield stability performance was assessed by using different univariate and multivariate statistics. Analysis of variance revealed significant differences among genotypes, locations, and G x E interaction for mean squares (p < 0.05) of major yield contributing traits. All the studied traits except for number of tillers per plant revealed higher genotypic variance than environmental variance. Broad sense heritability was estimated in the range of 44.36% to 98.60%. Based on ASV, ASI, bi, Wi 2 , σ 2 i and WAAS statistics, the genotypes G1, G4, G5, G8, G11 and G12 revealed lowest values for parametric statistics and considered more stable genotypes based on  paddy yield. The additive main effects and multiplicative interaction (AMMI) model revealed significant variation (p < 0.05) for genotypes, non-signification for environment and highly significant for G × E interaction. The variation proportion of PC1 and PC2 from interaction revealed 67.2% variability for paddy yield. Based on 'mean verses stability analysis of GGE biplot', 'Which-won-where' GGE Biplot, 'discriminativeness vs. representativeness' pattern of stability, 'IPCA and WAASB/GY' ratio-based stability Heat-map, and ranking of genotypes, the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 were observed ideal genotypes with yield potential more than 8 tons ha -1 . Discriminativeness vs. representativeness' pattern of stability identifies two environments, E5 (D.I Khan, KPK) and E6 (Usta Muhammad, Baluchistan) were best suited for evaluating genotypic yield performance. Based on these findings we have concluded that the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 could be included in the commercial varietal development process and future breeding program., (© 2024. The Author(s).)

Software Development based on Scrum Agile in a distributed development environment plays a pivotal role in the contemporary software industry by facilitating software development across geographic boundaries. However, in the past different frameworks utilized to address the challenges like communication and collaboration in scrum agile distributed software development (SADSD) were notably inadequate in transparency, security, traceability, geographically dispersed location work agreements, geographically dispersed teamwork effectiveness, and trust. These deficiencies frequently resulted in delays in software development and deployment, customer dissatisfaction, canceled agreements, project failures, and disputes over payments between customers and development teams. To address these challenges of SADSD, this paper proposes a new framework called ChainAgile, which leverages blockchain technology. ChainAgile employs a private Ethereum blockchain to facilitate the execution of smart contracts. These smart contracts cover a range of functions, including acceptance testing, secure payments, requirement verification, task prioritization, sprint backlog, user story design and development and payments with the automated distribution of payments via digital wallets to development teams. Moreover, in the ChainAgile framework, smart contracts also play a pivotal role in automatically imposing penalties on customers for making late payments or for no payments and penalties on developers for completing the tasks that exceed their deadlines. Furthermore, ChainAgile effectively addresses the scalability limitations intrinsic in blockchain technology by incorporating the Interplanetary File System (IPFS) is used for storage solutions as an off-chain mechanism. The experimental results conclusively show that this innovative approach substantially improves transparency, traceability, coordination, communication, security, and trust for both customers and developers engaged in scrum agile distributed software development (SADSD)., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Qureshi, Farooq. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The wheat crop that fulfills 35% of human food demand is facing several problems due to a lack of transparency, security, reliability, and traceability in the existing agriculture supply chain. Many systems have been developed for the agriculture supply chain to overcome such issues, however, monopolistic centralized control is the biggest hurdle to realizing the use of such systems. It has eventually gained consumers' trust in branded products and rejected other products due to the lack of traceable supply chain information. This study proposes a blockchain-based framework for supply chain traceability which provides trustable, transparent, secure, and reliable services for the wheat crop. A crypto token called wheat coin (WC) has been introduced to keep track of transactions among the stakeholders of the wheat supply chain. Moreover, an initial coin offering (ICO) of WC, crypto wallets, and an economic model are proposed. Furthermore, a smart contract-based transaction system has been devised for the transparency of wheat crop transactions and conversion of WC to fiat and vice versa. We have developed the interplanetary file system (IPFS) to improve data availability, security, and transparency which stores encrypted private data of farmers, businesses, and merchants. Lastly, the results of the experiments show that the proposed framework shows better performance as compared to previous crop supply chain solutions in terms of latency to add-blocks, per-minute transactions, average gas charge for the transaction, and transaction verification time. Performance analysis with Bitcoin and Ethereum shows the superior performance of the proposed system., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Farooq et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Manipulation of the subcellular localization of transcription factors by preventing their shuttling via the nuclear pore complex (NPC) emerges as a novel therapeutic strategy against cancer. One transmembrane component of the NPC is POM121, encoded by a tandem gene locus POM121A/C on chromosome 7. Overexpression of POM121 is associated with metabolic diseases (e.g., diabetes) and unfavorable clinical outcome in patients with colorectal cancer (CRC). Peroxisome proliferator-activated receptor-gamma (PPARγ) is a transcription factor with anti-diabetic and anti-tumoral efficacy. It is inhibited by export from the nucleus to the cytosol via the RAS-RAF-MEK1/2-ERK1/2 signaling pathway, a major oncogenic driver of CRC. We therefore hypothesized that POM121 participates in the transport of PPARγ across the NPC to regulate its transcriptional activity on genes involved in metabolic and tumor control. We found that POM121A/C mRNA was enriched and POM121 protein co-expressed with PPARγ in tissues from CRC patients conferring poor prognosis. Its interactome was predicted to include proteins responsible for tumor metabolism and immunity, and in-silico modeling provided insights into potential 3D structures of POM121. A peptide region downstream of the nuclear localization sequence (NLS) of POM121 was identified as a cytoplasmic interactor of PPARγ. POM121 positivity correlated with the cytoplasmic localization of PPARγ in patients with KRAS mutant CRC. In contrast, POM121A/C silencing by CRISPR/Cas9 sgRNA or siRNA enforced nuclear accumulation of PPARγ and activated PPARγ target genes promoting lipid metabolism and cell cycle arrest resulting in reduced proliferation of human CRC cells. Our data suggest the POM121-PPARγ axis as a potential drugable target in CRC., (© 2024. The Author(s).)

Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing., (© 2023 The Author(s).)

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

Background: Alkaline-salt is one of the abiotic stresses that slows plant growth and developmental processes and threatens crop yield. Long non-coding RNAs (lncRNAs) are endogenous RNA found in plants that engage in a variety of cellular functions and stress responses., Method: lncRNAs act as competing endogenous RNAs (ceRNA) and constitute a new set of gene control. The precise regulatory mechanism by which lncRNAs function as ceRNAs in response to alkaline-salt stress remains unclear. We identified alkaline-salt responsive lncRNAs using transcriptome-wide analysis of two varieties including alkaline-salt tolerant [WD20342 (WD)] and alkaline-salt sensitive [Caidao (CD)] rice cultivar under control and alkaline-salt stress treated [WD20342 (WDT, and Caidao (CDT)] conditions., Results: Investigating the competitive relationships between mRNAs and lncRNAs, we next built a ceRNA network involving lncRNAs based on the ceRNA hypothesis. Expression profiles revealed that a total of 65, 34, and 1549 differentially expressed (DE) lncRNAs, miRNAs, and mRNAs were identified in alkaline-salt tolerant WD (Control) vs. WDT (Treated). Similarly, 75 DE-lncRNAs, 34 DE-miRNAs, and 1725 DE-mRNAs (including up-regulated and down-regulated) were identified in alkaline-salt sensitive CD (Control) vs. CDT (Treated), respectively. An alkaline-salt stress ceRNA network discovered 321 lncRNA-miRNA-mRNA triplets in CD and CDT, with 32 lncRNAs, 121 miRNAs, and 111 mRNAs. Likewise, 217 lncRNA-miRNA-mRNA triplets in WD and WDT revealed the NONOSAT000455-osa&#95;miR5809b-LOC&#95;Os11g01210 triplet with the highest degree as a hub node with the most significant positive correlation in alkaline-salt stress response., Conclusion: The results of our investigation indicate that osa-miR5809b is dysregulated and plays a part in regulating the defense response of rice against alkaline-salt stress. Our study highlights the regulatory functions of lncRNAs acting as ceRNAs in the mechanisms underlying alkaline-salt resistance in rice., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults)., (© 2023. The Author(s).)

The Internet of things (IoT) is an emerging technology that enables ubiquitous devices to connect with the Internet. IoT technology has revolutionized the medical and healthcare industry by interconnecting smart devices and sensors. IoT-based devices and biosensors are ideal to detect diabetes disease by collecting the accurate value of glucose continuously. Diabetes is one of the well-known and major chronic diseases that has a worldwide social impact on community life. Blood glucose monitoring is a challenging task, and there is a need to propose a proper architecture of the noninvasive glucose sensing and monitoring mechanism, which could make diabetic people aware of self-management techniques. This survey presents a rigorous discussion of diabetes types and presents detection techniques based on IoT technology. In this research, an IoT-based healthcare network infrastructure has been proposed for monitoring diabetes disease based on big data analytics, cloud computing, and machine learning. The proposed infrastructure could handle the symptoms of diabetes, collect data, analyze it, and then transmit the results to the server for the next action. Besides, presented an inclusive survey on IoT-based diabetes monitoring applications, services, and proposed solutions. Furthermore, based on IoT technology the diabetes disease management taxonomy has also been presented. Finally, presented the attacks taxonomy as well as discussed challenges, and proposed a lightweight security model in order to secure the patient's health data., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians' ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.

The major challenge faced by autonomous vehicles today is driving through busy roads without getting into an accident, especially with a pedestrian. To avoid collision with pedestrians, the vehicle requires the ability to communicate with a pedestrian to understand their actions. The most challenging task in research on computer vision is to detect pedestrian activities, especially at nighttime. The Advanced Driver-Assistance Systems (ADAS) has been developed for driving and parking support for vehicles to visualize sense, send and receive information from the environment but it lacks to detect nighttime pedestrian actions. This article proposes a framework based on Deep Reinforcement Learning (DRL) using Scale Invariant Faster Region-based Convolutional Neural Networks (SIFRCNN) technologies to efficiently detect pedestrian operations through which the vehicle, as agents train themselves from the environment and are forced to maximize the reward. The SIFRCNN has reduced the running time of detecting pedestrian operations from road images by incorporating Region Proposal Network (RPN) computation. Furthermore, we have used Reinforcement Learning (RL) for optimizing the Q-values and training itself to maximize the reward after getting the state from the SIFRCNN. In addition, the latest incarnation of SIFRCNN achieves near-real-time object detection from road images. The proposed SIFRCNN has been tested on KAIST, City Person, and Caltech datasets. The experimental results show an average improvement of 2.3% miss rate of pedestrian detection at nighttime compared to the other CNN-based pedestrian detectors.

Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students' interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE., Competing Interests: Conflict of interestNone., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Brassica napus L. (canola, oil seed rape) is one of the world's most important oil seed crops. In the last four decades, the discovery of cytoplasmic male-sterility (CMS) systems and the restoration of fertility ( Rf ) genes in B. napus has improved the crop traits by heterosis. The homologs of Rf genes, known as the restoration of fertility-like ( RFL ) genes, have also gained importance because of their similarities with Rf genes. Such as a high non-synonymous/synonymous codon replacement ratio (dN/dS), autonomous gene duplications, and a possible engrossment in fertility restoration. B. napus contains 53 RFL genes on chromosomes A9 and C8. Our research aims to study the function of BnaRFL11 in fertility restoration using the CRISPR/Cas9 genome editing technique. A total of 88/108 (81.48%) T 0 lines, and for T 1 , 110/145 (75%) lines carried T-DNA insertions. Stable mutations were detected in the T 0 and T 1 generations, with an average allelic mutation transmission rate of 81%. We used CRISPR-P software to detect off-target 50 plants sequenced from the T 0 generation that showed no off-target mutation, signifying that if the designed sgRNA is specific for the target, the off-target effects are negligible. We also concluded that the mutagenic competence of the designed sgRNAs mediated by U6-26 and U6-29 ranged widely from 31% to 96%. The phenotypic analysis of bnarfl11 revealed defects in the floral structure, leaf size, branch number, and seed production. We discovered a significant difference between the sterile line and fertile line flower development after using a stereomicroscope and scanning electron microscope. The pollen visibility test showed that the pollen grain had utterly degenerated. The cytological observations of homozygous mutant plants showed an anther abortion stage similar to nap-CMS, with a Orf222 , Orf139 , Ap3 , and nad5c gene upregulation. The bnarfl11 shows vegetative defects, including fewer branches and a reduced leaf size, suggesting that PPR-encoding genes are essential for the plants' vegetative and reproductive growth. Our results demonstrated that BnaRFL11 has a possible role in fertility restoration. The current study's findings suggest that CRISPR/Cas9 mutations may divulge the functions of genes in polyploid species and provide agronomically desirable traits through a targeted mutation.

Rice plants experience various biotic (such as insect and pest attack) and abiotic (such as drought, salt, heat, and cold etc. ) stresses during the growing season, resulting in DNA damage and the subsequent losses in rice production. DNA Replication Helicase / Nuclease2 (DNA2) is known to be involved in DNA replication and repair. In animals and yeast DNA2 are well characterized because it has the abilities of both helicase and nuclease, it plays a crucial role in DNA replication in the nucleus and mitochondrial genomes. However; they are not fully examined in plants due to less focused on plants damage repair. To fill this research gap, the current study focused on the genome-wide identification and characterization of OsDNA2 genes, along with analyses of their transcriptional expression, duplication, and phylogeny in rice. Overall, 17 OsDNA2 members were reported to be found on eight different chromosomes (2, 3, 4, 6, 7, 9, 10, and 11). Among these chromosomes (Chr), Chr4 contained a maximum of six OsDNA2 genes. Based on phylogenetic analysis, the OsDNA2 gene members were clustered into three different groups. Furthermore, the conserved domains, gene structures, and cis -regulatory elements were systematically investigated. Gene duplication analysis revealed that OsDNA2&#95;2 had an evolutionary relationship with OsDNA2&#95;14, OsDNA2&#95;5 with OsDNA2&#95;6, and OsDNA2&#95;1 with OsDNA2&#95;8. Moreover, results showed that the conserved domain (AAA&#95;11 superfamily) were present in the OsDNA2 genes, which belongs to the DEAD-like helicase superfamily. In addition, to understand the post-transcriptional modification of OsDNA2 genes, miRNAs were predicted, where 653 miRNAs were reported to target 17 OsDNA2 genes. The results indicated that at the maximum, OsDNA2&#95;1 and OsDNA2&#95;4 were targeted by 74 miRNAs each, and OsDNA2&#95;9 was less targeted (20 miRNAs). The three-dimensional (3D) structures of 17 OsDNA2 proteins were also predicted. Expression of OsDNA2 members was also carried out under drought and salt stresses, and conclusively their induction indicated the possible involvement of OsDNA2 in DNA repair under stress when compared with the control. Further studies are recommended to confirm where this study will offer valuable basic data on the functioning of DNA2 genes in rice and other crop plants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Saleem, Farooq, Rehman, Aqeel, Farooq, Naeem, Inam, Ajmal, Rahim, Chen, Kalsoom, Uzair, Fiaz, Attia, Alafari, Khan and Yu.)

This paper presents the design, development, and testing of an IoT-enabled smart stick for visually impaired people to navigate the outside environment with the ability to detect and warn about obstacles. The proposed design employs ultrasonic sensors for obstacle detection, a water sensor for sensing the puddles and wet surfaces in the user's path, and a high-definition video camera integrated with object recognition. Furthermore, the user is signaled about various hindrances and objects using voice feedback through earphones after accurately detecting and identifying objects. The proposed smart stick has two modes; one uses ultrasonic sensors for detection and feedback through vibration motors to inform about the direction of the obstacle, and the second mode is the detection and recognition of obstacles and providing voice feedback. The proposed system allows for switching between the two modes depending on the environment and personal preference. Moreover, the latitude/longitude values of the user are captured and uploaded to the IoT platform for effective tracking via global positioning system (GPS)/global system for mobile communication (GSM) modules, which enable the live location of the user/stick to be monitored on the IoT dashboard. A panic button is also provided for emergency assistance by generating a request signal in the form of an SMS containing a Google maps link generated with latitude and longitude coordinates and sent through an IoT-enabled environment. The smart stick has been designed to be lightweight, waterproof, size adjustable, and has long battery life. The overall design ensures energy efficiency, portability, stability, ease of access, and robust features.

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.

Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients' lateral positions both ways, comfortably massage patients' backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff's safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans.

The widespread impacts of projected global and regional climate change on rice yield have been investigated by different indirect approaches utilizing various simulation models. However, direct approaches to assess the impacts of climatic variabilities on rice growth and development may provide more reliable evidence to evaluate the effects of climate change on rice productivity. Climate change has substantially impacted rice production in the mid-high latitudes of China, especially in Northeast China (NEC). Climatic variabilities occurring in NEC since the 1970s have resulted in an obvious warming trend, which made this region one of the three major rice-growing regions in China. However, the projections of future climate change have indicated the likelihood of more abrupt and irregular climatic changes, posing threats to rice sustainability in this region. Hence, understanding the self-adaptability and identifying adjustive measures to climate variability in high latitudes has practical significance for establishing a sustainable rice system to sustain future food security in China. A well-managed field study under randomized complete block design (RCBD) was conducted in 2017 and 2018 at two study sites in Harbin and Qiqihar, located in Heilongjiang province in NEC. Four different cultivars were evaluated: Longdao-18, Longdao-21 (longer growth duration), Longjing-21, and Suijing-18 (shorter growth duration) to assess the inter-relationships among grain-filling parameters, grain yield and yield components, and grain quality attributes. To better compare the adaptability mechanisms between grain-filling and yield components, the filling phase was divided into three sub-phases (start, middle, and late). The current study evaluated the formation and accumulation of the assimilates in superior and inferior grains during grain-filling, mainly in the middle sub-phase, which accounted for 59.60% of the yield. The grain yields for Suijing-18, Longjing-21, Longdao-21, and Longdao-18 were 8.02%, 12.78%, 17.19%, and 20.53% higher in Harbin than those in Qiqihar, respectively in 2017, with a similar trend observed in 2018. At Harbin, a higher number of productive tillers was noticed in Suijing-18, with averages of 17 and 15 in 2017 and 2018, respectively. The grain-filling parameters of yield analysis showed that the filling duration in Harbin was conducive to increased yield but the low dry weight of inferior grains was a main factor limiting the yield in Qiqihar. The average protein content values in Harbin were significantly higher (8.54% and 9.13%) than those in Qiqihar (8.34% and 9.14%) in 2017 and 2018, respectively. The amylose content was significantly higher in Harbin (20.03% and 22.27%) than those in Qiqihar (14.44% and 14.67%) in 2017 and 2018, respectively. The chalkiness percentage was higher in Qiqihar, indicating that Harbin produced good quality rice. This study provides more direct evidence of the relative changes in rice grain yield due to changes in grain-filling associated with relative changes in environmental components. These self-adaptability mechanisms to climatic variability and the inter-relationships between grain-filling and grain yield underscore the urgent to investigate and explore measures to improve Japonica rice sustainability, with better adaptation to increasing climatic variabilities. These findings may also be a reference for other global rice regions at high latitudes in addressing the impacts of climate change on future rice sustainability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Farooq, Khaskheli, Uzair, Xu, Wattoo, Rehman, Amatus, Fatima, Khan, Fiaz, Yousuf, Ramzan Khan, Khan, Attia, Ercisli and Golokhvast.)

Response to cancer immunotherapy in primary versus metastatic disease has not been well-studied. We found primary pancreatic ductal adenocarcinoma (PDA) is responsive to diverse immunotherapies whereas liver metastases are resistant. We discovered divergent immune landscapes in each compartment. Compared to primary tumor, liver metastases in both mice and humans are infiltrated by highly anergic T cells and MHCII lo IL10 + macrophages that are unable to present tumor-antigen. Moreover, a distinctive population of CD24 + CD44 - CD40 - B cells dominate liver metastases. These B cells are recruited to the metastatic milieu by Muc1 hi IL18 hi tumor cells, which are enriched >10-fold in liver metastases. Recruited B cells drive macrophage-mediated adaptive immune-tolerance via CD200 and BTLA. Depleting B cells or targeting CD200/BTLA enhanced macrophage and T-cell immunogenicity and enabled immunotherapeutic efficacy of liver metastases. Our data detail the mechanistic underpinnings for compartment-specific immunotherapy-responsiveness and suggest that primary PDA models are poor surrogates for evaluating immunity in advanced disease., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Rice ( Oryza sativa L.) is considered as a staple food for more than half of the global population, and sustaining productivity under a scarcity of resources is challenging to meet the future food demands of the inflating global population. The aerobic rice system can be considered as a transformational replacement for traditional rice, but the widespread adaptation of this innovative approach has been challenged due to higher losses of nitrogen (N) and reduced N-use efficiency (NUE). For normal growth and developmental processes in crop plants, N is required in higher amounts. N is a mineral nutrient and an important constituent of amino acids, nucleic acids, and many photosynthetic metabolites, and hence is essential for normal plant growth and metabolism. Excessive application of N fertilizers improves aerobic rice growth and yield, but compromises economic and environmental sustainability. Irregular and uncontrolled use of N fertilizers have elevated several environmental issues linked to higher N losses in the form of nitrous oxide (N 2 O), ammonia (NH 3 ), and nitrate (NO 3 - ), thereby threatening environmental sustainability due to higher warming potential, ozone depletion capacities, and abilities to eutrophicate the water resources. Hence, enhancing NUE in aerobic rice has become an urgent need for the development of a sustainable production system. This article was designed to investigate the major challenge of low NUE and evaluate recent advances in pathways of the N cycle under the aerobic rice system, and thereby suggest the agronomic management approaches to improve NUE. The major objective of this review is about optimizing the application of N inputs while sustaining rice productivity and ensuring environmental safety. This review elaborates that different soil conditions significantly shift the N dynamics via changes in major pathways of the N cycle and comprehensively reviews the facts why N losses are high under the aerobic rice system, which factors hinder in attaining high NUE, and how it can become an eco-efficient production system through agronomic managements. Moreover, it explores the interactive mechanisms of how proper management of N cycle pathways can be accomplished via optimized N fertilizer amendments. Meanwhile, this study suggests several agricultural and agronomic approaches, such as site-specific N management, integrated nutrient management (INM), and incorporation of N fertilizers with enhanced use efficiency that may interactively improve the NUE and thereby plant N uptake in the aerobic rice system. Additionally, resource conservation practices, such as plant residue management, green manuring, improved genetic breeding, and precision farming, are essential to enhance NUE. Deep insights into the recent advances in the pathways of the N cycle under the aerobic rice system necessarily suggest the incorporation of the suggested agronomic adjustments to reduce N losses and enhance NUE while sustaining rice productivity and environmental safety. Future research on N dynamics is encouraged under the aerobic rice system focusing on the interactive evaluation of shifts among activities and diversity in microbial communities, NUE, and plant demands while applying N management measures, which is necessary for its widespread adaptation in face of the projected climate change and scarcity of resources., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Farooq, Wang, Uzair, Fatima, Fiaz, Maqbool, Rehman, Yousuf and Khan.)

Several smart city ideas are introduced to manage various problems caused by overpopulation, but the futuristic smart city is a concept based on dense and artificial-intelligence-centric cities. Thus, massive device connectivity with huge data traffic is expected in the future where communication networks are expected to provide ubiquity, high quality of service, and on-demand content for a large number of interconnected devices. The sixth-generation (6G) network is considered the problem-solving network of futuristic cities, with huge bandwidth and low latency. The expected 6G of the radio access network is based on terahertz (THz) waves with the capability of carrying up to one terabit per second (Tbps). THz waves have the capability of carrying a large amount of data but these waves have several drawbacks, such as short-range and atmospheric attenuation. Hence, these problems can introduce complications and hamper the performance of the 6G network. This study envisions futuristic smart cities using 6G and proposes a conceptual terrestrial network (TN) architecture for 6G. The nested Bee Hive is a scalable multilayer architecture designed to meet the needs of futuristic smart cities. Moreover, we designed the multilayer network infrastructure while considering the expectations from a network of futuristic smart cities and the complications of THz waves. Extensive simulations are performed using different pathfinding algorithms in the 3D multilayer domain to evaluate the performance of the proposed architecture and set the dynamics of futuristic communication of 6G.

Climatic variability has been acquiring an extensive consideration due to its widespread ability to impact food production and livelihoods. Climate change has the potential to intersperse global approaches in alleviating hunger and undernutrition. It is hypothesized that climate shifts bring substantial negative impacts on food production systems, thereby intimidating food security. Vast developments have been made addressing the global climate change, undernourishment, and hunger for the last few decades, partly due to the increase in food productivity through augmented agricultural managements. However, the growing population has increased the demand for food, putting pressure on food systems. Moreover, the potential climate change impacts are still unclear more obviously at the regional scales. Climate change is expected to boost food insecurity challenges in areas already vulnerable to climate change. Human-induced climate change is expected to impact food quality, quantity, and potentiality to dispense it equitably. Global capabilities to ascertain the food security and nutritional reasonableness facing expeditious shifts in biophysical conditions are likely to be the main factors determining the level of global disease incidence. It can be apprehended that all food security components (mainly food access and utilization) likely be under indirect effect via pledged impacts on ménage, incomes, and damages to health. The corroboration supports the dire need for huge focused investments in mitigation and adaptation measures to have sustainable, climate-smart, eco-friendly, and climate stress resilient food production systems. In this paper, we discussed the foremost pathways of how climate change impacts our food production systems as well as the social, and economic factors that in the mastery of unbiased food distribution. Likewise, we analyze the research gaps and biases about climate change and food security. Climate change is often responsible for food insecurity issues, not focusing on the fact that food production systems have magnified the climate change process. Provided the critical threats to food security, the focus needs to be shifted to an implementation oriented-agenda to potentially cope with current challenges. Therefore, this review seeks to have a more unprejudiced view and thus interpret the fusion association between climate change and food security by imperatively scrutinizing all factors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Farooq, Uzair, Raza, Habib, Xu, Yousuf, Yang and Ramzan Khan.)

Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities.

The abundance and structural composition of nitrogen (N) transformation-related microbial communities under certain environmental conditions provide sufficient information about N cycle under different soil conditions. This study aims to explore the major challenge of low N use efficiency (NUE) and N dynamics in aerobic rice systems and reveal the agronomic-adjustive measures to increase NUE through insights into the ecophysiology of ammonia oxidizers. Water-saving practices, like alternate wetting and drying (AWD), dry direct seeded rice (DDSR), wet direct seeding, and saturated soil culture (SSC), have been evaluated in lowland rice; however, only few studies have been conducted on N dynamics in aerobic rice systems. Biological ammonia oxidation is majorly conducted by two types of microorganisms, ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB). This review focuses on how diversified are ammonia oxidizers (AOA and AOB), whose factors affect their activities and abundance under different soil conditions. It summarizes findings on pathways of N cycle, rationalize recent research on ammonia oxidizers in N-cycle, and thereby suggests adjustive agronomic measures to reduce N losses. This review also suggests that variations in soil properties significantly impact the structural composition and abundance of ammonia oxidizers. Nitrification inhibitors (NIs) especially nitrapyrin, reduce the nitrification rate and inhibit the abundance of bacterial amoA without impacting archaeal amoA . In contrast, some NIs confine the hydrolysis of synthetic N and, therefore, keep low NH 4 + -N concentrations that exhibit no or very slight impact on ammonia oxidizers. Variations in soil properties are more influential in the community structure and abundance of ammonia oxidizers than application of synthetic N fertilizers and NIs. Biological nitrification inhibitors (BNIs) are natural bioactive compounds released from roots of certain plant species, such as sorghum, and could be commercialized to suppress the capacity of nitrifying soil microbes. Mixed application of synthetic and organic N fertilizers enhances NUE and plant N-uptake by reducing ammonia N losses. High salt concentration promotes community abundance while limiting the diversity of AOB and vice versa for AOA, whereas AOA have lower rate for potential nitrification than AOB, and denitrification accounts for higher N 2 production. Archaeal abundance, diversity, and structural composition change along an elevation gradient and mainly depend on various soil factors, such as soil saturation, availability of NH 4 + , and organic matter contents. Microbial abundance and structural analyses revealed that the structural composition of AOA was not highly responsive to changes in soil conditions or N amendment. Further studies are suggested to cultivate AOA and AOB in controlled-environment experiments to understand the mechanisms of AOA and AOB under different conditions. Together, this evaluation will better facilitate the projections and interpretations of ammonia oxidizer community structural composition with provision of a strong basis to establish robust testable hypotheses on the competitiveness between AOB and AOA. Moreover, after this evaluation, managing soils agronomically for potential utilization of metabolic functions of ammonia oxidizers would be easier., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Farooq, Uzair, Maqbool, Fiaz, Yousuf, Yang and Khan.)