Search Results (391)

One of the aggressive and lethal cancers, pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and resistance to conventional treatments. Moreover, the tumor immune microenvironment (TIME) plays a crucial role in the progression and therapeutic resistance of PDAC. It is associated with T-cell exhaustion, leading to the progressive loss of T-cell functions with an impaired ability to kill tumor cells. Therefore, this study employed single-cell RNA sequencing (scRNA-seq) analysis of a publicly available human PDAC dataset, with cells isolated from the primary tumor and adjacent normal tissues, identifying upregulated genes of T-cells and cancer cells in two groups (“cancer cells_vs_all-PDAC” and “cancer-PDAC_vs_all-normal”). Common and unique markers of cancer cells from both groups were identified. The Reactome pathways of cancer and T-cells were selected, while the genes implicated in those pathways were used to perform PPI analysis, revealing the hub genes of cancer and T-cells. The gene expression validation of cancer and T-cells hub-genes was performed using GEPIA2 and TISCH2, while the overall survival analysis of cancer cells hub-genes was performed using GEPIA2. Conclusively, this study unraveled 16 novel markers of cancer and T-cells, providing the groundwork for future research into the immune landscape of PDAC, particularly T-cell exhaustion. However, further clinical studies are needed to validate these novel markers as potential therapeutic targets in PDAC patients. Full article

Extremophilic microorganisms offer an untapped potential for producing unique bioactive metabolites with therapeutic applications. In the current study, bacterial isolates were obtained from samples collected from Chamalang cave located in Kohlu District, Balochistan, Pakistan. The cave-derived isolate C1 (Rhodococcus jialingiae) exhibits prominent antibacterial activity against multidrug-resistant pathogens (MDR), including Escherichia coli, Staphylococcus aureus, and Micrococcus luteus. It also demonstrates substantial antioxidant activity, with 71% and 58.39% DPPH radical scavenging. Optimization of physicochemical conditions, such as media, pH, temperature, and nitrogen and carbon sources and concentrations substantially enhanced both biomass and metabolite yields. Optimal conditions comprise specialized media, a pH of 7, a temperature of 30 °C, peptone (1.0 g/L) as the nitrogen source, and glucose (0.5 g/L) as the carbon source. HPLC and QTOF-MS analyses uncovered numerous metabolites, including a phenolic compound, 2-[(E)-3-hydroxy-3-(4-methoxyphenyl) prop-2-enoyl]-4-methoxyphenolate, Streptolactam C, Puromycin, and a putative aromatic polyketide highlighting the C1 isolate chemical. Remarkably, one compound (C₁₄H₃₆N₇) demonstrated a special molecular profile, signifying structural novelty and warranting further characterization by techniques such as ¹H and ¹³C NMR. These findings highlight the biotechnological capacity of the C1 isolate as a source of novel antimicrobials and antioxidants, linking environmental adaptation to metabolic potential and supporting natural product discovery pipelines against antibiotic resistance. Full article

The Lahore–Islamabad Motorway (M-2) is a critical transportation corridor in Pakistan, where contamination in roadside dust by potentially toxic elements (PTEs) presents potential environmental and health concerns. This study evaluates the concentration, spatial distribution, and ecological risks of PTEs (Mn, Ni, Cr, Cu, Pb, Zn, Cd, Ag, Fe) in road dust along the M-2. PTE concentrations were determined using standard protocols and by analysis using an atomic absorption spectrometer. The findings indicate substantial variability in metal concentrations, with Fe (CV% = 9.35%) and Pb (CV% = 7.06%) displaying the highest consistency, whereas Ni exhibited the greatest fluctuation (CV% = 168.80%). Contamination factor analysis revealed low to moderate contamination for Ni and Fe, while Zn contamination was significant in 60% of samples. Cr and Cd exhibited persistently high contamination, and Pb was uniformly elevated across all locations. Ecological risk assessment categorized Ni, Zn, and Cu as low-risk elements, while Pb posed a substantial risk. Cd concentrations indicated high to extreme ecological hazards, emphasizing the necessity for urgent mitigation measures. Factor analysis suggested an interaction of various sources, including industrial, vehicular emissions, and construction materials. Strengthened pollution control strategies and systematic monitoring are essential for mitigating contamination and ensuring environmental sustainability along the motorway. Full article

Zinc oxide (ZnO) photocatalysts are recognized for their ease of synthesis, cost-effectiveness, efficiency, scalability, and environmental compatibility, making them highly suitable for addressing wastewater contamination. In this study, various compositions of kaolinite were used for the hydrothermal deposition of ZnO, including 0.5%, 0.75%, 1%, and 1.25%. The main purpose of this study was to evaluate the effect of kaolinite toward the enhanced performance of ZnO through modification of particle size, morphology and surface functional groups. Several analytical techniques were employed to obtain structural and optical results, including scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and UV–visible spectroscopy, revealing significant changes in particle shape, particle size, surface functional groups, and optical band gap when kaolinite was added. The ZnO/kaolinite composite (sample 4) with 1.25% kaolinite content demonstrated outstanding photocatalytic performance for the degradation of methylene blue in natural sunlight. For sample 4, 15 mg of the dye in a 3.4 × 10⁻⁵ M dye solution exhibited a degradation efficiency of 99%. In contrast, when using 15 mg of catalyst dose and 1.5 × 10⁻⁵ M dye solution, the degradation efficiency was observed to be almost 100%, thus indicating that catalyst dose and dye concentration affect degradation efficiency. The reusability test revealed that sample 4 retained degradation efficiency of 98% after five cycles without showing any morphological changes. By decorating ZnO with kaolinite mineral clay, this study provides exciting findings and insights into the development of low-cost photocatalysts, which could be used to produce solar-powered hydrogen and treat wastewater. Full article

Often thought of as a mesic paradise, forest ecosystems are a mosaic of microhabitats with temporal oscillations that cause significant environmental stresses, providing habitats for extremophilic and extremotolerant fungi. Adapted to survive and thrive under conditions lethal to most mesophiles (e.g., extreme temperatures, pH, water potential, radiation, salinity, nutrient scarcity, and pollutants), these species are increasingly recognized as vital yet underappreciated elements of forest biodiversity and function. This review examines the current understanding of the roles of extremophilic fungi in forests, scrutinizing their presence in these ecosystems with a critical eye. Particularly under severe environmental conditions, extremophilic fungi play a crucial role in forest ecosystems, as they significantly enhance decomposition and nutrient cycling, and foster mutualistic interactions with plants that increase stress resilience. This helps to maintain ecosystem stability. We examine the definition of “extreme” within forest settings, survey the known diversity and distribution of these fungi across various forest stress niches (cold climates, fire-affected areas, acidic soils, canopy surfaces, polluted sites), and delve into their possible ecological functions, including decomposition of recalcitrant matter, nutrient cycling under stress, interactions with plants (pathogenesis, endophytism, perhaps mycorrhizae), bioremediation, and contributions to soil formation. However, the review stresses significant methodological difficulties, information gaps, and field-based natural biases. We recommend overcoming cultural constraints, enhancing the functional annotation of “omics” data, and planning investigations that clarify the specific activities and interactions of these cryptic creatures within the forest matrix to further advance the field. Here, we demonstrate that moving beyond simple identification to a deeper understanding of function will enable us to more fully appreciate the value of extremophilic fungi in forest ecosystems, particularly in relation to environmental disturbances and climate change. Full article

Highlighting the importance of artificial intelligence and machine learning approaches in engineering and fluid mechanics problems, especially in heat transfer applications is main goal of the presented article. With the advancement in Artificial Intelligence (AI) and Machine Learning (ML) techniques, the computational efficiency and accuracy of numerical results are enhanced. The theme of the study is to use machine learning techniques to examine the thermal analysis of MHD boundary layer flow of Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder embedded in a porous medium with heat source/sink and viscous dissipation effects. The considered base fluid is water ($H_{2}O$) and hybrid nanoparticles titanium oxide (${TiO}_2$) and Copper oxide ($CuO$). The governing flow equations are nonlinear PDEs. Non-similar system of PDEs are obtained with efficient conversion variables. The dimensionless PDEs are truncated using a local non-similarity approach up to third level and numerical solution is evaluated using MATLAB built-in-function bvp4c. Artificial Neural Networks (ANNs) simulation approach is used to trained the networks to predict the solution behavior. Thermal boundary layer improves with the enhancement in the value of $Rd$. The accuracy and reliability of ANNs predicted solution is addressed with computation of correlation index and residual analysis. The RMSE is evaluated [0.04892, 0.0007597, 0.0007596, 0.01546, 0.008871, 0.01686] for various scenarios. It is observed that when concentration of hybrid nanoparticles increases then thermal characteristics of the Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder. Full article

Accurate crop yield modeling (CYM) is inherently challenging due to the complex, nonlinear, and temporally dynamic interactions of biotic and abiotic factors. Crop traits, which historically capture the cumulative effect of these factors, exhibit functional relationships critical for optimizing productivity. This underscores the necessity of multi-trait-based CYM approaches. Crop growth models enable trait dynamics with reflectance data and spectral indices as proxies for crop health and traits, respectively, to have real-time, spatially explicit monitoring. The Agricultural Production Systems sIMulator was calibrated to simulate multiple traits across the growth season based on geo-tagged wheat field ground information. Reflectance and spectral indices were processed for the geo-tagged fields across temporal observations to enable real-time, spatially explicit monitoring. Based on these parameters, this study addresses a critical gap in existing CYM frameworks by proposing a machine learning-based model that synergized multiple crop traits with reflectance and spectral indices to generate site-specific yield estimates. The performance evaluation revealed that the Long Short-Term Memory (LSTM) model achieved superior accuracy for the integrated parameters (RMSE = 250.68 kg/ha, MAE = 193.76 kg/ha, and R² = 0.84), followed by traits alone. The Random Forest model followed the LSTM model, with an RMSE = 293.56 kg/ha, MAE = 230.68 kg/ha, and R² = 0.78 for integrated parameters, and an RMSE = 291.73 kg/ha, MAE = 223.17 kg/ha, and R² = 0.78 for crop traits. The superior prediction demonstrated the dominant role of multiple crop traits with satellite-derived reflectance metrics to develop robust CYM frameworks capable of capturing intra- and inter-field yield variability. Full article

A Ppy/Ag-ZnO catalyst was successfully synthesized at room temperature using a novel, green methodology. It involves a mechanically assisted metathesis reaction. The Ppy/Ag-ZnO catalyst was analyzed via X-ray diffraction Technique (XRD), Thermogravimetric analysis (TGA), Differential scanning calorimetry (DSC), Fourier Transform Infrared (FTIR), Scanning Electron Microscopy (SEM), UV–visible spectroscopy, Brunauer–Emmett–Teller (BET), and zeta potential. Debye Scherrer’s calculation suggested a crystallite size of 2.30 nm for Ppy/Ag-ZnO nanocomposite. SEM confirmed the production of aggregated particles with an average size of 2.65 μm, endorsing the -ve zeta potential value (−6.78 mV) due to the presence of Van der Waals forces among the particles of Ppy/Ag-ZnO. DSC confirms that the strong interfacial interaction between Ag-ZnO and the polar segments of Ppy is responsible for the higher Tg (107 °C) and Tm (270 °C) in Ppy/Ag-ZnO. The surface area and average pore size of Ppy/Ag-ZnO catalyst were determined to be 47.08 cm³/g and 21.72 Å, respectively. Methyl orange (MO) was used as a probe in a photocatalytic reaction of fabricated material, which demonstrated exceptional efficiency, exhibiting a removal rate of 91.11% with a rate constant of 0.028 min⁻¹. Photocatalytic degradation of MO was shown to follow pseudo-first-order kinetics. Full article

Consistent and accurate data on forest biomass and carbon dynamics are essential for optimizing carbon sequestration, advancing sustainable management, and developing natural climate solutions in various forest ecosystems. This study quantifies the forest biomass in designated forests based on GEDI LiDAR datasets with a unique compartment-level monitoring of unexplored hilly areas of Mansehra. The integration of multisource explanatory variables, employing machine learning models, adds further innovation to the study of reliable above ground biomass (AGB) estimation. Integrating Landsat-9 vegetation indices with ancillary datasets improved forest biomass estimation, with the random forest algorithm yielding the best performance (R² = 0.86, RMSE = 28.03 Mg/ha, and MAE = 19.54 Mg/ha). Validation with field data on a point-to-point basis estimated a mean above-ground biomass (AGB) of 224.61 Mg/ha, closely aligning with the mean ground measurement of 208.13 Mg/ha (R² = 0.71). The overall mean AGB model estimated a forest biomass of 189.42 Mg/ha in the designated moist temperate forests of the study area. A critical deficit in the carbon sequestration potential was analysed, with the estimated AGB in 2022, at 19.94 thousand tons, with a deficit of 0.83 thousand tons to nullify CO₂ emissions (20.77 thousand tons). This study proposes improved AGB estimation reliability and offers insights into the CO₂ sequestration potential, suggesting a policy shift for sustainable decision-making and climate change mitigation policies. Full article

Approaches to retain or improve wave-transparent composite properties received ongoing attention. Silica glass fiber composites are being utilized in wave transparency applications owing to their excellent dielectric properties. During operational service life, they are exposed to ambient and harsh environments, which degrade their performance and properties. The objective is to evaluate the progressive degradation of silica fiber wave-transparent composite material’s properties and overall performance. Silica fiber/epoxy wave-transparent composites (SFWCs) were fabricated by stacking high-silica glass cloth (HSG) plies via multi-layer compression and curing at 150 °C (14 hrs) and were investigated upon one-year real-time weathering and 20-year accelerated aging (Hallberg peck model). The morphology of one-year-aged SFWC composite was found to be better than that of 20-year-aged SFWC, where relatively weakened interfacial bonding and composite structure were observed. One year weathering the dielectric constant (ε_r) was increased to 4.34%, and dielectric loss (δ) was found to be 5.6%, whereas upon accelerated conditions (equivalent to 20 yrs of ambient conditions), ε_r was significantly raised 30.63% from its original value (3.2), and δ was increased 22.8% (0.035). In the 20-year aged SFWC composite, the maximum absorbed moisture was 3.1%. Tensile strength dropped from 147.8 MPa to 136.48 MPa, and compressive strength from 388.54 MPa to 374.41 MPa. Upon aging (from 1 year of weathering to 20 years of accelerated aging), SFWC composite properties and functional performance were lowered but remained reasonable. SFWC properties, as revealed by microscale characterization, can contribute to the determination of the impact of deterioration and useful service life in respective microelectronics wave transparency applications. Full article

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner–Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing. Full article

The processing and disposal of municipal solid waste (MSW) are global problems, particularly in low- to middle-income states like Pakistan. These economic systems will need to tackle problems regarding municipal solid waste disposal to accomplish a sustainable future in waste management. Still, the determination of MSW procedures is frequently influenced by unstable, vague, and inadequately stated criteria. To deal with this issue, we designed an interactive model that uses intuitionistic fuzzy hypersoft sets (IFHSSs) to find the optimal thermochemical processing system for MSW. The main objective of this research is to define interactional operational laws for intuitionistic fuzzy hypersoft numbers and to use these laws to build interaction aggregation operators (AOs) and ordered AOs along with their basic characteristics. Based on developed operators, a novel Evaluation Based on the Distance from the Average Solution (EDAS) technique is proposed to integrate multiple attribute group decision making (MAGDM) issues. The suggested strategy is used to analyze five thermochemical treatment techniques for MSW, using a case study focusing on Pakistan’s particular MSW administration problems to choose the most economical technique. Therefore, the new structure is assessed with established methodologies to illustrate its stability. The comparison of results proves that the implications of the stated approach will be more effective and capable than the existing approaches. Full article

On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently. Currently, the most widely used method is Reverse Transcription Polymerase Chain Reaction (RT-PCR), a clinical technique for infection identification. However, RT-PCR is expensive, has limited sensitivity, and requires specialized medical expertise. One of the major challenges in the rapid diagnosis of COVID-19 is the need for reliable imaging, particularly X-ray imaging. This work takes advantage of artificial intelligence (AI) techniques to enhance diagnostic accuracy by automating the detection of COVID-19 infections from chest X-ray (CXR) images. We obtained and analyzed CXR images from the Kaggle public database (4035 images in total), including cases of COVID-19, viral pneumonia, pulmonary opacity, and healthy controls. By integrating advanced techniques with transfer learning from pre-trained convolutional neural networks (CNNs), specifically InceptionV3, ResNet50, and Xception, we achieved an accuracy of 95%, significantly higher than the 85.5% achieved with ResNet50 alone. Additionally, our proposed method, CXR-DNNs, can accurately distinguish between three different types of chest X-ray images for the first time. This computer-assisted diagnostic tool has the potential to significantly enhance the speed and accuracy of COVID-19 diagnoses. Full article

Practical Byzantine Fault Tolerance (PBFT) is one of the most popular consensus mechanisms for the consortium and private blockchain technology. It has been recognized as a candidate consensus mechanism for the Internet of Things networks as it offers lower resource requirements and high performance when compared with other consensus mechanisms such as proof of work. In this paper, by considering the blockchain nodes are wirelessly connected, we model the network nodes distribution and transaction arrival rate as Poisson point process and we develop a framework for evaluating the performance of the wireless PBFT network. The framework utilizes slotted ALOHA as its multiple access technique. We derive the end-to-end success probability of the wireless PBFT network which serves as the basis for obtaining other key performance indicators namely, the optimal transmission interval, the transaction throughput and delay, and the viable area. The viable area represents the minimum PBFT coverage area that guarantees the liveness, safety, and resilience of the PBFT protocol while satisfying a predefined end-to-end success probability. Results show that the transmission interval required to make the wireless PBFT network viable can be reduced if either the end-to-end success probability requirement or the number of faulty nodes is lowered. Full article

Background: Immunization saves millions of lives, and globally, vaccines have significantly contributed to reducing mortality and morbidity due to more than 20 life-threatening illnesses. However, there are considerable disparities in vaccination coverage among countries and within populations. This study evaluates the reduction in disparities in vaccination coverage across various socio-economic groups by adopting an integrated community-engagement approach combined with maternal and child health services through mobile health camps. Methods: This secondary analysis is based on a community-based demonstration project conducted between 2014 and 2016 across 146 union councils in polio high-risk districts of Sindh, Khyber Pakhtunkhwa (KP) and Baluchistan in Pakistan. The intervention involved structured community engagement and mobile health camps providing routine immunization alongside maternal and child health services. Data were collected through cross-sectional independent surveys using the WHO two-stage cluster technique at the baseline and the endline, covering over 120,000 children under 5 years old. Four key outcome indicators were analyzed: fully vaccinated children, under-immunized children, unvaccinated children, and polio zero-dose children for equity in vaccine uptake. Results: The proportion of fully vaccinated children increased in the lowest wealth quintile from 28.5% (26.7%, 30.3%) at the baseline to 51.6% (49.5%, 53.8%) at the endline. In comparison, the increase in the richest quantities was 16.2% (14.0%, 18.4%) from the baseline 56.4% (54.6%, 58.2%) to the endline 72.7% (71.1%, 74.2%). Under-vaccination dropped by 10.2% (95% CI: −11.4%, −9.1%), with the poorest quintile showing an 11.8% reduction. The gap between the highest and lowest wealth quintiles in full immunization narrowed by 6.9%, from 27.9% to 21.0% at the baseline and the endline, respectively. The prevalence of zero-dose children significantly decreased across all quintiles, with the highest reduction observed in the lowest quintile of −11.3% (−13.6%, −9.1%). The difference between the highest and lowest wealth quintiles reduced from 6.2% to 3.8%. A significant reduction in polio zero-dose children was achieved, as 13.5% (95% CI: −14.8%, −12.2%), from 29.2% (95% CI: 28.0%, 30.3%) to 15.6% (14.8%, 16.5%). Conclusions: This study shows that integrating community engagement with maternal and child health services through health camps can significantly enhance immunization coverage and reduce wealth-based disparities in high-risk, hard-to-reach areas. The approach improved coverage for zero-dose and fully vaccinated children, suggesting a potential for scaling in regions with access issues, conflict, and vaccine hesitancy. Full article