Future advances and challenges of nanomaterial-based technologies for electromagnetic interference-based technologies: A review.

The emerging growth of the electronic devices applications has arisen the serious problems of electromagnetic (EM) wave pollution which resulting in equipment malfunction. Therefore, polymer-based composites have been considered good candidates for better EMI shielding due to their significant characteristics including, higher flexibility, ultrathin, lightweight, superior conductivity, easy fabrication processing, environmentally friendly, corrosion resistive, better adhesion with physical, chemical and thermal stability. This review article focused on the concept of the EMI shielding mechanism and challenges with the fabrication of polymer-based composites. Subsequently, recent advancements in the polymer composites applications have been critically reviewed. In addition, the impact of polymers and polymer nanocomposites with different fillers such as organic, inorganic, 2D, 3D, mixture and hybrid nano-fillers on EMI shielding effectiveness has been explored. Lastly, future research directions have been proposed to overcome the limitations of current technologies for further advancement in EMI shielding materials for industrial applications. Based on reported literature, it has been found that the low thickness based lightweight polymer is considered as a best material for excellent material for next-generation electronic devices. Optimization of polymer composites during the fabrication is required for better EMI shielding. New nano-fillers such as functionalization and composite polymers are best to enhance the EMI shielding and conductive properties.

Dynamic light scattering and zeta-potential as a tool for understanding the mechanism of pesticides binding toward individual components of transition metal nanoparticles and graphene oxide hybrids.

Pesticides present in their commercial formulations are studied for their preferable binding toward carbon-based graphene oxide (GO) or transition metal nanoparticles (Fe, Co, Ni, and Cu), present as hybrids. This simple study also reveals the mechanism of interaction of few selected different classes of pesticides, namely, λ-cyhalothrin, imidacloprid, and metsulfuron-methyl toward these hybrids. Individually, to study this comparative binding when hybrids are not used, the understanding of preferred binding toward any of these selected compounds could be challenging, costly, and time-consuming. Dynamic light scattering (DLS) is used to study the changes observed for hydrodynamic radius and zeta potential for the stability of the resulting products. This simple method can also be extended to identify the binding mechanism for other diverse set of combinations. These studies are supported by binding of GO with nanoparticles in batch adsorption and the best fit using Langmuir and Freundlich isotherms is presented. Moreover, pesticide adsorption toward GO-nanoparticle composites is also evidenced.

Heavy metal sequestration from wastewater by metal-organic frameworks: a state-of-the-art review of recent progress.

Metal-organic frameworks (MOFs) have emerged as highly promising adsorbents for removing heavy metals from wastewater due to their tunable structures, high surface areas, and exceptional adsorption capacities. This review meticulously examines and summarizes recent advancements in producing and utilizing MOF-based adsorbents for sequestering heavy metal ions from water. It begins by outlining and contrasting commonly employed methods for synthesizing MOFs, such as solvothermal, microwave, electrochemical, ultrasonic, and mechanochemical. Rather than delving into the specifics of adsorption process parameters, the focus shifts to analyzing the adsorption capabilities and underlying mechanisms against critical metal(loid) ions like chromium, arsenic, lead, cadmium, and mercury under various environmental conditions. Additionally, this article discusses strategies to optimize MOF performance, scale-up production, and address environmental implications. The comprehensive review aims to enhance the understanding of MOF-based adsorption for heavy metal remediation and stimulate further research in this critical field. In brief, this review article presents a comprehensive overview of the contemporary information on MOFs as an effective adsorbent and the challenges being faced by these adsorbents for heavy metal mitigation (including stability, cost, environmental issues, and optimization), targeting to develop a vital reference for future MOF research.

A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection.

Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT's limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. The fusion of global and local features addresses the challenge of robust feature vector creation. Our approach showcases enhanced learning capabilities through modified ViT architecture and ResNet architecture. The dual-pathway training enriches feature extraction, culminating in a comprehensive feature vector. Preliminary results demonstrate significant potential for accurate detection of MI. Our study introduces a hybrid ResNet-ViT model for advanced MI detection, highlighting the synergy between global and local feature extraction. This approach holds promise for elevating MI classification accuracy, with implications for improved patient care. Further validation and clinical applicability exploration are warranted.

Abnormal dynamic functional connectivity and topological properties of cerebellar network in male obstructive sleep apnea.

PURPOSE: To investigate dynamic functional connectivity (dFC) within the cerebellar-whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients. METHODS: Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum-whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed. RESULTS: Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep. CONCLUSION: Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high-order and low-level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA.

HViT: Hybrid vision inspired transformer for the assessment of carotid artery plaque by addressing the cross-modality domain adaptation problem in MRI.

BACKGROUND: Medical image classification is crucial for accurate and efficient diagnosis, and deep learning frameworks have shown significant potential in this area. When a general learning deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, which degrades the performance of deep learning models and leads to inaccurate predictions. PURPOSE: This study aims to propose a framework that utilized the cross-modality domain adaptation and accurately diagnose and classify MRI scans and domain knowledge into stable and vulnerable plaque categories by a modified Vision Transformer (ViT) model for the classification of MRI scans and transformer model for domain knowledge classification. METHODS: This study proposes a Hybrid Vision Inspired Transformer (HViT) framework that employs a convolutional layer for image pre-processing and normalization and a 3D convolutional layer to enable ViT to classify 3D images. Our proposed HViT framework introduces a slim design with a multi-branch network and channel attention, improving patch embedding extraction and information learning. Auxiliary losses target shallow features, linking them with deeper ones, enhancing information gain, and model generalization. Furthermore, replacing the MLP Head with RNN enables better backpropagation for improved performance. Moreover, we utilized a modified transformer model with LSTM positional encoding and Golve word vector to classify domain knowledge. By using ensemble learning techniques, specifically stacking ensemble learning with hard and soft prediction, we combine the predictive power of both models to address the cross-modality domain adaptation problem and improve overall performance. RESULTS: The proposed framework achieved an accuracy of 94.32% for carotid artery plaque classification into stable and vulnerable plaque by addressing the cross-modality domain adaptation problem and improving overall performance. CONCLUSION: The model was further evaluated using an independent dataset acquired from different hardware protocols. The results demonstrate that the proposed deep learning model significantly improves the generalization ability across different MRI scans acquired from different hardware protocols without requiring additional calibration data.

End to end vision transformer architecture for brain stroke assessment based on multi-slice classification and localization using computed tomography.

BACKGROUND: Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes. PURPOSE: In this study, we aimed to enhance the vision transformer architecture for the multi-slice classification of CT scans of each patient into three categories, including Normal, Infarction, Hemorrhage, and patient-wise stroke localization, based on end-to-end vision transformer architecture. This framework can provide an automated, objective, and consistent approach to stroke diagnosis and localization, enabling personalized treatment plans based on the location and extent of the stroke. METHODS: We modified the Vision Transformer (ViT) in combination with neural network layers for the multi-slice classification of brain CT scans of each patient into normal, infarction, and hemorrhage classes. For stroke localization, we used the ViT architecture and convolutional neural network layers to detect stroke and localize it by bounding boxes for infarction and hemorrhage regions in a patient-wise manner based on multi slices. RESULTS: Our proposed framework achieved an overall accuracy of 87.51% in classifying brain CT scan slices and showed high precision in localizing the stroke patient-wise. Our results demonstrate the potential of our method for accurate and reliable stroke diagnosis and localization. CONCLUSION: Our study enhanced ViT architecture for automated stroke diagnosis and localization using brain CT scans, which could have significant implications for stroke management and treatment. The use of deep learning algorithms can provide a more objective and consistent approach to stroke diagnosis and potentially enable personalized treatment plans based on the location and extent of the stroke. Further studies are needed to validate our method on larger and more diverse datasets and to explore its clinical utility in real-world settings.

The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective.

CONCEPTUAL INTRODUCTION: To introduce the concept of cybernetical intelligence, deep learning, development history, international research, algorithms, and the application of these models in smart medical image analysis and deep medicine are reviewed in this paper. This study also defines the terminologies for cybernetical intelligence, deep medicine, and precision medicine. REVIEW OF METHODS: Through literature research and knowledge reorganization, this review explores the fundamental concepts and practical applications of various deep learning techniques and cybernetical intelligence by conducting extensive literature research and reorganizing existing knowledge in medical imaging and deep medicine. The discussion mainly centers on the applications of classical models in this field and addresses the limitations and challenges of these basic models. EVALUATION AND DISCUSSIONS: In this paper, the more comprehensive overview of the classical structural modules in convolutional neural networks is described in detail from the perspective of cybernetical intelligence in deep medicine. The results and data of major research contents of deep learning are consolidated and summarized. CONCLUSION: There are some problems in machine learning internationally, such as insufficient research techniques, unsystematic research methods, incomplete research depth, and incomplete evaluation research. Some suggestions are given in our review to solve the problems existing in the deep learning models. Cybernetical intelligence has proven to be a valuable and promising avenue for advancing various fields, including deep medicine and personalized medicine.

Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI.

BACKGROUND AND OBJECTIVE: Traditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the corpus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy. Residual learning can facilitate network training, Bi-Directional Convolutional LSTM (BDC-LSTM) can exploit interlayer spatial dependencies, and HDC can expand the receptive domain without losing resolution. METHODS: In this paper, we propose a segmentation method by combining BDC-LSTM and U-Net to segment the corpus callosum from multiple angles of brain images based on computed tomography (CT) and magnetic resonance imaging (MRI) in which two types of sequence, namely T2-weighted imaging as well as the Fluid Attenuated Inversion Recovery (Flair), were utilized. The two-dimensional slice sequences are segmented in the cross-sectional plane, and the segmentation results are combined to obtain the final results. Encoding, BDC- LSTM, and decoding include convolutional neural networks. The coding part uses asymmetric convolutional layers of different sizes and dilated convolutions to get multi-slice information and extend the convolutional layers' perceptual field. RESULTS: This paper uses BDC-LSTM between the encoding and decoding parts of the algorithm. On the image segmentation of the brain in multiple cerebral infarcts dataset, accuracy rates of 0.876, 0.881, 0.887, and 0.912 were attained for the intersection of union (IOU), dice similarity coefficient (DS), sensitivity (SE), and predictive positivity value (PPV). The experimental findings demonstrate that the algorithm outperforms its rivals in accuracy. CONCLUSION: This paper obtained segmentation results for three images using three models, ConvLSTM, Pyramid-LSTM, and BDC-LSTM, and compared them to verify that BDC-LSTM is the best method to perform the segmentation task for faster and more accurate detection of 3D medical images. We improve the convolutional neural network segmentation method to obtain medical images with high segmentation accuracy by solving the over-segmentation problem.

Influence of Co3O4-based catalysts on N2O catalytic decomposition and NO conversion.

This study investigated the effect of different Co3O4-based catalysts on the catalytic decomposition of nitrous oxide (N2O) and on nitric oxide (NO) conversion. The experiments were carried out using various reaction temperatures, alkaline solutions, pH, mixing conditions, aging times, space velocities, impregnation loads, and compounds. The results showed that Co3O4 catalysts prepared by precipitation methods have the highest catalytic activity and N2O conversion, even at low reaction temperatures, while the commercial nano and powder forms of Co3O4 (CS) have the lowest performance. The catalysts become inactive at temperatures below 400 °C, and their activity is strongly influenced by the mixing temperature. Samples without stirring during the aging process have higher catalytic activity than those with stirring, even at low reaction temperatures (200-300 °C). The catalytic activity of Co3O4 PM1 decreases with low W/F values and low reaction temperatures. Additionally, the catalyst's performance tends to increase with the reduction process. The study suggests that cobalt-oxide-based catalysts are effective in N2O catalytic decomposition and NO conversion. The findings may be useful in the design and optimization of catalytic systems for N2O and NO control. The results obtained provide important insights into the development of highly efficient, low-cost, and sustainable catalysts for environmental protection.

A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography.

Background and Objective: Bone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors. Materials and Methods: Our dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation. Results: The proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance. Conclusion: Our novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.

Multiobjective Optimization and Sustainability Assessment of an Improved Wet Sulfuric Acid-Based Ionic Liquid Process for the Utilization of Hydrogen Sulfide Using a Symmetry Approach.

Ionic liquids (ILs) are efficient media for the liquid-phase sulfuric acid reaction. Under mild situations, the reaction of H2S with CH4 in ILs happens extremely quick and virtually complete, resulting in liquid sulfuric acid (H2SO4(l)). 1-hexyl-3-methylimidazolium chloride ([hmim][Cl]) ILs were formerly the most effective at capturing and converting H2S. It can convert H2S to H2SO4(l) with a proportion of up to 96%. This study aimed to develop cutting-edge techniques and assess their applicability for different acidic gas capacities and H2S amounts by considering three sustainability metrics which are people (safety), planet (ecological), and profit. Then, to maximize profit while lowering the global warming potential (GWP), fire explosion damage index (FEDI), and toxicity damage index (TDI), a multiobjective optimization (MOO) case was performed. The trade-off between economic, environmental, and safety performance was expressed through Pareto-optimal solutions. The improved wet sulfuric acid (WSA)-based IL method was safer (lower fire and explosion damage index), ecologically friendly (lower GWP), and portable. The findings indicate that the improved WSA-based on IL gives the optimum results compared to conventional WSA processes, such as the profit of 5688$/h increased from 1896$/h, the GWP of 0.0138-ton CO2-eq decreased from 0.0275-ton CO2-eq, the TDI of 6.72 decreased from 13.44, and the FEDI of 6.18 decreased from 20.6, respectively. This discovery opens the door to a viable strategy for capturing and converting H2S from an acid gas stream.

Growth Response in Oryctolagus cuniculus to Selenium Toxicity Exposure Ameliorated with Vitamin E.

The adverse impacts of high temperature during the summer season on the rabbit industry have gained increased global attention. In this study, the comparative effects of biological (BIO) and chemical (CH) nanoselenium (nano-Se) combined with vitamin E on the growth and immune performances of rabbits were observed. A total of 200 white male rabbits of similar age (90 days) were divided into five treatment groups (T0, T1, T2, T3, and T4), 40 animals in each treatment. The rabbits in the first treatment group (T0) was fed basal diet; (T1) basal diet supplemented with 35 mg biological synthesized nanoselenium/kg diet; (T2) basal diet with 35 mg biological nanoselenium/kg diet+150 mg Vit. E/kg; (T3) basal diet+35 m chemically synthesized nanoselenium/kg diet; and (T4) basal diet+35 mg of chemical nanoselenium/kg diet+150 mg Vit. E/kg. The duration of this experiment was 63 days. The body weight of each rabbit was recorded weekly. Results revealed a significant (P < 0.05) increase in live body weight (LBW), total body gain (TBG), and feed conversion ratio (FCR) of rabbits treated with BIO-Se+Vit. E (T2) compared to the other groups. Selenium concentrations in the kidneys and liver were significantly higher (P < 0.05) in animals fed with BIO-Se+Vit. E (T2). The concentrations of serum urea, glutamyl transferase (GGT), and triglycerides (TG) were lower in untreated (T0) and treated groups (T1, T2, T3, and T4). From the results of this study, it can be concluded that biological nano-Se gave maximum improvement for the parameters under study compared to the chemically synthesized nanoselenium by playing a role in alleviating heat stress, increasing the growth performance, and enhancing the immunity of growing white male rabbits. Further addition of Vit. E is an alternative method to maximize productivity with no adverse effects during the fattening period of growing white male rabbits.

Investigation on the Redox Properties of a Novel Cu-Based Pr-Modified Oxygen Carrier for Chemical Looping Combustion.

CO2 levels in the atmosphere are growing as a result of the burning of fossil fuels to meet energy demands. The introduction of chemical looping combustion (CLC) as an alternative to traditional combustion by transporting oxygen emphasizes the need to develop greener and more economical energy systems. Metal oxide, also defined as an oxygen carrier (OC), transports oxygen from the air to the fuel. Several attempts are being made to develop an OC with a reasonable material cost for superior fuel conversion and high oxygen transport capacity (OTC). This study aims to synthesize a potential OC using the wet impregnation method for the CLC process. Thermogravimetric analysis (TGA) was used to determine the cyclic redox properties using 5% CH4/N2 and air as reducing and oxidizing gases, respectively. The 10CuPA-based OC retained a high OTC of about 0.0267 mg O2/mg of OC for 10 cycles that was higher than 10CuA-based OC. Furthermore, the oxygen transfer rate for 10CuPA-based OC was relatively higher compared to 10CuA-based OC over 10 cycles. In comparison to 10CuA-based OC, the 10CuPA-based OC presented a steady X-ray diffraction (XRD) pattern after 10 redox cycles, implying that the phase was stably restored due to praseodymium-modified γ alumina support.

Cost-Effective Processing of Carbon-Rich Materials in Ionic Liquids: An Expeditious Approach to Biofuels.

This work presents a cost-effective approach for processing of renewable carbon-rich biomass using pyridinium-based Lewis acidic ionic liquids (LAILs). Rice husk as carbon-rich lignocellulosic waste was pretreated with a series of neutral and Lewis acidic ionic liquids to yield valuable intermediate platform monosaccharides. Novelty in the work lies in direct conversion of lignocellulosic carbohydrates into reducing sugars without their further conversion into 5-hydroxymethylfurfural or any other platform chemicals that are fermentation inhibitors for bioethanol production. The unconverted cellulose-rich material (CRM) is regenerated as a delignified material by the simultaneous addition of antisolvents. CRM and recovered lignin obtained after pretreatment were analyzed via scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and Fourier transform infrared (FTIR) spectroscopy. The process was optimized with respect to a high yield of platform sugars and the quantity as well as quality of recovered CRM and lignin contents. Various reaction parameters involving the molecular structure of ionic liquids (ILs), Lewis acidic strength of ILs, biomass loading into IL, time, temperature, and biomass particle size were screened thoroughly. From all of the tested ILs, unsymmetrical 3-methylpyridinium IL having N-octyl substitution and chloroaluminate anion showed a greater conversion efficiency at 100 °C for 1.5 h. FTIR and SEM analyses of recovered CRM justify >90% lignin removal from rice husk. From all of the removed lignin, 60 wt % of original lignin content was recovered. The Lewis acidic system possessed recycling ability up to 3 times for subsequent treatment of rice husk without a significant loss of efficiency.

Role of High Resolution Computed Tomography chest in the diagnosis and evaluation of COVID -19 patients -A systematic review and meta-analysis.

BACKGROUND: Recent studies reported that CT scan findings could be implicated in the diagnosis and evaluation of COVID-19 patients. OBJECTIVE: To identify the role of High-Resolution Computed Tomography chest and summarize characteristics of chest CT imaging for the diagnosis and evaluation of SARS-CoV-2 patients. METHODOLOGY: Google Scholar, PubMed, Science Direct, Research Gate and Medscape were searched up to 31 January 2020 to find relevant articles which highlighted the importance of thoracic computed tomography in the diagnosis as well as the assessment of SARS-CoV-2 infected patients. HRCT abnormalities of SARS-CoV-2 patients were extracted from the eligible studies for meta-analysis. RESULTS: In this review, 28 studies (total 2655 patients) were included. Classical findings were Ground Glass Opacities (GGO) (71.64 %), GGO with consolidation (35.22 %), vascular enlargement (65.41 %), subpleural bands (52.54 %), interlobular septal thickening (43.28 %), pleural thickening (38.25 %), and air bronchograms sign (35.15 %). The common anatomic distribution of infection was bilateral lung infection (71.55 %), peripheral distribution (54.63 %) and multiple lesions (74.67 %). The incidences were higher in in the left lower lobe (75.68 %) and right lower lobe (73.32 %). A significant percentage of patients had over 2 lobes involvement (68.66 %). CONCLUSION: Chest CT-scan is a helpful modality in the early detection of COVID-19 pneumonia. The GGO in the peripheral areas of lungs with multiple lesions is the characteristic pattern of COVID-19. The correct interpretation of HRCT features makes it easier to detect COVID-19 even in the early phases and the disease progression can also be accessed with the help of the follow-up chest scans.

Holistic process evaluation of non-conventional palm oil mill effluent (POME) treatment technologies: A conceptual and comparative review.

Thriving oil palm agroindustry comes at a price of voluminous waste generation, with palm oil mill effluent (POME) as the most cumbersome waste due to its liquid state, high strength, and great discharge volume. In view of incompetent conventional ponding treatment, a voluminous number of publications on non-conventional POME treatments is filed in the Scopus database, mainly working on alternative or polishing POME treatments. In dearth of such comprehensive review, all the non-conventional POME treatments are rigorously reviewed in a conceptual and comparative manner. Herein, non-conventional POME treatments are sorted into the five major routes, viz. biological (bioconversions - aerobic/anaerobic biodegradation), physical (flotation & membrane filtration), chemical (Fenton oxidation), physicochemical (photooxidation, steam reforming, coagulation-flocculation, adsorption, & ultrasonication), and bioelectrochemical (microbial fuel cell) pathways. For aforementioned treatments, the constraints, pros, and cons are qualitatively and quantitatively (with compiled performance data) detailed to indicate their process maturity. Authors recommended (i) bioconversions, adsorption, and steam reforming as primary treatments, (ii) flotation and ultrasonication as pretreatments, (iii) Fenton oxidation, photooxidation, and membrane filtration as polishing treatments, and (iv) microbial fuel cell and coagulation-flocculation as pretreatment or polishing treatment. Life cycle assessments are required to evaluate the environmental, economic, and energy aspects of each process.

Overview of Feedstocks for Sustainable Biodiesel Production and Implementation of the Biodiesel Program in Pakistan.

The energy demand of the world is skyrocketing due to the exponential economic growth and population expansion. To meet the energy requirement, the use of fossil fuels is not a good decision, causing environmental pollution such as CO2 emissions. Therefore, the use of renewable energy sources like biofuels can meet the energy crisis especially for countries facing oil shortages such as Pakistan. This review describes the comparative study of biodiesel synthesis for various edible oils, non-edible oils, and wastes such as waste plastic oil, biomass pyrolysis oil, and tyre pyrolysis oil in terms of their oil content and extraction, cetane number, and energy content. The present study also described the importance of biodiesel synthesis via catalytic transesterification and its implementation in Pakistan. Pakistan is importing an extensive quantity of cooking oil that is used in the food processing industries, and as a result, a huge quantity of waste cooking oil (WCO) is generated. The potential waste oils for biodiesel synthesis are chicken fat, dairy scum, WCO, and tallow oil that can be used as potential substrates of biodiesel. The implementation of a biodiesel program as a replacement of conventional diesel will help to minimize the oil imports and uplift the country's economy. Biodiesel production via homogeneous and heterogeneous catalyzed transesterification is more feasible among all transesterification processes due to a lesser energy requirement and low cost. Therefore, biodiesel synthesis and implementation could minimize the imports of diesel by significantly contributing to the overall Gross Domestic Product (GDP). Although, waste oil can meet the energy needs, more available cultivation land should be used for substrate cultivation. In addition, research is still needed to explore innovative solvents and catalysts so that overall biodiesel production cost can be minimized. This would result in successful biodiesel implementation in Pakistan.

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

INTRODUCTION: Various genome wide association studies have manifested that Major Histocompatibility Complex (MHC) region on chromosome 6p21 houses many potential candidate genes for asthma. OBJECTIVE: This Case-Control association study was planned to determine the association of 10 Single Nucleotide Polymorphisms (SNPs), residing within and around MHC genes' region on chromosome 6p21, with Asthma in Punjabi population of Lahore, Pakistan. METHODS: A total of 161 subjects, 61 physician-diagnosed asthma patients and 100 age-matched healthy controls, were recruited from Lahore, a city in Punjab. Ten single nucleotide polymorphisms (SNPs) (rs9378249, rs2070600, rs404860, rs6689, rs1049124, rs1063355, rs1049225, rs1049219, rs7773955 and rs928976) located within or near AGER, NOTCH and HLA genes in MHC region, were genotyped in both patients and controls using single base extension reaction and capillary electrophoresis-based genetic analyser. Statistical models were applied using SHEsis Plus. Results were adjusted for various cofactors (age, gender and environment) and by applying multiple corrections. Haplotype and linkage disequilibrium analyses were performed on Haploview software v4.1. RESULTS: Three of the studied SNPs rs1049124, rs1049219 and rs7773955 show independent significant association with asthma under allelic and genotypic models. Two of the haplotypes, H7 and H13, "CTAATTT" and "CCACTAT", respectively, for rs2070600, rs404860, rs6689, rs1049124, rs1063355, rs1049219 and rs7773955, are found to be significantly associated with the disease. CONCLUSION: This study reports association of SNP variants residing on HLA-DQB1 and HLA-DQA2 genes and haplotypes H7 and H13 on genomic region 6p21 with Asthma in the Punjabi population of Lahore, Pakistan.

Conversion of flaxseed oil into biodiesel using KOH catalyst: Optimization and characterization dataset.

The dataset presented here are part of the data planned to produce biodiesel from flaxseed. Biodiesel production from flaxseed oil through transesterification process using KOH as catalyst, and the operating parameters were optimized with the help of face-centered central composite design (FCCD) of response surface methodology (RSM). The operating independent variables selected such as, methanol oil ratio (4:1 to 6:1), catalyst (KOH) weight (0.40-1.0%), temperature (35 °C-65 °C), and reaction time (30 min-60 min) were optimized against biodiesel yield as response. The maximum yield (98.6%) of biodiesel from flaxseed can achieved at optimum methanol oil ratio (5.9:1), catalyst (KOH) weight (0.51%), reaction temperature (59.2 °C), and reaction time (33 min). The statistical significance of the data set was tested through the analysis of variance (ANOVA). These data were the part of the results reported in "Optimization of process variables for biodiesel production by transesterification of flaxseed oil and produced biodiesel characterizations" Renewable Energy [1].