Search results for: Bilal Okudan

Authors: Nathalie Elia, Samer Aouad, Jane Estephane, Christophe Poupin, Bilal Nsouli, Edmond Abi Aad

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Carbon dioxide is one of the main contributors to greenhouse effect and hence to climate change. As a result, the methanation reaction CO2(g) + 4H2(g) →CH4(g) + 2H2O (ΔH°298 = -165 kJ/mol), also known as Sabatier reaction, has received great interest as a process for the valorization of the greenhouse gas CO2 into methane which is a hydrogen-carrier gas. The methanation of CO2 is an exothermic reaction favored at low temperature and high pressure. However, this reaction requires a high energy input to activate the very stable CO2 molecule, and exhibits serious kinetic limitations. Consequently, the development of active and stable catalysts is essential to overcome these difficulties. Catalytic methanation of CO2 has been studied using catalysts containing Rh, Pd, Ru, Co and Ni on various supports. Among them, the Ni-based catalysts have been extensively investigated under various conditions for their comparable methanation activity with highly improved cost-efficiency. The addition of promoters are common strategies to increase the performance and stability of Ni catalysts. In this work, a small amount of Ru was used as a promoter for Ni catalysts supported on ceria and tested in the CO2 methanation reaction. The nickel loading was 5 wt. % and ruthenium loading is 0.5wt. %. The catalysts were prepared by successive impregnation method using Ni(NO3)2.6H2O and Ru(NO)(NO3)3 as precursors. The calcined support was impregnated with Ni(NO3)2.6H2O, dried, calcined at 600°C for 4h, and afterward, was impregnated with Ru(NO)(NO3)3. The resulting solid was dried and calcined at 600°C for 4 h. Supported monometallic catalysts were prepared likewise. The prepared solids Ru(0.5%)/CeO2, Ni(5%)/CeO2 and Ru(0.5%)-Ni(5%)/CeO2 were then reduced prior to the catalytic test under a flow of 50% H2/Ar (50 ml/min) for 4h at 500°C. Finally, their catalytic performances were evaluated in the CO2 methanation reaction, in the temperature range of 100–350°C by using a gaseous mixture of CO2 (10%) and H2 (40%) in Ar balanced at a total flow rate of 100 mL/min. The effect of pressure on the CO2 methanation was studied by varying the pressure between 1 and 10 bar. The various catalysts showed negligible CO2 conversion at temperatures lower than 250°C. The conversion of CO2 increases with increasing reaction temperature. The addition of Ru as promoter to Ni/CeO2 improved the CO2 methanation. It was shown that the CO2 conversion increases from 15 to 70% at 350°C and 1 bar. The effect of pressure on CO2 conversion was also studied. Increasing the pressure from 1 to 5 bar increases the CO2 conversion from 70% to 87%, while increasing the pressure from 5 to 10 bar increases the CO2 conversion from 87% to 91%. Ru–Ni catalysts showed excellent catalytic performance in the methanation of carbon dioxide with respect to Ni catalysts. Therefore the addition of Ru onto Ni catalysts improved remarkably the catalytic activity of Ni catalysts. It was also found that the pressure plays an important role in improving the CO2 methanation.

Keywords: CO2, methanation, nickel, ruthenium

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Authors: Bilal Ahmed, Muhammad Rafiq, Choongjae Im

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The tech-savvy Gen Z's transfer towards interactive concept learning is hammering the demand for online collaborative learning environments, renovating conventional education approaches. The authors propose a novel approach to enhance learning outcomes to improve retention in 3D interactive education by connecting virtual reality (VR) and non-VR devices in the classroom and distance learning. The study evaluates students' experiences with VR interconnectivity devices in human anatomy lectures using real-time 3D interactive data visualization. Utilizing the renowned "Guo & Pooles Inventory" and the "Flow for Presence Questionnaires," it used an experimental research design with a control and experimental group to assess this novel connecting strategy's effectiveness and significant potential for in-person and online educational settings during the sessions. The experimental group's interactions, engagement levels, and usability experiences were assessed using the "Guo & Pooles Inventory" and "Flow for Presence Questionnaires," which measure their sense of presence, engagement, and immersion throughout the learning process using a 5-point Likert scale. At the end of the sessions, we used the "Perceived Usability Scale" to find our proposed system's overall efficiency, effectiveness, and satisfaction. By comparing both groups, the students in the experimental group used the integrated VR environment and VR to non-VR devices, and their sense of presence and attentiveness was significantly improved, allowing for increased engagement by giving students diverse technological access. Furthermore, learners' flow states demonstrated increased absorption and focus levels, improving information retention and Perceived Usability. The findings of this study can help educational institutions optimize their technology-enhanced teaching methods for traditional classroom settings as well as distance-based learning, where building a sense of connection among remote learners is critical. This study will give significant insights into educational technology and its ongoing progress by analyzing engagement, interactivity, usability, satisfaction, and presence.

Keywords: interactive learning environments, human-computer interaction, virtual reality, computer- supported collaborative learning

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Authors: Wahhaj Ahmed Farooqi, Bilal Ahmad, Sandra Maritza Zambrano Bernal

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In view of structural engineering, monitoring of structural responses over time is of great importance with respect to recent developments of construction technologies. Recently, developments of advanced computing tools have enabled researcher’s better execution of structural health monitoring (SHM) and control systems. In the last decade, building information modeling (BIM) has substantially enhanced the workflow of planning and operating engineering structures. Typically, building information can be stored and exchanged via model files that are based on the Industry Foundation Classes (IFC) standard. In this study a modeling approach for semantic modeling of SHM and control systems is integrated into the BIM methodology using the IFC standard. For validation of the modeling approach, a laboratory test structure, a four-story shear frame structure, is modeled using a conventional BIM software tool. An IFC schema extension is applied to describe information related to monitoring and control of a prototype SHM and control system installed on the laboratory test structure. The SHM and control system is described by a semantic model applying Unified Modeling Language (UML). Subsequently, the semantic model is mapped into the IFC schema. The test structure is composed of four aluminum slabs and plate-to-column connections are fully fixed. In the center of the top story, semi-active tuned liquid column damper (TLCD) is installed. The TLCD is used to reduce effects of structural responses in context of dynamic vibration and displacement. The wireless prototype SHM and control system is composed of wireless sensor nodes. For testing the SHM and control system, acceleration response is automatically recorded by the sensor nodes equipped with accelerometers and analyzed using embedded computing. As a result, SHM and control systems can be described within open BIM, dynamic responses and information of damages can be stored, documented, and exchanged on the formal basis of the IFC standard.

Keywords: structural health monitoring, open building information modeling, industry foundation classes, unified modeling language, semi-active tuned liquid column damper, nondestructive testing

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Authors: Huzaifa Bilal

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Russian wheat aphid (Diuraphis noxia, Kurdjumov) is considered an economically important wheat (Triticum aestivum L.) pest worldwide and in South Africa. The RWA damages wheat plants and reduces annual yields by more than 10%. Even though pest management by pesticides and resistance breeding is an attractive option, chemicals can cause harm to the environment. Furthermore, the evolution of resistance-breaking aphid biotypes has out-paced the release of resistant cultivars. An alternative strategy to reduce the impact of aphid damage on plants, such as priming, which sensitizes plants to respond effectively to subsequent attacks, is necessary. In this study, wheat plants at the seedling and flag leaf stages were primed by salicylic acid and isolate representative of two races of the leaf rust pathogen Puccinia triticina Eriks. (Pt), before RWA (South African RWA biotypes 1 and 4) infestation. Randomized complete block design experiments were conducted in the greenhouse to study plant-pest interaction in primed and non-primed plants. Analysis of induced aphid damage indicated salicylic acid differentially primed wheat cultivars for increased resistance to the RWASA biotypes. At the seedling stage, all cultivars were primed for enhanced resistance to RWASA1, while at the flag leaf stage, only PAN 3111, SST 356 and Makalote were primed for increased resistance. The Puccinia triticina efficaciously primed wheat cultivars for excellent resistance to RWASA1 at the seedling and flag leaf stages. However, Pt failed to enhance the four Lesotho cultivars' resistance to RWASA4 at the seedling stage and PAN 3118 at the flag leaf stage. The induced responses at the seedling and flag leaf stages were positively correlated in all the treatments. Primed plants induced high activity of antioxidant enzymes like peroxidase, ascorbate peroxidase and superoxide dismutase. High antioxidant activity indicates activation of resistant responses in primed plants (primed by salicylic acid and Puccina triticina). Isolates of avirulent Pt races can be a worthy priming agent for improved resistance to RWA infestation. Further confirmation of the priming effects needs to be evaluated at the field trials to investigate its application efficiency.

Keywords: Russian wheat aphis, salicylic acid, puccina triticina, priming

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Authors: Muhammad Bilal, Amelia Jane Llyod, Habsah Mohamad, Jia Hui Wong, Abdul Aziz Mohamed Yusoff, Jafri Malin Abdullah, Jingli Zhang

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Epilepsy is one of the major brain afflictions occurs with uncontrolled excitation of cortex; disturbed 50 million of world’s population. About 25 percent of patients subjected to adverse effects from antiepileptic drugs (AEDs) such as depression, nausea, tremors, gastrointestinal symptoms, osteoporosis, dizziness, weight change, drowsiness, fatigue are commonly observed indications; therefore, new drugs are required to cure epilepsy. GABA is principle inhibitory neurotransmitter, control excitation of the brain. Mutation or dysfunction of GABA receptor is one of the primary causes of epilepsy, which is confirmed from many acquired models of epilepsy like traumatic brain injury, kindling, and status epilepticus models of epilepsy. GABA receptor has 3 distinct types such as GABA (A), GABA (B), GABA(C).GABA (A) receptor has 20 different subunits, α1β2γ2 subunits composition of GABA (A) receptor is the most used combination of subunits for screening of compounds against epilepsy. We expressed α1β2γ2s subunits of GABA (A) Receptor in Xenopus leavis oocytes and examined the enhancement potential of 4-Hydroxybenzoic acid compound on GABA (A) receptor via two-electrode voltage clamp current recording technique. Bamboo shoots are the young, tender offspring of bamboo, which are usually harvested after a cultivating period of 2 weeks. Proteins, acids, fat, starch, carbohydrate, fatty acid, vitamin, dietary fiber, and minerals are the major constituent found systematically in bamboo shoots. These shoots reported to have anticancer, antiviral, antibacterial activity, also possess antioxidant properties due to the presence of phenolic compounds. Student t-test analysis suggested that 4- hydroxybenzoic acid positively allosteric GABA (A) receptor, increased normalized current amplitude to 1.0304±0.0464(p value 0.032) compared with vehicle. 4-Hydrobenzoic acid, a compound from Dendrocalamus Asper bamboo shoot gives new insights for future studies on bamboo shoots with motivation for extraction of more compounds to investigate their effects on human and rodents against epilepsy, insomnia, and anxiety.

Keywords: α1β2γ2S, antiepileptic, bamboo shoots, epilepsy GABA (A) receptor, two-microelectrode voltage clamp, xenopus laevis oocytes

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Authors: Muhammad Ather Nadeem, Bilal Ahmad Khan, Hussam F. Najeeb Alawadi, Athar Mahmood, Aneela Nijabat, Tasawer Abbas, Muhammad Habib, Abdullah

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The global prevalence of Avena fatua infestation poses a significant challenge to wheat sustainability. While chemical control stands out as an efficient and rapid way to control weeds, concerns over developing resistance in weeds and environmental pollution have led to criticisms of herbicide use. Consequently, this study was designed to address these challenges through the chemical synthesis, characterization, and optimization of chitosan-based nanoparticles containing clodinofop Propargyl and fenoxaprop-P-ethyl for the effective management of A. fatua. Utilizing the ionic gelification technique, chitosan-based nanoparticles of clodinofop Propargyl and fenoxaprop-P-ethyl were prepared. These nanoparticles were applied at the 3-4 leaf stage of Phalaris minor weed, applying seven altered doses. These nanoparticles were applied at the 3-4 leaf stage of Phalaris minor weed, applying seven altered doses (D0 (Check weeds), D1 (Recommended dose of traditional-herbicide (TH), D2 (Recommended dose of Nano-herbicide (NPs-H)), D3 (NPs-H with 05-fold lower dose), D4 ((NPs-H) with 10-fold lower dose), D5 (NPs-H with 15-fold lower dose), and D6 (NPs-H with 20-fold lower dose)). Characterization of the chitosan-containing herbicide nanoparticles (CHT-NPs) was conducted using FT-IR analysis, demonstrating a perfect match with standard parameters. UV–visible spectrum further revealed absorption peaks at 310 nm for NPs of clodinofop propargyl and at 330 nm for NPs of fenoxaprop-p-ethyl. This research aims to contribute to sustainable weed management practices by addressing the challenges associated with chemical herbicide use. The application of chitosan-based nanoparticles (CHT-NPs) containing fenoxaprop-P-ethyl and clodinofop-propargyl at the recommended dose of the standard herbicide resulted in 100% mortality and visible injury to weeds. Surprisingly, when applied at a lower dose with 5-folds, these chitosan-containing nanoparticles of clodinofop Propargyl and fenoxaprop-P-ethyl demonstrated extreme control efficacy. Furthermore, at a 10-fold lower dose compared to standard herbicides and the recommended dose of clodinofop-propargyl and fenoxaprop-P-ethyl, the chitosan-based nanoparticles exhibited comparable effects on chlorophyll content, visual injury (%), mortality (%), plant height (cm), fresh weight (g), and dry weight (g) of A. fatua. This study indicates that chitosan/tripolyphosphate-loaded nanoparticles containing clodinofop-propargyl and fenoxaprop-P-ethyl can be effectively utilized for the management of A. fatua at a 10-fold lower dose, highlighting their potential for sustainable and efficient weed control.

Keywords: mortality, chitosan-based nanoparticles, visual injury, chlorophyl contents, 5-fold lower dose.

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Authors: Showkat Ahmad Bhat, Iqra Reyaz, Falaque ul Afshan, Ahmad Arif Reshi, Muneeb U. Rehman, Manzoor R. Mir, Sabhiya Majid, Sonallah, Sheikh Bilal, Ishraq Hussain

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Background: Gastric cancer is the fourth most common cancer and the second leading cause of worldwide cancer-related deaths, with a wide variation in incidence rates across different geographical areas. The current view of cancer is that a malignancy arises from a transformation of the genetic material of a normal cell, followed by successive mutations and by chain of alterations in genes such as DNA repair genes, oncogenes, Tumor suppressor genes. Minerals are necessary for the functioning of several transcriptional factors, proteins that recognize certain DNA sequences and have been found to play a role in gastric cancer. Material Methods:The present work was a case control study and its aim was to ascertain the role of minerals and promoter hypermethylation of CpG islands of DAP-K gene in Gastric cancer patients among the Kashmiri population. Serum was extracted from all the samples and mineral estimation was done by AAS from serum, DNA was also extracted and was modified using bisulphite modification kit. Methylation-specific PCR was used for the analysis of the promoter hypermethylation status of DAP-K gene. The epigenetic analysis revealed that unlike other high risk regions, Kashmiri population has a different promoter hypermethylation profile of DAP-K gene and has different mineral profile. Results: In our study mean serum copper levels were significantly different for the two genders (p<0.05), while as no significant differences were observed for iron and zinc levels. In Methylation-specific PCR the methylation status of the promoter region of DAP-K gene was as 67.50% (27/40) of the gastric cancer tissues showed methylated DAP-K promoter and 32.50% (13/40) of the cases however showed unmethylated DAP-K promoter. Almost all 85% (17/20) of the histopathologically confirmed normal tissues showed unmethylated DAP-K promoter except only in 3 cases where DAP-K promoter was found to be methylated. The association of promoter hypermethylation with gastric cancer was evaluated by χ2 (Chi square) test and was found to be significant (P=0.0006). Occurrence of DAP-K methylation was found to be unequally distributed in males and females with more frequency in males than in females but the difference was not statistically significant (P =0.7635, Odds ratio=1.368 and 95% C.I=0.4197 to 4.456). When the frequency of DAP-K promoter methylation was compared with clinical staging of the disease, DAP-K promoter methylation was found to be certainly higher in Stage III/IV (85.71%) compared to Stage I/ II (57.69%) but the difference was not statistically significant (P =0.0673). These results suggest that DAP-K aberrant promoter hypermethylation in Kashmiri population contributes to the process of carcinogenesis in Gastric cancer and is reportedly one of the commonest epigenetic changes in the development of Gastric cancer.

Keywords: gastric cancer, minerals, AAS, hypermethylation, CpG islands, DAP-K gene

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Authors: Muhammad Qamar Masood, Syed Abbas Raza, Umar Yousaf Raja, Imran Hassan, Bilal Afzal, Muhammad Aleem Zahir, Atika Shaheer

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Background: Cardiovascular disease (CVD) is a major burden among people with type 2 diabetes (T2D) with 1 in 3 reported to have CVD. Therefore, understanding real-world clinical characteristics and prescribing patterns could help in better care. Objective: The CONVERGE (Cardiovascular Outcomes and Value in the Real world with GLP-1RAs) study characterized demographics and medication usage patterns in T2D intensified (add-on to metformin) overall population. The data were further divided into subgroups {dipeptidyl peptidase-4 inhibitors (DPP-4is), sulfonylureas (SUs), insulins, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is)}, according to the latest prescribed antidiabetic agent (ADA) in India/Pakistan/Thailand. Here, we report findings from Pakistan. Methods: A multi-center retrospective study utilized data from medical records between 13-Sep-2008 (post-market approval of GLP-1RAs) and 31-Dec-2017 in adults (≥18-year-old). The data for this study were collected from 05 centers / institutes located in major cities of Pakistan, including Karachi, Lahore, Islamabad, and Multan. These centers included National Hospital, Aga Khan University Hospital, Diabetes Endocrine Clinic Lahore, Shifa International Hospital, Mukhtar A Sheikh Hospital Multan. Data were collected at start of medical record and at 6 or 12-months prior to baseline based on variable type; analyzed descriptively. Results: Overall, 1,010 patients were eligible. At baseline, overall mean age (SD) was 51.6 (11.3) years, T2D duration was 2.4 (2.6) years, HbA1c was 8.3% (1.9) and 35% received ≥1CVD medications in the past 1-year (before baseline). Most frequently prescribed ADAs post-metformin were DPP-4is and SUs (~63%). Only 6.5% received GLP-1RAs and SGLT-2is were not available in Pakistan during the study period. Overall, it took a mean of 4.4 years and 5 years to initiate GLP-1RAs and SGLT-2is, respectively. In comparison to other subgroups, more patients from GLP-1RAs received ≥3 types of ADA (58%), ≥1 CVD medication (64%) and had higher body mass index (37kg/m2). Conclusions: Utilization of GLP-1RAs and SGLT-2is was low, took longer time to initiate and not before trying multiple ADAs. This may be due to lack of evidence for CV benefits for these agents during the study period. The planned phase 2 of the CONVERGE study can provide more insights into utilization and barriers to prescribe GLP-1RAs and SGLT-2is post 2018 in Pakistan.

Keywords: type 2 diabetes, GLP-1RA, treatment intensification, cardiovascular disease

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Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Minhas Alam, Muhammad Hidayat Rasool, Mohsin Khurshid, Bilal Aslam

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The genus Acinetobacter has emerged as a significant concern in hospital-acquired infections, particularly due to the versatility of Acinetobacter baumannii in causing nosocomial infections. The organism's remarkable metabolic adaptability allows it to thrive in various environments, including the environment, animals, and humans. However, the extent of antimicrobial resistance in Acinetobacter species from veterinary settings, especially in developing countries like Pakistan, remains unclear. This study aimed to isolate and characterize Acinetobacter spp. from veterinary settings in Punjab, Pakistan. A total of 2,230 specimens were collected, including 1,960 samples from veterinary settings (nasal and rectal swabs from dairy and beef cattle), 200 from the environment, and 70 from human clinical settings. Isolates were identified using routine microbiological procedures and confirmed by polymerase chain reaction (PCR). Antimicrobial susceptibility was determined by the disc diffusion method, and minimum inhibitory concentration (MIC) was measured by the micro broth dilution method. Molecular techniques, such as PCR and DNA sequencing, were used to screen for antimicrobial-resistant determinants. Genetic diversity was assessed using standard techniques. The results showed that the overall prevalence of A. baumannii in cattle was 6.63% (65/980). However, among cattle, a higher prevalence of A. baumannii was observed in dairy cattle, 7.38% (54/731), followed by beef cattle, 4.41% (11/249). Out of 65 A. baumannii isolates, the carbapenem resistance was found in 18 strains, i.e. 27.7%. The prevalence of A. baumannii in nasopharyngeal swabs was higher, i.e., 87.7% (57/65), as compared to rectal swabs, 12.3% (8/65). Class D β-lactamases genes blaOXA-23 and blaOXA-51 were present in all the CRAB from cattle. Among carbapenem-resistant isolates, 94.4% (17/18) were positive for class B β-lactamases gene blaIMP, whereas the blaNDM-1 gene was detected in only one isolate of A. baumannii. Among 70 clinical isolates of A. baumannii, 58/70 (82.9%) were positive for the blaOXA-23-like gene, and 87.1% (61/70) were CRAB isolates. Among all clinical isolates of A. baumannii, blaOXA-51-like gene was present. Hence, the co-existence of blaOXA-23 and blaOXA-51 was found in 82.85% of clinical isolates. From the environmental settings, a total of 18 A. baumannii isolates were recovered; among these, 38.88% (7/18) strains showed carbapenem resistance. All environmental isolates of A. baumannii harbored class D β-lactamases genes, i.e., blaOXA-51 and blaOXA-23 were detected in 38.9% (7/18) isolates. Hence, the co-existence of blaOXA-23 and blaOXA-51 was found in 38.88% of isolates. From environmental settings, 18 A. baumannii isolates were recovered, with 38.88% showing carbapenem resistance. All environmental isolates harbored blaOXA-51 and blaOXA-23 genes, with co-existence in 38.88% of isolates. MLST results showed ten different sequence types (ST) in clinical isolates, with ST 589 being the most common in carbapenem-resistant isolates. In veterinary isolates, ST2 was most common in CRAB isolates from cattle. Immediate control measures are needed to prevent the transmission of CRAB isolates among animals, the environment, and humans. Further studies are warranted to understand the mechanisms of antibiotic resistance spread and implement effective disease control programs.

Keywords: Acinetobacter baumannii, carbapenemases, drug resistance, MSLT

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Authors: S. A. Bhat, Ahmad Arif, Muneeb U. Rehman, Manzoor R Mir, S. Bilal, Ishraq Hussain, H. M Khan, S. Shanaz, M. I Mir, Sabhiya Majid

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Background: Geologically Climatic conditions of the state range from sub-tropical (Jammu), temperate (Kashmir) to cold artic (Ladakh) zones, which exerts significant influence on its agro-climatic conditions. Gastrointestinal parasitism is a major problem in sheep production worldwide. Materials and Methods: The present study was to evaluate the resistance status of sheep breeds reared in Kashmir Valley for natural resistance against Haemonchus contortus by natural pasture challenge infection. Ten microsatellite markers were used in the study for evaluation of association of Ovar-MHC with parasitic resistance in association with biochemical and parasitological parameters. Following deworming, 500 animals were subjected to selected contaminated pastures in a vicinity of the livestock farms of SKUAST-K and Sheep Husbandry Kashmir. For each animal about 10-15 ml blood was collected aseptically for molecular and biochemical analysis. Weekly fecal samples (3g) were taken, directly from the rectum of all experimental animals and examined for Fecal egg count (FEC) with modified McMaster technique. Packed cell volume (PCV) was determined within 2-5 h of blood collection, all the biochemical parameters were determined in serum by semi automated analyzer. DNA was extracted from all the blood samples with phenol-chloroform method. Microsatellite analysis was done by denaturing sequencing gel electrophoresis Results: Overall sheep from Bakerwal breed followed by Corriediale breed performed relatively better in the trial; however difference between breeds remained low. Both significant (P<0.05) and non-significant differences with respect to resistance against haemonchosis were noted at different intervals in all the parameters.. All the animals were typed for the microsatellites INRA132, OarCP73, DRB1 (U0022), OLA-DQA2, BM1818, TFAP2A, HH56, BM1815, IL-3 and BM-1258. An association study including the effect of FEC, PCV, TSP, SA, LW, and the number of alleles within each marker was done. All microsatellite markers showed degree of heterozygosity of 0.72, 0.72, 0.75, 0.62, 0.84, 0.69, 0.66, 0.65, 0.73 and 0.68 respectively. Significant association between alleles and the parameters measured were only found for the OarCP73, OLA-DQA2 and BM1815 microsatellite marker. Standard alleles of the above markers showed significant effect on the TP, SA and body weight. The three sheep breeds included in the study responded differently to the nematode infection, which may be attributed to their differences in their natural resistance against nematodes. Conclusion: Our data confirms that some markers (OarCP73, OLA-DQA2 and BM1815) within Ovar-MHC are associated with phenotypic parameters of resistance and suggest superiority of Bakerwal sheep breed in natural resistance against Haemonchus contortus.

Keywords: Ovar-Mhc, ovine leukocyte antigen (OLA), sheep, parasitic resistance, Haemonchus contortus, phenotypic & genotypic markers

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Jia H. Wong, Jingli Zhang, Habsah Mohamad, Iswatun H. Abdullah Ripain, Muhammad Bilal, Amelia J. Lloyd, Abdul A. Mohamed Yusoff, Jafri M. Abdullah

Abstract:

Epilepsy is one of the most common neurological diseases affecting more than 50 million of people worldwide. Epilepsy is a state of recurrent, spontaneous seizures with multiple syndromes and symptoms of different causes of brain dysfunction, prognosis, and treatments; characterized by transient, occasional and stereotyped interruptions of behavior whereby the excitatory-inhibitory activities within the central nervous system (CNS) are thrown out of balance due to various kinds of interferences. The goal of antiepileptic treatment is to enable patients to be free from seizures or to achieve control of seizures through surgical treatment and/or pharmacotherapy. Pharmacotherapy through AED plays an important role especially in countries with epilepsy treatment gap due to costs and availability of health facilities, skills and resources, yet there are about one-third of the people with epilepsy have drug-resistant seizures. Hence, this poses considerable challenges to the healthcare system and the effort in providing cost-effective treatment as well as the search for alternatives to treatment and management of epilepsy. Enhancement of γ-aminobutyric acid (GABA)-mediated inhibitory neurotransmission is one of the key mechanisms of actions of antiepileptic drugs. GABA type > a receptors (GABAAR) are ligand-gated ion channels that mediate rapid inhibitory neurotransmission upon the binding of GABA with a heteropentameric structure forming a central pore that is permeable to the influx of chloride ions in its activated state. The major isoform of GABAA receptors consists of two α1, two β2, and one γ2 subunit. It is the most abundantly expressed combinations in the brain and the most commonly researched through Xenopus laevis oocytes. With the advancing studies on ethnomedicine and traditional treatments using medicinal plants, increasing evidence reveal that spice and herb plants with medicinal properties play an important role in the treatment of ailments within communities across different cultures. Capsaicin is the primary natural capsaicinoid in hot peppers of plant genus Capsicum, consist of an aromatic ring, an amide linkage and a hydrophobic side chain. The study showed that capsaicins conferred neuroprotection in status epilepticus mouse models through anti-ictogenic, hypothermic, antioxidative, anti-inflammatory, and anti-apoptotic actions in a dose-dependent manner. In this study, five capsaicin derivatives were tested for their ability to increase the GABA-induced chloride current on α1β2γ2S of GABAAR expressed on Xenopus laevis oocytes using the method of two-microelectrode voltage clamp. Two of the capsaicin derivatives, IS5 (N-(4-hydroxy-3-methoxybenzyl)-3-methylbutyramide) and IS10 (N-(4-hydroxy-3-methoxybenzyl)-decanamide) at a concentration of 30µM were able to significantly increase the GABA-induced chloride current with p=0.002 and p=0.026 respectively. This study were able to show the enhancement effect of two capsaicin derivatives with moderate length of hydrocarbon chain on this receptor subtype, revealing the promising inhibitory activity of capsaicin derivatives through enhancement of GABA-induced chloride current and further investigations should be carried out to verify its antiepileptic effects in animal models.

Keywords: α1β2γ2 GABAA receptors, α1β2γ2S, antiepileptic, capsaicin derivatives, two-microelectrode voltage clamp, Xenopus laevis oocytes

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