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8. Design and development of thermo-electromagnetic system for spinodal decompositions of FeCrCo alloys

Author

Ali Haider, Muhammad Ali Khan, Syed Husain Imran Jaffery, Muhammad Iftikhar Faraz, Mohammed Jameel, Jana Petru, and Shaxnoza Saydaxmetova

Subjects
Magnetic field, Electromagnet, Thermomagnetic treatment, Sustainable energy, Renewable energy, Spinodal decomposition, Mining engineering. Metallurgy, TN1-997
Abstract

Permanent magnets are essential components of electromechanical devices. Majority of magnets are used in permanent magnet motors that have extensive application in relation to energy efficiency and sustainability like electric vehicles. This research is aimed for efficient manufacturing of FeCrCo permanent magnets. Electromagnets could be utilized for the generation of continuous magnetic field to use in number of manufacturing processes. A two-pole electromagnet, comprising of two solenoids each having 2200 turns of copper wire, was developed. The system was designed to produce magnetic field up to 10 kilo Gauss for spinodal decomposition of FeCrCo alloy samples under thermomagnetic treatment process. Being rare earth free alloys, FeCrCo magnet is gaining research focus as an alternative magnetic alloy for advanced applications. The electromagnetic system design was refined and confirmed by using the Finite Element Method. The experimental values, of magnetic field generated by the two-pole electromagnet setup, were well close to the simulation results. The magnetizing setup was utilized to treat the FeCrCo magnetic alloy samples simultaneously at high temperature (700 °C) and magnetic field (7 kilo Gauss). This thermo-magnetic setup helped to improve the metallurgical structures of FeCrCo to grow and develop more efficiently. Treated samples of FeCrCo alloy demonstrated enhanced magnetic properties due to effective spinodal decomposition. The improvement in magnetic properties was attributed to the elimination of retained alpha phase and formation of more alpha-1 phase.

Published
2024
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9. Precision harvesting: comparative analysis of machine learning and generative AI-based classifiers for guava fruit maturity assessment using thermal imaging

Author

Zeeshan Ali Haq, Zainul Abdin Jaffery, and Shabana Mehfuz

Subjects
Fuzzy logic system, machine learning, generative AI, thermal image, quality assessment, Nutrition. Foods and food supply, TX341-641, Food processing and manufacture, TP368-456
Abstract

Guava is a highly nutritious fruit with abundance of health benefits and is medically recommended for daily consumption. Therefore, quality assessment of guava is of significance. Manual quality grading of fruits is being performed for a long time which suffers from subjective biases. Consequently, development of a computer vision-based automated system for quality grading of fruits is essential. In this paper, maturity assessment of guava is performed using various machine learning classifiers, including neural network, fuzzy logic system, and generative AI models. Thermal images of guava are used for maturity assessment. Performance of these models is evaluated by determining the confusion matrix and classification report, considering both class-wise and overall classification. After careful observation, FLS-based ANFIS is highly recommended for grading the guava on the basis of its maturity level. Furthermore, thermal imaging is also very significant for the development of a holistic computer vision-based fruit quality assessment model.

Published
2024
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10. Deep Image Coding in the Fractional Wavelet Transform Domain based on High-Frequency Sub-bands Prediction

Author

Ahmad, Nadeem, Jaffery, Zainul Abdin, Irshad, Khan, Shaheen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Malik, Hasmat, editor, Mishra, Sukumar, editor, Sood, Y. R., editor, Iqbal, Atif, editor, and Ustun, Taha Selim, editor

Published
2024
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11. Effects of machining parameters, ultrasonic vibrations and cooling conditions on cutting forces and tool wear in meso scale ultrasonic vibrations assisted end-milling (UVAEM) of Ti–6Al–4V under dry, flooded, MQL and cryogenic environments – A statistical analysis

Author

Adil Rauf, Muhammad Ali Khan, Syed Husain Imran Jaffery, and Shahid Ikramullah Butt

Subjects
Meso scale, Ultrasonic vibrations assisted end milling, Ti–6Al–4V, MQL, Cryogenic, Force, Mining engineering. Metallurgy, TN1-997
Abstract

Ti–6Al–4V, an alloy of titanium has recently gained focus of research to overcome its machinability challenges. Miniaturization in devices has forced a shift towards micro and meso-scale machining. Sustainability and green manufacturing concepts have shifted focus in machining from cutting fluids towards modern cooling approaches to minimize or eliminate them. In present research, cutting forces & tool wear in Ultrasonic Vibrations Assisted End Milling of Ti–6Al–4V at meso-scale under Dry, Flooded, Minimum Quantity Lubrication & Cryogenic cooling have been analyzed to develop optimum machining parameters. Taguchi L16 orthogonal array was designed to perform experiments keeping Cutting Speed, Feed per tooth, Depth of cut, Ultrasonic vibrations amplitude & Cooling environment as inputs. ANOVA was used to analyze contribution ratios by these parameters towards cutting forces & tool wear. Results indicated that machining conditions, cooling environment and Ultrasonic Vibrations influence cutting forces & tool wear. Depth of cut had the highest influence with 63.41% contribution towards cutting forces while cutting speed remained the highest influencing factor with 39.86% contribution towards tool wear. Cutting forces were reduced by 33.49%, 16.93% and 4.91% while tool wear was reduced by 26.43%, 9.48% and 5.17% under Minimum Quantity Lubrication environment compared to dry, flooded and cryogenic cooling respectively. Cutting forces and tool wear under ultrasonic vibrations were reduced by 24.52% and 13.16% respectively as compared to Conventional Machining (CM). For optimum results, Minimum Quantity Lubrication with low Cutting Speed, Depth of cut, Feed per tooth and high amplitude of ultrasonic vibrations is recommended to machine Ti–6Al–4V.

Published
2024
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12. Processing and characterization of beryllium free Ni–Cr–Mo biomaterial doped with cerium, boron and titanium for dental applications

Author

Ali Haider, Syed Husain Imran Jaffery, Aamir Nusair Khan, Shahid Ikramullah Butt, and Xiubing Jing

Subjects
Ni-Cr-Mo biomaterial, Dental restoration, Corrosion, Wear, Coefficient of friction, Potentiodynamic polarization, Mining engineering. Metallurgy, TN1-997
Abstract

Dental restoration has been a major concern of healthcare since centuries. Several alloys are in use for dental prostheses including Ni–Cr–Mo alloy. The Ni–Cr–Mo alloy has advantage of economy and density over other alloys, while possessing equivalent corrosion and biocompatibility characteristics. However, beryllium in Ni–Cr–Mo dental casting alloys has associated health hazards, being carcinogenic. In this research Ni–Cr–Mo alloys with minor additions of boron (0.2 wt %), cerium (0.4 wt %) and titanium (2 wt %), to replace beryllium were developed through vacuum induction melting and investment casting technique. Manufacturing steps are detailed for the alloy development to have medically clean alloy, suitable for biomedical applications. The cast alloy samples were tested for microstructure, phases, melting temperatures, wear and corrosion characteristics. Corrosion and wear test results demonstrate that the composition with minor addition of cerium had a significant effect to have low corrosion rate (25.92 × 10−3 mills per year), low wear rate (0.04 × 10−4 mm3/N.m) and lowest coefficient of friction.

Published
2024
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13. MTA-cooperative PRMT5 inhibitors enhance T cell-mediated antitumor activity in MTAP-loss tumors

Author

Wei He, Weiyi Peng, Si Chen, Jordi Rodon Ahnert, Jiakai Hou, Leilei Shi, Ritu Bohat, Yue Lu, Han Xu, Sana Sharif, Roshni Jaffery, Ashley Guerrero, Rongjie Fu, Ningbo Zheng, Nicholas A Egan, Chengtai Yu, Shuyue Wang, Donjeta Gjuka, Everett M Stone, Pooja Anil Shah, Taiping Chen, Xinli Liu, and Mark T Bedford

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background Hyperactivated protein arginine methyltransferases (PRMTs) are implicated in human cancers. Inhibiting tumor intrinsic PRMT5 was reported to potentiate antitumor immune responses, highlighting the possibility of combining PRMT5 inhibitors (PRMT5i) with cancer immunotherapy. However, global suppression of PRMT5 activity impairs the effector functions of immune cells. Here, we sought to identify strategies to specifically inhibit PRMT5 activity in tumor tissues and develop effective PRMT5i-based immuno-oncology (IO) combinations for cancer treatment, particularly for methylthioadenosine phosphorylase (MTAP)-loss cancer.Methods Isogeneic tumor lines with and without MTAP loss were generated by CRISPR/Cas9 knockout. The effects of two PRMT5 inhibitors (GSK3326595 and MRTX1719) were evaluated in these isogenic tumor lines and T cells in vitro and in vivo. Transcriptomic and proteomic changes in tumors and T cells were characterized in response to PRMT5i treatment. Furthermore, the efficacy of MRTX1719 in combination with immune checkpoint blockade was assessed in two syngeneic murine models with MTAP-loss tumor.Results GSK3326595 significantly suppresses PRMT5 activity in tumors and T cells regardless of the MTAP status. However, MRTX1719, a methylthioadenosine-cooperative PRMT5 inhibitor, exhibits tumor-specific PRMT5 inhibition in MTAP-loss tumors with limited immunosuppressive effects. Mechanistically, transcriptomic and proteomic profiling analysis reveals that MRTX1719 successfully reduces the activation of the PI3K pathway, a well-documented immune-resistant pathway. It highlights the potential of MRTX1719 to overcome immune resistance in MTAP-loss tumors. In addition, MRTX1719 sensitizes MTAP-loss tumor cells to the killing of tumor-reactive T cells. Combining MRTX1719 and anti-PD-1 leads to superior antitumor activity in mice bearing MTAP-loss tumors.Conclusion Collectively, our results provide a strong rationale and mechanistic insights for the clinical development of MRTX1719-based IO combinations in MTAP-loss tumors.

Published
2024
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14. Classification and monitoring of arm exercises using machine learning and wrist-worn band

Author

Aamer Bilal Asghar, Maham Majeed, Abdullah Taseer, Muhammad Burhan Khan, Khazina Naveed, Mujtaba Hussain Jaffery, Ahmed Sayed Mohammed Metwally, Krzysztof Ejsmont, and Mirosław Nejman

Subjects
KNN, SVM, Boosted Trees, Classification, IoT, Sensors, Electronic computers. Computer science, QA75.5-76.95
Abstract

Exercise is essential for a healthy lifestyle, thus it is important to consider how to keep proper posture when performing arm exercises at home. This work uses wrist-worn bands with the MPU6050 sensor to address these issues, which collects motion data using acceleration measurements. The individuals in the dataset are completing a variety of activities at varying ranges of motion. Machine learning-based classification methods are then applied after the pre-processing and feature extraction of the gathered data. An App prototype integrated with a WiFi module and Cloud infrastructure is created to enable real-time data collecting and storage. The Arduino IDE is used to send the collected data to the ThingSpeak platform, where it is subsequently sent to MATLAB for additional analysis. The studied data is then returned to ThingSpeak, where the program displays the findings. This approach reduces the risk of injuries caused by bad posture by enabling people to continue regular workouts at home without requiring a personal trainer or a particular environment. The findings of this work shed important light on the performance of Boosted Trees, Quadratic SVM, Subspace KNN, and Fine KNN algorithms for arm exercises employing a wrist-worn band with an MPU6050 sensor. The Fine KNN has the highest accuracy of 91.3% among all implemented algorithms.

Published
2024
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15. Achieving sustainable machining of titanium grade 3 alloy through optimization using grey relational analysis (GRA)

Author

Adnan Ahmad, Muhammad Ali Khan, Sohail Akram, Muhammad Iftikhar Faraz, Syed Husain Imran Jaffery, Tahir Iqbal, and Jana Petru

Subjects
Titanium grade 3 alloy, Specific cutting energy, Tool wear rate, Sustainable machining, Cleaner production, Multi-objective optimization, Technology
Abstract

In the contemporary landscape of advanced manufacturing, there is an increasing demand for machining processes that excel in both quality and energy efficiency. One of the prime objectives is to excel in an era of machining practices that are environmentally conscious and economically sustainable. This research addresses this challenge by undertaking a comprehensive exploration of multi-objective optimization, specifically tailored to address the machining challenges presented by Titanium Grade 3 alloy. A robust Taguchi-Grey integrated approach was adopted with research aim to strike an optimum balance among specific cutting energy, tool wear, surface finish, and material removal rate. The impact of machining inputs i.e., feed rate, cutting speed, and depth of cut were analyzed. Best machining setting was identified using grey relational analysis. Feed rate was identified as the most influential member affecting grey relational grade having contribution ratio of 73.95%. Furthermore, surface plots and contour plots were developed for aid of machinists on job floor in selection of best cutting conditions. Harnessing the usefulness of response surface optimization, the machinability was further enhanced by 9% reduction in specific cutting energy and 7% improvement in tool wear. However, it is imperative to acknowledge the trade-offs as a result of MOO; surface roughness increases by 12%, potentially necessitating additional post-processing steps.

Published
2024
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16. Environmental aging of reinforced polymer composite radome: reliability and performance investigation

Author

Imran Haider, Iftikhar Hussain Gul, Shahid Aziz, Muhammad Iftikhar Faraz, Muhammad Ali Khan, Syed Husain Imran Jaffery, and Dong-Won Jung

Subjects
wave transparent fiber composite, wideband frequency, dielectric properties, tensile properties, reliability, Technology
Abstract

In high-speed microelectronic communication, efficient and reliable radome-enclosed antenna performance is highly desired, which depends on consistent dielectric, mechanical properties, and low moisture absorption. The purpose of this study is to investigate the dielectric properties of fiber-polymer matrix composite (PMC) radome over wideband frequency and the impact of environmental aging on its performance. The dielectric constant (Ɛr) of the SF/E0.8 (80% fiber loading) composite radome material decreased to 4% from its original value (3.93), and dielectric loss (δ) was reduced by 11% from 0.035 (2–18 GHz), while SEM morphology indicated fair interface bonding. Employing the Hallberg and Peck model, equivalent aging time (5–25 years), upon accelerated environmental aging, Ɛr was increased up to 3.69%, δ to 9.68%, and the moisture uptake in the SF/E0.8 composite was increased from 1.13% to 1.67%, while tensile strength was retained up to 90.62% of its original value (147.83 MPa), compression strength up to 93.56% of its original value (388.54 MPa), flexural strength up to 85.44% of its original value (286.77 MPa), and interlaminar shear strength up to 77.66% of its original value (22.03 MPa), respectively. SF/E0.8 radome-enclosed antenna gain was decreased to 1%, and the voltage standing wave ratio (VSWR) was increased to 1.04% from their original values. This gradual and small deviation of SF/Ex composite properties and radome electrical performance over the extended aging time is referred to as reliable and effective for radome applications.

Published
2024
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17. Sustainability assessment of machining Al 6061-T6 using Taguchi-grey relation integrated approach

Author

Sajid Raza Zaidi, Shahid Ikramullah Butt, Muhammad Ali Khan, Muhammad Iftikhar Faraz, Syed Husain Imran Jaffery, and Jana Petru

Subjects
Al 6061-T6, Specific cutting energy, Process optimisation, Grey relational analysis, Sustainable manufacturing, Clean materials, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Modern machining requires reduction in energy usage, surface roughness, and burr width to produce finished or near-finished parts. To ensure high surface quality in machining processes, it is crucial to minimize surface finish and minimize burr width, which are considered as significant parameters as specific cutting energy. The objective of this study was to identify the optimal machining parameters for milling in order to minimize surface roughness, burr width, and specific cutting energy. To achieve this, the research investigated the impact of feed per tooth, cutting speed, depth of cut, and number of inserts on the responses across three intervals using Taguchi L9 array. Observing the responses by varying these parameters, underlined the need for multi objective optimisation. Machining conditions of 0.14 mm/tooth fz, 350 m/min Vc and 2 mm ap using 1 cutting insert (exp no 9) was identified as the best machining run using grey relational analysis owing to its highest grey relational grade of 0.936. ANOVA examination identified cutting speed as the leading factor impacting the grey relational grade with 31.07 % contribution ratio, with the number of inserts, depth of cut, and feed per tooth also making notable contributions. Conclusively, machining parameters identified through response surface optimisation resulted in 21.69 % improvement in surface finish, 11.39 % reduction in specific energy consumption, and 6.2 % decrease in burr width on the down milling side albeit with an increase of 9 % in burr width on the up-milling side.

Published
2024
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19. EdD Graduate Perspectives: Uplifting Our Own Voices

Author

Martin, Staci B., Jung, Su-Jin, Gournaris, Kara, Xiang, Li, Jaffery, Zafreen, Anderson, Ingrid, Hatfield, Lisa, and Caskey, Micki M.

Abstract

The purpose of this essay is to share the voices of EdD graduates who are often underrepresented or missing in the literature. To begin, we invited EdD graduates to co-author this article about the connection among their EdD program experiences and interactions and their activism. We included our definition of activism and posed three open-ended questions. Six program graduates and one professor agreed to organize the graduates' responses by the question topics and salient themes. We asked about our experiences in the EdD program and how these influence--positively and negatively--what we are doing now (post-program). We found (a) relationships with faculty and cohort mattered; (b) instructional scaffolding was vital; and (c) faculty and cohorts reflected how lived experiences cultivated a sense of belonging and collectiveness. We also asked about our interactions with peers, cohort, advisor(s), instructors, or mentors, as well as, in what ways did these interactions affect--positively and negatively--what we are doing now (post-program). Lastly, we asked, in what ways, did the EdD program affect--positively or negatively--our activism in the classroom, community, or place of employment. We found examples of how we are shifting the landscape of academia to honor more voices in research and publication, more culturally responsive to impacted communities, and challenging the status quo. We focused on our experiences and interactions in an EdD program and how these experiences and interactions prompted activism in our current practice so that having a diversity of voices not only challenge other students, regardless of their background, to think differently about who creates, produces, and defines knowledge, as well as, support faculty that say they want to expand their curriculum and instruction, yet rely on what they know or what was taught to them in their courses.

Published
2021

24. Integrated multi-omics analyses identify anti-viral host factors and pathways controlling SARS-CoV-2 infection

Author

Jiakai Hou, Yanjun Wei, Jing Zou, Roshni Jaffery, Long Sun, Shaoheng Liang, Ningbo Zheng, Ashley M. Guerrero, Nicholas A. Egan, Ritu Bohat, Si Chen, Caishang Zheng, Xiaobo Mao, S. Stephen Yi, Ken Chen, Daniel J. McGrail, Nidhi Sahni, Pei-Yong Shi, Yiwen Chen, Xuping Xie, and Weiyi Peng

Subjects
Science
Abstract

Abstract Host anti-viral factors are essential for controlling SARS-CoV-2 infection but remain largely unknown due to the biases of previous large-scale studies toward pro-viral host factors. To fill in this knowledge gap, we perform a genome-wide CRISPR dropout screen and integrate analyses of the multi-omics data of the CRISPR screen, genome-wide association studies, single-cell RNA-Seq, and host-virus proteins or protein/RNA interactome. This study uncovers many host factors that are currently underappreciated, including the components of V-ATPases, ESCRT, and N-glycosylation pathways that modulate viral entry and/or replication. The cohesin complex is also identified as an anti-viral pathway, suggesting an important role of three-dimensional chromatin organization in mediating host-viral interaction. Furthermore, we discover another anti-viral regulator KLF5, a transcriptional factor involved in sphingolipid metabolism, which is up-regulated, and harbors genetic variations linked to COVID-19 patients with severe symptoms. Anti-viral effects of three identified candidates (DAZAP2/VTA1/KLF5) are confirmed individually. Molecular characterization of DAZAP2/VTA1/KLF5-knockout cells highlights the involvement of genes related to the coagulation system in determining the severity of COVID-19. Together, our results provide further resources for understanding the host anti-viral network during SARS-CoV-2 infection and may help develop new countermeasure strategies.

Published
2024
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25. Formability characterization of steel tailor welded blanks formed through single point incremental forming

Author

Usman Attique, Shahid Ikramullah Butt, Syed Hussain Imran Jaffery, Faping Zhang, and Ghulam Hussain

Subjects
Steel tailor welded blanks, Manual tungsten inert gas (TIG) welding, Material characterization, SEM/EDX analysis, Strength differential, Thickness differential, Mining engineering. Metallurgy, TN1-997
Abstract

TWBs are being well formed by a simple, highly innovative method known as Incremental Sheet Forming (ISF). Both aluminum and steel sheets welded through friction stir welding (FSW), laser beam welding (LBW) or electron beam welding (EBW) have been explored, extensively, by researchers. However, no research is available on the formability characterization of selected steel sheets having thickness and strength differential, manually welded using Tungsten Inert Gas (TIG) welding technique and formed through Single Point Incremental Forming (SPIF). The same is addressed in this study. Variable wall angle truncated pyramid was used as test geometry. Tensile testing, microhardness and scanning electron microscope (SEM)/Energy Dispersive X-ray (EDX) analysis were performed before forming. Material selected for the study included deep drawing quality (DDQ) steel (DC06) and stainless steel (SS) (AISI 201). Because of huge difference in both strength and hardness values of DDQ steel and SS, low formability was achieved in case of strength differential with minimum and maximum forming depths of 1.8 mm and 3.6 mm, respectively. Whereas formability achieved in case of thickness differential was quite high with minimum and maximum values of forming depths of 27.8 mm and 36.6 mm, respectively since strength and hardness values were not varied much. Percentage thinning was used to assess formability achieved. Final thicknesses achieved during forming were verified by cosine law also. Research investigations may be applied in similar fashion for precise study of material characterization of various kinds of TWBs being used in multiple industries including automotive, vessel, medical, etc.

Published
2024
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32. A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Author

Ovais A Jaffery, Lea Melki, Gregory Slabaugh, Wilson W Good, and Caroline H Roney

Subjects
Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.

Published
2024
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33. Assessment of normal renal size in healthy individuals in Pakistan and its influencing factors: A cross-sectional study

Author

Syed Muhammad Kashif Kazmi, Adnan Hashmi, Umm-e-Rabab, Najiya Amanat Khan, Misbah Fatima Shaikh, Finza Kanwal, Zakiuddin G. Oonwala, Inshal Jaffery, and Sehar Gul Sadruddin

Subjects
kidney size, ultrasound imaging, cross-sectional study, demographic factors, Medicine (General), R5-920, Surgery, RD1-811
Abstract

Background and objectives. In order to address the need to establish normative benchmark of kidney size in healthy population of Pakistan, this study aimed to assess and quantify the renal sizes in healthy individuals within the Pakistani population using ultrasound imaging. Secondary objectives included examining age-related variations, gender-specific differences, and the correlation between Body Mass Index (BMI) and kidney measurements. Materials and methods. A five-year retrospective investigation of 509 patients undergoing non-renal abdominal ultrasounds at NIKUD Research Hospital employed strict participant criteria, excluding those with diabetes, hypertension, abnormal eGFR, or urinalysis. Ultrasound data was collected from medical record of patients aged 18-80 years without a history or ongoing renal disease, excluding any conditions impacting renal size. Results. In 480 participants meeting stringent health criteria, the right kidney measured 10.45 ± 0.97 cm, and the left kidney measured 10.64 ± 0.95 cm. Age-related variations included increasing renal length until the fifth decade, followed by decline. Gender-specific differences, though statistically non-significant, suggested slightly larger renal sizes in females. A positive BMI and renal length relationship indicated increased sizes in obese individuals. Conclusions. This study establishes renal size benchmarks in a healthy Pakistani population, highlighting demographic factors' role in assessments. Variations across age, gender, and BMI underscore the need for population-specific norms in clinical evaluations.

Published
2023
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34. The multidimensional benefits of eicosapentaenoic acid: from heart health to inflammatory control

Author

Mahmoud Nassar, Aelia Jaffery, Bassel Ibrahim, Bahaaeldin Baraka, and Hazem Abosheaishaa

Subjects
Eicosapentaenoic acid, Cardiovascular health, Inflammation, Cholesterol, Blood clots, Vascepa, Internal medicine, RC31-1245
Abstract

Abstract Eicosapentaenoic acid (EPA) is an omega-3 fatty acid found in fatty fish and fish oil supplements. Over the past few decades, research has suggested that EPA has various potential health benefits, particularly for heart health. EPA has been associated with reduced inflammation, improved cholesterol levels, and reduced blood pressure, all of which can contribute to a lower risk of heart disease. Additionally, EPA has been found to reduce the risk of blood clots, which can lead to heart attacks and strokes. This comprehensive review article aims to summarize the current state of knowledge regarding the potential health benefits of EPA. We focus on its effects on cardiovascular health, inflammation, atherosclerotic plaques, blood clots, diabetes, obesity, and cancer. Finally, we provide an overview of the recommended daily dose of EPA for optimal health benefits. This review highlights the importance of EPA in promoting overall health and well-being and provides insights into its potential therapeutic applications.

Published
2023
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41. Analysis of the satellite navigational data in the Baseband signal processing of Galileo E5 AltBOC signal

Author

Khan, Subhan, Zhu, Yiqun, Jawad, Muhammad, Safder, Muhammad Umair, Jaffery, Mujtaba Hussain, and Javid, Salman

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

The advancement in the world of global satellite systems has enhanced the accuracy and reliability between various constellations. However, these enhancements have made numerous challenges for the receivers designer. For instance, comparing the acquisition and tracking of the Galileo signals turn into relatively complex processes after the utilization of Alternate Binary Offset Carrier AltBOC modulation scheme. This paper presents an efficient and unique method for comparing baseband signal processing of the complex receiver structure of the Galileo E5 AltBOC signal. More specifically, the data demodulation has attained after comparing the noisy satellite data sets with the clean data sets. Moreover, the paper presents the implementation of signal acquisition, code tracking, multipath noise characteristics, and carrier tracking for various datasets of the satellite after the eradication of noise. The results obtained in the paper are promising and provide the through treatment to the problem.

Published
2021

44. Malaysia Consumers Green Purchasing Behaviour Towards Organic Products: A Review

Author

Jaffery, Nur Shahafiqah Nadiah, Annuar, Sharifah Nurafizah Syed, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Bhar, Sareen Kaur, editor, and Rahmat, Hawa, editor

Published
2023
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45. Immunomodulatory therapy in children with paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS, MIS-C; RECOVERY): a randomised, controlled, open-label, platform trial

Author

Faust, Saul N, Haynes, Richard, Jones, Christine E, Staplin, Natalie, Whittaker, Elizabeth, Jaki, Thomas, Juszczak, Ed, Spata, Enti, Wan, Mandy, Bamford, Alasdair, Dimitri, Paul, Finn, Adam, Furness, John, Ramanan, Athimalaipet V, Gale, Christopher, Cathie, Katrina, Drysdale, Simon, Bernatoniene, Jolanta, Murray, Clare, Roehr, Charles C, Fleming, Paul, Riordan, Andrew, Bandi, Srini, Jyothish, Deepthi, Evans, Jennifer, Emonts, Marieke, Kelly, Dominic, Pathan, Nazima, Davies, Patrick, Hague, Rosie, Pollock, Louisa, Semple, Malcolm G, Peto, Leon, Baillie, J Kenneth, Buch, Maya, Jeffery, Katie, Knight, Marian, Lim, Wei Shen, Montgomery, Alan, Mukherjee, Aparna, Mumford, Andrew, Rowan, Kathryn, Thwaites, Guy, Mafham, Marion, Emberson, Jonathan, Landray, Martin J, Horby, Peter W, Chappell, Lucy, Day, Jeremy, Dung, DV, Quang, NN, Binh, Prof, Burhan, E, Alisjahbana, B, Koirala, J, Basnet, S, Kestelyn, E, Basnyat, B, Gyanwali, P, Hamers, RL, Sandercock, P, Darbyshire, J, DeMets, D, Fowler, R, Lalloo, D, Munavvar, M, Roberts, I, Warris, A, Wittes, J, Craddock-Bamford, A, Barton, J, Basoglu, A, Brown, R, Brudlo, W, Denis, E, Fletcher, L, Howard, S, Musini, S, Taylor, K, Cui, G, Goodenough, B, King, A, Lay, M, Murray, D, Stevens, W, Wallendszus, K, Welsh, R, Crichton, C, Davies, J, Goldacre, R, Harper, C, Knight, F, Nunn, M, Salih, H, Welch, J, Campbell, M, Pessoa-Amorim, G, Zayed, M, Wiles, J, Bagley, G, Cameron, S, Chamberlain, S, Farrell, B, Freeman, H, Kennedy, A, Whitehouse, A, Wilkinson, S, Wood, C, Reith, C, Davies, K, Halls, H, Holland, L, Truell, R, Wilson, K, Lingwood, Emma, Howie, L, Lunn, M, Rodgers, Penelope, Amuasi, J, Baird, K, Bao, T, Bittaye, M, Bonney, J, D'Alessandro, U, Dhimal, M, Huyen, T, Jagne, A, Karkey, A, Maiga, O, Matey, E, Nadjm, B, Nel, J, Pant, S, Puspatriani, K, Rahardjani, M, Raijal, S, Rees, H, Rimainar, A, Roberts, C, Rocca, A, Shrestha, S, Usuf, E, Vidaillic, C, Wulandari, F, Alexander, A, Amezaga, M, Armah, C, Asghar, A, Aubrey, P, Barker-Williams, K, Barnard, A, Bartholomew, J, Beaumont-Jewell, D, Beddows, S, Beety, J, Birch, C, Boyle, P, Brend, M, Brookes, M, Callens, C, Campbell, D, Campbell, H, Cate, H, Chambers, E, Chapman, K, Coe, A, Collins, J, Crawshaw, S, Das, S, De Soyza, A, Denton, E, Dicks, P, Dolman, M, Donaldson, K, Dowden, L, Dowling, S, Drake, C, Duckles-Leech, H, Duffy, H, Gerdes, L, Gibbons, K, Gill, L, Gilmour, K, Goodwin, J, Graham, J, Green, C, Hall, C, Hall, A, Hampshire, K, Hanson, J, Harman, T, Harper, H, Harrison, S, Hicks, J, Hodgson, H, Hough, M, Jackson, S, Johnson, G, Kirk, J, Lemoine, N, Levett, C, Lock, S, Lockett, T, Logan, M, Lomme, K, Luo, J, McMillan, N, Membrey, H, Mguni, N, Minton, J, Monaghan, H, Murphy, S, Muzengi, N, Naz, M, Nickson, I, Odam, M, Ohia, U, Okell, E, Oliver, A, Pearson, K, Pine, K, Pleass, R, Porter, D, Porter, L, Potter, A, Poustie, V, Pratt, S, Price, A, Priest, A, Rodgers, A, Rook, C, Ryan, P, Sam, S, Sharratt, P, Sheffield, J, Shenton, J, Shirley, K, Skinner, T, Slade, H, Sutu, M, Taylor, J, Thomas, E, Tinkler, H, Unsworth, A, Usher, R, van't Hoff, W, Walker, S, Walters, J, Walton, E, Warmington, J, Wayman, E, Williams, P, Williamson, J, Williamson, D, Yelnoorkar, F, Knight, M, Hodson, K, Pavord, S, Williamson, C, Bowman, L, Chen, F, Clarke, R, Goonasekara, M, Herrington, W, Judge, P, Ng, S, Preiss, D, Sammons, E, Zhu, D, Arkley, C, Bowker, P, Bruce, J, Byrne-Watts, V, Chapman, G, Coleman, G, Denwood, T, Gray, J, Harrison, D, Mather, N, Pinches, H, Rees, A, Rees, S, Turner, G, Abani, O, Abbas, A, Abbas, F, Abbas, J, Abbas, K, Abbas, M, Abbasi, S, Abbass, H, Abbott, A, Abdallah, N, Abdelaziz, A, Abdelfattah, M, Abdelqader, B, Abdul, A, Abdul, B, Abdul, S, Abdul Rasheed, A, Abdulakeem, A, Abdul-Kadir, R, Abdullah, A, Abdulmumeen, A, Abdul-Raheem, R, Abdulshukkoor, N, Abdusamad, K, Abed El Khaleq, Y, Abedalla, M, Abeer Ul Amna, A, Abel, L, Abernethy, K, Abeywickrema, M, Abhinaya, C, Aboaba, A, Aboagye-Odei, A, Aboelela, H, Abo-Leyah, H, Abouelela, K, Abou-Haggar, A, Abouibrahim, M, Abousamra, A, Abouzaid, M, Abraham, M, Abraham, T, Abraheem, A, Abrams, J, Abrams, R, Abu, H, Abu-Arafeh, A, Abubacker, S, Abung, A, Abusamra, Y, Aceampong, Y, Achara, A, Acharya, D, Acheampong, S, Acheampong, P, Acheson, J, Achieng, S, Acosta, A, Acquah, R, Acton, C, Adabie-Ankrah, J, Adair, P, Adam, F, Adam, M, Adamali, H, Adamczyk, M, Adams, C, Adams, D, Adams, K, Adams, L, Adams, N, Adams, R, Adams, T, Adamu-Ikeme, L, Adati, K, Adcock, K, Addo, A, Adeagbo, O, Adebiyi, A, Adedeji, O, Adegeye, Y, Adegoke, K, Adell, V, Adenwalla, S, Adeoye, F, Adesemoye, O, Adewunmi, E, Adeyanju, A, Adeyemi, J, Adeyemo, T, Adhikary, R, Aditya, A, Adkins, G, Adnan, A, Aeron-Thomas, J, Affleck, D, Afnan, C, Afridi, M, Aftab, Z, Agarwal, M, Agbeko, R, Agbo, C, Aggarwal, S, Aghababaie, A, Aguilar Jimenez, L, Ahamed Sadiq, S, Ahammed Nazeer, M, Ahmed, A, Ahmed, B, Ahmed, F, Ahmed, H, Ahmed, I, Ahmed, K, Ahmed, L, Ahmad, M, Ahmed, N, Ahmed, O, Ahmed, R, Ahmad, S, Ahmed, S H, Ahmed Ali, R, Ahmed Mohamud, B, Ahmer, S, Ahonia, A, Aiken, C, Ail, D, Ainsworth, M, Aissa, M, Aitken, L, Ajay, B, Ajibode, A, Ajmi, A, Akhtar, N, Akili, S, Akinbiyi, B, Akindolie, O, Akinfenwa, Y, Akinkugbe, O, Akinpelu, I, Akram, M, Aktinade, O, Akudi, U, Al Aaraj, A, Al Balushi, A, Al Dakhola, M, Al Swaifi, A, Al-Abadi, E, Alabi, A, Aladangady, N, Alafifi, M, Alam, A, Alam, S, Al-Asadi, A, Alatzoglou, K, Albert, P, Albon, L, Alcala, A, Alcorn, G, Alcorn, S, Aldana, A, Alderdice, D, Aldesouki, A, Aldouri, R, Aldridge, J, Aldridge, N, Alegria, A, Alexander, C, Alexander, J, Alexander, P, Al-Fori, J, Alghazawi, L, Alhabsha, O, Al-Hakim, B, Alhameed, R, Al-Hayali, M, Al-Hity, S, Ali, A, Ali, F, Ali, J, Ali, M, Ali, N, Ali, O, Ali, R, Ali, S, Aliberti, E, Alin, J, Alina, A, Alipustain, A, Aliyuda, F, Alizaedeh, K, Al-Jibury, M, Al-Juboori, S, Al-Khalil, M, Alkhudhayri, A, Alkhusheh, M, Allan, F, Allan, N, Allanson, A, Allcock, R, Allen, E, Allen, J, Allen, K, Allen, L, Allen, P, Allen, R, Allen, S, Allen, T, Alli, A, Allison, K, Allman, B, Allsup, D, Allsop, H, Allsop, L, Almahroos, A, Al-Moasseb, H, Al-Obaidi, M, Alomari, L, Al-Rabahi, A, Al-Ramadhani, B, Al-Saadi, Z, Al-Sammarraie, R, Alshaer, I, Al-Shahi Salman, R, Al-Shamkhani, W, Alsheikh, F, Al-Sheklly, B, Altaf, S, Alty, A, Alvarez, M, Alvarez Corral, M, Alveyn, E, Alzetani, M, Amamou, S, Amar, N, Ambalavanan, S, Ambrogetti, R, Ambrose, C, Ameen, A, Ames, K, Amin, A, Amin, K, Amin, S, Amin, T, Amit, B, Amjad, A, Amjad, N, Amosun, V, Amsal, M, Amsha, K, Amutio Martin, N, Amy, P, Anada, A, Anand, A, Anandappa, S, Anantapatnaikuni, S, Anderson, E, Anderson, J, Anderson, L, Anderson, M, Anderson, N, Anderson, R, Anderson, S, Anderson, W, Andreou, P, Andrews, A, Andrews, J, Aneke, K, Ang, A, Ang, W, Angel, T, Angela, A, Angelini, P, Anguvaa, L, Anichtchik, O, Anim-Somuah, M, Aniruddhan, K, Annett, J, Anning, L, Anstey, P, Anstey, R, Anthony, A, Anthony-Pillai, A, Antill, P, Antonnia, Z, Anu, V, Anwar, M, Apetri, E, Apostolopoulos, A, Appleby, S, Appleyard, D, Aquino, M, Araba, B, Aransiola, S, Araujo, M, Archer, A, Archer, D, Archer, S, Arcoria, D, Ardley, C, Arias, A, Aribike, O, Arimoto, R, Armata, I, Armistead, J, Armitage, A, Armstrong, C, Armstrong, M, Armstrong, S, Armstrong, W, Armtrong, P, Arndt, H, Arnison-Newgass, C, Arnold, D, Arnold, R, Arnott, A, Arora, D, Arora, R, Arora, K, Arora, P, Arter, A, Arumaithurai, A, Arya, A, Arya, R, Asandei, D, Asghar, M, Ashab, M, Ashbrook-Raby, C, Ashby, H, Ashcroft, J, Ashcroft, S, Asher, G, Ashfak, Z, Ashfaq, A, Ashish, A, Ashley, D, Ashman-Flavell, S, Ashok, S, Ashour, A, Ashraf, S, Ashraf, M, Ashraq, M, Ashton, D, Ashton, S, Ashworth, A, Ashworth, F, Ashworth, R, Aslam, A, Aslam, I, Aslam, S, Aslett, L, Asogan, H, Asrar, A, Assaf, O, Astin-Chamberlain, R, Athorne, D, Atkins, B, Atkins, C, Atkins, S, Atkinson, J, Atkinson, V, Atomode, A, Atraskiewicz, B, Attia, A, Attubato, E, Attwood, M, Auer, Z, Aujayeb, A, Aung, A, Aung, H, Aung, K, Aung, N, Aung, Y, Aung, Z, Austin, E, Austin, K, Auwal, A, Avari, M, Avery, M, Aveyard, N, Avis, J, Aviss, G, Avram, C, Avram, P, Awadelkareem, A, Awadzi, G, Awaly, M, Awan, A, Awoseyila, A, Aya, A, Ayaz, E, Ayers, A, Azam, J, Azeem, A, Azharuddin, M, Aziz, A, Aziz, G, Aziz, I, Aziz, N, Azkoul, A, Azman Shah, A, Azzopardi, G, Azzoug, H, Babatunde, F, Babi, M, Babiker, B, Babington, G, Babirecki, M, Babores, M, Babs-Osibodu, A, Bacciarelli, S, Bachar, R, Bachour, M, Bacon, G, Bacon, J, Badal, B, Bader, M, Badhan, G, Badhrinarayanan, S, Bae, J, Baggaley, A, Baggott, A, Bagmane, D, Bagshaw, L, Bahadori, K, Bailey, A, Bailey, J, Bailey, K, Bailey, L, Bailey, M, Bailey, P, Bailey, S, Baillie, H, Baillie, J, Bain, J, Bains, V, Baird, D, Baird, E, Baird, S, Baird, T, Baird, Y, Bajandouh, A, Baker, D, Baker, E, Baker, J, Baker, K, Baker, M, Baker, R, Baker, T, Baker, V, Bakere, H, Bakerly, N, Baker-Moffatt, M, Bakhai, A, Bakhtiar, N, Bakoulas, P, Bakthavatsalam, D, Balachandran, N, Balan, A, Balasingam, P, Balaskas, T, Balasubramania, M, Balatoni, N, Balcombe, A, Baldwin, A, Baldwin, C, Baldwin, D, Baldwin, F, Baldwin-Jones, R, Bale, N, Balfour, J, Ball, R, Ball, M, Ballard, K, Balluz, I, Balmforth, C, Balogh, E, Baltmr, A, Baluwala, A, Bambridge, G, Bamford, P, Bamford, A, Bamgboye, A, Bancroft, H, Bancroft, E, Banda, J, Bandaru, K, Bandla, N, Bandyopadhyam, S, Banerjee, A, Banerjee, R, Bani-Saad, O, Banks, H, Banks, L, Banks, P, Bann, C, Bannister, H, Bannister, O, Banton, L, Baptist, M, Baqai, T, Baral, A, Baramova, D, Barbe, R, Barbon, E, Barbosa, M, Barbour, J, Barclay, A, Barclay, C, Bardsley, G, Bareford, S, Bari, S, Barker, A, Barker, D, Barker, E, Barker, H, Barker, J, Barker, L, Barker, O, Barkha, S, Barla, J, Barlow, G, Barlow, R, Barlow, V, Barnacle, J, Barnes, D, Barnes, N, Barnes, R, Barnes, T, Barnetson, C, Barnett, A, Barnett-Vanes, A, Barnsley, W, Barr, A, Barr, C, Barr, D, Barr, J, Barratt, N, Barratt, S, Barrera, M, Barrett, A, Barrett, F, Barrett, J, Barrett, S, Barrow, E, Bartlett, C, Bartlett, G, Bartlett, J, Bartlett, L, Bartley, S, Bartolmeu-Pires, S, Barton, A, Barton, G, Barton, L, Barton, R, Baruah, R, Baryschpolec, S, Bashir, H, Bashyal, A, Basker, B, Basran, A, Bassett, G, Bassett, J, Bassford, C, Bassoy, B, Bastion, V, Basumatary, A, Basvi, P, Bataduwaarachchi, V, Bate, T, Bateman, H, Bateman, K, Bateman, V, Bates, E, Bates, H, Bates, M, Bates, S, Batham, S, Batista, A, Batla, A, Batra, D, Batty, A, Batty, H, Batty, T, Baum, M, Baumbe, R, Bautista, C, Bawa, T, Bawa, F, Bawani, F, Bax, S, Baxter, M, Baxter, N, Baxter, Z, Bayes, H, Bayo, L-A, Bazari, F, Bazaz, R, Bazli, A, Beacham, L, Beadles, W, Beadon, K, Beak, P, Beale, A, Beard, K, Beardsal, K, Bearpark, J, Beasley, A, Beattie, S, Beaumont, K, Beaver, T, Beavis, S, Beazley, C, Beck, S, Beckett, V, Beckitt, R, Beckley, S, Beddall, H, Beeby, D, Beeby, S, Beech, G, Beecroft, M, Beer, S, Beer, N, Bega, G, Begg, A, Begg, S, Beghini, S, Begum, A, Begum, S, Behan, T, Behrouzi, R, Beishon, J, Beith, C, Belcher, J, Belfield, H, Belfield, K, Belgaumkar, A, Bell, D, Bell, G, Bell, J, Bell, L, Bell, N, Bell, P, Bell, S, Bellamu, J, Bellamy, M, Bellamy, T, Bellini, A, Bellis, A, Bellis, F, Bendall, L, Benesh, N, Benetti, N, Benham, L, Benison-Horner, G, Benkenstein, S, Benn, T, Bennett, A, Bennett, C, Bennett, D, Bennett, G, Bennett, K, Bennett, L, Bennett, M, Bennett, S, Bennion, K, Benoy, G, Benson, V, Bentley, A, Bentley, J, Benton, I, Beranova, E, Beresford, M, Bergin, C, Bergstrom, M, Berriman, T, Berry, Z, Best, K, Best, F, Bester, A, Beuvink, Y, Bevan, E, Bevins, S, Bewick, T, Bexley, A, Beyatli, S, Beynon, F, Bhadi, A, Bhagani, S, Bhakta, S, Bhalla, R, Bhandal, K, Bhandari, A, Bhandari, S, Bhanich Supapol, J, Bhanot, A, Bhanot, R, Bhasin, S, Bhat, A, Bhat, P, Bhatnagar, R, Bhatt, K, Bhayani, J, Bhojwani, D, Bhuie, P, Bhuiyan, S, Bibby, A, Bibi, F, Bibi, N, Bibi, S, Bicanic, T, Bidgood, S, Bigg, J, Biggs, S, Biju, A, Bikov, A, Billingham, S, Billings, J, Binns, A, BinRofaie, M, Bintcliffe, O, Birch, J, Birchall, K, Bird, S, Birt, M, Bishop, C, Bishop, K, Bishop, L, Bisnauthsing, K, Biswas, N, Biuk, S, Blachford, K, Black, E, Black, H, Black, K, Black, M, Black, P, Black, S, Blackgrove, H, Blackledge, B, Blackler, J, Blackley, S, Blackman, H, Blackstock, C, Blair, C, Blakemore, F, Blamey, H, Bland, A, Blane, S, Blankley, S, Blaxill, P, Blaylock, K, Blazeby, J, Blencowe, N, Bloom, B, Bloomfield, J, Bloss, A, Blowers, A, Blows, S, Bloxham, H, Blundell, L, Blunsum, A, Blunt, M, Blunt, T, Blyth, I, Blyth, K, Blythe, A, Blythe, K, Boampoaa, M, Board, S, Bobie, B, Bobruk, K, Bodalia, P, Bodasing, N, Boden, M, Bodenham, T, Boehmer, G, Boffito, M, Bohanan, K, Bohmov, K, Bohnacker, N, Bokhandi, S, Bokhar, M, Bokhari, S, Bokobza, I, Boles, A, Bolger, C, Bond, C, Bond, H, Bond, S, Bond, T, Bone, A, Boniface, G, Bonney, L, Booker, L, Boot, S, Boothroyd, M, Borbone, J, Borman, N, Bosence, S, Bostock, K, Botting, N, Bottrill, F, Bouattia, H, Bough, L, Boughton, H, Boult, Z, Boumrah, T, Bourke, M, Bourke, S, Bourne, M, Bousfield, R, Boustred, L, Bowes, A, Bowker, T, Bowler, H, Bowman, S, Bowmer, R, Bowring, A, Bowyer, H, Boyd, A, Boyd, J, Boyd, L, Boyer, N, Boyle, N, Boyle, R, Boyles, L, Brace, L, Bracken, A, Bradder, J, Bradley, C, Bradley, P, Bradley-Potts, J, Bradshaw, L, Bradshaw, Z, Brady, C, Brady, R, Brady, S, Braga Sardo, P, Braganza, D, Braithwaite, M, Brammer, S, Branch, M, Brankin-Frisby, T, Brannigan, J, Brattan, S, Bray, F, Bray, N, Brazil, M, Brear, L, Brear, T, Brearey, S, Bremner, L, Bresges, X, Bressington, C, Bretland, G, Brewer, C, Bridgett, M, Bridgwood, G, Brigham, S, Bright, J, Brightling, C, Brigstock, T, Brimfield, L, Brinksman, P, Brinkworth, E, Brittain-Long, R, Britten, V, Britton, H, Broad, L, Broadhead, S, Broadhurst, R, Broadley, A, Broadway, M, Brockelsby, C, Brocken, M, Brockley, T, Brodsky, M, Brogan, F, Brohan, L, Brokke, F, Brolly, J, Bromley, D, Brooke-Ball, H, Brooker, V, Brooking, D, Brooks, A, Brooks, D, Brooks, J, Brooks, K, Brooks, N, Brooks, P, Brooks, R, Brooks, S, Broom, M, Broomhead, N, Broughton, N, Broughton, C, Brouns, M, Brown, A, Brown, C, Brown, E, Brown, H, Brown, J, Brown, L, Brown, N, Brown, P, Brown, S, Brown, T, Browne, B, Browne, C, Browne, D, Browne, M, Browne, N, Brownlee, S, Brraka, A, Bruce, M, Brunchi, A, Brunskill, N, Brunton, A, Brunton, M, Bryant, M, Bryden, E, Brzezicki, H, Buazon, A, Buchan, R, Buchanan, R, Buche, D, Buck, L, Buck, A, Buckland, M, Buckley, C, Buckley, L, Buckley, P, Buckley, S, Buckman, C, Budds, A, Bugg, G, Bujazia, R, Bukhari, M, Bukhari, S, Bulbulia, R, Bull, A, Bull, D, Bull, K, Bull, R, Bull, T, Bullock, E, Bullock, S, Bulteel, N, Bumunarachchi, K, Bungue-Tuble, R, Burbidge, O, Burchett, C, Burda, D, Burden, C, Burden, T, Burgess, M, Burgess, R, Burgess, S, Burhan, H, Burke, H, Burke, K, Burman, A, Burnard, S, Burnett, C, Burnett, S, Burns, A, Burns, C, Burns, J, Burns, K, Burrage, D, Burrows, K, Burston, C, Burton, A, Burton, B, Burton, F, Burton, H, Burton, M, Butcher, D, Butler, A, Butler, E, Butler, J, Butler, P, Butler, S, Butt, A, Butt, M, Butterworth, C, Butterworth-Cowin, N, Buttery, R, Buttle, T, Button, H, Buttress, D, Bye, H, Byrne, J, Byrne, W, C Ooi, H, Cabandugama, A, Cabrero, L, Caddy, S, Cade, R, Cadwgan, A, Cahilog, Z, Cairney, D, Calderwood, J, Caldow, D, Cale, E, Calisti, G, Callaghan, D, Callaghan, J, Callum, D, Calver, C, Cambell-Kelly, M, Camburn, T, Cameron, D, Cameron, E, Cameron, F, Camm, C, Cammack, R, Campbell, A, Campbell, B, Campbell, J, Campbell, K, Campbell, R, Campbell, W, Campbell Hewson, Q, Camsooksai, J, Canclini, L, Candido, S, Candlish, J, Caneja, C, Cann, A, Cann, J, Cannan, R, Cannon, A, Cannon, E, Cannon, M, Cannon, P, Cannons, V, Canonizado, E, Cantliff, J, Caplin, B, Capocci, S, Caponi, N, Capp, A, Capstick, R, Capstick, T, Caraenache, C, Card, A, Cardwell, M, Carey, C, Carey, R, Carley, S, Carlin, F, Carlin, T, Carmichael, S, Carmody, M, Carnahan, M, Caroline, C, Carpenter, J, Carr, S, Carrasco, A, Carrington, Z, Carroll, A, Carroll, P, Carson, R, Cart, C, Carter, E, Carter, J, Carter, M, Carter, N, Carter, P, Cartwright, D, Cartwright, J, Carty, C, Carty, L, Carungcong, J, Carver, C, Carver, E, Carver, R, Casey, S, Cassells, A, Castiello, T, Castle, G, Castles, B, Caswell, M, Catana, A, Catelan Zborowski, A, Cathcart, S, Catibog, D, Catley, C, Catlow, L, Caudwell, M, Cavazza, A, Cave, A, Cave, L, Cavinato, S, Cawa, F, Cawley, K, Caws, C, Cawthorne, K, Cendl, H, Century, H, Cernova, J, Cesay, M, Cetti, E, Chabane, S, Chablani, M, Chabo, C, Chacko, C, Chadwick, R, Chadwick, J, Chadwick, D, Chakkaraphani, E, Chakraborty, A, Chakraborty, M, Chakravorty, M, Chalakova, P, Chalmers, R, Chalmers, J, Chamberlain, G, Chambers, J, Chambers, L, Chambers, N, Chan, A, Chan, C, Chan, E, Chan, J, Chan, K, Chan, P, Chan, R, Chan, X, Chandler, C, Chandler, H, Chandler, K, Chandler, Z, Chandra, S, Chandran, N, Chandrasekaran, B, 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Zulaikha, R, Zulfikar, S, Zullo, C, and Zuriaga-Alvaro, A

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2024
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47. Faslpr gene dosage tunes the extent of lymphoproliferation and T cell differentiation in lupus

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Bohat, Ritu, Liang, Xiaofang, Chen, Yanping, Xu, Chunyu, Zheng, Ningbo, Guerrero, Ashley, Hou, Jiakai, Jaffery, Roshni, Egan, Nicholas A., Li, Yaxi, Tang, Yitao, Unsal, Esra, Robles, Adolfo, Chen, Si, Major, Angela M., Elldakli, Hadil, Chung, Sang-Hyuk, Liang, Han, Hicks, M. John, Du, Yong, Lin, Jamie S., Chen, Xiqun, Mohan, Chandra, and Peng, Weiyi

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2024
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