Search

Your search keyword '"Murtaza A"' showing total 30,219 results
Start Over Author "Murtaza A"
30,219 results on '"Murtaza A"'

51. Enhancing Video Transmission with Machine Learning based Routing in Software-Defined Networks

Author

İpek, Anıl Dursun, Cicioğlu, Murtaza, and Çalhan, Ali

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Our study uses the centralized, flexible, dynamic, and programmable structure of Software-Defined networks (SDN) to overcome the problems. Although SDN effectively addresses the challenges present in traditional networks, it still requires further enhancements to achieve a more optimized network architecture. The Floodlight controller utilized in this study employs metrics such as hop count, which provides limited information for routing. In scenarios such as video transmission, this situation is insufficient and the need for optimization arises. For this purpose, an artificial intelligence (AI) based routing algorithm is proposed between the server and the client in the scenario based on NSFNET topology. The topology designed with the Floodlight controller in the Mininet simulation environment includes a client, a server, and 14 switches. A realistic network environment is provided by adding different receivers and creating TCP traffic between these receivers using the iperf3 tool. In three scenarios, video streaming is performed using the FFmpeg tool, and 49 path metrics such as RTT, throughput, and loss are recorded. In these scenarios, PSNR and SSIM calculations are made to observe the differences between the transmitted and the original video in congested and uncongested environments. Due to the lack of a dataset suitable for the proposed network environment in the literature, a new dataset consisting of 876 records is created using continuously transmitted video traffic. Low and high traffic levels are created within the dataset, and different machine learning techniques such as KNN, Random Forest, SVM, AdaBoost, Logistic Regression and XGBoost are applied using the features that affect the traffic levels.

Published
2024

52. Neural MP: A Generalist Neural Motion Planner

Author

Dalal, Murtaza, Yang, Jiahui, Mendonca, Russell, Khaky, Youssef, Salakhutdinov, Ruslan, and Pathak, Deepak

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The current paradigm for motion planning generates solutions from scratch for every new problem, which consumes significant amounts of time and computational resources. For complex, cluttered scenes, motion planning approaches can often take minutes to produce a solution, while humans are able to accurately and safely reach any goal in seconds by leveraging their prior experience. We seek to do the same by applying data-driven learning at scale to the problem of motion planning. Our approach builds a large number of complex scenes in simulation, collects expert data from a motion planner, then distills it into a reactive generalist policy. We then combine this with lightweight optimization to obtain a safe path for real world deployment. We perform a thorough evaluation of our method on 64 motion planning tasks across four diverse environments with randomized poses, scenes and obstacles, in the real world, demonstrating an improvement of 23%, 17% and 79% motion planning success rate over state of the art sampling, optimization and learning based planning methods. Video results available at mihdalal.github.io/neuralmotionplanner, Comment: Website at mihdalal.github.io/neuralmotionplanner. Main paper: 7 pages, 4 figures, 2 tables. Appendix: 9 pages, 5 figures, 6 tables

Published
2024

53. Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach

Author

Sharma, Vidushi, Tek, Andy, Nguyen, Khanh, Giammona, Max, Zohair, Murtaza, Sundberg, Linda, and La, Young-Hye

Subjects
Computer Science - Machine Learning, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Materials Science
Abstract

Higher loading of active electrode materials is desired in batteries, especially those based on conversion reactions, for enhanced energy density and cost efficiency. However, increasing active material loading in electrodes can cause significant performance depreciation due to internal resistance, shuttling, and parasitic side reactions, which can be alleviated to a certain extent by a compatible design of electrolytes. In this work, a data-driven approach is leveraged to find a high-performing electrolyte formulation for a novel interhalogen battery custom to the target cathode loading. An electrolyte design consisting of 4 solvents and 4 salts is experimentally devised for a novel interhalogen battery based on a multi-electron redox reaction. The experimental dataset with variable electrolyte compositions and active cathode loading, is used to train a graph-based deep learning model mapping changing variables in the battery's material design to its specific capacity. The trained model is used to further optimize the electrolyte formulation compositions for enhancing the battery capacity at a target cathode loading by a two-fold approach: large-scale screening and interpreting electrolyte design principles for different cathode loadings. The data-driven approach is demonstrated to bring about an additional 20% increment in the specific capacity of the battery over capacities obtained from the experimental optimization., Comment: 34 Pages, 5 Figures, 2 Tables

Published
2024

54. Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos

Author

Murtaza, Shakeeb, Pedersoli, Marco, Sarraf, Aydin, and Granger, Eric

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Weakly-Supervised Video Object Localization (WSVOL) involves localizing an object in videos using only video-level labels, also referred to as tags. State-of-the-art WSVOL methods like Temporal CAM (TCAM) rely on class activation mapping (CAM) and typically require a pre-trained CNN classifier. However, their localization accuracy is affected by their tendency to minimize the mutual information between different instances of a class and exploit temporal information during training for downstream tasks, e.g., detection and tracking. In the absence of bounding box annotation, it is challenging to exploit precise information about objects from temporal cues because the model struggles to locate objects over time. To address these issues, a novel method called transformer based CAM for videos (TrCAM-V), is proposed for WSVOL. It consists of a DeiT backbone with two heads for classification and localization. The classification head is trained using standard classification loss (CL), while the localization head is trained using pseudo-labels that are extracted using a pre-trained CLIP model. From these pseudo-labels, the high and low activation values are considered to be foreground and background regions, respectively. Our TrCAM-V method allows training a localization network by sampling pseudo-pixels on the fly from these regions. Additionally, a conditional random field (CRF) loss is employed to align the object boundaries with the foreground map. During inference, the model can process individual frames for real-time localization applications. Extensive experiments on challenging YouTube-Objects unconstrained video datasets show that our TrCAM-V method achieves new state-of-the-art performance in terms of classification and localization accuracy.

Published
2024

55. Total Variation Filter via Multiquadric Radial Basis Function Approximation Scheme for Additive Noise Removal

Author

Mushtaq Ahmad Khan, Ahmed B. Altamimi, Zawar Hussain Khan, Khurram Shehzad Khattak, Murtaza Ali, Asmat Ullah, Sheraz Khan, Muhammad Sohail Khan, and Muhammad Faisal Abrar

Subjects
Image denoising, digital total variation (DTV) filter, multiquadric radial basis function (MQ-RBF), restoration equation, traditional scheme, meshless scheme, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The Digital Total Variation (DTV) scheme is a digitized energy regularization scheme used for image denoising. This technique takes advantage of being applied to arbitrarily located data points and also has the edge detective property. This article aims to introduce a novel meshless scheme using DTV filtering and Radial Basis Functions (RBFs) to solve the associated equation with the DTV model numerically which results in the image denoising to remove additive noise from image information. This meshless algorithm based on local collocation and Multiquadric Radial Basis Function. These appearances allow this algorithm not only to remove the additive noise from images but also to resolve the discontinuities sharply. It is also noticed that the proposed meshless scheme is simple, fast, computationally effective, requires simply post-processing, and can be easily implemented mathematically. Experimental results confirm that the peak signal-to-noise ratio, the structural similarity, signal-to-noise ratio, the visual effect, and the computational performance of this new meshless scheme are improved compared with state-of-the-art denoising schemes. Furthermore, the proposed scheme can be applied to colour images as well.

Published
2020
Full Text
View/download PDF

56. Conformal and Disformal Structure of 3D Circularly Symmetric Static Metric in f(R) Theory of Gravity

Author

Muhammad Ramzan, Murtaza Ali, and Fiaz Hussain

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Science
Abstract

conformal vector fields are treated as generalization of homothetic vector fields while disformal vector fields are defined through disformal transformations which are generalization of conformal transformations, therefore it is important to study conformal and disformal vector fields. In this paper, conformal and disformal structure of 3D (Three Dimensional) circularly symmetric static metric is discussed in the framework of f(R) theory of gravity. The purpose of this paper is twofold. Firstly, we have found some dust matter solutions of EFEs (Einstein Field Equations) by considering 3D circularly symmetric static metric in the f(R) theory of gravity. Secondly, we have found CKVFs (Conformal Killing Vector Fields) and DKVFs (Disformal Killing Vector Fields) of the obtained solutions by means of some algebraic and direct integration techniques. A metric version of f(R) theory of gravity is used to explore the solutions and dust matter as a source of energy momentum tensor. This study reveals that no proper DVFs exists. Here, DVFs for the solutions under consideration are either HVFs (Homothetic Vector Fields) or KVFs (Killing Vector Fields) in the f(R) theory of gravity. In this study, two cases have been discussed. In the first case, both CKVFs and DKVFs become HVFs with dimension three. In the second case, there exists two subcases. In the first subcase, DKVFs become HVFs with dimension seven. In the second subcase, CKVFs and DKVFs become KVFs having dimension four.

Published
2020
Full Text
View/download PDF

57. Unilateral Biportal Endoscopic Tumor Removal and Percutaneous Stabilization for Extradural Tumors: Technical Case Report and Literature Review

Author

Seung-kook Kim, Riyad Bendardaf, Murtaza Ali, Hyun-a Kim, Eun-jung Heo, and Su-chan Lee

Subjects
biportal endoscopic spine surgery, endoscopic spine surgery, spinal cord tumor, tumor biopsy, unilateral biportal endoscopy, Surgery, RD1-811
Abstract

Background:Extradural spinal tumors arise from soft or bony tissues in the spine and account for majority of spinal tumors. Interest in the unilateral biportal endoscopic (UBE) technique is rising, because it can easily decompress the bony spinal canal and accommodate all open surgical instruments under endoscopic guidance. However, reports of this technique have been limited to certain diseases. This study first demonstrates the UBE technique for extradural tumor biopsy and removal, and percutaneous stabilization in a 72-year-old female patient with dramatic symptom improvement.MethodsWe used the UBE technique for decompression and the percutaneous screw fixation technique for stabilization in a patient with an extradural mass compressing the thecal sac and destroying the posterior element. Under endoscopic guidance, a unilateral approach was used, and decompression and flavectomy were performed bilaterally. After decompression, tumor removal and biopsy were performed using various forceps and biopsy needles. After confirming sufficient spinal canal decompression, the screw was placed percutaneously. We evaluated the technical process of the procedure, the patient's pre- and postoperative pain (using the visual analog scale), and operative radiology and pathologic results.ResultsPostoperative pain and disability improved clinically, and spinal alignment stabilized radiologically. As the pathology findings confirmed an aneurysmal bone cyst, the treatment was completed without adjuvant therapy.ConclusionsWe treated an unstable spine due to an extradural tumor with the UBE and percutaneous screw techniques.

Published
2022
Full Text
View/download PDF

58. Hospital Variation in Management and Outcomes of Acute Respiratory Distress Syndrome Due to COVID-19

Author

Shelsey W. Johnson, MD, Michael A. Garcia, MD, Emily K. Q. Sisson, MA, Christopher R. Sheldrick, PhD, Vishakha K. Kumar, MD, MBA, Karen Boman, BA, Scott Bolesta, PharmD, Vikas Bansal, MBBS, MPH, Amos Lal, MBBS, J. P. Domecq, MD, MS, Roman R. Melamed, MD, Amy B. Christie, MD, Abdurrahman Husain, MD, Santiago Yus, MBBS, Ognjen Gajic, MD, MSc, Rahul Kashyap, MBBS, MBA, Allan J. Walkey, MD, MSc, from the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS): COVID-19 Registry Investigator Group, Oscar Y. Gavidia, Felipe Pachon, Yeimy A. Sanchez, Mohamed El Kassas, Mohamed Badr, Ahmed Tawheed, Hend Yahia, Wataru Matsuda, Reina Suzuki, Masamitsu Sanui, Sho Horikita, Yuki Itagaki, Akira Kodate, Yuki Takahashi, Koyo Moriki, Muhammad Sohaib, Asghar, Mashaal Syed, Syed Anosh, Ali Naqvi, Borisovich Zabolotskikh, Konstantin Dmitrievich Zybin, Sergey Vasilevich Sinkov, Tatiana Sergeevna Musaeva, Mohammed A. Almazyad, Mohammed I. Alarifi, Jara M. Macarambon, Ahmad Abdullah Bukhari, Hussain A. Albahrani, Kazi N. Asfina, Kaltham M. Aldossary, Marija Zdravkovic, Zoran Todorovic, Viseslav Popadic, Slobodan Klasnja, Predrag D. Stevanovic, Dejan S. Stojakov, Duska K. Ignjatovic, Suzana C. Bojic, Marina M. Bobos, Irina B. Nenadic, Milica S. Zaric, Marko D. Djuric, Vladimir R. Djukic, Santiago Yus, Belen C. Martin, Uluhan Sili, Huseyin Bilgin, Pinar Ay, Roman R. Melamed, David M. Tierney, Love A. Patel, Vino S. Raj, Barite U. Dawud, Narayana Mazumder, Abbey Sidebottom, Alena M. Guenther, Benjamin D. Krehbiel, Nova J. Schmitz, Stacy L. Jepsen, Lynn Sipsey, Anna Schulte, Whitney Wunderlich, Cecely Hoyt, Kenneth W. Dodd, Nicholas Goodmanson, Kathleen Hesse, Paige Bird, Chauncey Weinert, Nathan Schoenrade, Abdulrahman Altaher, Esmael Mayar, Matthew Aronson, Tyler Cooper, Monica Logan, Brianna Miner, Gisele Papo, Abdulrahman Al-Fares, Anmol Kharbanda, Sunil Jhajhria, Zachary Fyffe, Amy B. Christie, Dennis W. Ashley, Rajani Adiga, Andrea Sikora Newsome, Christy C. Forehand, Rebecca Bruning, Timothy W. Jones, Moldovan Sabov, Fatema Zaidi, Fiona Tissavirasingham, Dhatri Malipeddi, Valerie M. Banner-Goodspeed, Somnath Bose, Lauren E. Kelly, Melisa Joseph, Marie McGourty, Krystal Capers, Benjamin Hoenig, Maria C. Karamourtopoulos, Anica C. Law, Elias N. Baedorf Kassis, Allan J. Walkey, Sushrut S. Waikar, Michael A. Garcia, Mia Colona, Zoe Kibbelaar, Michael Leong, Daniel Wallman, Kanupriya Soni, Jennifer Maccarone, Joshua Gilman, Ycar Devis, Joseph Chung, Munizay Paracha, David N. Lumelsky, Madeline DiLorenzo, Najla Abdurrahman, Shelsey Johnson, Kimberly Zammit, Patrick J. McGrath, William Loeffler, Maya R. Chilbert, Jean-Baptiste Mesland, Pierre Henin, Hélène Petre, Isabelle Buelens, Anne-Catherine Gerard, Rayan E. Ihle, Shelda A. Martin, Elaine A. Davis, Salim Surani, Joshua White, Aftab Khan, Rahul Dhahwal, Sreekanth Cheruku, Farzin Ahmed, Christopher Deonarine, Ashley Jones, Mohammad-Ali Shaikh, David Preston, Jeanette Chin, Steven K. Daugherty, Sam Atkinson, Kelly Shrimpton, Raquel R. Bartz, Vijay Krishnamoorthy, Bryan Kraft, Aaron Pulsipher, Eugene Friedman, Sachin Mehta, David P. Yamane, Ivy Benjenk, Nivedita Prasanna, Katharine Nault, Ronald A. Reilkoff, Julia A. Heneghan, Sarah Eichen, Lexie Goertzen, Scott Rajala, Ghislaine Feussom, Ben Tang, Mayo Clinic Arizona, Rodrigo Cartin-Ceba, Ayan Sen, Amanda Palacios, Giyth M. Mahdi, Rahul Kashyap, Ognjen Gajic, Vikas Bansal, Aysun Tekin, Amos Lal, John C. O’Horo, Neha N. Deo, Mayank Sharma, Shahraz Qamar, Juan Pablo Domecq, Romil Singh, Alex Niven, Mayo Clinic, Eau Claire, Abigail T. La Nou, Marija Bogojevic, Barbara Mullen, Devang Sanghavi, Pramod Guru, Pablo Moreno Franco, Karthik Gnanapandithan, Hollie Saunders, Zachary Fleissner, Juan Garcia, Alejandra Yu Lee Mateus, Siva Naga Yarrarapu, Nirmaljot Kaur, Abhisekh Giri, Syed Anjum Khan, Nitesh Kumar Jain, Thoyaja Koritala, Chakradhar Venkata, Miriam Engemann, Annamarie Mantese, Yasir Tarabichi, Adam Perzynski, Christine Wang, Dhatri Kotekal, Bhagat S. Aulakh, Sandeep Tripathi, Jennifer A. Bandy, Lisa M. Kreps, Dawn R. Bollinger, Roger Scott Stienecker, Andre G. Melendez, Tressa A. Brunner, Sue M. Budzon, Jessica L. Heffernan, Janelle M. Souder, Tracy L. Miller, Andrea G. Maisonneuve, Antonia L. Vilella, Sara B. Kutner, Kacie Clark, Danielle Moore, Harry L. Anderson, III, Dixy Rajkumar, Ali Abunayla, Jerrilyn Heiter, Howard A. Zaren, Stephanie J. Smith, Grant C. Lewis, Lauren Seames, Cheryl Farlow, Judy Miller, Gloria Broadstreet, Anthony Martinez, Micheal Allison, Aniket Mittal, Rafael Ruiz, Aleta Skaanland, Robert Ross, Neha Gupta, Tracy L. Jones, Shonda C. Ayers, Amy B. Harrell, Dr. Brent R. Brown, Abdurrahman Husain, Atul Malhotra, Qais Zawaydeh, Dragana Markotić, Ivana Bošnjak, Emily A. Vail, Susannah Nicholson, Rachelle B. Jonas, AnnaRose E. Dement, William Tang, Mark DeRosa, Robert E. Villarreal, Patrick W. McGonagill, Colette Galet, Janice Hubbard, David Wang, Lauren Allan, Aditya Badheka, Madhuradhar Chegondi, Roger A. Alvarez, Amarilys Alarcon-Calderon, Marie Anne Sosa, Sunita K. Mahabir, Mausam J. Patel, Faraaz Ali Shah, Byron Chuan, Sagar L. Rawal, Manal Piracha, Joseph E. Tonna, Nicholas M. Levin, Kayte Suslavich, Rachel Tsolinas, Zachary T. Fica, Chloe R. Skidmore, Renee D. Stapleton, Anne E. Dixon, Olivia Johnson, Sara S. Ardren, Stephanie Burns, Anna Raymond, Erika Gonyaw, Kevin Hodgdon, Chloe Housenger, Benjamin Lin, Karen McQuesten, Heidi Pecott-Grimm, Julie Sweet, Sebastian Ventrone, Murtaza Akhter, Rania Abdul Rahman, Mary Mulrow, Ashish K. Khanna, Lynne Harris, Bruce Cusson, Jacob Fowler, David Vaneenenaam, Glen McKinney, Imoh Udoh, Kathleen Johnson, Vishwanath Pattan, Jessica Papke, Ismail Jimada, Nida Mhid, and Samuel Chakola

Subjects
Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

OBJECTIVES:. To describe hospital variation in use of “guideline-based care” for acute respiratory distress syndrome (ARDS) due to COVID-19. DESIGN:. Retrospective, observational study. SETTING:. The Society of Critical Care Medicine’s Discovery Viral Infection and RESPIRATORY ILLNESS UNIVERSAL STUDY COVID-19 REGISTRY. PATIENTS:. Adult patients with ARDS due to COVID-19 between February 15, 2020, and April 12, 2021. INTERVENTIONS:. Hospital-level use of “guideline-based care” for ARDS including low-tidal-volume ventilation, plateau pressure less than 30 cm H2O, and prone ventilation for a Pao2/Fio2 ratio less than 100. MEASUREMENTS AND MAIN RESULTS:. Among 1,495 adults with COVID-19 ARDS receiving care across 42 hospitals, 50.4% ever received care consistent with ARDS clinical practice guidelines. After adjusting for patient demographics and severity of illness, hospital characteristics, and pandemic timing, hospital of admission contributed to 14% of the risk-adjusted variation in “guideline-based care.” A patient treated at a randomly selected hospital with higher use of guideline-based care had a median odds ratio of 2.0 (95% CI, 1.1–3.4) for receipt of “guideline-based care” compared with a patient receiving treatment at a randomly selected hospital with low use of recommended therapies. Median-adjusted inhospital mortality was 53% (interquartile range, 47–62%), with a nonsignificantly decreased risk of mortality for patients admitted to hospitals in the highest use “guideline-based care” quartile (49%) compared with the lowest use quartile (60%) (odds ratio, 0.7; 95% CI, 0.3–1.9; p = 0.49). CONCLUSIONS:. During the first year of the COVID-19 pandemic, only half of patients received “guideline-based care” for ARDS management, with wide practice variation across hospitals. Strategies that improve adherence to recommended ARDS management strategies are needed.

Published
2022
Full Text
View/download PDF

59. Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence.

Author

Young Sin Ko, Yoo Mi Choi, Mujin Kim, Youngjin Park, Murtaza Ashraf, Willmer Rafell Quiñones Robles, Min-Ju Kim, Jiwook Jang, Seokju Yun, Yuri Hwang, Hani Jang, and Mun Yong Yi

Subjects
Medicine, Science
Abstract

BackgroundColorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists.Methods and findingsOur whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7-10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17-30 times the number of potential human errors were detected within an average of 1.2 days.ConclusionsThe AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.

Published
2022
Full Text
View/download PDF

60. A Quasi Experimental study to Compare Thermo-regulator blanket with conventional method (incubator) for temperature regulations in preterm, Low Birth weight neonates landing at Emergency department of a tertiary care pediatric facility

Author

Murtaza Ali Gowa, Allah Rakhia, Muhammad Hayat Bozdar, Mehboob Noonari, and Syed Jamal Raza

Subjects
Medicine
Abstract

Objective: To compare incubator and thermal blanket for thermoregulation in preterm and low birth weight neonates. Method: The pilot study was conducted from January to March 2018 at the emergency department of the National Institute of Child Health, Karachi, and comprised preterm and low birth weight neonates without any co-morbidity who were randomised to control and interventional groups. Data was collected using a proforma about demographics, weight, gestational age and brief clinical history. Temperature, blood pressure and pulse were monitored along with other routine care after every 15 minutes in the incubator, and the same procedure was adopted for thermal blankets. Data was analysed using Statistical Package for Social Sciences (SPSS) Version 23. Results: Of the 38 subjects, 19(50%) each were cases and controls. The mean gestational age between the groups was not significantly different (p>0.05). Temperature for the first 60 minutes remained similar in the two groups, but at 75 minutes and onwards, the incubator showed a significantly better results (p

Published
2022
Full Text
View/download PDF

61. Human development as one of the factors of the transition to the knowledge economy

Author

Bimagambetova Zhibek T., Murtaza Alma Sh., Nurgalieva Mayra N., Tovma Nataliya A., Mishenko I., and Tleppayev Arsen

Subjects
Environmental sciences, GE1-350
Abstract

Human development is defined as a process of growth of human capabilities - a person must live a long life, not get sick, be educated, enjoy political and economic freedoms, human rights, public respect for the individual. The purpose of the study is to study the patterns of human development. In the course of the study, the following methods were used: analysis, synthesis, induction, deduction, sociological survey. The ratio of the components of the human capital index was studied, tools and criteria for improving the quality of human capital were identified.

Published
2023
Full Text
View/download PDF

62. Prophylaxis Against Thromboembolic Events During Chemotherapy for Germ Cell Cancer

Author

Xiaosong Meng, Murtaza Ahmed, Kevin D. Courtney, Waddah Arafat, Ibrahim Ibrahim, Vitaly Margulis, Craig Nichols, and Aditya Bagrodia

Subjects
germ cell tumor, thromboembolic event, chemotherapy, anticoagulation, cisplatin, khorana risk score, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

IntroductionPatients with advanced germ cell tumors (GCT) receiving cisplatin-based chemotherapy have high rates of thromboembolic events (TEE) which can negatively affect their overall survival. While primary TEE prophylaxis during chemotherapy may prevent these events, it is unclear which patients will benefit in this setting.Materials and MethodsA review of PubMed/Medline was conducted in December 2020 and all pertinent articles were evaluated for relevancy and quality of data for inclusion in the review.ResultsStudies on patients receiving initial cisplatin-based chemotherapy for advanced GCT have reported up to a 19% rate of TEE. This high rate may be associated with multiple factors including retroperitoneal lymphadenopathy, advanced clinical stage, high risk Khorana scores and presence of a central line. Large phase III clinical trials have demonstrated the benefit of low-molecular-weight-heparin and direct oral anticoagulants for primary prophylaxis and against recurrent TEE. However, primary prophylaxis is currently underutilized with GCT patients starting chemotherapy.ConclusionPrecise models to predict TEE risk and consideration of anticoagulation are difficult to develop owing to the relatively uncommon nature of GCT and lack of representation in primary TEE prophylaxis clinical trials. Despite these limitations, we believe that the benefits of prophylactic anticoagulation outweigh the risk of major bleeding in select GCT patients with higher risk of TEE. We have developed a simple algorithm to help guide TEE prophylaxis selection based on patient factors and route of chemotherapy administration. Given the high rate of TEE in GCT patients, we believe better utilization of primary prophylaxis in patient starting cisplatin-based chemotherapy will have clinical benefit.

Published
2021
Full Text
View/download PDF

63. Incidental case finding of a 19‐year‐old woman with Tuberous Sclerosis Complex: A step‐wise Multidisciplinary approach

Author

Sajjad Ali, Samahir Tariq Khan, Hamza Usman, Ali Hamza Khan, Zain Douba, and Murtaza Ali

Subjects
MRI, neurocutaneous, shagreen patch, tuberous sclerosis complex, Medicine, Medicine (General), R5-920
Abstract

Abstract Radiological imaging plays a vital role in clinically diagnosing TSC. TSC prognosis is largely determined by the severity and the extent of the systems affected by it. TSC patients are symptomatically managed, since no cure is present. Healthcare professionals must frequently check‐up TSC patients who have a lifelong disorder.

Published
2021
Full Text
View/download PDF

64. Risk Factors for Critical Coronavirus Disease 2019 and Mortality in Hospitalized Young Adults: An Analysis of the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS) Coronavirus Disease 2019 Registry

Author

Sandeep Tripathi, MD, MS, Imran A. Sayed, MD, Heda Dapul, MD, Jeremy S. McGarvey, MS, Jennifer A. Bandy, RN, Karen Boman, BS, Vishakha K. Kumar, MD, MBA, Vikas Bansal, MBBS, MPH, Lynn Retford, CAE, Sreekanth Cheruku, MD, MPH, Margit Kaufman, MD, FASA, Smith F. Heavner, MS, RN, Valerie C. Danesh, PhD, RN, Catherine A. St. Hill, DVM, PhD, Ashish K. Khanna, MD, Utpal Bhalala, MD, Rahul Kashyap, MBBS, MBA, Ognjen Gajic, MD, MS, Allan J. Walkey, MD, MS, Katja M. Gist, DO, MSc, for The Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS): COVID-19 Registry Investigator Group, Jean-Baptiste Mesland, Pierre Henin, Hélène Petre, Isabelle Buelens, Anne-Catherine Gerard, Philippe Clevenbergh, Rolando Claure-Del Granado, Jose A. Mercado, Esdenka Vega-Terrazas, Maria F. Iturricha-Caceres, Ruben Garza, Eric Chu, Victoria Chan, Oscar Y Gavidia, Felipe Pachon, Yeimy A Sanchez, Mohamed El Kassas, Mohamed Badr, Ahmed Tawheed, Hend Yahia, Sierra-Hoffman, Fernando Valerio, Oscar Diaz, Jose Luis Ramos Coello, Guillermo Perez, Ana Karen Vallecillo Lizardo, Gabina María Reyes Guillen, Helin Archaga Soto, Mradul Kumar Daga, Munisha Agarwal, Ishan Rohtagi, Anusha Cherian, Sreejith Parameswaran, Magesh Parthiban, Menu Priya A., Girish Vadgaonkar, Rekha Ediga, Shilpa Basety, Shwetha Dammareddy, Phani Sreeharsha Kasumalla, Sridhar Papani, Mahesh Kamuram, Smitha S. Segu, Tuhin Chakraborty, Epcebha Joyce, Umamaheswara Raju, Janaki Manduva, Naresh Kolakani, Shreeja Sripathi, Sheetal Chaitanya, Surapaneni Krishna Mohan, Ekambaram Jyothisree, Kamlesh Kumar Agrawal, Vijendra Baghel, Kirti Kumar Patel, Nooshin Dalili, Mohsen Nafa, Sandeep Tripathi, Yuki Itagaki, Akira Kodate, Reina Suzuki, Yuki Takahashi, Koyo Moriki, Michihito Kyo, Masamitsu Sanui, Sho Horikita, Wataru Matsuda, Shu Tahara, Mineji Hayakawa, Kunihiko Maekawa, Takuya Shiga, Yudai Iwasaki, Abdulrahman AlFares, Rene Rodriguez-Gutierrez, Jose Gerardo Gonzalez-Gonzalez, Alejandro Salcido-Montenegro, Adrian Camacho-Ortiz, Fatimah Hassan-Hanga, Hadiza Galadanci, Abubakar Shehu Gezawa, Halima M. S. Kabara, Taiwo Gboluwaga Amole, Halima Kabir, Dalha Gwarzo, Haliru, Abdullahi S Ibrahim, Muhammad Sohaib Asghar, Mashaal Syed, Syed Anosh Ali Naqvi, Igor Borisovich Zabolotskikh, Konstantin Dmitrievich Zybin, Sergey Vasilevich Sinkov, Tatiana Sergeevna Musaeva, Marwa Ridha Amer, Mohammed Abdullah Bawazeer, Talal I. Dahhan, Eiad Kseibi, Abid Shahzad Butt, Syed Moazzum Khurshid, Muath Rabee, Mohammed Abujazar, Razan Alghunaim, Maal Abualkhair, Abeer Turki AlFirm, Razan K Alamoudi, Hassan M. AlSharif, Sarah A. Almazwaghi, Mohammed S Elsakran, Mohamed A Aid, Mouaz A Darwich, Omnia M Hagag, Salah A Ali, Alona rocacorba, Kathrine Supeña, Efren Ray Juane, Jenalyn Medina, Jowany Baduria, Mohammed A Almazyad, Mohammed I Alarifi, Jara M Macarambon, Ahmad Abdullah Bukhari, Hussain A. Albahrani, Kazi N Asfina, Kaltham M Aldossary, Predrag D Stevanovic, Dejan S Stojakov, Duska K Ignjatovic, Suzana C Bojic, Marina M Bobos, Irina B Nenadic, Milica S Zaric, Marko D Djuric, Vladimir R Djukic, Bojan Kovacevic, Jovana Bojicic, Marija Zdravkovic, Zoran Todorovic, Viseslav Popadic, Slobodan Klasnja, Santiago Y. Teruel, Belen C. Martin, Himat Sulaimonov, Firuza Khudonazarova, Nabi Bakhtibekov, Nekruz Jamshedov, Uluhan Sili, Huseyin Bilgin, Pinar Ay, Varsha P Gharpure, Usman Raheemi, Kenneth W. Dodd, Nicholas Goodmanson, Kathleen Hesse, Paige Bird, Chauncey Weinert, Nathan Schoenrade, Abdulrahman Altaher, Esmael Mayar, Matthew Aronson, Tyler Cooper, Monica Logan, Brianna Miner, Gisele Papo, Eric M. Siegal, Phyllis Runningen, Catherine A. St. Hill, Roman R. Melamed, David M. Tierney, Love A. Patel, Vino S. Raj, Barite U. Dawud, Narayana Mazumder, Abbey Sidebottom, Alena M. Guenther, Benjamin D. Krehbiel, Nova J. Schmitz, Stacy L. Jepsen, Abhijit A Raval, Andrea Franks, Katherine Irby, Ronald C. Sanders, Jr., Glenda Hefley, Anmol Kharbanda, Sunil Jhajhria, Zachary Fyffe, Stephen Capizzi, Bethany Alicie, Martha Green, Lori Crockarell, Amelia Drennan, Kathleen Dubuque, Tonya Fambrough, Nikole Gasaway, Briana Krantz, Peiman Nebi, Jan Orga, Margaret Serfass, Alina Simion, Kimberly Warren, Cassie Wheeler, CJ Woolman, Andrew S. Moyer, George M. Verghese, Andrea Sikora Newsome, Christy C. Forehand, Rebecca Bruning, Timothy W. Jones, Moldovan Sabov, Fatema Zaidi, Fiona Tissavirasingham, Dhatri Malipeddi, Jarrod M Mosier, Karen Lutrick, Beth Salvagio Campbell, Cathleen Wilson, Patrick Rivers, Jonathan Brinks, Mokenge Ndiva Mongoh, Boris Gilson, Donna Lee Armaignac, Don Parris, Maria Pilar Zuniga, Ilea Vargas, Viviana Boronat, Anneka Hutton, Navneet Kaur, Prashank Neupane, Nohemi Sadule-Rios, Lourdes M. Rojas, Aashish Neupane, Priscilla Rivera, Carlos Valle Carlos, Gregory Vincent, Mahesh Amin, Mary E Schelle, Amanda Steadham, Christopher M Howard, Cameron McBride, Jocelyn Abraham, Orlando Garner, Katherine Richards, Keegan Collins, Preethi Antony, Sindhu Mathew, Valerie C. Danesh, Gueorgui Dubrocq, Amber L. Davis, Marissa J Hammers, ill M. McGahey, Amanda C. Farris, Elisa Priest, Robyn Korsmo, Lorie Fares, Kathy Skiles, Susan M. Shor, Kenya Burns, Corrie A Dowell, Melody Flores, Lindsay Newman, Debora A Wilk, Jason Ettlinger, Jaccallene Bomar, Himani Darji, Alejandro Arroliga, Alejandro C Arroliga, Corrie A. Dowell, Gabriela Hope Conzales, Debora A. Wilk, Paras B. Khandhar, Elizabeth Kring, Valerie M. Banner-Goodspeed, Somnath Bose, Lauren E. Kelly, Melisa Joseph, Marie McGourty, Krystal Capers, Benjamin Hoenig, Maria C. Karamourtopoulos, Anica C. Law, Elias N. Baedorf Kassis, Allan J. Walkey, Sushrut S. Waikar, Michael A. Garcia, Mia Colona, Zoe Kibbelaar, Michael Leong, Daniel Wallman, Kanupriya Soni, Jennifer Maccarone, Joshua Gilman, Ycar Devis, Joseph Chung, Munizay Paracha, David N. Lumelsky, Madeline DiLorenzo, Najla Abdurrahman, Shelsey Johnson, Andrew M. Hersh, Stephanie L Wachs, Brittany S. Swigger, Lauren A. Sattler, Michael N. Moulton, Kimberly Zammit, J Patrick, William McGrath, Maya Loeffler, R Chilbert, Aaron S. Miller, Edwin L. Anderson, Rosemary Nagy, Ravali R. Inja, Raghavendra Tirupathi, Alymer Tang, Arshad Safi, Cindy Green, Jackie Newell, Rayan E. Ihle, Shelda A. Martin, Elaine A. Davis, Katja M. Gist, Imran A Sayed, John Brinton, Larisa Strom, Kathleen Chiotos, Allison M. Blatz, Giyoung Lee, Ryan H. Burnett, Guy I. Sydney, Danielle M. Traynor, Karissa Nauert, Annika Gonzalez, Mariel Bagley, Anita Santpurkar, Salim Surani, Joshua White, Aftab Khan, Rahul Dhahwal, Sreekanth Cheruku, Farzin Ahmed, Christopher Deonarine, Ashley Jones, Mohammad-Ali Shaikh, David Preston, Jeanette Chin, Vidula Vachharajani, Abhijit Duggal, Prabalini Rajendram, Omar Mehkri, Siddharth Dugar, Michelle Biehl, Gretchen Sacha, Stuart Houltham, Alexander King, Kiran Ashok, Bryan Poynter, Mary Beukemann, Richard Rice, Susan Gole, Valerie Shaner, Adarsh Conjeevaram, Michelle Ferrari, Narendrakumar Alappan, Steven Minear, Jaime Hernandez-Montfort, Syed Sohaib Nasim, Ravi Sunderkrishnan, Debasis Sahoo, Patrick S. Milligan, Sandeep K. Gupta, Joy M. Koglin, Regina Gibson, Lana Johnson, Felicia Preston, Crimson Scott, Bethany Nungester, Steven K. Daugherty, Sam Atkinson, Kelly Shrimpton, Sidney Ontai, Brian Contreras, Uzoma Obinwanko, Nneka Amamasi, Amir Sharafi, Sarah Lee, Zahia Esber, Chetna Jinjvadia, Kimberly Welker, Francis M. Maguire, Jessica Timmer, Raquel R Bartz, Vijay Krishnamoorthy, Bryan Kraft, Aaron Pulsipher, Eugene Friedman, Sachin Mehta, Margit Kaufman, Gregg Lobel, Nisha Gandhi, Amr Abdelaty, Elizabeth Shaji, Kiana Lim, Juan Marte, Dani Ashley Sosa, David P. Yamane, Ivy Benjenk, Nivedita Prasanna, Smith F. Heavner-Sullivan, Prera J. Roth, Banu Sivaraj, Haley Fulton, Madison G Herin, Marissa Crum, Morgan E. Fretwell, Emily-Rose Zhou, Christine Waller, Kara Kallies, Jonean Thorsen, Alec Fitzsimmons, Haley Olsen, Heda R. Dapul, Sourabh Verma, Alan Salas, Ariel Daube, Michelle Korn, Michelle Ramirez, Logi Rajagopalan, Laura Santos, Orma Smalls, Atul Malhotra, Abdurrahman Husain, Qais Zawaydeh, J.H. Steuernagle, Steven Q. Davis, Valentina Jovic, Max Masuda, Amanda Hayes, Katharine Nault, Michael Smith, William Snow, Riley Liptak, Hannah Durant, Valerie Pendleton, Alay Nanavati, Risa Mrozowsk, LiManoj K Gupta, Franscene E. Oulds, Akshay Nandavar, Yuk Ming Liu, Sarah Zavala, Esther Shim, Ronald A. Reilkoff, Julia A. Heneghan, Sarah Eichen, Lexie Goertzen, Scott Rajala, Ghislaine Feussom, Ben Tang, Christine C. Junia, Robert Lichtenberg, Hasrat Sidhu, Diana Espinoza, Shelden Rodrigues, Maria Jose Zabala, Daniela Goyes, Ammu Susheela, Buddhi Hatharaliyadda, Naveen Rameshkumar, Amulya Kasireddy, Genessis Maldonado, Lisseth Beltran, Akshata Chaugule, Hassan Khan, Namrata Patil, Ruhi Patil, Rodrigo Cartin-Ceba, Ayan Sen, Amanda Palacios, Giyth M. Mahdi, Rahul Kashyap, Ognjen Gajic, Vikas Bansal, Aysun Tekin, Amos Lal, John C. O’Horo, Neha N. Deo, Mayank Sharma, Shahraz Qamar, Cory J. Kudrna, Juan Pablo Domecq Garces, Abigail T. La Nou, Marija Bogojevic, Devang Sanghavi, Pramod Guru, Karthik Gnanapandithan, Hollie Saunders, Zachary Fleissner, Juan Garcia, Alejandra Yu Lee Mateus, Siva Naga Yarrarapu, Syed Anjum Khan, Juan Pablo Domecq, Nitesh Kumar Jain, Thoyaja Koritala, Alexander Bastidas, Gabriela Orellana, Adriana Briceno Bierwirth, Eliana Milazzo, Juan Guillermo Sierra, Thao Dang, Amy B. Christie, Dennis W. Ashley, Rajani Adiga, Rahul S Nanchal, Paul A Bergl, Jennifer L Peterson, Travis Yamanaka, Nicholas A. Barreras, Michael Markos, Anita Fareeduddin, Rohan Mehta, Chakradhar Venkata, Miriam Engemann, Annamarie Mantese, Yasir Tarabichi, Adam Perzynski, Christine Wang, Dhatri Kotekal, Adriana C Briceno Bierwirth, Gabriela M Orellana, Gerardo Catalasan, Shohana Ahmed, Carlos F Matute, Ahmad Hamdan, Ivania Salinas, Genesis Del Nogal, Angel Tejada, Anna Eschler, Mary Hejna, Emily Lewandowski, Kristen Kusmierski, Clare Martin, Jen-Ting Chen, Aluko Hope, Zoe Tsagaris, Elise Ruen, Aram Hambardzumyan, Prithvi Sendi, Meghana Nadiger, Balagangadhar Totapally, Bhagat S. Aulakh, Jennifer A. Bandy, Lisa M. Kreps, Dawn R. Bollinger, Roger Scott Stienecker, Andre G. Melendez, Tressa A. Brunner, Sue M Budzon, Jessica L. Heffernan, Janelle M. Souder, Tracy L. Miller, Andrea G. Maisonneuve, Roberta E. Redfern, Jessica Shoemaker, Jennifer Micham, Lynn Kenney, Gabriel Naimy, Sara Utley, Holly Balcer, Kerry P. J. Pulver, Jennifer Yehle, Alicia Weeks, Terra Inman, Brian L. Delmonaco, Anthony Franklin, Mitchell Heath, Antonia L. Vilella, Sara B. Kutner, Kacie Clark, Danielle Moore, Shina Menon, John K McGuire, Deana Rich, Harry L. Anderson, III, Dixy Rajkumar, Ali Abunayla, Jerrilyn Heiter, Howard A. Zaren, Stephanie J. Smith, Grant C. Lewis, Lauren Seames, Cheryl Farlow, Judy Miller, Gloria Broadstreet, John Lin, Cindy Terrill, Brock Montgomery, Sydney Reyes, Summer Reyes, Alex Plattner, Anthony Martinez, Micheal Allison, Aniket Mittal, Rafael Ruiz, Aleta Skaanland, Robert Ross, Umang Patel, Jordesha Hodge, KrunalKumar Patel, Shivani Dalal, Himanshu Kavani, Sam Joseph, Michael A. Bernstein, Ian K. Goff, Matthew Naftilan, Amal Mathew, Deborah Williams, Sue Murdock, Maryanne Ducey, Kerianne Nelson, Paul K Mohabir, Connor G O’Brien, Komal Dasani, William Marx, Ioana Amzuta, Asad J. Choudhry, Mohammad T. Azam, Kristina L Carter, Michael A Olmos, Brittany M Parker, Julio Quintanilla, Tara A Craig, Brendon J Clough, Jeffrey T Jameson, Neha Gupta, Tracy L Jones, Shonda C Ayers, Amy B Harrell, Dr.Brent R Brown, Utpal S. Bhalala, Joshua Kuehne, Melinda Garcia, Morgan Beebe, Heather Herrera, Chris Fiack, Stephanie Guo, May Vawer, Beth Blackburn, Megan Edwards, Caleb Darby, Kristy Page, Amanda Brown, Jessie McAbee, Katherine A. Belden, Michael Baram, Devin M. Weber, Rosalie DePaola, Yuwei Xia, Hudson Carter, Aaron Tolley, Mark Steele, Laurie Kemble, Joshua L. Denson, A. Scott Gillet, Margo Brown, Rachael Stevens, Andrew Wetherbie, Kevin Tea, Mathew Moore, Benjamin J Sines, Thomas J Bice, Emily A. Vail, Susannah Nicholson, Rachelle B. Jonas, AnnaRose E. Dement, William Tang, Mark DeRosa, Robert E. Villarreal, Rajany V. Dy, Alfredo Iardino, Jill Sharma, Richard Czieki, Julia Christopher, Ryan Lacey, Marwan Mashina, Kushal Patel, Erica C. Bjornstad, Nancy M. Tofil, Scott House, Isabella Aldana, Nikhil K. Meena, Jose D. Caceres, Nikhil K Meena, Sarenthia M. Epps, Harmeen Goraya, Kelsey R. Besett, Ryan James, Lana Y. Abusalem, Akash K. Patel, Lana S Hasan, Dina Gomaa, Michael Goodman, Devin Wakefield, Anthony Spuzzillo, John O. Shinn, II, Azra Bihorac, Tezcan Ozrazgat Baslanti, George Omalay, Haleh Hashemighouchani, Julie S. Cupka, Matthew M Ruppert, Patrick W. McGonagill, Colette Galet, Janice Hubbard, David Wang, Lauren Allan, Aditya Badheka, Madhuradhar Chegondi, Usman Nazir, Garrett Rampon, Jake Riggle, Nathan Dismang, Vicki Montgomery, Janice Sullivan, Sarah Morris, Jennifer Nason, Roger A. Alvarez, Amarilys Alarcon-Calderon, Marie Anne Sosa, Sunita K. Mahabir, Mausam J. Patel, Pauline Park, Andrew Admon, Sinan Hanna, Rishi Chanderraj, Maria Pliakas, Ann Wolski, Jennifer Cirino, Dima Dandachi, Hariharan Regunath, Maraya N. Camazine, Grant. E. Geiger, Abdoulie O. Njai, Baraa M. Saad, Faraaz Ali Shah, Byron Chuan, Sagar L. Rawal, Manal Piracha, Joseph E. Tonna, Nicholas M. Levin, Kayte Suslavich, Rachel Tsolinas, Zachary T. Fica, Chloe R. Skidmore, Renee D. Stapleton, Anne E. Dixon, Olivia Johnson, Sara S. Ardren, Stephanie Burns, Anna Raymond, Erika Gonyaw, Kevin Hodgdon, Chloe Housenger, Benjamin Lin, Karen McQuesten, Heidi Pecott-Grimm, Julie Sweet, Sebastian Ventrone, Nita Khandelwal, T. Eoin West, Ellen S. Caldwell, Lara Lovelace-Macon, Navya Garimella, Denisse B. Dow, Murtaza Akhter, Rania Abdul Rahman, Mary Mulrow, Erin M. Wilfong, Kelsi Vela, Ashish K. Khanna, Lynne Harris, Bruce Cusson, Jacob Fowler, David Vaneenenaam, Glen McKinney, Imoh Udoh, Kathleen Johnson, Patrick G. Lyons, Andrew P Michelson, Sara S. Haluf, Lauren M. Lynch, Nguyet M. Nguyen, Aaron Steinberg, Nicholas Braus, Vishwanath Pattan, Jessica Papke, Ismail Jimada, Nida Mhid, and Samuel Chakola

Subjects
Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

IMPORTANCE:. Even with its proclivity for older age, coronavirus disease 2019 has been shown to affect all age groups. However, there remains a lack of research focused primarily on the young adult population. OBJECTIVES:. To describe the epidemiology and outcomes of coronavirus disease 2019 and identify the risk factors associated with critical illness and mortality in hospitalized young adults. DESIGN, SETTINGS, AND PARTICIPANTS:. A retrospective cohort study of the Society of Critical Care Medicine’s Viral Infection and Respiratory Illness Universal Study registry. Patients 18–40 years old, hospitalized from coronavirus disease 2019 from March 2020 to April 2021, were included in the analysis. MAIN OUTCOMES AND MEASURES:. Critical illness was defined as a composite of mortality and 21 predefined interventions and complications. Multivariable logistic regression was used to assess associations with critical illness and mortality. RESULTS:. Data from 4,005 patients (152 centers, 19 countries, 18.6% non-U.S. patients) were analyzed. The median age was 32 years (interquartile range, 27–37 yr); 51% were female, 29.4% Hispanic, and 42.9% had obesity. Most patients (63.2%) had comorbidities, the most common being hypertension (14.5%) and diabetes (13.7%). Hospital and ICU mortality were 3.2% (129/4,005) and 8.3% (109/1,313), respectively. Critical illness occurred in 25% (n = 996), and 34.3% (n = 1,376) were admitted to the ICU. Older age (p = 0.03), male sex (adjusted odds ratio, 1.83 [95% CI, 1.2–2.6]), and obesity (adjusted odds ratio, 1.6 [95% CI, 1.1–2.4]) were associated with hospital mortality. In addition to the above factors, the presence of any comorbidity was associated with critical illness from coronavirus disease 2019. Multiple sensitivity analyses, including analysis with U.S. patients only and patients admitted to high-volume sites, showed similar risk factors. CONCLUSIONS:. Among hospitalized young adults, obese males with comorbidities are at higher risk of developing critical illness or dying from coronavirus disease 2019.

Published
2021
Full Text
View/download PDF

65. Utility of Flexible Fiberoptic Nasolaryngoscopy in Assessment of Laryngeal Lesions Among Suspected Patients Living in Karachi, Pakistan

Author

Syeda Uzma Naqvi, Sadaf Zia, Muhammad Shuja Farrukh, Murtaza Ahsan Ansari, and Atif Hafeez Siddiqui

Subjects
fiberoptic laryngoscopy, laryngeal diseases, hoarseness, throat symptoms, Medicine
Abstract

Objective: To assess the utility of lexible iberoptic nasolaryngoscopy in diagnosing laryngeal lesions and identifying factors contributing to the development of serious laryngeal pathologies among suspicious patients. Methods: A cross-sectional study was conducted from August 2017 to August 2018 at Dow University Hospital. Patients who came to an ENT outpatient clinic with a complaint of hoarseness, dysphagia, and sortness of breath (SOB) were asked to undergo Fiber optic direct laryngoscopy (FODL) examination. Laryngeal pathologies were noted on FODL along with demographic characteristics, presenting complaints, and aggravating factors. Results: Findings of the FODL showed that amongst 86 patients with regular complains of upper respiratory disease, lesions were found to be higher in vocal cords (n=34, 39.5%), followed by posterior pharyngeal walls (n=19, 22.1%), and pyriform fossa (n=13, 15.1%). A signiicant association of pyriform was observed with history of radiaton (p-value 0.023). Similarly, posterior commissure was also found signiicantly associated with history of radiation (p-value 0.041). Moreover, exposure to air pollution was also found signiicantly associated with ary epiglottic folds (p-value 0.036), ventricular bands (p-value 0.004), vocal cords (p-value 0.004), anterior commissure (p-value 0.017), sub-glottis (p-value 0.016), and posterior pharyngeal walls (p-value 0.046). Conclusion: Eficacy of iberoptic nasolaryngoscopy in diagnosing laryngeal lesions among patients with regular complains of upper respiratory tract was found signiicant especially in thick populated industrial city. We recommend flexible nasolaryngoscopy examination in routine chekups at primary and secondary health units as it is low-cost and free of anesthesia risk and therefore can reduce burden of advanced disease from society.

Published
2019
Full Text
View/download PDF

66. On the Families of Graphs With Unbounded Metric Dimension

Author

Heng Pan, Murtaza Ali, Gohar Ali, Muhammad Tariq Rahim, and Xiaopeng Yang

Subjects
Metric dimension, basis, resolving set, splitting graph, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

A family G of connected graphs is a family with unbounded metric dimension if dim(G) is not constant and depends on the order of graph. In this paper, we compute the metric dimension of the splitting graphs S(Pn) and S(Cn) of a path and cycle. We prove that the metric dimension of these graphs varies and depends on the number of vertices of the graph.

Published
2019
Full Text
View/download PDF

67. SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution

Author

Belharbi, Soufiane, Whitford, Mara KM, Hoang, Phuong, Murtaza, Shakeeb, McCaffrey, Luke, and Granger, Eric

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Confocal fluorescence microscopy is one of the most accessible and widely used imaging techniques for the study of biological processes at the cellular and subcellular levels. Scanning confocal microscopy allows the capture of high-quality images from thick three-dimensional (3D) samples, yet suffers from well-known limitations such as photobleaching and phototoxicity of specimens caused by intense light exposure, limiting its applications. Cellular damage can be alleviated by changing imaging parameters to reduce light exposure, often at the expense of image quality. Machine/deep learning methods for single-image super-resolution (SISR) can be applied to restore image quality by upscaling lower-resolution (LR) images to yield high-resolution images (HR). These SISR methods have been successfully applied to photo-realistic images due partly to the abundance of publicly available data. In contrast, the lack of publicly available data partly limits their application and success in scanning confocal microscopy. In this paper, we introduce a large scanning confocal microscopy dataset named SR-CACO-2 that is comprised of low- and high-resolution image pairs marked for three different fluorescent markers. It allows the evaluation of performance of SISR methods on three different upscaling levels (X2, X4, X8). SR-CACO-2 contains the human epithelial cell line Caco-2 (ATCC HTB-37), and it is composed of 2,200 unique images, captured with four resolutions and three markers, forming 9,937 image patches for SISR methods. We provide benchmarking results for 16 state-of-the-art methods of the main SISR families. Results show that these methods have limited success in producing high-resolution textures. The dataset is freely accessible under a Creative Commons license (CC BY-NC-SA 4.0). Our dataset, code and pretrained weights for SISR methods are available: https://github.com/sbelharbi/sr-caco-2., Comment: 27 pages, 15 figures, NeurIPS 2024

Published
2024

68. Speech Loudness in Broadcasting and Streaming

Author

Torcoli, Matteo, Halimeh, Mhd Modar, Leitz, Thomas, Grewe, Yannik, Kratschmer, Michael, Neugebauer, Bernhard, Murtaza, Adrian, Fuchs, Harald, and Habets, Emanuël A. P.

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

The introduction and regulation of loudness in broadcasting and streaming brought clear benefits to the audience, e.g., a level of uniformity across programs and channels. Yet, speech loudness is frequently reported as being too low in certain passages, which can hinder the full understanding and enjoyment of movies and TV programs. This paper proposes expanding the set of loudness-based measures typically used in the industry. We focus on speech loudness, and we show that, when clean speech is not available, Deep Neural Networks (DNNs) can be used to isolate the speech signal and so to accurately estimate speech loudness, providing a more precise estimate compared to speech-gated loudness. Moreover, we define critical passages, i.e., passages in which speech is likely to be hard to understand. Critical passages are defined based on the local Speech Loudness Deviation (SLD) and the local Speech-to-Background Loudness Difference (SBLD), as SLD and SBLD significantly contribute to intelligibility and listening effort. In contrast to other more comprehensive measures of intelligibility and listening effort, SLD and SBLD can be straightforwardly measured, are intuitive, and, most importantly, can be easily controlled by adjusting the speech level in the mix or by enabling personalization at the user's end. Finally, examples are provided that show how the detection of critical passages can support the evaluation and control of the speech signal during and after content production., Comment: Accepted for presentation at the Audio Engineering Society (AES) 156th Convention, June 2024, Madrid, Spain

Published
2024

69. Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks

Author

Dalal, Murtaza, Chiruvolu, Tarun, Chaplot, Devendra, and Salakhutdinov, Ruslan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e.g. picking, placing, pulling, pushing, navigating). However, LLM planning does not address how to design or learn those behaviors, which remains challenging particularly in long-horizon settings. Furthermore, for many tasks of interest, the robot needs to be able to adjust its behavior in a fine-grained manner, requiring the agent to be capable of modifying low-level control actions. Can we instead use the internet-scale knowledge from LLMs for high-level policies, guiding reinforcement learning (RL) policies to efficiently solve robotic control tasks online without requiring a pre-determined set of skills? In this paper, we propose Plan-Seq-Learn (PSL): a modular approach that uses motion planning to bridge the gap between abstract language and learned low-level control for solving long-horizon robotics tasks from scratch. We demonstrate that PSL achieves state-of-the-art results on over 25 challenging robotics tasks with up to 10 stages. PSL solves long-horizon tasks from raw visual input spanning four benchmarks at success rates of over 85%, out-performing language-based, classical, and end-to-end approaches. Video results and code at https://mihdalal.github.io/planseqlearn/, Comment: Published at ICLR 2024. Website at https://mihdalal.github.io/planseqlearn/ 9 pages, 3 figures, 3 tables; 14 pages appendix (7 additional figures)

Published
2024

70. Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

Author

Guichemerre, Alexis, Belharbi, Soufiane, Mayet, Tsiry, Murtaza, Shakeeb, Shamsolmoali, Pourya, McCaffrey, Luke, and Granger, Eric

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Camelyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification., Comment: 16 pages, 21 figures, 5 tables, CVPRw 2024

Published
2024

71. NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Author

Liu, Xiaoning, Wu, Zongwei, Li, Ao, Vasluianu, Florin-Alexandru, Zhang, Yulun, Gu, Shuhang, Zhang, Le, Zhu, Ce, Timofte, Radu, Jin, Zhi, Wu, Hongjun, Wang, Chenxi, Ling, Haitao, Cai, Yuanhao, Bian, Hao, Zheng, Yuxin, Lin, Jing, Yuille, Alan, Shao, Ben, Guo, Jin, Liu, Tianli, Wu, Mohao, Feng, Yixu, Hou, Shuo, Lin, Haotian, Zhu, Yu, Wu, Peng, Dong, Wei, Sun, Jinqiu, Zhang, Yanning, Yan, Qingsen, Zou, Wenbin, Yang, Weipeng, Li, Yunxiang, Wei, Qiaomu, Ye, Tian, Chen, Sixiang, Zhang, Zhao, Zhao, Suiyi, Wang, Bo, Luo, Yan, Zuo, Zhichao, Wang, Mingshen, Wang, Junhu, Wei, Yanyan, Sun, Xiaopeng, Gao, Yu, Huang, Jiancheng, Chen, Hongming, Chen, Xiang, Tang, Hui, Chen, Yuanbin, Zhou, Yuanbo, Dai, Xinwei, Qiu, Xintao, Deng, Wei, Gao, Qinquan, Tong, Tong, Li, Mingjia, Hu, Jin, He, Xinyu, Guo, Xiaojie, Sabarinathan, Uma, K, Sasithradevi, A, Bama, B Sathya, Roomi, S. Mohamed Mansoor, Srivatsav, V., Wang, Jinjuan, Sun, Long, Chen, Qiuying, Shao, Jiahong, Zhang, Yizhi, Conde, Marcos V., Feijoo, Daniel, Benito, Juan C., García, Alvaro, Lee, Jaeho, Kim, Seongwan, A, Sharif S M, Khujaev, Nodirkhuja, Tsoy, Roman, Murtaza, Ali, Khairuddin, Uswah, Faudzi, Ahmad 'Athif Mohd, Malagi, Sampada, Joshi, Amogh, Akalwadi, Nikhil, Desai, Chaitra, Tabib, Ramesh Ashok, Mudenagudi, Uma, Lian, Wenyi, Lian, Wenjing, Kalyanshetti, Jagadeesh, Aralikatti, Vijayalaxmi Ashok, Yashaswini, Palani, Upasi, Nitish, Hegde, Dikshit, Patil, Ujwala, C, Sujata, Yan, Xingzhuo, Hao, Wei, Fu, Minghan, choksy, Pooja, Sarvaiya, Anjali, Upla, Kishor, Raja, Kiran, Yan, Hailong, Zhang, Yunkai, Li, Baiang, Zhang, Jingyi, and Zheng, Huan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field., Comment: NTIRE 2024 Challenge Report

Published
2024

72. Tinker or Transfer? A Tale of Two Techniques in Teaching Visualization

Author

Hyland, Adam and Ali, Murtaza

Subjects
Computer Science - Human-Computer Interaction
Abstract

In education there exists a tension between two modes of learning: traditional lecture-based instruction and more tinkering-based creative learning. In this paper, we outline our efforts as two Ph.D. students (who are skilled in visualization but are not, importantly, professionally trained visualization experts) to implement creative learning activities in an information visualization course in our home department. We describe our motivation for doing so, and how what began out of necessity turned into an endeavor whose utility we strongly believe in. In implementing these activities, we received largely positive reviews from students, along with constructive feedback which helped us iteratively improve the activities. Finally, we also detail our future plans for turning this work into a formal design inquiry with students to build a new class centered entirely around creative learning.

Published
2024

73. A Realistic Protocol for Evaluation of Weakly Supervised Object Localization

Author

Murtaza, Shakeeb, Belharbi, Soufiane, Pedersoli, Marco, and Granger, Eric

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-level labels. The absence of bounding box (bbox) supervision during training raises challenges in the literature for hyper-parameter tuning, model selection, and evaluation. WSOL methods rely on a validation set with bbox annotations for model selection, and a test set with bbox annotations for threshold estimation for producing bboxes from localization maps. This approach, however, is not aligned with the WSOL setting as these annotations are typically unavailable in real-world scenarios. Our initial empirical analysis shows a significant decline in LOC performance when model selection and threshold estimation rely solely on class labels and the image itself, respectively, compared to using manual bbox annotations. This highlights the importance of incorporating bbox labels for optimal model performance. In this paper, a new WSOL evaluation protocol is proposed that provides LOC information without the need for manual bbox annotations. In particular, we generated noisy pseudo-boxes from a pretrained off-the-shelf region proposal method such as Selective Search, CLIP, and RPN for model selection. These bboxes are also employed to estimate the threshold from LOC maps, circumventing the need for test-set bbox annotations. Our experiments with several WSOL methods on ILSVRC and CUB datasets show that using the proposed pseudo-bboxes for validation facilitates the model selection and threshold estimation, with LOC performance comparable to those selected using GT bboxes on the validation set and threshold estimation on the test set. It also outperforms models selected using class-level labels, and then dynamically thresholded based solely on LOC maps., Comment: 13 pages, 5 figures

Published
2024

74. Association between body mass index, dosing strategy, and efficacy of immune checkpoint inhibitors

Author

Yang Xie, Vinita Popat, Shaheen Khan, Saad A Khan, Edward K Wakeland, Quan-Zhen Li, Murtaza Ahmed, Mitchell S von Itzstein, Thomas Sheffield, Farjana Fattah, Jason Y Park, Jessica M Saltarski, Yvonne Gloria-McCutchen, David Hsiehchen, Jared Ostmeyer, Nazima Sultana, and David E Gerber

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

Background Increased body mass index (BMI) has been associated with improved response to immune checkpoint inhibitors (ICIs) in multiple cancer types. We evaluated associations between BMI, ICI dosing strategy, and clinical outcomes.Methods We abstracted clinical data on patients with cancer treated with ICI, including age, sex, cancer type, BMI, ICI type, dosing strategy (weight-based or fixed), radiographic response, overall survival (OS), and progression-free survival (PFS). We compared clinical outcomes between low-BMI and high-BMI populations using Kaplan-Meier curves, Cox regressions, and Pearson product-moment correlation coefficients.Results A total of 297 patients were enrolled, of whom 40% were women and 59% were overweight (BMI≥25). Of these, 204 (69%) received fixed and 93 (31%) received weight-based ICI dosing. In the overall cohort, overweight BMI was associated with improved PFS (HR 0.69; 95% CI 0.51 to 0.94; p=0.02) and had a trend toward improved OS (HR 0.77; 95% CI 0.57 to 1.04; p=0.08). For both endpoints, improved outcomes in the overweight population were limited to patients who received weight-based ICI dosing (PFS HR 0.53; p=0.04 for weight-based; vs HR 0.79; p=0.2 for fixed dosing) (OS HR 0.56; p=0.03 for weight-based; vs HR 0.89; p=0.54 for fixed dosing). In multivariable analysis, BMI was not associated with PFS or OS. However, the interaction of BMI≥25 and weight-based dosing had a trend toward association with PFS (HR 0.53; 95% CI 0.26 to 1.10; p=0.09) and was associated with OS (HR 0.50; 95% CI 0.25 to 0.99; p=0.05). Patients with BMI

Published
2021
Full Text
View/download PDF

75. Pattern and Frequency of Respiratory Illnesses at Emergency Department of a Tertiary Care Pediatric Health Facility of Karachi, Pakistan

Author

Allah Rakhia, Murtaza Ali Gova, Taj Muhammad Laghari, Hayat Bozdar, Safia Bibi, and Mehboob Nonari

Subjects
Emergency room, pediatric, respiratory, ailments, pneumonia, Medicine
Abstract

Background: Respiratory ailments contribute a major chunk of pediatric emergency visits, morbidity and mortality. Knowledge of Burden and pattern of diseases at any setting may help in provision of effective health services. Therefore, this study was conducted to estimate the burden and pattern of respiratory ailments at emergency department of a leading public sector pediatric health facility. Objective: To determine the patterns and frequency of respiratory illnesses among pediatric patients visiting the emergency (ER) department of National Institute of Child Health (NICH), Karachi Pakistan. Study type, settings & duration: The Retrospective observational study was conducted at Emergency Department of National institute of Child health (NICH), Karachi from January to December 2017. Methodology: In this retrospective study, a pre designed questionnaire was used for data collection after the approval from Institutional ethical review board of National Institute of child health, Karachi. Patient’s data was obtained from patients files and was entered on SPSS version 21. Frequencies and percentages were calculated for qualitative variables while mean and standard deviation were calculated for quantitative variables. Results: Total 22364 patients visited the ER of NICH during the study period, 49.7% were males and 50.3% females. Mean age was 7.82 years and mean weight was 18.95 Kgs. Pneumonia was the leading respiratory ailment (37.26%) among the subjects followed by viral Upper respiratory tract infections (URTIs) (20.83%) and Bronchiolitis (19.28%). Majority (78%) patients were treated in indoor room, 13% patients were treated in resuscitation room while 9% patients were treated and discharged from outdoor station..........

Published
2021

76. Integrin Alpha E (CD103) Limits Virus-Induced IFN-I Production in Conventional Dendritic Cells

Author

Vikas Duhan, Vishal Khairnar, Simo Kitanovski, Thamer A. Hamdan, Andrés D. Klein, Judith Lang, Murtaza Ali, Tom Adomati, Hilal Bhat, Sarah-Kim Friedrich, Fanghui Li, Philippe Krebs, Anthony H. Futerman, Marylyn M. Addo, Cornelia Hardt, Daniel Hoffmann, Philipp A. Lang, and Karl S. Lang

Subjects
GWAS, genome wide association screen, Itgae, CD103, vesicular stomatitis virus, IFN-I, Immunologic diseases. Allergy, RC581-607
Abstract

Early and strong production of IFN-I by dendritic cells is important to control vesicular stomatitis virus (VSV), however mechanisms which explain this cell-type specific innate immune activation remain to be defined. Here, using a genome wide association study (GWAS), we identified Integrin alpha-E (Itgae, CD103) as a new regulator of antiviral IFN-I production in a mouse model of vesicular stomatitis virus (VSV) infection. CD103 was specifically expressed by splenic conventional dendritic cells (cDCs) and limited IFN-I production in these cells during VSV infection. Mechanistically, CD103 suppressed AKT phosphorylation and mTOR activation in DCs. Deficiency in CD103 accelerated early IFN-I in cDCs and prevented death in VSV infected animals. In conclusion, CD103 participates in regulation of cDC specific IFN-I induction and thereby influences immune activation after VSV infection.

Published
2021
Full Text
View/download PDF

77. The State of Working Pennsylania 2023

Author

Keystone Research Center (KRC), Herzenberg, Stephen, Kovach, Claire, and Murtaza, Maisum

Abstract

"The State of Working Pennsylvania 2022" centered on the continued recovery from the COVID-19 recession, highlighting that Pennsylvania was at a policy crossroads: would political leaders embrace policies to strengthen the individual and collective worker power evident a year ago? Or would austerity and anti-worker policies after the 2022 elections undercut the possibility of a more sustained period of shared prosperity, a "New Deal for a New Economy?" In the past year, policymakers have not made a decisive choice between these two policy options. In part because the 2022 elections led to divided government. Executive authority at both the federal and state level remains in the hands of policymakers supportive of workers and labor unions, which makes possible small steps to enhance workers' power. But divided legislative branches rule out big changes in state and federal laws. On the upside, there have not yet been a reversal of the 2021-22 federal investments in climate, infrastructure, and innovation now sustaining a strong economy. On the downside, a federal or state minimum wage increase has not yet passed and there is zero chance of enacting a major pro-worker labor law reform (i.e., Protecting the Right to Organize, or PRO Act) federally before 2025. In this context of divided government and a President and Governor generally supportive of workers, a Pennsylvania job market favorable to workers has been sustained and stabilized, the latter in part because inflation--as expected--has quickly fallen to around 3%. Some readers may see the policy recommendations in this report as too ambitious and far-reaching. The authors disagree--they are only as forceful and creative as required to bring an end to nearly a half century of rising inequality. If readers agree that there is need to restore shared prosperity and share the authors' view that doing so would lay the foundation for social unity and a team effort to address climate change, then readers should embrace these policy recommendations as the best way to achieve these vital goals. [For the 2022 report, see ED624091.]

Published
2023

79. Template-assisted covalent modification underlies activity of covalent molecular glues

Author

Li, Yen-Der, Ma, Michelle W., Hassan, Muhammad Murtaza, Hunkeler, Moritz, Teng, Mingxing, Puvar, Kedar, Rutter, Justine C., Lumpkin, Ryan J., Sandoval, Brittany, Jin, Cyrus Y., Schmoker, Anna M., Ficarro, Scott B., Cheong, Hakyung, Metivier, Rebecca J., Wang, Michelle Y., Xu, Shawn, Byun, Woong Sub, Groendyke, Brian J., You, Inchul, Sigua, Logan H., Tavares, Isidoro, Zou, Charles, Tsai, Jonathan M., Park, Paul M. C., Yoon, Hojong, Majewski, Felix C., Sperling, Haniya T., Marto, Jarrod A., Qi, Jun, Nowak, Radosław P., Donovan, Katherine A., Słabicki, Mikołaj, Gray, Nathanael S., Fischer, Eric S., and Ebert, Benjamin L.

Published
2024
Full Text
View/download PDF

93. Synthesis and Applications of Zinc Oxide Nanorods, Copper-Doped Zinc Oxide Nanorods, Nickel Hydroxide/Zinc Oxide Nanorods, Iron (III) Oxide/Zinc Oxide Nanorods and Zinc Oxide/Graphene Oxide Nanorods for Batch Adsorption, Fixed-Bed Column Study, and Degradation of Cationic Dye (Blue Tur-XGB B-3) from Wastewater

Author

Munawwar, Humna, Munir, Ruba, Muneer, Amna, Zaheer, Fatima, Bashir, Muhammad Zeeshan, Sayed, Murtaza, Zahid, Muhammad, Nadeem, Raziya, Jahan, Nazish, and Noreen, Saima

Published
2024
Full Text
View/download PDF

94. The Hydrogen Storage and Extraction Abilities of Cesium-Based Perovskite Hydrides Cs2Tl\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\varvec{B}}^{\varvec{{\prime\:}}}$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\varvec{B}}^{\varvec{{\prime\:}}}$$\end{document}H6(\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\varvec{B}}^{\varvec{{\prime\:}}}$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\varvec{B}}^{\varvec{{\prime\:}}}$$\end{document}=In, Ga): First-Principles Calculations

Author

Murtaza, Hudabia, Munir, Junaid, Ain, Quratul, Aldwayyan, Abdullah S., Ghaithan, Hamid M., Ahmed, Abdullah Ahmed Ali, and Qaid, Saif M. H.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results