Index IntroductionReadability of the articleScientific validity of the articleConclusion and recommendationA group of researchers has discovered a way to detect fasciculation in muscles, which serves to detect early signs of motor neuron disease motor neurons (MND) ). The article, Ultrasound-based detection of fasciculations in healthy and diseased muscles, was published by IEEE Xplore. This article has been peer-reviewed and can be found in Transactions on Biomedical Engineering or online, where other articles are also published monthly. The intended audience for their article is other researchers, engineers and scientists, and even students looking for articles to analyze to complete their assignments. The four authors, Peter John Harding, Ian D. Loram, Nicholas Combes and Emma Hodson-Tole, live in the United Kingdom, where they all obtained their degrees. Harding is a member of the Cognitive Motor Function Research Group at Manchester Metropolitan University, where he works with two of his co-authors, Loram and Hodson-Tole. Some of his academic interests include biomedical imaging, medical diagnostics, computational optimization and parallelism. Additionally, he is a STEM ambassador. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay In addition to this article, he has made many other publications with other authors, such as Mutual Information Based Gesture Recognition and Automated Measurement of Human Skeletal Calf Muscle Contraction. Loram received his Ph.D. from the University of Birmingham and currently teaches neuromuscular control of human movement at Manchester Metropolitan University. His academic interests include human performance optimization, fear of falling, stress, human performance, coordination and muscle synergies. His research interests range from visual hand tracking, postural control, muscle proprioception, muscle tendon interactions, and real-time muscle ultrasound. He was awarded the Leverhulme Early Career Fellowship in 2004 and was appointed Reader in Neuromuscular Control of Human Movement, Institute for Biomedical Research into Human Movement and Health. Hodson-Tole is a member of numerous professional associations, editorial boards, and holds a Ph.D. in the physiology and biomechanics of skeletal muscle. The Wellcome Trust awarded her a Sir Henry Wellcome Postdoctoral Research Fellowship. She is also a principal investigator on two projects: MND Diagnosis: The Utility of Standard Frame Rate B-Mode Ultrasound Imaging and Imaging Motor Unit Recruitment Patterns. His academic interests include the diagnosis of neurodegenerative diseases, the structure and function of motor units, and the spatial and temporal dynamics of skeletal muscle activation. Combes holds an MD from the University of Birmingham and consults in neurophysiology at the Royal Preston Hospital. Introduction Motor neuron disease (MND) is a neurodegenerative disease in which the motor neuron begins to die or become unstable, causing involuntary contractions known as fasciculation (Harding, 2016). The process of identifying these fasciculations is called electromyography (EMG). It involves inserting needles into different points of the body, making the process invasive and painful. Furthermore, the electrodes detect these muscle movements in small portions of the muscles, which leads to an inaccuracy of the detector, causing the practitioner to potentially “miss” the fasciculation (Harding, 2016). Alternatively, ultrasonography (US) has been proposed for the detection of MND. TheUltrasound can evaluate multiple layers and areas of the skin and is highly sensitive to movements up to 5 micrometers (Harding, 2016). To demonstrate this hypothesis, the article provides the process of collecting US data on fasciculation in muscles and compares that data with that taken from EMG. The organization of the article, diction, use of images and sentence structure contributed to the understanding of its contents. However, there are also errors and repetitions in the sentence structure that make for confusing reading. However, the validity of the methodology used demonstrated that the experiment practiced real science: measurement, formulation and modification of hypotheses. The methodology demonstrated the author's contention that ultrasound imaging provides more accurate detection of fasciculation in MND.Readability of the articleThe article has strong and weak points. First analyze the organization of the contents: the authors build the article in chronological order, which is the best solution for the article. It shows the phases of their experiment, what steps were performed, how the data was analyzed, etc. The article has been divided into subsections, and those subsections have been further divided into sections with subheadings. The subsections helped readers identify the steps of the experiment, while the subheadings added details to the steps. These subparts allow readers to easily navigate the article because they know what each section is about. Second, the use of professional diction is suitable for the intended audience: engineers, scientists and researchers. The article used words related to the intended audience such as “hypothesize”, “magnitude”, identified”, “accuracy” and “dataset”. Furthermore, they provided adequate details of the process and used precise words. The article provided details very specific about the experiment, such as age, gender and muscle health condition of the participants. To judge the quality of the experiment, such details are important to the readers' knowledge the word “operator,” which specifies the type of equipment used in the experiment. Furthermore, the article used non-sexist language. For example, in the sentence “To evaluate the correctness and level of agreement between operators, the ratings of the operators have been combined in different ways” (Harding, 2016) no gender roles have been imposed on the object. Thirdly, the use of images helps readers understand the content clearer way; they used colors effectively, chose appropriate graphs and created effective tables. Since the experiment compares two muscle areas between two different types of muscles (healthy and diseased), the article used line graphs to help readers visualize the data. As shown below, two line graphs are presented to compare the data from the two muscles tested in the experiment. The placement of the graphs shows the visible difference between the data. Legends and colors help readers identify data even in graphs. In addition to the line graphs, tables were created to display the data, which was also an effective visual. As shown in Figure 2, the data between different muscle areas and their health conditions are shown numerically. Unlike line graphs that show increase and decrease, the table shows the difference between the data. Finally, the article contains sentences that are too long and repetitive. For example, the phrase “When evaluating the agreement between observer-identified muscle contractions and MI…” (Harding, 2016) was repeated twice in the article. Even if the contents of the.