1,720,980 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Investigation of Sleep Neural Dynamics in Intracranial EEG Patients
No full textIntracranial electroencephalography (iEEG) provides superior diagnostic and research benefits over non-invasive EEG in terms of spatial resolution and the level of electrophysiological detail. Post-operative Computed Tomography (CT) scans provide the precision in electrode localization required for clinical purposes; however, to use this data for basic sleep research the challenge lies in identifying the precise locations of the implanted electrodes’ recording sites in terms of neuroanatomical regions as well as reliable scoring of their sleep data without the aid of facial electrodes. While existing methods can be combined to determine their exact locations in three-dimensional space, they fail to identify the functionally relevant gray matter areas that lie closest to them, especially if the points lie in the white matter. We introduce an iterative sphere inflation algorithm in conjunction with a unified pipeline to detect the exact as well as nearest regions of interest for these recording sites. Next, for sleep scoring purposes, we establish differences observed in alpha band activity between wakefulness and rapid eye movement (REM) sleep in frontal and temporal regions of iEEG patients. Lastly, we implement an automated sleep scoring method relying on the variations in alpha and delta bands power during sleep which can be applied to large sets of iEEG data recorded without accompanying electrooculogram (EOG) and electromyogram (EMG) electrodes available across labs for use in studies pertaining to neural dynamics during sleep.M.S.Patients with epilepsy (a neurological disorder characterized by seizures) who do not respond to medication often undergo invasive monitoring of their brains’ electrical activity using intracranial electroencephalography (iEEG). iEEG requires a surgery in which electrodes are inserted directly into the patient’s brain for better measurements. While they are monitored, these patients offer a unique opportunity for research studies that investigate the role of sleep in various learning, memory mechanisms and other health-related areas. This is because the direct contact of the electrodes with the brain tissue provides far superior quality and resolution of brain activity data in comparison to non-invasive cap-based EEG that healthy subjects wear over their scalp. However, in order to derive meaningful conclusions from these invasive recordings, we must first know the exact areas of the brain from which each site records the electrical data. We must then be able to identify which stage of sleep the patient is in at any given point in time, to be able to successfully correlate specific sleep stage-related activity with our research objectives; these patients often lack the facial electrodes used for standard sleep scoring procedures. To solve the first problem, we present an electrode localization method along with an algorithm to determine which neighboring regions contribute most to a given site’s recorded data. For the second problem, we first establish a difference in the behavior of alpha waves in the brain between wakefulness and rapid eye movement (REM) sleep. Lastly, we present an automated method to classify sleep data into different stages based on the variation in alpha waves and delta waves found during sleep
Investigation of Future Voluntary Movement Prediction for Pathological Tremor-Alleviating Exoskeletons
No full textPathological tremor, a common neurological disorder, significantly impacts patients' daily quality of life and causes difficulties with performing simple daily tasks. Those tremors usually interfere with the patient's fine motor control and may also cause psychological anxiety and social barriers. Traditional treatments, such as medication and physical therapy, can alleviate symptoms to a certain extent; however, their effects are limited, and they may also have side effects. Therefore, rehabilitation exoskeletons have emerged as an assistive technology and have become an essential supplement to traditional treatments. Then, performance optimization is particularly critical to fully realizing the potential of exoskeletons. The ideal tremor suppressor exoskeleton not only needs to suppress tremors effectively but also must be able to distinguish and predict the patient's autonomous movements. These requirements can ensure that the exoskeleton minimizes the influence of involuntary tremors while facilitating the patient's voluntary movements, enabling patients to experience a smooth and natural operational experience similar to that of normal movement.
The wrist is a critical component of human operational capability, with its flexibility and precision playing an important role in daily activities. However, it is also a common site for pathological tremors. Our research laboratory has developed a wearable exoskeleton termed TAWE to address this issue. TAWE uses a 6-degree-of-freedom (DOF) rigid link mechanism, which can precisely replicate the natural range of motion of the wrist while simultaneously providing real-time suppression of pathological tremors without compromising the user's freedom of movement. Therefore, we developed a deep learning model based on a convolutional neural network (CNN) and self-attention mechanism to accurately extract and predict patients' voluntary movement intentions from tremor-affected motion data. This model enables real-time motion planning for the exoskeleton, achieving both tremor suppression and zero-latency performance. This model is capable of directly predicting voluntary movement trajectories approximately 100 milliseconds in advance from real-time input data.
Finally, we comprehensively evaluated the model's performance and its real-time capabilities when integrated into the exoskeleton system through simulation experiments. Overall, the CNN-Self-Attention-based model has strong performance and can predict autonomous motion trajectories for the next 100 milliseconds in real-time, regardless of whether the input data included tremor interference. However, the results also revealed certain model limitations under extreme conditions, such as high-frequency and large-amplitude tremors. In these cases, the output trajectory remained insufficiently smooth even after processing, resulting in slight stuttering during exoskeleton movement. These problems need further research and improvement.Master of SciencePathological tremor, a common neurological disorder, significantly impacts patients' daily quality of life and poses challenges in performing simple daily tasks. Those tremors usually interfere with patients' fine motor control and may also cause psychological anxiety and social barriers. Traditional treatments, such as medication and physical therapy, can alleviate symptoms to a certain extent; however, their effects are limited, and they may also have side effects. Therefore, rehabilitation exoskeletons have emerged as assistive technology and have become essential supplements to traditional treatments. The ideal tremor suppressor exoskeleton not only needs to suppress tremors effectively but also must be able to distinguish and predict the patient's autonomous movements, which can help patients have a smooth and natural operational experience similar to their usual experience.
The wrist plays an essential role in daily activities and is a common site for pathological tremors. To address this issue, our research laboratory has developed a wearable exoskeleton called TAWE. TAWE can provide real-time suppression of pathological tremors without compromising the user's freedom of movement. We also developed a deep-learning model to evaluate patients' voluntary movement intentions. This model can predict voluntary movement trajectories in real-time for approximately 100 milliseconds, allowing the exoskeleton to achieve tremor suppression and zero-latency performance simultaneously.
Finally, we comprehensively evaluated the model's performance. Overall, the model has strong performance and can predict autonomous motion trajectories for the next 100 milliseconds in real-time, regardless of whether the input data included tremor interference. However, the results also revealed certain model limitations under extreme conditions, such as high-frequency and large-amplitude tremors. In these cases, the output trajectory remained insufficiently smooth even after processing, resulting in slight stuttering in the exoskeleton movement. These problems need further research and improvement
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Lateral Parabrachial Choline Acetyltransferase Neurons and the Decision to Eat
No full textFood choice is a modifiable health factor which involves neural, hormonal, and metabolic signals. The lateral parabrachial nucleus is a brain structure in the pons that integrates multiple aspects of food choice. It receives information from the homeostatic melanocortin hypothalamic system and projects to the mesolimbic dopamine reward system. The lateral parabrachial is molecularly diverse and expresses the acetylcholine synthesis enzyme: choline acetyltransferase (ChAT). In this study, we used ChAT-CRE mice to investigate the anatomical projections, the calcium dynamics, and the causal role of the LPBN ChAT neurons. Anatomical projection results were produced using CRE dependent viral vectors to express mRuby tagged synaptophysin, the highest output being the central amygdala. Calcium dynamics were measured at the amygdala using the genetically encoded calcium indicator GCaMP. The dynamics around the decision to consume food were seen to be different between fasted and sated satiety states. Activation of the circuit showed a pronounced latency to food consumption and time to finish for a single calorie of food. These data demonstrate a possible node that integrates homeostatic feeding information and relays it to higher order brain systems that modify reward value.Master of ScienceHealth can be impacted by the food an individual decides to eat, and this choice is controlled by the brain. There are many regions of the brain that are recruited when an individual decides to eat, but the two major circuits recruited are the homeostatic feeding circuit and the reward feeding circuit. The homeostatic feeding circuit involves the hypothalamus, the structure that controls basic essential functions of the body and circulating hunger hormones to signal energy availability. The second circuit is the reward circuitry which uses the neurotransmitter dopamine to signal pleasure and motivation for food. At the middle of the two circuits sits the parabrachial nucleus which expresses choline acetyltransferase, the enzyme that creates the neurotransmitter acetylcholine. To harness the molecular and anatomical specificity, we employed viral dependent protein expression to measure the anatomical output, the activity when a mouse is engaged in feeding behavior, and the causal role of the identified circuit during feeding behavior. The results showed the anatomical output to be the central amygdala, a modifier of food reward and value. The activity of the cells while feeding was seen to be higher when sated, and the activation of the circuit saw an increased latency to eat food and increased the time to consume a calorie. Together, we have demonstrated a circuit from the parabrachial nucleus the amygdala which integrates homeostatic information and projects to a brain structure that modifies food value and reward
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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