Добірка наукової літератури з теми "Intra-manual processing"

From birth, infants encounter an environment full of objects and learn rapidly about their spatial characteristics. According to Newcombe, Uttal, and Sauter (2013), spatial development includes (1) the development of intraobject representations with the ability to transform them by mental rotation, and (2) the development of interobject representations with the ability to find and predict certain object locations. Infants’ remarkable improvements of these two strands of spatial object processing raise the major question of which factors may drive them. In this article, we discuss the extent to which infants’ development of intra- and interobject representations is related to their emerging motor skills. In particular, we provide a review on how far infants’ development of mental object rotation ability and their ability to localize objects are related to their manual object exploration and locomotion skills. We document a bulk of evidence suggesting such a link between infants’ motor development and their spatial object processing and also discuss and critically reconsider the implications of these studies.

Automated Text Summarization is becoming important due to the vast amount of data being generated. Manual processing of documents is tedious, mostly due to the absence of standards. Therefore, there is a need for a mechanism to reduce text size, structure it, and make it readable for users. Natural Language Processing (NLP) is critical for analyzing large amounts of unstructured, text-heavy data. This project aims to address concerns with extractive and abstractive text summarization by introducing a new neural network model that deals with repetitive and incoherent phrases in longer documents. The model incorporates a novel Seq2Seq architecture that enhances the standard attentional model with an intra-attention mechanism. Additionally, a new training method that combines supervised word prediction and reinforcement learning is employed. The model utilizes a hybrid pointer-generator network, which distinguishes it from the standard encoder-decoder model. This approach produces higher quality summaries than existing models.

Quantifying the pathological features of flexor tendon pulleys is essential for grading the trigger finger since it provides clinicians with objective evidence derived from microscopic images. Although manual grading is time consuming and dependent on the observer experience, there is a lack of image processing methods for automatically extracting pulley pathological features. In this paper, we design and develop a color-based image segmentation system to extract the color and shape features from pulley microscopic images. Two parameters which are the size ratio of abnormal tissue regions and the number ratio of abnormal nuclei are estimated as the pathological progression indices. The automatic quantification results show clear discrimination among different levels of diseased pulley specimens which are prone to misjudgments for human visual inspection. The proposed system provides a reliable and automatic way to obtain pathological parameters instead of manual evaluation which is with intra- and interoperator variability. Experiments with 290 microscopic images from 29 pulley specimens show good correspondence with pathologist expectations. Hence, the proposed system has great potential for assisting clinical experts in routine histopathological examinations.

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

IntroductionBody dysmorphic disorder (BDD) is a debilitating disorder, characterized by obsessions and compulsions relating specifically to perceived appearance, and which has been newly classified within the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) Obsessive-Compulsive and Related Disorders grouping. Until now, little research has been conducted into the cognitive profile of this disorder.MethodsParticipants with BDD (n=12) and participants without BDD (n=16) were tested using a computerized neurocognitive battery investigating attentional set-shifting (Intra/Extra Dimensional Set Shift Task), decision-making (Cambridge Gamble Task), motor response-inhibition (Stop-Signal Reaction Time Task), and affective processing (Affective Go-No Go Task). The groups were matched for age, IQ, and education.ResultsIn comparison to controls, patients with BDD showed significantly impaired attentional set-shifting, abnormal decision-making, impaired response inhibition, and greater omission and commission errors on the emotional processing task.ConclusionDespite the modest sample size, our results showed that individuals with BDD performed poorly compared to healthy controls on tests of cognitive flexibility, reward and motor impulsivity, and affective processing. Results from separate studies in OCD patients suggest similar cognitive dysfunction. Therefore, these findings are consistent with the reclassification of BDD alongside OCD. These data also hint at additional areas of decision-making abnormalities that might contribute specifically to the psychopathology of BDD.

Accurate determination of tumor and radiotherapy target volume is crucial to prevent local and regional failure in lung cancer. Radiotherapy planning considers critical normal tissue structures or Organs at Risk (OAR). While manual contouring is the gold standard in radiotherapy planning, it is still susceptible to intra- and inter-observer variation. A digital image processing system capable of automatically and semi-automatically determining tumor targets and OARs through computer programming can assist in the contouring process. The study aims to create a digital contouring computer program to aid in planning radiotherapy irradiation for lung cancer cases, guided by CT-Simulator image guidance. The research involved contouring CT-Simulator images of lung cancer cases using automatic segmentation, semi-automatic segmentation, and a combination of automatic, semi-automatic, and manual segmentation. Contour accuracy was assessed by a radiation oncologist. Automatic segmentation showed high accuracy for lungs (>95%), moderate for tumor targets (72.22%) and the heart (73.33%), but low accuracy for the esophagus (34.44%) and spinal cord (36.67%). Semi-automatic segmentation achieved high accuracy for the esophagus (96.67%) and spinal cord (95.56%), and moderate accuracy for tumor targets (73.33%), heart (66.67%), right lung (66.25%), and left lung (72.94%). The combination of automatic, semi-automatic, and manual segmentation resulted in high accuracy for tumor targeting and OARs (>95%). Automatic or semi-automatic segmentation using location and gray level classification methods for lung cancer cases did not produce optimal results. However, a contouring program combining automatic, semi-automatic, and manual segmentation proved more effective, balancing human interaction and automation in the lung cancer contouring process. This digital contouring program offers valuable support for radiation oncologists, potentially leading to improved treatment outcomes for lung cancer patients

The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis.

A comet assay is a trusted and widely used method for assessing DNA damage in individual eukaryotic cells. However, it is time-consuming and requires extensive monitoring and sample manipulation by the user. This limits the throughput of the assay, increases the risk of errors, and contributes to intra- and inter-laboratory variability. Here, we describe the development of a device which automates high throughput sample processing for a comet assay. This device is based upon our patented, high throughput, vertical comet assay electrophoresis tank, and incorporates our novel, patented combination of assay fluidics, temperature control, and a sliding electrophoresis tank to facilitate sample loading and removal. Additionally, we demonstrated that the automated device performs at least as well as our “manual” high throughput system, but with all the advantages of a fully “walkaway” device, such as a decreased need for human involvement and a decreased assay run time. Our automated device represents a valuable, high throughput approach for reliably assessing DNA damage with the minimal operator involvement, particularly if combined with the automated analysis of comets.

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

Patients diagnosed with Parkinson’s disease present sleep disorders with a higher frequency than the general population. The sleep architecture in these patients shows variations with respect to the normal population, so in this work it was decided to investigate the characteristics of the macroarchitecture of sleep in patients diagnosed with Parkinson’s disease. A polysomnographic study was carried out on 77 patients diagnosed with Parkinson’s disease. All the studies were processed according to the AASM Manual for the Scoring of Sleep and Associated Events v.2.2, and to the criteria of the International Classification of Sleep Disorders 3rd ed. (2014). Processing was carried out using descriptive statistics, as well as non-parametric analysis for comparison between cases and controls. The group of patients showed significant reductions of the N2, N3, and REM sleep stages when compared with a control group, as well as a significant increase in intra-sleep wakefulness. The number of REM–NoREM sleep cycles and sleep efficiency showed marked reduction compared to the control group. There was a statistically significant difference in the macroarchitecture of sleep between patients diagnosed with Parkinson’s disease and healthy controls.

Les enfants prématurés reçoivent des stimulations sensorielles inappropriées lors d'une période critique de leur développement cérébral. Le toucher semble être une modalité clé chez les enfants prématurés. L'enjeu de ce travail de thèse était d'étudier les compétences tactiles manuelles précoces des enfants prématurés. Pour cela, nous nous sommes intéressés au traitement haptique (tactilo-kinesthésique) manuel de la forme des objets sans contrôle de la vision. Ce champ de recherche était resté jusqu'alors inexploré chez les enfants prématurés. Une première étude qui nous a servi d'étude pilote, nous a permis d'explorer les compétences (intra-main) à percevoir d'une main la différence entre deux formes d'objets (prisme vs. Cylindre) chez des enfants prématurés âgés de 33 à 34+6 SA (Semaines d'Aménorrhée). Dans une seconde étude, nous avons examiné l'évolution de ces compétences tactiles manuelles en fonction du degré de prématurité (trois groupes). Enfin, dans une troisième étude, nous avons étudié les compétences (inter-main) des enfants prématurés âgés de 33 à 34+6 SA à percevoir et mémoriser une forme d'objet avec une main et détecter une différence de forme avec l'autre main. Nos résultats révèlent l'existence d'une habituation et d'une discrimination manuelle de la forme des objets chez les enfants prématurés dès 28 SA. De plus, les enfants prématurés âgés de 28 à 34 SA présentent également des compétences de reconnaissance d'un objet familier suite à une interférence (présentation du nouvel objet) contrairement aux enfants âgés de plus de 34 SA. Les résultats particuliers de ce dernier groupe d'âge mettent en évidence des différences qualitatives avec les deux autres groupes de prématurité. Cependant les résultats ne montrent pas de différence quantitative des performances tactiles manuelles entre les trois groupes de prématurité. Enfin, les résultats révèlent qu'un transfert inter-manuel de la forme est présent chez les enfants prématurés témoignant de l'existence d'une communication entre les deux hémisphères cérébraux dès 33 SA. L'ensemble des résultats montre que l'enfant prématuré est déjà doté de compétences tactiles précoces. Ce travail de thèse apporte de nouvelles connaissances concernant le développement de la perception tactile et fournit des pistes de réflexion dans le cadre des soins de développement
Preterm infants receive inadequate sensory stimulations during a critical period of brain development. Touch seems to be a key modality in preterm infants. The aim of this thesis was to investigate early manual abilities in preterm infants. Therefore, we focused on manual haptic processing of object shape without vision control. This field of research remained hitherto unexplored in preterm infants. The first study investigated the abilities (intra-manual) to perceive in one hand the difference between the shape of two objects (prism vs. Cylinder) in preterm infants from 33 to 34+6 GW (Gestational Weeks). In a second study, we examined the development of these manual abilities depending on the degree of prematurity (three groups). Finally, in a third study, we investigated the abilities (inter-manual) of preterm infants from 33 to 34+6 GW to perceive and memorize an object's shape with one hand and to detect differences between two shapes in the opposite hand. Our results reveal that manual habituation and discrimination of object shape are present in preterm infants from 28 GW. In addition, preterm infants from 28 to 34 GW show recognition memory after haptic interference (presentation of a novel object) contrary to infants from 34 GW. This last result indicates qualitative differences between groups of prematurity. However, our results show no quantitative difference in manual performance between the three groups of prematurity. Finally, results reveal that an inter-manual transfer of shape information is present in preterm infants at 33 GW demonstrating the existence of communication between the two cerebral hemispheres. Overall, our results show that the preterm infant is already endowed with early tactile abilities. This thesis provides new theoretical insights concerning the development of tactile perception and opens new perspectives in the context of developmental care

The current approach for monitoring water quality is mostly manual, labor-intensive, and involves a tedious process. This research suggests a sensor-based system for monitoring water quality, where several sensors; a microcontroller for system processing, a communication system for inter- and intra-node communication, and other parts, make up a Wireless Sensor Network (WSN) required to automate this process. This new suggested technique for controlling and enhancing the quality of water considers three nodes namely (Ifraz1, Ifraz2, and Ifraz3) in Erbil city in Iraqi Kurdistan region. Each monitoring node is connected to a ESP32 microcontroller to enable transmitting and receiving capability. The node sends its statues over the wireless network through the Worldwide Interoperability for Microwave Access (WiMAX) network communication technique to Data Management Center (DMC) that is used to manage and monitor the reports received from the nodes. The proposed method shows its effectiveness in water monitoring systems through real-time water monitoring and simple configuration compared to traditional systems.