Hasil untuk "Human anatomy"

R. Rungger, B. Hartmann, S. Kirchengast

Caesarean sections (CS) are the most common surgical procedures performed on women of reproductive age. They should only be performed when medically indicated or in case of acute birth complications. Assessing risk factors that could necessitate a CS is therefore of great interest. The aim of this retrospective medical record-based study was to analyse the significance of maternal height as a risk factor for emergency CS using a data set of 11,110 term births in Vienna, Austria. The emergency CS rate was 8.2%. Mothers experiencing emergency CS were significantly older, shorter, but heavier and more likely to be first-time mothers than women experiencing spontaneous vaginal childbirth. Very short mothers (< 156 cm) had the significantly highest (p < 0.001) emergency CS rate, while women with a height of > 175 cm had the lowest. Maternal height was an independent risk factor for emergency CS. For every centimetre decrease in height, the risk of an emergency CS increased significantly by 6.7%. Maternal height should therefore be considered a risk factor for birth complications that could require an emergency CS, even in a population with a very high standard of prenatal care.

Tatyana Chernyavskaya

No text.

Sadaf Naqvi, Ibraheem, Gulshan Sharma et al.

Abstract Analyzing power system disturbances for automatic generation control (AGC) requires solving a large set of differential equations, which remains computationally demanding even in linearized form and hence limits the practical implementation of most control strategies. In addition, the presence of time constants and delays further complicates the modeling by influencing the response of generators, governors, and control mechanisms especially for hydro dominated power systems. To address these challenges, Model Order Reduction (MOR) techniques play an important role in solving these issues for higher order and complex systems. This paper applies the Hankel norm approximation (HNA) method to develop reduced-order models for AGC in a hydro-dominated power system. An eleventh-order model is reduced to seventh, eighth and ninth-order representations. Stability margins of these reduce order models are evaluated through eigenvalue analysis, and the dynamic responses of the reduced order models are compared against the original model. Results demonstrate that the reduced order models preserve the essential dynamics while substantially lowering computational effort in AGC. To further assess the effectiveness of HNA, a Truncation-based reduction approach is also applied, and comparative results are offered to show the benefits of the proposed work.

Weishuai Guo, Youngsuk Kim, Chaojie Wu et al.

Context: Balance ability is a crucial component of human motor function, essential for maintaining postural stability in both static and dynamic conditions. It plays a fundamental role in everyday activities such as standing and walking, as well as in sports performance and injury prevention.Objective: To examine the comparative effects of static stretching (SS) and dynamic stretching (DS) on balance performance in healthy adults using meta-analysis.Methods: Following PRISMA and PERSIST guidelines, a systematic literature search was conducted in July 2024 across PubMed, Web of Science, Cochrane, Embase, EBSCO, and China National Knowledge Infrastructure (CNKI) databases for randomised controlled trials evaluating the impact of SS and DS on balance outcomes. Fourteen studies involving 346 participants met the inclusion criteria.Results: The primary analysis indicated that SS significantly impaired static balance compared to DS (effect size = −0.05). No significant differences were observed for dynamic balance or centre of pressure (COP). Meta-regression identified stretching duration as a significant source of heterogeneity, with durations between 20 and 200 s associated with better balance outcomes. A visual distribution of effect sizes further supported this optimal duration range for static balance enhancement.Conclusion: Dynamic stretching is more effective than static stretching for improving static balance in healthy adults. Stretching duration plays a critical role, and optimising both the type and timing of stretching interventions may enhance balance performance in athletic and clinical populations.

Ziyang Song, Zelin Zang, Zuyao Chen et al.

Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored, especially in clinical anatomical surgical images. Anatomy understanding tasks demand precise understanding and clinically coherent answers, which are difficult to achieve due to the complexity of medical data and the scarcity of high-quality expert annotations. These challenges limit the effectiveness of conventional Supervised Fine-Tuning (SFT) strategies. While recent work has demonstrated that Group Relative Policy Optimization (GRPO) can enhance reasoning in MLLMs without relying on large amounts of data, we find two weaknesses that hinder GRPO's reasoning performance in anatomy recognition: 1) knowledge cannot be effectively shared between different anatomical structures, resulting in uneven information gain and preventing the model from converging, and 2) the model quickly converges to a single reasoning path, suppressing the exploration of diverse strategies. To overcome these challenges, we propose two novel methods. First, we implement a progressive learning strategy called Anatomical Similarity Curriculum Learning by controlling question difficulty via the similarity of answer choices, enabling the model to master complex problems incrementally. Second, we utilize question augmentation referred to as Group Diversity Question Augmentation to expand the model's search space for difficult queries, mitigating the tendency to produce uniform responses. Comprehensive experiments on the SGG-VQA and OmniMedVQA benchmarks show our method achieves a significant improvement across the two benchmarks, demonstrating its effectiveness in enhancing the medical reasoning capabilities of MLLMs. The code can be found in https://github.com/tomato996/Anatomy-R1

Gabriel Skantze, Bahar Irfan

Turn-taking is a fundamental aspect of conversation, but current Human-Robot Interaction (HRI) systems often rely on simplistic, silence-based models, leading to unnatural pauses and interruptions. This paper investigates, for the first time, the application of general turn-taking models, specifically TurnGPT and Voice Activity Projection (VAP), to improve conversational dynamics in HRI. These models are trained on human-human dialogue data using self-supervised learning objectives, without requiring domain-specific fine-tuning. We propose methods for using these models in tandem to predict when a robot should begin preparing responses, take turns, and handle potential interruptions. We evaluated the proposed system in a within-subject study against a traditional baseline system, using the Furhat robot with 39 adults in a conversational setting, in combination with a large language model for autonomous response generation. The results show that participants significantly prefer the proposed system, and it significantly reduces response delays and interruptions.

Chunyuan Li, Yan Yang, Shiqi Luo et al.

Abstract The therapeutic options for Alzheimer’s disease (AD) are limited, underscoring the critical need for finding an effective regulator of Aβ42 production. In this study, with 489 human postmortem brains, we revealed that homotrimer G protein subunit gamma 5 (GNG5) expression is upregulated in the hippocampal–entorhinal region of pathological AD compared with normal controls, and is positively correlated with Aβ pathology. In vivo and in vitro experiments confirm that increased GNG5 significantly promotes Aβ pathology and Aβ42 production. Mechanically, GNG5 regulates the cleavage preference of γ-secretase towards Aβ42 by directly interacting with the γ-secretase catalytic subunit presenilin 1 (PS1). Moreover, excessive GNG5 increases the protein levels and the activation of Rab5, leading to the increased number of early endosomes, the major cellular organelle for production of Aβ42. Furthermore, immunoprecipitation and immunofluorescence revealed co-interaction of Aβ42 with GPCR family CXCR2, which is known as the receptor for IL-8, thus facilitating the dissociation of G-proteins βγ from α subunits. Treatment of Aβ42 in neurons combined with structure prediction indicated Aβ42 oligomers as a new ligand of CXCR2, upregulating γ subunit GNG5 protein levels. The co-localizations of GNG5 and PS1, CXCR2 and Aβ42 were verified in eight human brain regions. Besides, GNG5 is significantly reduced in extracellular vesicles (EVs) derived from cerebral cortex or serum of AD patients compared with healthy cognition controls. In brief, GNG5 is a novel regulator of Aβ42 production, suggesting its clinical potential as a diagnosis biomarker and the therapeutic target for AD. The GNG5 content in EVs derived from serum and brain tissue of patients with AD significantly reduced. The GNG5 expression in the hippocampal-entorhinal neurons of donors with pathological AD significantly increased, and can exist in homotrimer subtypes. GNG5 expression positively correlates with Aβ pathology and Aβ42 production. Homotrimer-GNG5 binds to the γ-secretase catalytic subunit PS1 and preferentially generates Aβ42 in early endosome. GNG5 leads to enhanced Rab5 protein and activation levels, increased number of early endosome, promoting Aβ42 production. Further, Aβ42 binds to CXCR2 to upregulate GNG5 levels in a feedback loop.

Maribel Miguel-Pérez, Pere Iglesias-Chamorro, Sara Ortiz-Miguel et al.

Background: Injuries of the proximal attachment of the hamstring muscles are common. The present study aimed to investigate the relationship of the proximal attachment of the hamstring muscles with neighboring structures comprehensively. Methods: A total of 97 hemipelvis from 66 cryopreserved specimens were evaluated via ultrasound, anatomical and histological samples. Results: The proximal attachment of the hamstring muscles presents a hyperechogenic line surrounding the origin of the semimembranosus and the long head of the biceps femoris muscles, as well as another hyperechogenic line covering the sciatic nerve. The anatomical and histological study confirms the ultrasound results and shows different layers forming the sacrotuberous ligament. Furthermore, it shows that the proximal attachment of the semimembranosus muscle has a more proximal origin than the rest of the hamstring muscles. Moreover, this muscle shares fibers with the long head of the biceps femoris muscle and expands to the adductor magnus muscle. The histological analysis also shows the dense connective tissue of the retinaculum covering the long head of the biceps femoris and semimembranosus muscles, as well as the expansion covering the sciatic nerve. Conclusions: These anatomical relationships could explain injuries at the origin of the hamstring muscles.

Young Seok Jeon, Hongfei Yang, Huazhu Fu et al.

Imposing key anatomical features, such as the number of organs, their shapes and relative positions, is crucial for building a robust multi-organ segmentation model. Current attempts to incorporate anatomical features include broadening the effective receptive field (ERF) size with data-intensive modules, or introducing anatomical constraints that scales poorly to multi-organ segmentation. We introduce a novel architecture called the Anatomy-Informed Cascaded Segmentation Network (AIC-Net). AIC-Net incorporates a learnable input termed "Anatomical Prior", which can be adapted to patient-specific anatomy using a differentiable spatial deformation. The deformed prior later guides decoder layers towards more anatomy-informed predictions. We repeat this process at a local patch level to enhance the representation of intricate objects, resulting in a cascaded network structure. AIC-Net is a general method that enhances any existing segmentation models to be more anatomy-aware. We have validated the performance of AIC-Net, with various backbones, on two multi-organ segmentation tasks: abdominal organs and vertebrae. For each respective task, our benchmarks demonstrate improved dice score and Hausdorff distance.

E. Sherafat, B. Farooq

Over the last decade, Autonomous Delivery Robots (ADRs) have transformed conventional delivery methods, responding to the growing e-commerce demand. However, the readiness of ADRs to navigate safely among pedestrians in shared urban areas remains an open question. We contend that there are crucial research gaps in understanding their interactions with pedestrians in such environments. Human Pose Estimation is a vital stepping stone for various downstream applications, including pose prediction and socially aware robot path-planning. Yet, the absence of an enriched and pose-labeled dataset capturing human-robot interactions in shared urban areas hinders this objective. In this paper, we bridge this gap by repurposing, fusing, and labeling two datasets, MOT17 and NCLT, focused on pedestrian tracking and Simultaneous Localization and Mapping (SLAM), respectively. The resulting unique dataset represents thousands of real-world indoor and outdoor human-robot interaction scenarios. Leveraging YOLOv7, we obtained human pose visual and numeric outputs and provided ground truth poses using manual annotation. To overcome the distance bias present in the traditional MPJPE metric, this study introduces a novel human pose estimation error metric called Mean Scaled Joint Error (MSJE) by incorporating bounding box dimensions into it. Findings demonstrate that YOLOv7 effectively estimates human pose in both datasets. However, it exhibits weaker performance in specific scenarios, like indoor, crowded scenes with a focused light source, where both MPJPE and MSJE are recorded as 10.89 and 25.3, respectively. In contrast, YOLOv7 performs better in single-person estimation (NCLT seq 2) and outdoor scenarios (MOT17 seq1), achieving MSJE values of 5.29 and 3.38, respectively.

Thandi Fasemore, Pilani Nkomozepi, Ejikeme Mbajiorgu

Arif Wicaksono

Background: Anatomy learning required practice to understanding and comprehend. Anatomical practice used aids such as cadaver. Cadaver was a corpse preserved for anatomy learning. Practice using cadavers was a way of studying the human body with very real details and experiences and must be done offline. The Coronavirus Disease 2019 (COVID-19) pandemic had limited lives, learning, and practice due to the risk of transmission. The interest in understanding some material offline must be facilitated with a supportive environment by medical institutions. Objective: This study will provide recommendation in implementing safe anatomy practice during COVID-19 pandemic. Methods: This article used literature review of anatomical practice and health protocols from books, journals, and guides from home and abroad Results: offline anatomy practice using cadaver should be implemented to help student understand human body more specific and more detail. Conclusion: Health protocol should and can be implemented in offline anatomy practice during COVID-19 pandemic. Anatomy practice can be performed during pandemic using strict health protocols.

Carmen Alonso-Martínez, Mario Rubio-Teves, Diana Casas-Torremocha et al.

Javad Alizadeh, Simone C. da Silva Rosa, Xiaohui Weng et al.

Different studies corroborate a role for ceramide synthases and their downstream products, ceramides, in modulation of apoptosis and autophagy in the context of cancer. These mechanisms of regulation, however, appear to be context dependent in terms of ceramides’ fatty acid chain length, subcellular localization, and the presence or absence of their downstream targets. Our current understanding of the role of ceramide synthases and ceramides in regulation of apoptosis and autophagy could be harnessed to pioneer the development of new treatments to activate or inhibit a single type of ceramide synthase, thereby regulating the apoptosis induction or cross talk of apoptosis and autophagy in cancer cells. Moreover, the apoptotic function of ceramide suggests that ceramide analogues can pave the way for the development of novel cancer treatments. Therefore, in the current review paper we discuss the impact of ceramide synthases and ceramides in regulation of apoptosis and autophagy in context of different types of cancers. We also briefly introduce the latest information on ceramide synthase inhibitors, their application in diseases including cancer therapy, and discuss approaches for drug discovery in the field of ceramide synthase inhibitors. We finally discussed strategies for developing strategies to use lipids and ceramides analysis in biological fluids for developing early biomarkers for cancer.

Horst Erich König, Sokol Duro, William Pérez

The objective of this paper was to describe the anatomy of the stifle joint (<i>Articulatio genus</i>) of the pampas deer (<i>Ozotoceros bezoarticus</i>, Linnaeus, 1758) by dissection and imaging studies. Twenty-six pelvic limbs were used for gross dissection, and four stifle regions from two animals were used for radiography and magnetic resonance imaging (MRI). The stifle joint of the pampas deer comprised the femoropatellar joint (joint between the distal part of the femur and the patella), and the femorotibial joint joined the femoral condyles to the proximal extremity of the tibia. The general anatomy of the stifle joint, including the overall morphology of the joint with its bones, complementary parts, means of attachment, and anatomical relationships, was like that of other ruminant species of similar size. Imaging techniques such as MRI allow adequate visualization of most components of the stifle joint.

Yong-Xia Xu, Guo-Ying Liu, Zhang-Zhang Ji et al.

In humans and animals, exposure to changes in internal or external environments causes acute stress, which changes sleep and enhances neurochemical, neuroendocrine, and sympathetic activities. Repeated stress responses play an essential role in the pathogenesis of psychiatric diseases and sleep disorders. However, the underlying mechanism of sleep changes and anxiety disorders in response to acute stress is not well established. In the current study, the effects of restraint stress (RS) on anxiety and sleep–wake cycles in mice were investigated. We found that after RS, the mice showed anxiety-like behavior after RS manipulation and increased the amounts of both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep in the dark period. The increase in sleep time was mainly due to the increased number of episodes of NREM and REM sleep during the dark period. In addition, the mice showed an elevation of the EEG power spectrum of both NREM and REM sleep 2 h after RS manipulation. There was a significant reduction in the EEG power spectrum of both NREM and REM sleep during the darkperiod in the RS condition. The expression of the c-Fos protein was significantly increased in the parabrachial nucleus, bed nucleus of the stria terminalis, central amygdala, and paraventricular hypothalamus by RS manipulation. Altogether, the findings from the present study indicated that neural circuits from the parabrachial nucleus might regulate anxiety and sleep responses to acute stress, and suggest a potential therapeutic target for RS induced anxiety and sleep alterations.

Timothy E. Wang, Alessandro Pinto

We survey the landscape of human operator modeling ranging from the early cognitive models developed in artificial intelligence to more recent formal task models developed for model-checking of human machine interactions. We review human performance modeling and human factors studies in the context of aviation, and models of how the pilot interacts with automation in the cockpit. The purpose of the survey is to assess the applicability of available state-of-the-art models of the human operators for the design, verification and validation of future safety-critical aviation systems that exhibit higher-level of autonomy, but still require human operators in the loop. These systems include the single-pilot aircraft and NextGen air traffic management. We discuss the gaps in existing models and propose future research to address them.

Vuthea Chheang, Shayla Sharmin, Rommy Marquez-Hernandez et al.

Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants.

Qiping Zhang, Nathan Tsoi, Mofeed Nagib et al.

Understanding human perceptions of robot performance is crucial for designing socially intelligent robots that can adapt to human expectations. Current approaches often rely on surveys, which can disrupt ongoing human-robot interactions. As an alternative, we explore predicting people's perceptions of robot performance using non-verbal behavioral cues and machine learning techniques. We contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in Virtual Reality, together with perceptions of robot performance provided by users on a 5-point scale. We then analyze how well humans and supervised learning techniques can predict perceived robot performance based on different observation types (like facial expression and spatial behavior features). Our results suggest that facial expressions alone provide useful information, but in the navigation scenarios that we considered, reasoning about spatial features in context is critical for the prediction task. Also, supervised learning techniques outperformed humans' predictions in most cases. Further, when predicting robot performance as a binary classification task on unseen users' data, the F1-Score of machine learning models more than doubled that of predictions on a 5-point scale. This suggested good generalization capabilities, particularly in identifying performance directionality over exact ratings. Based on these findings, we conducted a real-world demonstration where a mobile robot uses a machine learning model to predict how a human who follows it perceives it. Finally, we discuss the implications of our results for implementing these supervised learning models in real-world navigation. Our work paves the path to automatically enhancing robot behavior based on observations of users and inferences about their perceptions of a robot.

Nir Douer, Joachim Meyer

Intelligent systems have become a major part of our lives. Human responsibility for outcomes becomes unclear in the interaction with these systems, as parts of information acquisition, decision-making, and action implementation may be carried out jointly by humans and systems. Determining human causal responsibility with intelligent systems is particularly important in events that end with adverse outcomes. We developed three measures of retrospective human causal responsibility when using intelligent systems. The first measure concerns repetitive human interactions with a system. Using information theory, it quantifies the average human's unique contribution to the outcomes of past events. The second and third measures concern human causal responsibility in a single past interaction with an intelligent system. They quantify, respectively, the unique human contribution in forming the information used for decision-making and the reasonability of the actions that the human carried out. The results show that human retrospective responsibility depends on the combined effects of system design and its reliability, the human's role and authority, and probabilistic factors related to the system and the environment. The new responsibility measures can serve to investigate and analyze past events involving intelligent systems. They may aid the judgment of human responsibility and ethical and legal discussions, providing a novel quantitative perspective.