Search

Your search keyword '"Hernandez, Cristian"' showing total 231 results
Start Over Author "Hernandez, Cristian"
231 results on '"Hernandez, Cristian"'

1. Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation

Author

Aburub, Malek, Beltran-Hernandez, Cristian C., Kamijo, Tatsuya, and Hamaya, Masashi

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Robots hold great promise for performing repetitive or hazardous tasks, but achieving human-like dexterity, especially in contact-rich and dynamic environments, remains challenging. Rigid robots, which rely on position or velocity control, often struggle with maintaining stable contact and applying consistent force in force-intensive tasks. Learning from Demonstration has emerged as a solution, but tasks requiring intricate maneuvers, such as powder grinding, present unique difficulties. This paper introduces Diffusion Policies For Compliant Manipulation (DIPCOM), a novel diffusion-based framework designed for compliant control tasks. By leveraging generative diffusion models, we develop a policy that predicts Cartesian end-effector poses and adjusts arm stiffness to maintain the necessary force. Our approach enhances force control through multimodal distribution modeling, improves the integration of diffusion policies in compliance control, and extends our previous work by demonstrating its effectiveness in real-world tasks. We present a detailed comparison between our framework and existing methods, highlighting the advantages and best practices for deploying diffusion-based compliance control.

Published
2024

2. Robotic Object Insertion with a Soft Wrist through Sim-to-Real Privileged Training

Author

Fuchioka, Yuni, Beltran-Hernandez, Cristian C., Nguyen, Hai, and Hamaya, Masashi

Subjects
Computer Science - Robotics
Abstract

This study addresses contact-rich object insertion tasks under unstructured environments using a robot with a soft wrist, enabling safe contact interactions. For the unstructured environments, we assume that there are uncertainties in object grasp and hole pose and that the soft wrist pose cannot be directly measured. Recent methods employ learning approaches and force/torque sensors for contact localization; however, they require data collection in the real world. This study proposes a sim-to-real approach using a privileged training strategy. This method has two steps. 1) The teacher policy is trained to complete the task with sensor inputs and ground truth privileged information such as the peg pose, and then 2) the student encoder is trained with data produced from teacher policy rollouts to estimate the privileged information from sensor history. We performed sim-to-real experiments under grasp and hole pose uncertainties. This resulted in 100\%, 95\%, and 80\% success rates for circular peg insertion with 0, +5, and -5 degree peg misalignments, respectively, and start positions randomly shifted $\pm$ 10 mm from a default position. Also, we tested the proposed method with a square peg that was never seen during training. Additional simulation evaluations revealed that using the privileged strategy improved success rates compared to training with only simulated sensor data. Our results demonstrate the advantage of using sim-to-real privileged training for soft robots, which has the potential to alleviate human engineering efforts for robotic assembly., Comment: This paper has been accepted at IROS 2024

Published
2024

3. Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System

Author

Kamijo, Tatsuya, Beltran-Hernandez, Cristian C., and Hamaya, Masashi

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands, face issues of excessive contact forces due to their inability to adapt to contact with the environment, potentially causing damage. While compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors, they are hampered by the need for fine-tuning task-specific controller parameters. Learning from Demonstrations (LfD) offers an intuitive alternative, allowing robots to learn manipulations through observed actions. In this work, we introduce a novel system to enhance the teaching of dexterous, contact-rich manipulations to rigid robots. Our system is twofold: firstly, it incorporates a teleoperation interface utilizing Virtual Reality (VR) controllers, designed to provide an intuitive and cost-effective method for task demonstration with haptic feedback. Secondly, we present Comp-ACT (Compliance Control via Action Chunking with Transformers), a method that leverages the demonstrations to learn variable compliance control from a few demonstrations. Our methods have been validated across various complex contact-rich manipulation tasks using single-arm and bimanual robot setups in simulated and real-world environments, demonstrating the effectiveness of our system in teaching robots dexterous manipulations with enhanced adaptability and safety. Code available at: https://github.com/omron-sinicx/CompACT, Comment: Accepted to IROS 2024

Published
2024

4. A pro‐reparative bioelectronic device for controlled delivery of ions and biomolecules

Author

Asefifeyzabadi, Narges, Nguyen, Tiffany, Li, Houpu, Zhu, Kan, Yang, Hsin‐ya, Baniya, Prabhat, Lopez, Andrea Medina, Gallegos, Anthony, Hsieh, Hao‐Chieh, Dechiraju, Harika, Hernandez, Cristian, Schorger, Kaelan, Recendez, Cynthia, Tebyani, Maryam, Selberg, John, Luo, Le, Muzzy, Elana, Hsieh, Cathleen, Barbee, Alexie, Orozco, Jonathan, Alhamo, Moyasar A, Levin, Michael, Aslankoohi, Elham, Gomez, Marcella, Zhao, Min, Teodorescu, Mircea, Isseroff, Roslyn Rivkah, and Rolandi, Marco

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biotechnology, Bioengineering, bioelectronic device, ion pumps, wound healing, Dermatology & Venereal Diseases, Clinical sciences
Abstract

Wound healing is a complex physiological process that requires precise control and modulation of many parameters. Therapeutic ion and biomolecule delivery has the capability to regulate the wound healing process beneficially. However, achieving controlled delivery through a compact device with the ability to deliver multiple therapeutic species can be a challenge. Bioelectronic devices have emerged as a promising approach for therapeutic delivery. Here, we present a pro-reparative bioelectronic device designed to deliver ions and biomolecules for wound healing applications. The device incorporates ion pumps for the targeted delivery of H+ and zolmitriptan to the wound site. In vivo studies using a mouse model further validated the device's potential for modulating the wound environment via H+ delivery that decreased M1/M2 macrophage ratios. Overall, this bioelectronic ion pump demonstrates potential for accelerating wound healing via targeted and controlled delivery of therapeutic agents to wounds. Continued optimization and development of this device could not only lead to significant advancements in tissue repair and wound healing strategies but also reveal new physiological information about the dynamic wound environment.

Published
2024

5. SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing

Author

Beltran-Hernandez, Cristian C., Erbetti, Nicolas, and Hamaya, Masashi

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Cooking robots can enhance the home experience by reducing the burden of daily chores. However, these robots must perform their tasks dexterously and safely in shared human environments, especially when handling dangerous tools such as kitchen knives. This study focuses on enabling a robot to autonomously and safely learn food-cutting tasks. More specifically, our goal is to enable a collaborative robot or industrial robot arm to perform food-slicing tasks by adapting to varying material properties using compliance control. Our approach involves using Reinforcement Learning (RL) to train a robot to compliantly manipulate a knife, by reducing the contact forces exerted by the food items and by the cutting board. However, training the robot in the real world can be inefficient, and dangerous, and result in a lot of food waste. Therefore, we proposed SliceIt!, a framework for safely and efficiently learning robot food-slicing tasks in simulation. Following a real2sim2real approach, our framework consists of collecting a few real food slicing data, calibrating our dual simulation environment (a high-fidelity cutting simulator and a robotic simulator), learning compliant control policies on the calibrated simulation environment, and finally, deploying the policies on the real robot., Comment: Accepted to ICRA 2024

Published
2024

6. Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist

Author

Nguyen, Hai, Kozuno, Tadashi, Beltran-Hernandez, Cristian C., and Hamaya, Masashi

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This study tackles the representative yet challenging contact-rich peg-in-hole task of robotic assembly, using a soft wrist that can operate more safely and tolerate lower-frequency control signals than a rigid one. Previous studies often use a fully observable formulation, requiring external setups or estimators for the peg-to-hole pose. In contrast, we use a partially observable formulation and deep reinforcement learning from demonstrations to learn a memory-based agent that acts purely on haptic and proprioceptive signals. Moreover, previous works do not incorporate potential domain symmetry and thus must search for solutions in a bigger space. Instead, we propose to leverage the symmetry for sample efficiency by augmenting the training data and constructing auxiliary losses to force the agent to adhere to the symmetry. Results in simulation with five different symmetric peg shapes show that our proposed agent can be comparable to or even outperform a state-based agent. In particular, the sample efficiency also allows us to learn directly on the real robot within 3 hours., Comment: Accepted at ICRA-2024

Published
2024

7. Vision-Language Interpreter for Robot Task Planning

Author

Shirai, Keisuke, Beltran-Hernandez, Cristian C., Hamaya, Masashi, Hashimoto, Atsushi, Tanaka, Shohei, Kawaharazuka, Kento, Tanaka, Kazutoshi, Ushiku, Yoshitaka, and Mori, Shinsuke

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Large language models (LLMs) are accelerating the development of language-guided robot planners. Meanwhile, symbolic planners offer the advantage of interpretability. This paper proposes a new task that bridges these two trends, namely, multimodal planning problem specification. The aim is to generate a problem description (PD), a machine-readable file used by the planners to find a plan. By generating PDs from language instruction and scene observation, we can drive symbolic planners in a language-guided framework. We propose a Vision-Language Interpreter (ViLaIn), a new framework that generates PDs using state-of-the-art LLM and vision-language models. ViLaIn can refine generated PDs via error message feedback from the symbolic planner. Our aim is to answer the question: How accurately can ViLaIn and the symbolic planner generate valid robot plans? To evaluate ViLaIn, we introduce a novel dataset called the problem description generation (ProDG) dataset. The framework is evaluated with four new evaluation metrics. Experimental results show that ViLaIn can generate syntactically correct problems with more than 99\% accuracy and valid plans with more than 58\% accuracy. Our code and dataset are available at https://github.com/omron-sinicx/ViLaIn., Comment: ICRA 2024

Published
2023

8. A bioelectronic device for electric field treatment of wounds reduces inflammation in an in vivo mouse model.

Author

Hernandez, Cristian, Hsieh, Hao-Chieh, Zhu, Kan, Li, Houpu, Yang, Hsinya, Recendez, Cynthia, Asefifeyzabadi, Narges, Nguyen, Tiffany, Tebyani, Maryam, Baniya, Prabhat, Lopez, Andrea, Alhamo, Moyasar, Gallegos, Anthony, Hsieh, Cathleen, Barbee, Alexie, Orozco, Jonathan, Soulika, Athena, Sun, Yao-Hui, Aslankoohi, Elham, Teodorescu, Mircea, Gomez, Marcella, Norouzi, Narges, Isseroff, Roslyn, Zhao, Min, and Rolandi, Marco

Subjects
Animals, Mice, Wound Healing, Inflammation, Macrophages, Disease Models, Animal, Male, Wearable Electronic Devices
Abstract

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

Published
2024

9. Programmable Delivery of Fluoxetine via Wearable Bioelectronics for Wound Healing In Vivo

Author

Li, Houpu, Yang, Hsin‐ya, Asefifeyzabadi, Narges, Baniya, Prabhat, Lopez, Andrea Medina, Gallegos, Anthony, Zhu, Kan, Nguyen, Tiffany, Hernandez, Cristian, Zlobina, Ksenia, Recendez, Cynthia, Hsieh, Hao‐Chieh, Tebyani, Maryam, Carrión, Héctor, Selberg, John, Luo, Le, Alhamo, Moyasar A, Barbee, Alexie, Orozco, Jonathan, Hsieh, Cathleen, Soulika, Athena M, Levin, Michael, Norouzi, Narges, Gomez, Marcella, Zhao, Min, Teodorescu, Mircea, Isseroff, Roslyn Rivkah, and Rolandi, Marco

Subjects
Engineering, Biomedical Engineering, Biotechnology, Bioengineering, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Good Health and Well Being, bioelectronic ion pump, controlled drug delivery, wearables, wound healing, Chemical sciences, Physical sciences
Abstract

The ability to deliver drugs with precise dosages at specific time points can significantly improve disease treatment while reducing side effects. Drug encapsulation for gradual delivery has opened the doors for a superior treatment regimen. To expand on this ability, programming bioelectronic devices to deliver small molecules enables ad-hoc personalized therapeutic profiles that are more complex than gradual release. Here, a wearable bioelectronic device with an integrated electrophoretic ion pump that affords on-demand drug delivery with precise dose control is introduced. Delivery of fluoxetine to wounds in mice result in a 27.2% decrease in the macrophage ratio (M1/M2) and a 39.9% increase in re-epithelialization, indicating a shorter inflammatory phase and faster overall healing. Programmable drug delivery using wearable bioelectronics in wounds introduces a broadly applicable strategy for the long-term delivery of a prescribed treatment regimen with minimal external intervention.

Published
2024

10. Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters

Author

Park, Yunjeong, Hernandez, Sebastian, Hernandez, Cristian O, Schweiger, Hunter E, Li, Houpu, Voitiuk, Kateryna, Dechiraju, Harika, Hawthorne, Nico, Muzzy, Elana M, Selberg, John A, Sullivan, Frederika N, Urcuyo, Roberto, Salama, Sofie R, Aslankoohi, Elham, Knight, Heather J, Teodorescu, Mircea, Mostajo-Radji, Mohammed A, and Rolandi, Marco

Subjects
Biomedical and Clinical Sciences, Neurosciences, Physical Sciences, Bioengineering, Neurological, Good Health and Well Being, Cerebral Cortex, Neurons, Organoids, Brain, Neurotransmitter Agents, CP: Biotechnology, CP: Neuroscience, bioelectronic ion pumps, cortical organoids, ionic delivery, organoid models, stem cell models
Abstract

Precise modulation of brain activity is fundamental for the proper establishment and maturation of the cerebral cortex. To this end, cortical organoids are promising tools to study circuit formation and the underpinnings of neurodevelopmental disease. However, the ability to manipulate neuronal activity with high temporal resolution in brain organoids remains limited. To overcome this challenge, we introduce a bioelectronic approach to control cortical organoid activity with the selective delivery of ions and neurotransmitters. Using this approach, we sequentially increased and decreased neuronal activity in brain organoids with the bioelectronic delivery of potassium ions (K+) and γ-aminobutyric acid (GABA), respectively, while simultaneously monitoring network activity. This works highlights bioelectronic ion pumps as tools for high-resolution temporal control of brain organoid activity toward precise pharmacological studies that can improve our understanding of neuronal function.

Published
2024

11. A system for bioelectronic delivery of treatment directed toward wound healing.

Author

Tebyani, Maryam, Asefifeyzabadi, Narges, Nguyen, Tiffany, Hernandez, Cristian, Zhu, Kan, Li, Houpu, Selberg, John, Hsieh, Hao-Chieh, Pansodtee, Pattawong, Recendez, Cynthia, Hee, Wan, Aslankoohi, Elham, Gomez, Marcella, Rolandi, Marco, Isseroff, Roslyn Rivkah, Zhao, Min, Teodorescu, Mircea, Yang, Hsin-Ya, Keller, Gordon, and Baniya, Prabhat

Subjects
Animals, Mice, Wound Healing, Capillary Tubing, Dimethylpolysiloxanes, Disease Models, Animal
Abstract

The development of wearable bioelectronic systems is a promising approach for optimal delivery of therapeutic treatments. These systems can provide continuous delivery of ions, charged biomolecules, and an electric field for various medical applications. However, rapid prototyping of wearable bioelectronic systems for controlled delivery of specific treatments with a scalable fabrication process is challenging. We present a wearable bioelectronic system comprised of a polydimethylsiloxane (PDMS) device cast in customizable 3D printed molds and a printed circuit board (PCB), which employs commercially available engineering components and tools throughout design and fabrication. The system, featuring solution-filled reservoirs, embedded electrodes, and hydrogel-filled capillary tubing, is assembled modularly. The PDMS and PCB both contain matching through-holes designed to hold metallic contact posts coated with silver epoxy, allowing for mechanical and electrical integration. This assembly scheme allows us to interchange subsystem components, such as various PCB designs and reservoir solutions. We present three PCB designs: a wired version and two battery-powered versions with and without onboard memory. The wired design uses an external voltage controller for device actuation. The battery-powered PCB design uses a microcontroller unit to enable pre-programmed applied voltages and deep sleep mode to prolong battery run time. Finally, the battery-powered PCB with onboard memory is developed to record delivered currents, which enables us to verify treatment dose delivered. To demonstrate the functionality of the platform, the devices are used to deliver H[Formula: see text] in vivo using mouse models and fluoxetine ex vivo using a simulated wound environment. Immunohistochemistry staining shows an improvement of 35.86% in the M1/M2 ratio of H[Formula: see text]-treated wounds compared with control wounds, indicating the potential of the platform to improve wound healing.

Published
2023

12. Implementing QR codes in academia to improve sample tracking, data accessibility, and traceability in multicampus interdisciplinary collaborations.

Author

Hernandez, Cristian, Aslankoohi, Elham, Frolikov, Pavel, Li, Houpu, Kurniawan, Sri, and Rolandi, Marco

Subjects
Animals, COVID-19, Quality Control
Abstract

The growing number of multicampus interdisciplinary projects in academic institutions expedites a necessity for tracking systems that provide instantly accessible data associated with devices, samples, and experimental results to all collaborators involved. This need has become particularly salient with the COVID pandemic when consequent travel restrictions have hampered in person meetings and laboratory visits. Minimizing post-pandemic travel can also help reduce carbon footprint of research activities. Here we developed a Quick Response (QR) code tracking system that integrates project management tools for seamless communication and tracking of materials and devices between multicampus collaborators: one school of medicine, two engineering laboratories, three manufacturing cleanroom sites, and three research laboratories. Here we aimed to use this system to track the design, fabrication, and quality control of bioelectronic devices, in vitro experimental results, and in vivo testing. Incorporating the tracking system into our project helped our multicampus teams accomplish milestones on a tight timeline via improved data traceability, manufacturing efficiency, and shared experimental results. This tracking system is particularly useful to track device issues and ensure engineering device consistency when working with expensive biological samples in vitro and animals in vivo to reduce waste of biological and animal resources associated with device failure.

Published
2023

13. Decoupling Sleep and Brain Size in Childhood: An Investigation of Genetic Covariation in the Adolescent Brain Cognitive Development Study

Author

Hernandez, Leanna M, Kim, Minsoo, Hernandez, Cristian, Thompson, Wesley, Fan, Chun Chieh, Galván, Adriana, Dapretto, Mirella, Bookheimer, Susan Y, Fuligni, Andrew, and Gandal, Michael J

Subjects
Biological Psychology, Psychology, Basic Behavioral and Social Science, Behavioral and Social Science, Brain Disorders, Clinical Research, Mental Health, Neurosciences, Pediatric, Sleep Research, Prevention, Genetics, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Mental health, Neurological, Good Health and Well Being, ADHD, Brain, Childhood, Heritability, Insomnia
Abstract

BackgroundChildhood sleep problems are common and among the most frequent and impairing comorbidities of childhood psychiatric disorders. In adults, sleep disturbances are heritable and show strong genetic associations with brain morphology; however, little is known about the genetic architecture of childhood sleep and potential etiological links between sleep, brain development, and pediatric-onset psychiatric symptoms.MethodsUsing data from the Adolescent Brain Cognitive Development Study (n Phenotype = 4428 for discovery/replication, n Genetics = 4728; age 9-10 years), we assessed phenotypic relationships, heritability, and genetic correlations between childhood sleep disturbances (insomnia, arousal, breathing, somnolence, hyperhidrosis, sleep-wake transitions), brain size (surface area, cortical thickness, volume), and dimensional psychopathology.ResultsSleep disturbances showed widespread positive associations with multiple domains of childhood psychopathology; however, only insomnia showed replicable associations with smaller brain surface area. Among the sleep disturbances assessed, only insomnia showed significant heritability (h 2 SNP = 0.15, p < .05) and showed substantial genetic correlations with externalizing and attention-deficit/hyperactivity disorder symptomatology (r G s > 0.80, ps < .05). We found no evidence of genetic correlation between childhood insomnia and brain size. Furthermore, polygenic risk scores calculated from genome-wide association studies of adult insomnia and adult brain size did not predict childhood insomnia; instead, polygenic risk scores trained using attention-deficit/hyperactivity disorder genome-wide association studies predicted decreased surface area at baseline as well as insomnia and externalizing symptoms longitudinally.ConclusionsFindings demonstrate a distinct genetic architecture underlying childhood insomnia and brain size and suggest genetic overlap between childhood insomnia and attention-deficit/hyperactivity disorder symptomatology. Additional research is needed to examine how genetic risk manifests in altered developmental trajectories and comorbid sleep/psychiatric symptoms across adolescence.

Published
2023

14. Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study

Author

Beltran-Hernandez, Cristian C., Petit, Damien, Ramirez-Alpizar, Ixchel G., and Harada, Kensuke

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks. Despite the advances in RL, it is still not widely adopted in the industry due to the need for an expensive large amount of robot interaction with its environment. Curriculum Learning (CL) has been proposed to expedite learning. However, most research works have been only evaluated in simulated environments, from video games to robotic toy tasks. This paper presents a study for accelerating robot learning of contact-rich manipulation tasks based on Curriculum Learning combined with Domain Randomization (DR). We tackle complex industrial assembly tasks with position-controlled robots, such as insertion tasks. We compare different curricula designs and sampling approaches for DR. Based on this study, we propose a method that significantly outperforms previous work, which uses DR only (No CL is used), with less than a fifth of the training time (samples). Results also show that even when training only in simulation with toy tasks, our method can learn policies that can be transferred to the real-world robot. The learned policies achieved success rates of up to 86\% on real-world complex industrial insertion tasks (with tolerances of $\pm 0.01~mm$) not seen during the training., Comment: 12 pages, 11 figures, 4 tables, in journal review. Corresponding author: Cristian C. Beltran-Hernandez

Published
2022

15. A system for bioelectronic delivery of treatment directed toward wound healing

Author

Baniya, Prabhat, Tebyani, Maryam, Asefifeyzabadi, Narges, Nguyen, Tiffany, Hernandez, Cristian, Zhu, Kan, Li, Houpu, Selberg, John, Hsieh, Hao-Chieh, Pansodtee, Pattawong, Yang, Hsin-ya, Recendez, Cynthia, Keller, Gordon, Hee, Wan Shen, Aslankoohi, Elham, Isseroff, Roslyn Rivkah, Zhao, Min, Gomez, Marcella, Rolandi, Marco, and Teodorescu, Mircea

Published
2023
Full Text
View/download PDF

16. Pathophysiology of chikungunya virus infection associated with fatal outcomes

Author

de Souza, William M., Fumagalli, Marcilio J., de Lima, Shirlene T.S., Parise, Pierina L., Carvalho, Deyse C.M., Hernandez, Cristian, de Jesus, Ronaldo, Delafiori, Jeany, Candido, Darlan S., Carregari, Victor C., Muraro, Stefanie P., Souza, Gabriela F., Simões Mello, Leda M., Claro, Ingra M., Díaz, Yamilka, Kato, Rodrigo B., Trentin, Lucas N., Costa, Clauber H.S., Maximo, Ana Carolina B.M., Cavalcante, Karene F., Fiuza, Tayna S., Viana, Vânia A.F., Melo, Maria Elisabeth L., Ferraz, Clarissa P.M., Silva, Débora B., Duarte, Larissa M.F., Barbosa, Priscilla P., Amorim, Mariene R., Judice, Carla C., Toledo-Teixeira, Daniel A., Ramundo, Mariana S., Aguilar, Patricia V., Araújo, Emerson L.L., Costa, Fabio T.M., Cerqueira-Silva, Thiago, Khouri, Ricardo, Boaventura, Viviane S., Figueiredo, Luiz Tadeu M., Fang, Rong, Moreno, Brechla, López-Vergès, Sandra, Mello, Liana Perdigão, Skaf, Munir S., Catharino, Rodrigo R., Granja, Fabiana, Martins-de-Souza, Daniel, Plante, Jessica A., Plante, Kenneth S., Sabino, Ester C., Diamond, Michael S., Eugenin, Eliseo, Proença-Módena, José Luiz, Faria, Nuno R., and Weaver, Scott C.

Published
2024
Full Text
View/download PDF

18. Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning

Author

Wang, Yan, Beltran-Hernandez, Cristian C., Wan, Weiwei, and Harada, Kensuke

Subjects
Computer Science - Robotics
Abstract

Robotic assembly tasks involve complex and low-clearance insertion trajectories with varying contact forces at different stages. While the nominal motion trajectory can be easily obtained from human demonstrations through kinesthetic teaching, teleoperation, simulation, among other methods, the force profile is harder to obtain especially when a real robot is unavailable. It is difficult to obtain a realistic force profile in simulation even with physics engines. Such simulated force profiles tend to be unsuitable for the actual robotic assembly due to the reality gap and uncertainty in the assembly process. To address this problem, we present a combined learning-based framework to imitate human assembly skills through hybrid trajectory learning and force learning. The main contribution of this work is the development of a framework that combines hierarchical imitation learning, to learn the nominal motion trajectory, with a reinforcement learning-based force control scheme to learn an optimal force control policy, that can satisfy the nominal trajectory while adapting to the force requirement of the assembly task. To further improve the imitation learning part, we develop a hierarchical architecture, following the idea of goal-conditioned imitation learning, to generate the trajectory learning policy on the \textit{skill} level offline. Through experimental validations, we corroborate that the proposed learning-based framework is robust to uncertainty in the assembly task, can generate high-quality trajectories, and can find suitable force control policies, which adapt to the task's force requirements more efficiently.

Published
2021

22. Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach

Author

Beltran-Hernandez, Cristian C., Petit, Damien, Ramirez-Alpizar, Ixchel G., and Harada, Kensuke

Subjects
Computer Science - Robotics, Computer Science - Machine Learning
Abstract

Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensively researched, safely solving complex high precision assembly in an unstructured environment remains an open problem. Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using position-controlled manipulators. The main contribution of this work is a learning-based method to solve peg-in-hole tasks with position uncertainty of the hole. We proposed the use of an off-policy model-free reinforcement learning method and bootstrap the training speed by using several transfer learning techniques (sim2real) and domain randomization. Our proposed learning framework for position-controlled robots was extensively evaluated on contact-rich insertion tasks on a variety of environments., Comment: 17 pages,12 figures, supplemental video https://youtu.be/v4fREpMk7kU

Published
2020
Full Text
View/download PDF

23. Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots

Author

Beltran-Hernandez, Cristian Camilo, Petit, Damien, Ramirez-Alpizar, Ixchel G., Nishi, Takayuki, Kikuchi, Shinichi, Matsubara, Takamitsu, and Harada, Kensuke

Subjects
Computer Science - Machine Learning, Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control, Statistics - Machine Learning
Abstract

Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators. These challenges include the need for a robust controller to avoid undesired behavior, that risk damaging the robot and its environment, and constant supervision from a human operator. The main contributions of this work are, first, we proposed a learning-based force control framework combining RL techniques with traditional force control. Within said control scheme, we implemented two different conventional approaches to achieve force control with position-controlled robots; one is a modified parallel position/force control, and the other is an admittance control. Secondly, we empirically study both control schemes when used as the action space of the RL agent. Thirdly, we developed a fail-safe mechanism for safely training an RL agent on manipulation tasks using a real rigid robot manipulator. The proposed methods are validated on simulation and a real robot, an UR3 e-series robotic arm., Comment: 8 pages, 9 figures, version accepted for IROS RA-L 2020, for associated video file, see https://youtu.be/4wdIhQxD6cA

Published
2020
Full Text
View/download PDF

26. A pro‐reparative bioelectronic device for controlled delivery of ions and biomolecules

Author

Asefifeyzabadi, Narges, primary, Nguyen, Tiffany, additional, Li, Houpu, additional, Zhu, Kan, additional, Yang, Hsin‐ya, additional, Baniya, Prabhat, additional, Medina Lopez, Andrea, additional, Gallegos, Anthony, additional, Hsieh, Hao‐Chieh, additional, Dechiraju, Harika, additional, Hernandez, Cristian, additional, Schorger, Kaelan, additional, Recendez, Cynthia, additional, Tebyani, Maryam, additional, Selberg, John, additional, Luo, Le, additional, Muzzy, Elana, additional, Hsieh, Cathleen, additional, Barbee, Alexie, additional, Orozco, Jonathan, additional, Alhamo, Moyasar A., additional, Levin, Michael, additional, Aslankoohi, Elham, additional, Gomez, Marcella, additional, Zhao, Min, additional, Teodorescu, Mircea, additional, Isseroff, Roslyn Rivkah, additional, and Rolandi, Marco, additional

Published
2024
Full Text
View/download PDF

27. Galaxy detection and identification using deep learning and data augmentation

Author

González, Roberto E., Muñoz, Roberto P., and Hernández, Cristian A.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We present a method for automatic detection and classification of galaxies which includes a novel data-augmentation procedure to make trained models more robust against the data taken from different instruments and contrast-stretching functions. This method is shown as part of AstroCV, a growing open source computer vision repository for processing and analyzing big astronomical datasets, including high performance Python and C++ algorithms used in the areas of image processing and computer vision. The underlying models were trained using convolutional neural networks and deep learning techniques, which provide better results than methods based on manual feature engineering and SVMs in most of the cases where training datasets are large. The detection and classification methods were trained end-to-end using public datasets such as the Sloan Digital Sky Survey (SDSS), the Galaxy Zoo, and private datasets such as the Next Generation Virgo (NGVS) and Fornax (NGFS) surveys. Training results are strongly bound to the conversion method from raw FITS data for each band into a 3-channel color image. Therefore, we propose data augmentation for the training using 5 conversion methods. This greatly improves the overall galaxy detection and classification for images produced from different instruments, bands and data reduction procedures. The detection and classification methods were trained using the deep learning framework DARKNET and the real-time object detection system YOLO. These methods are implemented in C language and CUDA platform, and makes intensive use of graphical processing units (GPU). Using a single high-end Nvidia GPU card, it can process a SDSS image in 50 milliseconds and a DECam image in less than 3 seconds., Comment: Astronomy and Computing Accepted. Repository https://github.com/astroCV/astroCV

Published
2018

28. Factors associated with failed epidural blood patch after accidental dural puncture in obstetrics: a prospective, multicentre, international cohort study

Author

Casier, Isabelle, Bryon, Bart, Soetens, Filiep, Dewandre, Pierre-Yves, Lambert, Geraldine, Christiaen, Jan, Schepers, Roel, Van Houwe, Patrick, Kalmar, Alain, Vanoverschelde, Henk, Bauters, Monique, Roofthooft, Eva, Devroe, Sarah, Van de Velde, Marc, Jadrijevic, Ana, Jokic, Aleksandra, Marin, Damjan, Sklebar, Ivan, Mihaljević, Slobodan, Kosinova, Martina, Stourac, Petr, Adamus, Milan, Kufa, Christian, Volfová, Ivana, Zaoralová, Blažena, Froeslev-Friis, Christina, Mygil, Bjoern, Krebs Albrechtsen, Charlotte, Kavasmaa, Tomi, Alahuhta, Seppo, Mäyrä, Anne, Mennander, Susanna, Rautaneva, Kati, Hiekkanen, Tuula, Kontinen, Vesa, Linden, Kirsti, Toivakka, Sara, Boselli, Emmanuel, Greil, Pierre-Édouard, Mascle, Olivier, Courbon, Aurelie, Lutz, Jean., Simonet, Thérèse, Barbier, Marie, Hlioua, Tarik, Meniolle d’Hauthville, Fleur, Quintin, Christine, Bouattour, Karim, Lecinq, Agnès, Mercier, Frederic, Bonnet, Marie-Pierre, Carbonniere, Mathieu, Fischer, Catherine, Picard, Paola-Carla, Bonnin, Martine, Storme, Brigitte, Bouthors, Anne-Sophie, Detente, Thomas, Nguyen, Truong Minh, Keita, Hawa, Nebout, Sophie, Osse, Lauranne, Delmas, Anne, Vial, Florence, Kaufner, Lutz, Hoefing, Christoph, Mueller, Stefan, Becke, Karin, Blobner, Manfred, Lewald, Heidrun, Schaller, Stefan Josef, Muggleton, Ellis, Bette, Birgit, Neumann, Claudia, Weber, Stefan, Grünewald, Matthias, Ohnesorge, Henning, Helf, Antonia, Jelting, Yvonne, Kranke, Peter, von Heymann, Christian, Welfle, Sabine, Staikou, Chryssoula, Stavrianopoulou, Antonia, Stamelos, Mattheios, Kalopita, Konstantina, Loukeri, Anastasia, Valsamidis, Dimitrios, Matsota, Paraskevi, Thorsteinsson, Adalbjörn, Tome, Riad, Eidelman, Leonid A., Davis, Atara, Orbach-Zinger, Sharon, Ioscovich, Alexander, Ramona, Iacob, De Simone, Luigi, Pesetti, Barbara, Brazzi, Luca, Zito, Alessandro, Camorcia, Michela, Della Rocca, Giorgio, Aversano, Marco, Frigo, Maria Grazia, Todde, Cristina, Macas, Andrius, Keraitiene, Grazina, Rimaitis, Kestutis, Borg, Francis, Tua, Carl, Kuijpers-Visser, Agnes Geertje, Schyns-van den Berg, Alexandra, Hollmann, Markus W., van den Berg, Tijs, Koolen, Eric, Dons, Ilse, van der Knijff, Anouk, van der Marel, Caroline, Ruysschaert, Nele, Pelka, Michal, Pluymakers, Christine, Koopman, Seppe, Teunissen, Aart-Jan, Cornelisse, Dick, van Dasselaar, Nick, Verdouw, Bastiaan, Beenakkers, Ingrid, Dahl, Vegard, Hagen, Robert, Vivaldi, Francesco, Eriksen, John Reidar, Wiszt, Radovan, Aslam Tayyba, Naz, Ringvold, Else-Marie, Chutkowski, Radosław, Skirecki, Tomasz, Wódarski, Bartłomiej, Faria, Maria Aida, Ferreira, Amélia, Sampaio, Ana Catarina, Ferreira, Irene, Matias, Bernardo, Teixeira, Joana, Araujo, Rita, Cabido, Herminia, Fortuna, Rosario, Lemos, Paulo, Cardoso, Carolina, Moura, Fernando, Pereira, Cristiana, Pereira, Sandra, Abecasis, Manuel, Lança, Filipa, Muchacho, Paulo, Ormonde, Lucindo, Guedes-Araujo, Isabel, Pinho-Oliveira, Vitor, Paredes, Paulo, Bentes, Carla, Gouveia, Francisco, Milheiro, Ana, Castanheira, Cláudia, Neves, Miriam, Pacheco, Vânia, Cortez, Mara, Tranquada, Raquel, Tareco, Glória, Furtado, Inês, Pereira, Estela, Marinho, Luísa, Seabra, Manuel, Bulasevic, Aleksandra, Kendrisic, Mirjana, Jovanovic, Lidija, Pujić, Borislava, Kutlesic, Marija, Grochova, Monika, Simonova, Jana, Pavlovic, Gordana, Rozman, Ales, Blajic, Iva, Graovac, Dragan, Stopar Pintaric, Tatjana, Chiquito, Teresa, Monedero, Pablo, De Carlos-Errea, Joaquin, Guillén-Casbas, Roque, Veiga-Gil, Leonor, Basso, Morena, Garcia Bartolo, Carolina, Hernandez, Cristian, Ricol, Laura, De Santos, Maroto Pinar, Gràcia Solsona, Josep A., López-Baamonde, Manuel, Magaldi Mendaña, Marta, Plaza Moral, Ana María, Vendrell, Marina, Trillo, Lourdes, Perez Garcia, Anibal Ricardo, Alamillo Salas, Clara, Moret, Enric, Ramió, Laura, Aguilar Sanchez, Jose Luis, Soler Pedrola, Maria, Valldeperas Hernandez, Maria Inmaculada, Aldalur, Gorka, Bárcena, Estíbaliz, Herrera, Julia, Iturri, Fernando, Martínez, Alberto, Martínez, Leire, Serna, Rosa, Gilsanz, Fernando, Guasch Arevalo, Emilia, Iannuccelli, Fabrizio, Latorre, Julieta, Rodriguez Roca, Cristina, Pérez Pardo, Osvaldo Ceferino, Sierra Biddle, Natalia, Suárez Castaño, Ceferina, Hernández González, Lourdes, Remacha González, Caridad, Sánchez Nuez, Raquel, Anta, Diego, Beleña, Jose M., García-Cuadrado, Carmen, Garcia, Irene, Manrique, Susana, Suarez, Elena, Hein, Anette, Arbman, Elisabet, Hansson, Helena, Tillenius, Monika, Al-Taie, Ruaa, Ledin-Eriksson, Susanne, Lindén-Söndersö, Anja, Rosén, Ola, Austruma, Evija, Gillberg, Lars, Darvish, Bijan, Gupta, Anil, Jörnvall, Henrik, Nordström, Johan, Persson, Jan, Rosenberg, Jan, Brühne, Lars, Forshammar, Johan, Ugarph Edfeldt, Malin, Rolfsson, Håkan, Hellblom, Anna, Levin, Katarina, Rabow, Sofus, Thorlacius, Karin, Bansch, Peter, Robertson (Baeriswyl), Moira, Stamer, Ulrike, Mathivon, Stanislas, Savoldelli, Georges, Auf der Maur, Pia, Filipovic, Miodrag, Dullenkopf, Alexander, Brunner, Maya, Girard, Thierry, Vonlanthen, Claudia, Ozbilgin, Sule, Gunaydin D, Berrin, Corman Dincer, Pelin, Tas Tuna, Ayca, Daamen, Sylvia, Farsi, Slama, Feijten, Prisca, Harlet, Pierre, Leva, Brigitte, Plichon, Benoît, Magnuson, Anders, Guasch, Emilia, Mercier, Frédéric J., and Schyns-van den Berg, Alexandra M.J.V.

Published
2022
Full Text
View/download PDF

29. SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

Author

Cove, Michael V., Kays, Roland, Bontrager, Helen, Bresnan, Claire, Lasky, Monica, Frerichs, Taylor, Klann, Renee, Lee, Thomas E., Crockett, Seth C., Crupi, Anthony P., Weiss, Katherine C. B., Rowe, Helen, Sprague, Tiffany, Schipper, Jan, Tellez, Chelsey, Lepczyk, Christopher A., Fantle-Lepczyk, Jean E., Lapoint, Scott, Williamson, Jacque, Fisher-Reid, M. Caitlin, King, Sean M., Bebko, Alexandra J., Chrysafis, Petros, Jensen, Alex J., Jachowski, David S., Sands, Joshua, MacCombie, Kellyanne, Herrera, Daniel J., van Dermerwe, Marius, Knowles, Travis W., Horan, Robert V., Rentz, Michael S., Brandt, Laroy S. E., Nagy, Christopher, Barton, Brandon T., Thompson, Weston C., Maher, Sean P., Darracq, Andrea K., Hess, George, Parsons, Arielle W., Brennawells, Gary, Roemer, W., Hernandez, Cristian J., Gompper, Matthew E., Webb, Stephen L., Vanek, John P., Lafferty, Diana J. R., Bergquist, Amelia M., Hubbard, Tru, Forrester, Tavis, Clark, Darren, Cincotta, Connor, Favreau, Jorie, Facka, Aaron N., Halbur, Michelle, Hammerich, Steven, Gray, Morgan, Rega-Brodsky, Christine C., Durbin, Caleb, Flaherty, Elizabeth A., Brooke, Jarred M., Coster, Stephanie S., Lathrop, Richard G., Russell, Katarina, Bogan, Daniel A., Cliché, Rachel, Shamon, Hila, Hawkins, Melissa T. R., Marks, Sharyn B., Lonsinger, Robert C., O’Mara, M. Teague, Compton, Justin A., Fowler, Melinda, Barthelmess, Erika L., Andy, Katherine E., Belant, Jerrold L., Beyer, Dean E., Kautz, Todd M., Scognamillo, Daniel G., Schalk, Christopher M., Leslie, Matthew S., Nasrallah, Sophie L., Ellison, Caroline N., Ruthven, Chip, Fritts, Sarah, Tleimat, Jaquelyn, Ygay, Mand, Whittier, Christopher A., Neiswenter, Sean A., Pelletier, Robert, DeGregorio, Brett A., Kuprewicz, Erin K., Davis, Miranda L., Dykstra, Adrienne, Mason, David S., Baruzzi, Carolina, Lashley, Marcus A., Risch, Derek R., Price, Melissa R., Allen, Maximilian L., Whipple, Laura S., Sperry, Jinelle H., Hagen, Robert H., Mortelliti, Alessio, Evans, Bryn E., Studds, Colin E., Sirén, Alexej P. K., Kilborn, Jillian, Sutherland, Chris, Warren, Paige, Fuller, Todd, Harris, Nyeema C., Carter, Neil H., Trout, Edward, Zimova, Marketa, Giery, Sean T., Iannarilli, Fabiola, Higdon, Summer D., Revord, Ronald S., Hansen, Christopher P., Millspaugh, Joshua J., Zorn, Adam, Benson, John F., Wehr, Nathaniel H., Solberg, Jaylin N., Gerber, Brian D., Burr, Jessica C., Sevin, Jennifer, Green, Austin M., Şekercioğlu, Çağan H., Pendergast, Mary, Barnick, Kelsey A., Edelman, Andrew J., Wasdin, Joanne R., Romero, Andrea, O’Neill, Brian J., Schmitz, Noel, Alston, Jesse M., Kuhn, Kellie M., Lesmeister, Damon B., Linnell, Mark A., Appel, Cara L., Rota, Christopher, Stenglein, Jennifer L., Anhalt-Depies, Christine, Nelson, Carrie, Long, Robert A., Jaspers, Kodi Jo, Remine, Kathryn R., Jordan, Mark J., Davis, Daniel, Hernández-Yáñez, Haydée, Zhao, Jennifer Y., and McShea, Andwilliam J.

Published
2021

31. Comparison of End-to-End Testing Tools for Microservices: A Case Study

Author

Hernández, Cristian Martínez, Martínez, Alexandra, Quesada-López, Christian, Jenkins, Marcelo, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Álvaro, editor, Ferrás, Carlos, editor, López-López, Paulo Carlos, editor, and Guarda, Teresa, editor

Published
2021
Full Text
View/download PDF

32. Programmable Delivery of Fluoxetine via Wearable Bioelectronics for Wound Healing In Vivo

Author

Li, Houpu, primary, Yang, Hsin‐ya, additional, Asefifeyzabadi, Narges, additional, Baniya, Prabhat, additional, Lopez, Andrea Medina, additional, Gallegos, Anthony, additional, Zhu, Kan, additional, Nguyen, Tiffany, additional, Hernandez, Cristian, additional, Zlobina, Ksenia, additional, Recendez, Cynthia, additional, Hsieh, Hao‐Chieh, additional, Tebyani, Maryam, additional, Carrión, Héctor, additional, Selberg, John, additional, Luo, Le, additional, Alhamo, Moyasar A., additional, Barbee, Alexie, additional, Orozco, Jonathan, additional, Hsieh, Cathleen, additional, Soulika, Athena M., additional, Levin, Michael, additional, Norouzi, Narges, additional, Gomez, Marcella, additional, Zhao, Min, additional, Teodorescu, Mircea, additional, Isseroff, Roslyn Rivkah, additional, and Rolandi, Marco, additional

Published
2024
Full Text
View/download PDF

34. Management practices for postdural puncture headache in obstetrics: a prospective, international, cohort study

Author

Casier, Isabelle, Bryon, Bart, Soetens, Filiep, Dewandre, Pierre-Yves, Lambert, Geraldine, Christiaen, Jan, Schepers, Roel, Van Houwe, Patrick, Kalmar, Alain, Vanoverschelde, Henk, Bauters, Monique, Roofthooft, Eva, Devroe, Sarah, Van de Velde, Marc, Jadrijevic, Ana, Jokic, Aleksandra, Marin, Damjan, Sklebar, Ivan, Mihaljević, Slobodan, Kosinova, Martina, Stourac, Petr, Adamus, Milan, Kufa, Christian, Volfová, Ivana, Zaoralová, Blažena, Froeslev-Friis, Christina, Mygil, Bjoern, Krebs Albrechtsen, Charlotte, Kavasmaa, Tomi, Alahuhta, Seppo, Mäyrä, Anne, Mennander, Susanna, Rautaneva, Kati, Hiekkanen, Tuula, Kontinen, Vesa, Linden, Kirsti, Toivakka, Sara, Boselli, Emmanuel, Greil, Pierre-Édouard, Mascle, Olivier, Courbon, Aurelie, Lutz, Jean, Simonet, Thérèse, Barbier, Marie, Hlioua, Tarik, Meniolle d’Hauthville, Fleur, Quintin, Christine, Bouattour, Karim, Lecinq, Agnès, Soued, Mickaël, Bonnet, Marie-Pierre, Carbonniere, Mathieu, Fischer, Catherine, Picard, Paola-Carla, Bonnin, Martine, Storme, Brigitte, Bouthors, Anne-Sophie, Detente, Thomas, Nguyen Troung, Minh, Keita, Hawa, Nebout, Sophie, Osse, Lauranne, Delmas, Anne, Vial, Florence, Kaufner, Lutz, Hoefing, Christoph, Mueller, Stefan, Becke, Karin, Blobner, Manfred, Lewald, Heidrun, Schaller, Stefan Josef, Muggleton, Ellis, Bette, Birgit, Neumann, Claudia, Weber, Stefan, Grünewald, Matthias, Ohnesorge, Henning, Helf, Antonia, Jelting, Yvonne, Kranke, Peter, von Heymann, Christian, Welfle, Sabine, Staikou, Chryssoula, Stavrianopoulou, Antonia, Tsaroucha, Athanasia, Kalopita, Konstantina, Loukeri, Anastasia, Valsamidis, Dimitrios, Matsota, Paraskevi, Thorsteinsson, Adalbjörn, Tome, Riad, Eidelman, Leonid A., Davis, Atara, Orbach-Zinger, Sharon, Ioscovich, Alexander, Ramona, Iacob, De Simone, Luigi, Pesetti, Barbara, Brazzi, Luca, Zito, Alessandro, Camorcia, Michela, Della Rocca, Giorgio, Aversano, Marco, Frigo, Maria Grazia, Todde, Cristina, Morina, Qamile, Macas, Andrius, Keraitiene, Grazina, Rimaitis, Kestutis, Borg, Francis, Tua, Carl, Kuijpers-Visser, Agnes Geertje, Schyns-van den Berg, Alexandra, Hollmann, Markus W., Van den Berg, Tijs, Koolen, Eric, Dons, Ilse, van der Knijff, Anouk, van der Marel, Caroline, Ruysschaert, Nele, Pelka, Michal, Pluymakers, Christine, Koopman, Seppe, Teunissen, Aart-Jan, Cornelisse, Dick, van Dasselaar, Nick, Verdouw, Bastiaan, Beenakkers, Ingrid, Dahl, Vegard, Hagen, Robert, Vivaldi, Francesco, Eriksen, John Reidar, Wiszt, Radovan, Aslam Tayyaba, Naz, Ringvold, Else-Marie, Chutkowski, Radosław, Skirecki, Tomasz, Wódarski, Bartłomiej, Faria, Maria Aida, Ferreira, Amélia, Sampaio, Ana Catarina, Ferreira, Irene, Matias, Bernardo, Teixeira, Joana, Araujo, Rita, Cabido, Herminia, Fortuna, Rosario, Lemos, Paulo, Cardoso, Carolina, Moura, Fernando, Pereira, Cristiana, Pereira, Sandra, Tavares, Francisca, Vasconcelos, Pedro, Abecasis, Manuel, Lança, Filipa, Muchacho, Paulo, Ormonde, Lucindo, Guedes-Araujo, Isabel, Pinho-Oliveira, Vitor, Paredes, Paulo, Bentes, Carla, Gouveia, Francisco, Milheiro, Ana, Castanheira, Cláudia, Neves, Miriam, Pacheco, Vânia, Cortez, Mara, Tranquada, Raquel, Tareco, Glória, Furtado, Inês, Pereira, Estela, Marinho, Luísa, Seabra, Manue, Bulasevic, Aleksandra, Kendrisic, Mirjana, Jovanovic, Lidija, Pujić, Borislava, Kutlesic, Marija, Grochova, Monika, Simonova, Jana, Pavlovic, Gordana, Rozman, Ales, Blajic, Iva, Graovac, Dragan, Stopar Pintraic, Tatjana, Chiquito, Teresa, Monedero, Pablo, Carlos-Errea, De Joaquin, Guillén-Casbas, Roque, Veiga-Gil, Leonor, Basso, Morena, Garcia Bartolo, Carolina, Hernandez, Cristian, Ricol, Laura, De Santos, Maroto Pinar, Gràcia Solsona, Josep A., López-Baamonde, Manuel, Magaldi Mendaña, Marta, Plaza Moral, Ana María, Vendrell, Marina, Trillo, Lourdes, Perez Garcia, Anibal Ricardo, Alamillo Salas, Clara, Moret, Enric, Ramió, Laura, Aguilar Sanchez, Jose Luis, Soler Pedrola, Maria, Valldeperas Hernandez, Maria Inmaculada, Aldalur, Gorka, Bárcena, Estíbaliz, Herrera, Julia, Iturri, Fernando, Martínez, Alberto, Martínez, Leire, Serna, Rosa, Gilsanz, Fernando, Guasch Arevalo, Emilia, Iannuccelli, Fabrizio, Latorre, Julieta, Rodriguez Roca, Cristina, Pérez Pardo, Osvaldo Ceferino, Sierra Biddle, Natalia, Suárez Cendaña, Ceferina, Hernández González, Lourdes, Remacha González, Caridad, Sánchez Nuez, Raquel, Anta, Diego, Beleña, Jose M., García-Cuadrado, Carmen, Garcia, Irene, Manrique, Susana, Suarez, Elena, Hein, Anette, Arbman, Elisabet, Hansson, Helena, Tillenius, Monika, Al-Taie, Ruaa, Ledin-Eriksson, Susanne, Lindén-Söndersö, Anja, Rosén, Ola, Austruma, Evija, Gillberg, Lars, Darvish, Bijan, Gupta, Anil, Nordstöm, Johan L., Persson, Jan, Rosenberg, Jan, Brühne, Lars, Forshammar, Johan, Ugarph Edfeldt, Malin, Rolfsson, Håkan, Hellblom, Anna, Levin, Katarina, Rabow, Sofus, Thorlacius, Karin, Bansch, Peter, Robertson (Baeriswyl), Moira, Stamer, Ulrike, Mathivon, Stanislas, Savoldelli, Georges, Auf der Maur, Pia, Filipovic, Miodrag, Dullenkopf, Alexander, Brunner, Maya, Girard, Thierry, Vonlanthen, Claudia, Ozbilgin, Sule, Gunaydin D, Berrin, Corman Dincer, Pelin, Tas Tuna, Ayca, Magnuson, Anders, Fernando, Roshan, Mercier, Frédéric J., and Schyns-van den Berg, Alexandra M.J.V.

Published
2020
Full Text
View/download PDF

35. Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae).

Author

Maturana, Claudia S., Mejias, Tamara Contador, Simoes, Felipe L., Valladares, Moises A., Vidal, Paula M., Ganan, Melisa, Gonzalez-Wevar, Claudio A., Poulin, Elie, Sands, Chester J., Convey, Peter, Hernandez, Cristian E., Shimabukuro, Erika Mayumi, and Siri, Augusto

Subjects
LAST Glacial Maximum, REPRODUCTIVE isolation, EXTREME environments, GENETIC markers, CHIRONOMIDAE
Abstract

The terrestrial fauna of Antarctica consists of a limited number of species, notably insects, small crustaceans and other micro-invertebrates. Over long periods of evolutionary isolation, these organisms have developed varying degrees of tolerance to multifaceted environmental stresses. Recent molecular biogeographical research highlights the enduring persistence of much of Antarctica's current terrestrial fauna, with estimates spanning from hundreds of thousands to millions of years. Parochlus steinenii, commonly known as the Antarctic winged midge, stands out as one of the only two insect species native to Antarctica. Distributed across three biogeographic regions, southern South America and the Falkland/Malvinas Islands, sub-Antarctic South Georgia and the Maritime Antarctic South Shetland Islands, this midge raises questions about the temporal isolation of its populations and their divergence. Employing mitochondrial and nuclear genetic markers, we conducted phylogeographic and demographic analyses on 151 individuals of P. steinenii obtained across the three main biogeographic regions including the Magellanic sub-Antarctic Ecoregion (MSE) of southern South America, the sub-Antarctic Island of South Georgia (SG) and the South Shetland Islands (SSI) within the Maritime Antarctic (MA). Our data support the diversification of P. steinenii during the midPleistocene around 1.46 Mya. This period included a branching event between a clade containing only specimens from the MSE and a clade containing individuals from a broader range of locations including the SSI and SG. Based on intraspecific phylogeographic and demographic inferences, we detected strong evolutionary divergence between the three main biogeographic regions. We also detected a signal of population growth during the deglaciation process in SSI and SG, contrary to the pattern seen in the MSE. The different demographic and phylogeographic histories between the sampled biogeographic regions could result from the MA and SG experiencing a strong genetic bottleneck due to a reduction in population size during the Last Glacial Maximum, while the MSE maintained a significant effective population size. The high level of divergence detected between individuals from the MSE and the remaining biogeographic regions supports the hypothesis of a speciation process taking place in P. steinenii. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

36. Programmable delivery of fluoxetine via wearable bioelectronics for wound healing in vivo

Author

Li, Houpu, primary, Yang, Hsin-ya, additional, Asefifeyzabadi, Narges, additional, Baniya, Prabhat, additional, Zlobina, Ksenia, additional, Hsieh, Hao-Chieh, additional, Nguyen, Tiffany, additional, Hernandez, Cristian, additional, Carrion, Hector, additional, Tebyani, Maryam, additional, Selberg, John, additional, Gomez, Marcella, additional, Teodorescu, Mircea, additional, Lopez, Andrea Medina, additional, Gallegos, Anthony, additional, Zhu, Kan, additional, Recendez, Cynthia, additional, Luo, Le, additional, Alhamo, Moyasar A., additional, Norouzi, Narges, additional, Soulika, Athena M., additional, Levin, Michael, additional, Zhao, Min, additional, Isseroff, Roslyn Rivkah, additional, and Rolandi, Marco, additional

Published
2023
Full Text
View/download PDF

44. Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters

Author

Park, Yunjeong, primary, Hernandez, Sebastian, additional, Hernandez, Cristian O., additional, Schweiger, Hunter E., additional, Li, Houpu, additional, Voitiuk, Kateryna, additional, Dechiraju, Harika, additional, Hawthorne, Nico, additional, Muzzy, Elana M., additional, Selberg, John A., additional, Sullivan, Frederika N., additional, Urcuyo, Roberto, additional, Salama, Sofie R., additional, Aslankoohi, Elham, additional, Teodorescu, Mircea, additional, Mostajo-Radji, Mohammed A., additional, and Rolandi, Marco, additional

Published
2023
Full Text
View/download PDF

49. On-chip on-demand delivery of K+ for in vitro bioelectronics

Author

Dechiraju, Harika, primary, Selberg, John, additional, Jia, Manping, additional, Pansodtee, Pattawong, additional, Li, Houpu, additional, Hsieh, Hao-Chieh, additional, Hernandez, Cristian, additional, Asefifeyzabadi, Narges, additional, Nguyen, Tiffany, additional, Baniya, Prabhat, additional, Marquez, Giovanny, additional, Rasmussen-Ivey, Cody, additional, Bradley, Carrie, additional, Teodorescu, Mircea, additional, Gomez, Marcella, additional, Levin, Michael, additional, and Rolandi, Marco, additional

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results