Correction: Peisen et al. Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors? 2024, , 2669.

There was an error in the original publication [...

Simulation training with haptic feedback of instrument vibrations reduces resident workload during live robot-assisted sleeve gastrectomy.

Background: New surgeons experience heavy workload during robot-assisted surgery partially because they must use vision to compensate for the lack of haptic feedback. We hypothesize that providing realistic haptic feedback during dry-lab simulation training may accelerate learning and reduce workload during subsequent surgery on patients.

Methods: We conducted a single-blinded study with 12 general surgery residents (third and seventh post-graduate year, PGY) randomized into haptic and control groups.

Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light.

Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively.

Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series.

Neonatal apneas and hypopneas present a serious risk for healthy infant development. Treating these adverse events requires frequent manual stimulation by skilled personnel, which can lead to alarm fatigue. This study aims to develop and validate an interpretable model that can predict apneas and hypopneas.

Accelerating the pace of innovation in robotics by fostering diversity and inclusive leadership.

Diverse and inclusive teams are not merely a moral imperative but also a catalyst for scientific excellence in robotics. Drawing from literature, a comprehensive citation analysis, and expert interviews, we derive seven main benefits of diversity and inclusion and propose a leadership guide for roboticists to reap these benefits.

Generating realistic neurophysiological time series with denoising diffusion probabilistic models.

Denoising diffusion probabilistic models (DDPMs) have recently been shown to accurately generate complicated data such as images, audio, or time series. Experimental and clinical neuroscience also stand to benefit from this progress, as the accurate generation of neurophysiological time series can enable or improve many neuroscientific applications. Here, we present a flexible DDPM-based method for modeling multichannel, densely sampled neurophysiological recordings.

Stiffness-tunable velvet worm-inspired soft adhesive robot.

Considering the characteristics and operating environment of remotely controlled miniature soft robots, achieving delicate adhesion control over various target surfaces is a substantial challenge. In particular, the ability to delicately grasp wrinkled and soft biological and nonbiological surfaces with low preload without causing damage is essential. The proposed adhesive robotic system, inspired by the secretions from a velvet worm, uses a structured magnetorheological material that exhibits precise adhesion control with stability and repeatability by the rapid stiffness change controlled by an external magnetic field.

H and C NMR and FTIR Spectroscopic Analysis of Formic Acid Dissociation Dynamics in Water.

The formation and transport of ionic charges in formic acid-water (HCOOH-HO) mixtures with initial water mole fractions ranging from X = 0 to 1 were investigated using C and H NMR, FTIR spectroscopy, viscosity, conductivity, and pH measurements. The maximum molar concentration of ions (HO and HCOO), along with the relative differences between theoretical and experimental densities, spin-lattice relaxation times (), activation energies (), viscosity (η), and conductivity (σ), were identified within the range of X ≈ 0.5-0.

Heterogeneous multiple soft millirobots in three-dimensional lumens.

Miniature soft robots offer opportunities for safe and physically adaptive medical interventions in hard-to-reach regions. Deploying multiple robots could further enhance the efficacy and multifunctionality of these operations. However, multirobot deployment in physiologically relevant three-dimensional (3D) tubular structures is limited by the lack of effective mechanisms for independent control of miniature magnetic soft robots.

From Simulations to Inference: Using Machine Learning to Tune Patient-Specific Finite-Element Models of the Middle Ear Towards Objective Diagnosis.

Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. A major challenge in tuning these models is the large number of parameters involved. Traditional methods, such as one-by-one sensitivity analyses, are time-consuming, subjective, and often return only a single set of parameter values, focusing on reproducing averaged data rather than capturing the full variability of experimental measurements.

Model for efficient dynamical ranking in networks.

We present a physics-inspired method for inferring dynamic rankings in directed temporal networks-networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each node is real-valued and varies in time as each new edge, encoding an outcome like a win or loss, raises or lowers the node's estimated strength or prestige, as is often observed in real scenarios including sequences of games, tournaments, or interactions in animal hierarchies. Our method works by solving a linear system of equations and requires only one parameter to be tuned.

Hierarchical Nanostructures as Acoustically Manipulatable Multifunctional Agents in Dynamic Fluid Flow.

Acoustic waves provide a biocompatible and deep-tissue-penetrating tool suitable for contactless manipulation in in vivo environments. Despite the prevalence of dynamic fluids within the body, previous studies have primarily focused on static fluids, and manipulatable agents in dynamic fluids are limited to gaseous core-shell particles. However, these gas-filled particles face challenges in fast-flow manipulation, complex setups, design versatility, and practical medical imaging, underscoring the need for effective alternatives.

Reinforcement learning in cold atom experiments.

Cold atom traps are at the heart of many quantum applications in science and technology. The preparation and control of atomic clouds involves complex optimization processes, that could be supported and accelerated by machine learning. In this work, we introduce reinforcement learning to cold atom experiments and demonstrate a flexible and adaptive approach to control a magneto-optical trap.

Investigation of Oxide Layer Development of X6CrNiNb18-10 Stainless Steel Exposed to High-Temperature Water.

The oxide layer development of X6CrNiNb18-10 (AISI 347) during exposure to high-temperature water has been investigated. Stainless steels are known to form a dual oxide layer in corrosive environments. The secondary Fe-rich oxide layer has no significant protective effect.

Steerable current-driven emission of spin waves in magnetic vortex pairs.

The efficient excitation of spin waves is a key challenge in the realization of magnonic devices. We demonstrate current-driven generation of spin waves in antiferromagnetically coupled magnetic vortices. We use time-resolved x-ray microscopy to directly image the emission of spin waves upon the application of alternating currents flowing directly through the magnetic stack.

Controlling nanocluster growth through nanoconfinement: the effect of the number and nature of metal-organic framework functionalities.

Controlled nanocluster growth nanoconfinement is an attractive approach as it allows for geometry control and potential surface-chemistry modification simultaneously. However, it is still not a straight-forward method and much of its success depends on the nature and possibly concentration of functionalities on the cavity walls that surround the clusters. To independently probe the effect of the nature and number of functional groups on the controlled Pd nanocluster growth within the pores of the metal-organic frameworks, Pd-laden UiO-66 analogues with mono- and bi-functionalised linkers of amino and methyl groups were successfully prepared and studied in a combined experimental-computational approach.

Hexagonal electrohydraulic modules for rapidly reconfigurable high-speed robots.

Robots made from reconfigurable modular units feature versatility, cost efficiency, and improved sustainability compared with fixed designs. Reconfigurable modules driven by soft actuators provide adaptable actuation, safe interaction, and wide design freedom, but existing soft modules would benefit from high-speed and high-strain actuation, as well as driving methods well-suited to untethered operation. Here, we introduce a class of electrically actuated robotic modules that provide high-speed (a peak contractile strain rate of 4618% per second, 15.

Similarity and economy of scale in urban transportation networks and optimal transport-based infrastructures.

Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure but rather extracts this directly from a continuous space using only a few origin and destination points, generating networks from scratch.

Connectome-constrained networks predict neural activity across the fly visual system.

We can now measure the connectivity of every neuron in a neural circuit, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation.

Electrohydraulic musculoskeletal robotic leg for agile, adaptive, yet energy-efficient locomotion.

Robotic locomotion in unstructured terrain demands an agile, adaptive, and energy-efficient architecture. To traverse such terrains, legged robots use rigid electromagnetic motors and sensorized drivetrains to adapt to the environment actively. These systems struggle to compete with animals that excel through their agile and effortless motion in natural environments.

Cutaneous Electrohydraulic (CUTE) Wearable Devices for Pleasant Broad-Bandwidth Haptic Cues.

By focusing on vibrations, current wearable haptic devices underutilize the skin's perceptual capabilities. Devices that provide richer haptic stimuli, including contact feedback and/or variable pressure, are typically heavy and bulky due to the underlying actuator technology and the low sensitivity of hairy skin, which covers most of the body. This article presents a system architecture for compact wearable devices that deliver salient and pleasant broad-bandwidth haptic cues: Cutaneous Electrohydraulic (CUTE) devices combine a custom materials design for soft haptic electrohydraulic actuators that feature high stroke, high force, and electrical safety with a comfortable mounting strategy that places the actuator in a non-contact resting position.

Localization and recognition of human action in 3D using transformers.

Understanding a person's behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization.