The increasing susceptibility of aerospace integrated circuits (ICs) to single-event effects (SEEs) demands advanced radiation-hardening-by-design (RHBD) methodologies, especially for analog/mixed-signal circuits operating in extreme environments. While digital circuit hardening has seen extensive advancements, circuit-level radiation hardening techniques specifically targeting analog building blocks—such as high-speed current-mode logic (CML) latches used in frequency dividers—have received relatively less attention, largely due to the inherent trade-offs between radiation tolerance and high-speed performance. This work proposes a low-overhead RHBD strategy for CML latches in high-speed dividers, addressing single-event upsets (SEUs) through the integration of pMOS transistors at storage nodes to counteract charge collection induced by SEEs. The methodology is rigorously validated using calibrated double-exponential current source simulations, emulating SEEs. The hardened design demonstrates robust radiation tolerance across varied pMOS control signals and sizing parameters, with minimal sensitivity introduced at additional nodes. Postlayout simulations in sub-20 nm FinFET technology reveal a maximum operating frequency of 46 GHz, accompanied by ultralow implementation overheads: a 2% area penalty and 22% power increase. The strategy resolves critical trade-offs between radiation resilience and high-speed performance, offering a scalable solution for aerospace-grade circuits in radiation-intensive environments. This advancement underscores the viability of tailored RHBD approaches for analog/mixed-signal systems, enabling next-generation space electronics that harmonize reliability with cutting-edge functionality.

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback. • A new visual abstraction sequence for DNA-nano designs that combines spatial configurations and temporal MDS data. • A visual analytics framework combining the conformational space plot, the energy–time plot, and structural views. • A multi-scale dynamic visualization of DNA structures trajectories coupled with an H-bond status visualization, revealing design flaws in the structures.

We present VOICE, a novel approach to science communication that connects large language models' conversational capabilities with interactive exploratory visualization. VOICE introduces several innovative technical contributions that drive our conversational visualization framework. Based on the collected design requirements, we introduce a two-layer agent architecture that can perform task assignment, instruction extraction, and coherent content generation. We employ fine-tuning and prompt engineering techniques to tailor agents' performance to their specific roles and accurately respond to user queries. Our interactive text-to-visualization method generates a flythrough sequence matching the content explanation. In addition, natural language interaction provides capabilities to navigate and manipulate 3D models in real-time. The VOICE framework can receive arbitrary voice commands from the user and respond verbally, tightly coupled with a corresponding visual representation, with low latency and high accuracy. We demonstrate the effectiveness of our approach by implementing a proof-of-concept prototype and applying it to the molecular visualization domain: analyzing three 3D molecular models with multiscale and multi-instance attributes. Finally, we conduct a comprehensive evaluation of the system, including quantitative and qualitative analyses on our collected dataset, along with a detailed public user study and expert interviews. The results confirm that our framework and prototype effectively meet the design requirements and cater to the needs of diverse target users.

We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite large mesoscale models of complex protein assemblies and complete cellular structures that are based on measured cryo-EM data. The current approaches require manual fitting in three dimensions to start, resulting in approximately aligned structures followed by an automated fine-tuning of the alignment. The DiffFit approach enables domain scientists to fit new structures automatically and visualize the results for inspection and interactive revision. The fitting begins with differentiable three-dimensional (3D) rigid transformations of the protein atom coordinates followed by sampling the density values at the atom coordinates from the target cryo-EM volume. To ensure a meaningful correlation between the sampled densities and the protein structure, we proposed a novel loss function based on a multi-resolution volume-array approach and the exploitation of the negative space. This loss function serves as a critical metric for assessing the fitting quality, ensuring the fitting accuracy and an improved visualization of the results. We assessed the placement quality of DiffFit with several large, realistic datasets and found it to be superior to that of previous methods. We further evaluated our method in two use cases: automating the integration of known composite structures into larger protein complexes and facilitating the fitting of predicted protein domains into volume densities to aid researchers in identifying unknown proteins. We implemented our algorithm as an open-source plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization software in the field. All supplemental materials are available at osf. io/5tx4q.

The authors would like to make the following errata after correcting the initialization related bugs in the associated program.

Accurate detection and segmentation of pulmonary nodules in low-dose CT images is essential for early screening and treatment of lung cancer. Previous methods have often overlooked the critical role of segmentation in nodule feature learning, relying on relatively simple region proposal networks and false positive reduction modules. To address this limitation’ we introduce an segmentation branch to fully utilize the additional information such as nodule shape and boundary. Our proposed 3D U-Net detection model based on multi-task learning is optimized through bottom-layer parameter sharing to enhance prediction performance by fully utilizing complementary information between tasks. As for challenging problem of large nodule scale variety and complex background, we add more skip connections between the encoder and decoder structures, enhancing the fusion of features from different levels and facili-tating gradient flow, thus reducing model training difficulty. We also incorporate an efficient channel attention module in residual block to improve model learning and representation capability. Our method, named ECANodule, achieves an average detection sensitivity of 91.1% and a segmentation Dice score of 83.4% on the LIDC-IDRI dataset, surpassing many previous detection methods. In addition, we provide in-depth discussions on the multi-task strategy, network structure, and channel attention mechanism, offering valuable insights for future research.

This paper proposed a wireless 16-channel im-plantable system for long-term neural recording. In order to achieve stable and reliable neural signal acquisition, the im-plantable microsystem consists of an analog front end (AFE), a transmitter (TX), a small battery and a coil for energy harvesting to charge the battery. The AFE integrates 16-channel low-noise amplifiers (LNA), a SAR ADC and a digital interface. The TX integrates a rectifier, a bandgap voltage reference, regulators and a 427 MHz OOK modulated transmitter. The AFE and TX chips were fabricated in 180-nm technology. All the required functional modules are integrated in the chips with off-chip crystal, coil and antenna. The proposed microsystem weighs 2.1 g without a battery, and 3.7 g including the battery. The dimension is$20\times 18\times 7$mm 3. The total current of the system is 1.13 mA, and the battery life is about 50 hours with a capacity of 60 mA$h$. The charging current is 10mA under wireless power transmission.

This paper proposed a multiple-mode animal behavior analysis system integrating a wireless bio-potential recorder and two 120fps video streams. A 16-channel analog-front-end (AFE) featuring a chopper low noise amplifier (LNA) and a 12-bit successive approximation analog-to-digital converter (SAR ADC) is designed as the sensor interface. Bluetooth Low Energy (BLE) based in-the-air protocol is implemented for wireless data transmission. A precise synchronization method is proposed featuring a millisecond level synchronization accuracy between the video frames and the acquired bio-potential. The compact wireless bio-potential recorder features a size of 2 × 2.5 × 1cm and a battery life of 9 hours. In-vivo test has been performed on rats with long-term implantable electrodes. The proposed system successfully recorded the electroneurogram (ENG) signal from sciatic nerves and electromyography (EMG) signal from leg muscles. Also video-based gait analysis was performed and provided the labels to train an EMG-based gait phase classifier.

We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.

This paper presents a power efficient 12-bit successive aproximation register analog-to-digital converter (SAR ADC) operated at a supply voltage of 0.6V. A binary-scaled redundant technology for SAR ADC is proposed based on split-capacitor DAC architecture. It suppresses the decision error without sacrificing the resolution. In addition, a feedback controlled bias technique is applied to the comparator reducing the power consumption for comparison by 21.6%. The proposed ADC was fabricated in 0.18μm CMOS technology, occupying an core area of 0.07mm2. The measured DNL and INL is +0.46/-0.50 LSB and +0.98/-0.95 LSB, respectively. A SINAD of 68.1dB and SFDR of 83.0dB are achieved, respectively, while operating at a sampling rate of 100kS/s. The power consuming of the proposed ADC is 1.35uW, resulting in an FOM of 6.5fJ/Conversion-step.

This paper presents the design of a low-noise chopper amplifier for neural signal acquisition in the presence of the in-band stimulation artifacts that exist in the closed-loop neuromodulation system. In order to avoid saturation due to the artifacts, the gain of the amplifier is designed to be 26dB. A modified positive feedback loop is introduced to boost both inband and DC input impedance. The Gm-C integrator without external capacitors is used in the DC-Servo-Loop (DSL) to filter out the electrode offset. To further enhance the linearity, the voltage divider technique is employed at the input of the Gm-C integrator. The proposed work was fabricated using a 0.18um CMOS process. The amplifier consumes 3.42μW under 1.8V supply voltage, while occupying an area of 0.219mm2. The measured input-referred noise is 1.12μVrms(1 Hz-200 Hz) and 4.65μVrms(200 Hz-5 kHz). A DC input impedance of 90MΩ, a CMRR of 103dB, and a PSRR of 78dB are achieved as well.