Multiscale embedded printing of engineered human tissue and organ equivalents.

Creating tissue and organ equivalents with intricate architectures and multiscale functional feature sizes is the first step toward the reconstruction of transplantable human tissues and organs. Existing embedded ink writing approaches are limited by achievable feature sizes ranging from hundreds of microns to tens of millimeters, which hinders their ability to accurately duplicate structures found in various human tissues and organs. In this study, a multiscale embedded printing (MSEP) strategy is developed, in which a stimuli-responsive yield-stress fluid is applied to facilitate the printing process. A dynamic layer height control method is developed to print the cornea with a smooth surface on the order of microns, which can effectively overcome the layered morphology in conventional extrusion-based three-dimensional bioprinting methods. Since the support bath is sensitive to temperature change, it can be easily removed after printing by tuning the ambient temperature, which facilitates the fabrication of human eyeballs with optic nerves and aortic heart valves with overhanging leaflets on the order of a few millimeters. The thermosensitivity of the support bath also enables the reconstruction of the full-scale human heart on the order of tens of centimeters by on-demand adding support bath materials during printing. The proposed MSEP demonstrates broader printable functional feature sizes ranging from microns to centimeters, providing a viable and reliable technical solution for tissue and organ printing in the future.

Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.

Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app.

High-Precision Single-Cell microRNA Therapy by a Functional Nanopipette with Sensitive Photoelectrochemical Feedback.

This work proposes the concept of single-cell microRNA (miR) therapy and proof-of-concept by engineering a nanopipette for high-precision miR-21-targeted therapy in a single HeLa cell with sensitive photoelectrochemical (PEC) feedback. Targeting the representative oncogenic miR-21, the as-functionalized nanopipette permits direct intracellular drug administration with precisely controllable dosages, and the corresponding therapeutic effects can be sensitively transduced by a PEC sensing interface that selectively responds to the indicator level of cytosolic caspase-3. The experimental results reveal that injection of ca. 4.4 × 10-20 mol miR-21 inhibitor, i.e., 26488 copies, can cause the obvious therapeutic action in the targeted cell. This work features a solution to obtain the accurate knowledge of how a certain miR-drug with specific dosages treats the cells and thus provides an insight into futuristic high-precision clinical miR therapy using personalized medicine, provided that the prerequisite single-cell experiments are courses of personalized customization.

High-Precision Printing of Cermets by Collapsable Matrix Assisted Digital Light Processing.

Shaping hard and brittle materials, e.g. cermets, at micrometer resolution has long been known challenging for both mechanical machining and high energy beam based additive manufacturing. Digital light processing (DLP), which features great printing quality and decent precision, unfortunately lacks capability to deal with the popular slurry-typed cermet precursor due to the tremendous optical absorption by its particles. Here, an innovative protocol based on a versatile collapsable matrix is devised to allow high-precision printing of WC-Co cermets on DLP platform. By tuning the external environment, this matrix attenuates composite powders to facilitate photopolymerization at the printing stage, and shrinks to condense green parts prior to thermal sintering. The as-obtained samples by collapsable matrix assisted DLP can reach a relative density of ≈90%, a record-breaking resolution of ≈10 µm, and a microhardness of up to 14.5 GPa. Complex delicate structures, including school emblem, honeycomb, and micro-drill can be directly fabricated, which has never been achieved before. Impressively, the as-obtained micro-drill is able to be directly used in drilling tasks. The above strategy represents a great progress in DLP by enabling shaping strong light attenuating materials at high resolution. Such advantages are ideal for the next generation ceramic-metal composite additive manufacturing.

High-Precision Calorimetry-Based Analysis of Pupal-Pharate Adult Development in Drosophila melanogaster.

In holometabolous insects, the larval body is almost completely decomposed and reconstructed into the adult body during the pupal-pharate adult stages. Therefore, the total energetic cost of this process is a key thermodynamic quantity necessary for evaluating the benefit of their life history. Here, we measured whole-body thermal dissipation of single pupae of the fruit fly, Drosophila melanogaster, during the period from puparium formation to adult eclosion as a function of age, using a high-precision isothermal calorimeter at T = 298 K. The mass-specific energy consumption during the period from the onset of larval-pupal apolysis to adult eclosion was determined to be 2.3 kJ/g for an individual of mass (adult) = 1.0 mg, while it was observed to follow Kleiber's law for individuals smaller than mass (adult) = 1.0 mg. During the pupal-pharate adult period, in addition to the U-shaped variation, several characteristic thermal dissipations related to various events, including somatic muscle contractions, ecdyses, pulsatile hormone secretion in a pharate adult, and vaporization of the exuvial fluid, were observed. The periodic bursts in the pharate adult stage grew exponentially, suggesting that the positive feedback in the metabolic system synchronized with the progression of development, making the energy consumption in this stage more efficient. The present study showed that high-precision calorimetry is a powerful and credible method for measuring not only the total energy spent during development but also the energy spent during every specific developmental event in an organism.

BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors.

Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.

Research on Inertial Navigation and Environmental Correction Indoor Ultra-Wideband Ranging and Positioning Methods.

In contrast to outdoor environments, indoor positioning encounters signal propagation disruptions due to the presence of buildings, resulting in reduced accuracy and, at times, the inability to determine a location accurately. This research, leveraging the robust penetrative capabilities of Ultra-Wideband (UWB) signals in non-line-of-sight (NLOS) scenarios, introduces a methodology for refining ranging outcomes through a combination of inertial navigation and environmental adjustments to achieve high-precision spatial positioning. This approach systematically enhances the correction of signal propagation errors through walls. Initially, it digitalizes the spatial setting, preserving the error correction parameters. Subsequently, it employs inertial navigation to estimate spatial coordinates and delineate signal propagation pathways to achieve precise ranging results. It iteratively hones the positioning outcomes for enhanced precision. Empirical findings demonstrate that within NLOS conditions, compared to standalone UWB positioning and IMU/UWB fusion positioning using the ESKF algorithm, this positioning technique significantly enhances planar positioning accuracy while achieving a marginal elevation accuracy improvement, albeit with some residual deviations from actual values. Furthermore, this positioning methodology effectively rectifies results in NOLS settings, paving the way for a novel approach to optimize indoor positioning through UWB technology.

A Smith Predictor Modified with a Pseudo Feedforward Control for the Charge-Coupled Device-Based Optoelectronic Tracking System.

In the high-precision optoelectronic tracking system (OTS) based on a charge-coupled device (CCD), the boresight error extracted from the tracking image contains an undeniable delay, which directly limits the control bandwidth of visual tracking. High bandwidth means high response speed and tracking accuracy. Generally, a model-based delay compensation control method called the Smith predictor is utilized to separate time delay from the closed loop to promote the control bandwidth. However, due to the existence of errors between the established model and the real object, the improvement in the bandwidth is still limited to ensure system stability, resulting in insufficient tracking performance. In this paper, to solve the problem, a Smith predictor modified with pseudo feedforward control for the OTS is proposed. The experimental results demonstrate that the proposed method achieves significant improvements in tracking performance, reducing the maximum residual error at 1 Hz from 365 arcseconds (using the classic Smith predictor) to 283 arcseconds, a 22.5% improvement. Across the main frequency band (0.2 Hz to 2 Hz), the residual errors were consistently lower using the proposed method.

Cross-Country Ski Skating Style Sub-Technique Detection and Skiing Characteristic Analysis on Snow Using High-Precision GNSS.

A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study aimed to develop a method of classifying sub-techniques and analyzing skiing characteristics during cross-country skiing skating style timed races on snow using high-precision kinematic GNSS devices. The study involved attaching GNSS devices to the heads of two athletes during skating style timed races on cross-country ski courses. These devices provided precise positional data and recorded vertical and horizontal head movements and velocity over ground (VOG). Based on these data, sub-techniques were classified by defining waveform patterns for G2, G3, G4, and G6P (G6 with poling action). The validity of the classification was verified by comparing the GNSS data with video analysis, a process that yielded classification accuracies ranging from 95.0% to 98.8% for G2, G3, G4, and G6P. Notably, G4 emerged as the fastest technique, with sub-technique selection varying among skiers and being influenced by skiing velocity and course inclination. The study's findings have practical implications for athletes and coaches as they demonstrate that high-precision kinematic GNSS devices can accurately classify sub-techniques and detect skiing characteristics during skating style cross-country skiing races, thereby providing valuable insights for training and strategy development.

High-Precision Atom Interferometer-Based Dynamic Gravimeter Measurement by Eliminating the Cross-Coupling Effect.

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses an AI sensor and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement were ±0.42 mGal and ±0.46 mGal after optimizing the cross-coupling effect, which was improved by factors of 4.18 and 4.21 compared to the cases without optimization.

An Adaptive Multi-Population Approach for Sphericity Error Evaluation in the Manufacture of Hemispherical Shell Resonators.

The performance of a hemispherical resonant gyroscope (HRG) is directly affected by the sphericity error of the thin-walled spherical shell of the hemispherical shell resonator (HSR). In the production process of the HSRs, high-speed, high-accuracy, and high-robustness requirements are necessary for evaluating sphericity errors. We designed a sphericity error evaluation method based on the minimum zone criterion with an adaptive number of subpopulations. The method utilizes the global optimal solution and the subpopulations' optimal solution to guide the search, initializes the subpopulations through clustering, and dynamically eliminates inferior subpopulations. Simulation experiments demonstrate that the algorithm exhibits excellent evaluation accuracy when processing simulation datasets with different sphericity errors, radii, and numbers of sampling points. The uncertainty of the results reached the order of 10-9 mm. When processing up to 6000 simulation datasets, the algorithm's solution deviation from the ideal sphericity error remained around -3 × 10-9 mm. And the sphericity error evaluation was completed within 1 s on average. Additionally, comparison experiments further confirmed the evaluation accuracy of the algorithm. In the HSR sample measurement experiments, our algorithm improved the sphericity error assessment accuracy of the HSR's inner and outer contour sampling datasets by 17% and 4%, compared with the results given by the coordinate measuring machine. The experiment results demonstrated that the algorithm meets the requirements of sphericity error assessment in the manufacturing process of the HSRs and has the potential to be widely used in the future.

Determining the Antenna Phase Center for the High-Precision Positioning of Smartphones.

In recent years, smartphones have emerged as the primary terminal for navigation and location services among mass users, owing to their universality, portability, and affordability. However, the highly integrated antenna design within smartphones inevitably introduces interference from internal signal sources, leading to a misalignment between the antenna phase center (APC) and the antenna geometric center. Accurately determining a smartphone's APC can mitigate system errors and enhance positioning accuracy, thereby meeting the increasing demand for precise and reliable user positioning. This paper delves into a detailed analysis of the generation of Global Navigation Satellite System (GNSS) receiver antenna phase center errors and proposes a method for correcting the receiver antenna phase center. Subsequently, a smartphone positioning experiment was conducted by placing the smartphone on an observation column with known coordinates. The collected observations were processed in static relative positioning mode, referencing observations from geodetic-grade equipment, and the accuracy of the static relative positioning fixed solution was evaluated. Following weighted estimation, we determined the antenna phase center of the Xiaomi Mi8 and corrected the APC. A comparison of the positioning results of the Xiaomi Mi8 before and after APC correction revealed minimal impact on the standard deviations (STDs) but significant influence on the root mean square errors (RMSEs). Specifically, the RMSEs in the E/N/U direction were reduced by 59.6%, 58.5%, and 42.0%, respectively, after APC correction compared to before correction. Furthermore, the integer ambiguity fixing rate slightly improved after the APC correction. In conclusion, the determination of a smartphone's APC can effectively reduce system errors in the plane direction of GNSS positioning, thereby enhancing smartphone positioning accuracy. This research holds significant value for advancing high-precision positioning studies related to smartphones.

Dual-Locked Fluorescent Probes Activated by Aminopeptidase N and the Tumor Redox Environment for High-Precision Imaging of Tumor Boundaries.

Clear delineation of tumor margins is essential for accurate resection and decreased recurrence rate in the clinic. Fluorescence imaging is emerging as a promising alternative to traditional visual inspection by surgeons for intraoperative imaging. However, traditional probes lack accuracy in tumor diagnosis, making it difficult to depict tumor boundaries accurately. Herein, we proposed an offensive and defensive integration (ODI) strategy based on the "attack systems (invasive peptidase) and defense systems (reductive microenvironment)" of multi-dimensional tumor characteristics to design activatable fluorescent probes for imaging tumor boundaries precisely. Screened out from a series of ODI strategy-based probes, ANQ performed better than traditional probes based on tumor unilateral correlation by distinguishing between tumor cells and normal cells and minimizing false-positive signals from living metabolic organs. To further improve the signal-to-background ratio in vivo, derivatized FANQ, was prepared and successfully applied to distinguish orthotopic hepatocellular carcinoma tissues from adjacent tissues in mice models and clinical samples. This work highlights an innovative strategy to develop activatable probes for rapid diagnosis of tumors and high-precision imaging of tumor boundaries, providing more efficient tools for future clinical applications in intraoperative assisted resection.

Reappraisal of the role of radiation therapy in lymphoma treatment.

Radiation therapy (RT) for lymphomas has improved dramatically with modern imaging and treatment techniques, encompassing only the necessary volume with minimal doses to normal structures. Prescribed radiation doses are reduced, and fractionation schedules are under revision. With effective systemic treatment only initial macroscopic disease is irradiated. With no or less effective systemic treatment, possible microscopic disease is also included. Risks of long-term side effects of RT have diminished dramatically and should be weighed against risks from more systemic treatment or increased risk of relapse. Lymphoma patients are often elderly, they tolerate modern limited RT very well. Lymphomas refractory to systemic treatments often remain radioresponsive, and brief, mild RT may offer effective palliation. New roles for RT are emerging with immune therapies. RT for "bridging," keeping the lymphoma under control while waiting for immune therapy, is well established. Enhancement of the immune response to lymphomas, so-called "priming," is being intensively researched.

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI.

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.

A High-Precision Real-Time Pose Measurement Method for the Primary Lens of Large Aperture Space Telescope Based on Laser Ranging.

The aperture of space telescopes increases with their required resolution, and the transmission optical systems with long focal length and diffractive primary lens are becoming increasingly popular. In space, the changes in the pose of the primary lens relative to the rear lens group have a significant impact on the imaging performance of the telescope system. The measurement of the pose of the primary lens in real-time and with high-precision is one of the important techniques for a space telescope. In this paper, a high-precision real-time pose measurement method for the primary lens of a space telescope in orbit based on laser ranging is proposed, and a verification system is established. The pose change of the telescope's primary lens can be easily calculated through six high-precision laser distance changes. The measurement system can be installed freely, which solves the problems of complex system structure and low measurement accuracy in traditional pose measurement techniques. Analysis and experiments show that this method can accurately obtain the pose of the primary lens in real-time. The rotation error of the measurement system is 2 × 10-5 degrees (0.072 arcsecs), and the translation error is 0.2 µm. This study will provide a scientific basis for high-quality imaging of a space telescope.

In-Orbit Calibration of Phase Retardance for Channeled Spectropolarimeter.

The phase retardance of the optical system (PROS) is a crucial factor limiting the accuracy of the Stokes vector reconstruction for the channeled spectropolarimeter. The dependence on reference light with a specific angle of polarization (AOP) and the sensitivity to environmental disturbance brings challenges to the in-orbit calibration of PROS. In this work, we propose an instant calibration scheme with a simple program. A function with a monitoring role is constructed to precisely acquire a reference beam with a specific AOP. Combined with numerical analysis, high-precision calibration without the onboard calibrator is realized. The simulation and experiments prove the effectiveness and anti-interference characteristics of the scheme. Our research under the framework of fieldable channeled spectropolarimeter shows that the reconstruction accuracy of S2 and S3 in the whole wavenumber domain are 7.2 × 10-3 and 3.3 × 10-3, respectively. The highlight of the scheme is to simplify the calibration program and ensure that the PROS high-precision calibration is not disturbed by the orbital environment.

A CMOS Temperature Sensor with a Smart Calibrated Inaccuracy of ±0.11 (3σ).

This paper presents a BJT-based smart CMOS temperature sensor. The analog front-end circuit contains a bias circuit and a bipolar core; the data conversion interface features an incremental delta-sigma analog-to-digital converter. The circuit utilizes the chopping, correlated double sampling, and dynamic element matching techniques to mitigate the effects of process bias and nonideal device characteristics on measurement accuracy. Furthermore, based on the principle of charge conservation, the dynamic range utilization of the ADC increases. We propose a neural network that uses a multilayer convolutional perceptron to calibrate the sensor output results. Using the algorithm, the sensor achieves an inaccuracy of ±0.11 °C (3σ), exceeding the accuracy of ±0.23 °C (3σ) achieved without calibration. We implement the sensor in a 0.18 µm CMOS process, occupying an area of 0.42 mm2. It achieves a resolution of 0.01 °C and has a conversion time of 24 ms.

Arduino-Based Low-Cost Device for the Measurement of Detonation Times in Blasting Caps.

The use of equipment such as oscilloscopes, high-speed cameras or acoustic sensors is quite common to measure detonation times from surface connectors and detonators. However, these solutions are expensive and, sometimes, not adequate to use in field conditions, such as mining or civil works. In this regard, a low-cost portable device is designed and tested using the Arduino platform, achieving a simple, robust and precise system to carry out field measurements. This study describes the characteristics and working principles of the designed device, as well as the verifications carried out to check the accuracy of the Arduino ceramic oscillator. Additionally, a field test was carried out using 100 actual detonators and surface connectors to verify the correct operation of the designed equipment. We have designed a device, and a methodology, to measure detonation instants with a minimum accuracy of 0.1 ms, being sufficient to carry out subsequent studies of detonation time dispersion for non-electric detonators.

DYNet: A Printed Book Detection Model Using Dual Kernel Neural Networks.

Target detection has always been a hotspot in image processing/computer vision research, and small-target detection is a frequently encountered problem in the field of target detection. With the continuous innovation of target detection technology, people always hope that the detection of small targets can reach the real-time accuracy of large-target detection. In this paper, a small-target detection model based on dual-core convolutional neural networks (CNN) is proposed, which is mainly used for the intelligent detection of books in the production line of printed books. The model is mainly composed of two modules, including a region prediction module and suspicious target search module. The region prediction module uses a CNN to predict suspicious region blocks in a large context. The suspicious target search module uses a different CNN from the above to find tiny targets in the predicted region blocks. Comparative testing of four small book target samples using this model shows that this model has better book small-target detection accuracy compared to other models.