EphB1 promotes the differentiation and maturation of dendritic cells in non-small cell lung cancer.

EphB1 is implicated in numerous physiological and pathological processes, including nervous system diseases, cardiovascular diseases and cancers. It binds to membrane-bound ligands and drives bidirectional signaling. EphB1, along with its ligand ehrinB, plays a pivotal role in activating immune cells. However, despite its presence in dendritic cells (DCs), EphB1's involvement in the differentiation and maturation of DCs in cancers remains inadequately understood. In this study, we found compromised differentiation and maturation of DCs in EphB1-/- mice bearing lung adenocarcinoma syngeneic tumors. Our in vitro assays revealed that EphB1 phosphorylation induced DC differentiation and maturation. Cox-2, a key enzyme involved in the production of proinflammatory molecules, is implicated in DC differentiation induced by phosphorylated EphB1. Additionally, the study has identified lead compounds that specifically target EphB1 phosphorylation sites. Collectively, this research on EphB1 phosphorylation has provided valuable insights into the regulation of immune cell functionality and holds the potential for the development of innovative therapeutic strategies for a range of diseases.

A Real-Time and Robust Neural Network Model for Low-Measurement-Rate Compressed-Sensing Image Reconstruction.

Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for robust reconstruction results. This paper proposes a neural network called RootsNet, which integrates the CS mechanism into the network to prevent error propagation. So, RootsNet knows what will happen if some modules in the network go wrong. It also implements real-time and successfully reconstructed extremely low measurement rates that are impossible for traditional optimization-theory-based methods. For qualitative validation, RootsNet is implemented in two real-world measurement applications, i.e., a near-field microwave imaging system and a pipeline inspection system, where RootsNet easily saves 60% more measurement time and 95% more data compared with the state-of-the-art optimization-theory-based reconstruction methods. Without losing generality, comprehensive experiments are performed on general datasets, including evaluating the key components in RootsNet, the reconstruction uncertainty, quality, and efficiency. RootsNet has the best uncertainty performance and efficiency, and achieves the best reconstruction quality under super low-measurement rates.

Brain-like position measurement method based on improved optical flow algorithm.

In this paper, a brain-like navigation scheme based on fuzzy kernel C-means (FKCM) clustering assisted pyramid Lucas Kanade (LK) optical flow algorithm is developed to measure the position of vehicle. The Speed Cell and Place Cell in animals' brain are introduced to construct the brain-like navigation mechanism which involves the optical flow method and image template matching to imitate the cells above-mentioned separately. To eliminate the singular values during optical flow calculation, the output of pyramid LK algorithm is clustered by FKCM algorithm firstly. Then, the velocity is calculated and integrated to get the position of the vehicle, and the brain-like navigation scheme is introduced to correct the position measurement errors by eliminating the accumulated errors resulting from velocity integration. The prominent advantages of the presented method are: (i) a pure visual brain-like position measurement method based on the concept of speed cells and place cells is proposed, making visual navigation more accurate and intelligent; (ii) the FKCM algorithm is used to eliminate the singular value of the pyramid LK algorithm, which improves the calculated velocity accuracy. Also, experimental comparison with classical pyramid LK algorithm is given to illustrate the superiority of the proposed method in position measurement.

Efficient Near-Field Radiofrequency Imaging of Impact Damage on CFRP Materials with Learning-Based Compressed Sensing.

Carbon fiber-reinforced polymer (CFRP) is a widely-used composite material that is vulnerable to impact damage. Light impact damages destroy the inner structure but barely show obvious change on the surface. As a non-contact and high-resolution method to detect subsurface and inner defect, near-field radiofrequency imaging (NRI) suffers from high imaging times. Although some existing works use compressed sensing (CS) for a faster measurement, the corresponding CS reconstruction time remains high. This paper proposes a deep learning-based CS method for fast NRI, this plugin method decreases the measurement time by one order of magnitude without hardware modification and achieves real-time imaging during CS reconstruction. A special 0/1-Bernoulli measurement matrix is designed for sensor scanning firstly, and an interpretable neural network-based CS reconstruction method is proposed. Besides real-time reconstruction, the proposed learning-based reconstruction method can further reduce the required data thus reducing measurement time more than existing CS methods. Under the same imaging quality, experimental results in an NRI system show the proposed method is 20 times faster than traditional raster scan and existing CS reconstruction methods, and the required data is reduced by more than 90% than existing CS reconstruction methods.

Increasing the antioxidant capacity of ceria nanoparticles with catechol-grafted poly(ethylene glycol).

Ceria nanoparticles are remarkable antioxidants due to their large cerium(III) content and the possibility of recovering cerium(III) from cerium(IV) after reaction. Here we increase the cerium(III) content of colloidally stable nanoparticles (e.g., nanocrystals) using a reactive polymeric surface coating. Catechol-grafted poly(ethylene glycols) (PEG) polymers of varying lengths and architectures yield materials that are non-aggregating in a variety of aqueous media. Cerium(IV) on the ceria surface both binds and oxidizes the catechol functionality, generating a dark-red colour emblematic of surface-oxidized catechols with a concomitant increase in cerium(III) revealed by X-ray photoemission spectroscopy (XPS). The extent of ceria reduction depends sensitively on the architecture of the coating polymer; small and compact polymer chains pack with high density at the nanoparticle surface yielding the most cerium(III). Nanoparticles with increased surface reduction, quantified by the intensity of their optical absorption and thermogravimetric measures of polymer grafting densities, were more potent antioxidants as measured by a standard TEAC antioxidant assay. For the same core composition nanoparticle antioxidant capacities could be increased over an order of magnitude by tailoring the length and architecture of the reactive surface coatings.

An Anti-Turbulence Self-Alignment Method for SINS under Unknown Latitude Conditions.

For the alignment problem of strapdown inertial navigation system (SINS) under the complex environment of unknown latitude, angular oscillation interference, and line interference, the ant colony simulated annealing algorithm of gravity vector optimization is proposed to obtain the gravity apparent motion vector optimization equation, and the polynomial fitting method is proposed to simultaneously perform latitude estimation and self-alignment in combination with the alignment principle of SINS. Simulations and experiments show that the proposed method has more robust anti-interference capability than the traditional interference-based alignment method, the latitude estimation accuracy is improved by six times, the self-alignment yaw angle error RMSE value after obtaining the latitude is within 0.7°, and the roll angle and pitch angle error values are within 0.1°.

A D-2-hydroxyglutarate biosensor based on specific transcriptional regulator DhdR.

D-2-Hydroxyglutarate (D-2-HG) is a metabolite involved in many physiological metabolic processes. When D-2-HG is aberrantly accumulated due to mutations in isocitrate dehydrogenase or D-2-HG dehydrogenase, it functions in a pro-oncogenic manner and is thus considered a therapeutic target and biomarker in many cancers. In this study, DhdR from Achromobacter denitrificans NBRC 15125 is identified as an allosteric transcriptional factor that negatively regulates D-2-HG dehydrogenase expression and responds to the presence of D-2-HG. Based on the allosteric effect of DhdR, a D-2-HG biosensor is developed by combining DhdR with amplified luminescent proximity homogeneous assay (AlphaScreen) technology. The biosensor is able to detect D-2-HG in serum, urine, and cell culture medium with high specificity and sensitivity. Additionally, this biosensor is used to identify the role of D-2-HG metabolism in lipopolysaccharide biosynthesis of Pseudomonas aeruginosa, demonstrating its broad usages.

An interaction-modeling mechanism for context-dependent Text-to-SQL translation based on heterogeneous graph aggregation.

For the context-dependent Text-to-SQL task, the generation of SQL query is placed in a multi-turn interaction scenario. Each turn of Text-to-SQL must take historical interactive information and database schema into account. Accordingly, how to encode and integrate these different types of texts (the question sentence, the corresponding SQL query, and database schema) is a tough problem. In previous work, these series of texts are usually concatenated into sequences and encoded by various variants of recurrent neural networks (RNN). However, the RNNs cannot model the intrinsic relationship of the text directly. To this end, we propose an interaction-modeling mechanism to represent and aggregate these texts. Firstly, different types of texts are represented as individual graphs. Then, heterogeneous graph aggregation is used to capture the interactions and aggregate graphs into a holistic representation. Finally, the corresponding SQL query is generated based on the current question and the aggregated information. We evaluate our model on the SparC and CoSQL dataset to demonstrate the benefits of interaction-modeling. Experimentally, our model has a competitive performance and space-time cost.

Comparison of complications and visual outcomes between big-bubble deep anterior lamellar keratoplasty and penetrating keratoplasty for fungal keratitis.

BACKGROUND: To compare the postoperative complications and visual outcomes of big-bubble deep anterior lamellar keratoplasty (BB-DALK) and penetrating keratoplasty (PK) for fungal keratitis (FK). METHODS: This retrospective study included 94 cases of BB-DALK for FK and 161 cases of PK for FK from a tertiary ophthalmology care centre. RESULTS: The most common FK pathogens were Fusarium (n = 84, 32.9%) and Aspergillus (n = 67, 26.3%). The recurrence rates after BB-DALK and PK were 3.2 and 5%, respectively (p = 0.723). The follow-up duration was 31.9 ± 15.8 months in the BB-DALK group and 33.9 ± 15.0 months in the PK group. The immune rejection rate was significantly lower in the BB-DALK group than in the PK group (1.1 vs. 18.6%, p < 0.001), as was the incidence of secondary glaucoma (p = 0.018). Endothelial cell density in the BB-DALK group tended to be stable at postoperative month 6, whereas the PK group still attenuated at a hyper-physiological rate. Postoperative best-corrected visual acuity (BCVA) significantly improved in both groups (p < 0.001). No significant difference between-group was observed in BCVA, refractive cylinder, and spherical equivalent postoperatively. CONCLUSION: Big-bubble DALK is a useful and safe alternative to PK for medically uncontrolled FK.

2D Gadolinium Oxide Nanoplates as T1 Magnetic Resonance Imaging Contrast Agents.

Millions of people a year receive magnetic resonance imaging (MRI) contrast agents for the diagnosis of conditions as diverse as fatty liver disease and cancer. Gadolinium chelates, which provide preferred T1 contrast, are the current standard but face an uncertain future due to increasing concerns about their nephrogenic toxicity as well as poor performance in high-field MRI scanners. Gadolinium-containing nanocrystals are interesting alternatives as they bypass the kidneys and can offer the possibility of both intracellular accumulation and active targeting. Nanocrystal contrast performance is notably limited, however, as their organic coatings block water from close interactions with surface Gadoliniums. Here, these steric barriers to water exchange are minimized through shape engineering of plate-like nanocrystals that possess accessible Gadoliniums at their edges. Sulfonated surface polymers promote second-sphere relaxation processes that contribute remarkable contrast even at the highest fields (r1 = 32.6 × 10-3 m Gd-1 s-1 at 9.4 T). These noncytotoxic materials release no detectable free Gadolinium even under mild acidic conditions. They preferentially accumulate in the liver of mice with a circulation half-life 50% longer than commercial agents. These features allow these T1 MRI contrast agents to be applied for the first time to the ex vivo detection of nonalcoholic fatty liver disease in mice.

Unambiguous measurement range and error tolerance in dual-wavelength interferometry.

In dual or multiwavelength interferometry, the traditional equivalent wavelength method is widely used for phase recovery to enlarge the unambiguous measurement range (UMR). In fact, however, this method ignores information of size and sign (positive or negative) of single wavelength wrapped phases, and the extension of the UMR is not sufficient. For the reflective measurement, the largest UMR of the dual or multiwavelength interferometry is half of the least-common multiple (LCM) of single wavelengths, called the LCM effective wavelength, which is often several times the equivalent wavelength. But why do we often use the equivalent wavelength and seldom use the larger UMR in practice? Existing research reveals that the actual UMR is related to the measurement error of single-wavelength-wrapped phases, and half of the LCM effective wavelength is only the theoretical value. But how do errors affect the UMR? We think the quantitative analysis and description are lacking. In this paper, we continue to study this problem, analyze it in a graphical method, and give quantitative descriptions. The simulation experiments are carried out and verify our analysis.

Libraries of Uniform Magnetic Multicore Nanoparticles with Tunable Dimensions for Biomedical and Photonic Applications.

Multicore iron oxide nanoparticles, also known as colloidal nanocrystal clusters, are magnetic materials with diverse applications in biomedicine and photonics. Here, we examine how both of their characteristic dimensional features, the primary particle and sub-micron colloid diameters, influence their magnetic properties and performance in two different applications. The characterization of these basic size-dependent properties is enabled by a synthetic strategy that provides independent control over both the primary nanocrystal and cluster dimensions. Over a wide range of conditions, electron microscopy and X-ray diffraction reveal that the oriented attachment of smaller nanocrystals results in their crystallographic alignment throughout the entire superstructure. We apply a sulfonated polymer with high charge density to prevent cluster aggregation and conjugate molecular dyes to particle surfaces so as to visualize their collection using handheld magnets. These libraries of colloidal clusters, indexed both by primary nanocrystal dimension (dp) and overall cluster diameter (Dc), form magnetic photonic crystals with relatively weak size-dependent properties. In contrast, their performance as MRI T2 contrast agents is highly sensitive to cluster diameter, not primary particle size, and is optimized for materials of 50 nm diameter (r2 = 364 mM-1 s-1). These results exemplify the relevance of dimensional control in developing applications for these versatile materials.

Metabolic Engineering of Bacillus licheniformis for Production of Acetoin.

Acetoin is a potential platform compound for a variety of chemicals. Bacillus licheniformis MW3, a thermophilic and generally regarded as safe (GRAS) microorganism, can produce 2,3-butanediol with a high concentration, yield, and productivity. In this study, B. licheniformis MW3 was metabolic engineered for acetoin production. After deleting two 2,3-butanediol dehydrogenases encoding genes budC and gdh, an engineered strain B. licheniformis MW3 (ΔbudCΔgdh) was constructed. Using fed-batch fermentation of B. licheniformis MW3 (ΔbudCΔgdh), 64.2 g/L acetoin was produced at a productivity of 2.378 g/[L h] and a yield of 0.412 g/g from 156 g/L glucose in 27 h. The fermentation process exhibited rather high productivity and yield of acetoin, indicating that B. licheniformis MW3 (ΔbudCΔgdh) might be a promising acetoin producer.

A rapid measurement method for structured surface in white light interferometry.

White light interferometry (WLI) is an effective and widely-used technique for structured surface measurement. However, it requires multiframe interferograms with vertical scanning to realise large-scale measurement, which is time consuming and computationally intensive. This paper proposes a rapid surface measurement method to realise surface recovery with a single interferogram by white light interferometry. First, the feasibility to solve the wrapped phase of a single white-light interferogram by Hilbert transform is certified. Then, unwrapped phases against zero optical path difference position (OPD) are achieved by a zero optical path difference detection algorithm applied to unwrapping process, which provides efficient surface recovery. To ensure the accuracy of phase solution in the proposed method, the necessary number and width of the interference fringes in the interferogram are analysed and determined based on Hilbert transform and sampling analysis. Finally, measurement results of a standard step sample and a standard reticle template are presented, which prove the accuracy and efficiency of the proposed method. LAY DESCRIPTION: As an effective and widely-used technique for structured surface measurement, white light interferometry (WLI) has the major advantage to measure noncontinuous surfaces using the short coherence length of a wide bandwidth source. However, frequently vertical scanning is required to get series of white light interferograms at different axial positions for surface recovery by recovered algorithms. The vertical scanning process is complicated and time consuming. This paper proposes a fast and efficient method to realise rapid surface measurement using only a single-frame interferogram based on WLI. First, the feasibility of using only one single white light interferogram to solve wrapped phases by Hilbert transform (HT) is discussed. Next, unwrapping process and zero optical path difference（OPD） detection algorithms are combined to unwrap phases against zero OPD position, which makes the structured surface recovery much easier. After that, the feasible number and width of interference fringes are determined based on sampling analysis and HT to guarantee the reliability and accuracy of phase solution in the proposed method. Finally, the accuracy and efficiency of this method is verified by measurement experiments of a standard step sample and a standard reticle template.

Regulation of Glutarate Catabolism by GntR Family Regulator CsiR and LysR Family Regulator GcdR in Pseudomonas putida KT2440.

Glutarate, a metabolic intermediate in the catabolism of several amino acids and aromatic compounds, can be catabolized through both the glutarate hydroxylation pathway and the glutaryl-coenzyme A (glutaryl-CoA) dehydrogenation pathway in Pseudomonas putida KT2440. The elucidation of the regulatory mechanism could greatly aid in the design of biotechnological alternatives for glutarate production. In this study, it was found that a GntR family protein, CsiR, and a LysR family protein, GcdR, regulate the catabolism of glutarate by repressing the transcription of csiD and lhgO, two key genes in the glutarate hydroxylation pathway, and by activating the transcription of gcdH and gcoT, two key genes in the glutaryl-CoA dehydrogenation pathway, respectively. Our data suggest that CsiR and GcdR are independent and that there is no cross-regulation between the two pathways. l-2-Hydroxyglutarate (l-2-HG), a metabolic intermediate in the glutarate catabolism with various physiological functions, has never been elucidated in terms of its metabolic regulation. Here, we reveal that two molecules, glutarate and l-2-HG, act as effectors of CsiR and that P. putida KT2440 uses CsiR to sense glutarate and l-2-HG and to utilize them effectively. This report broadens our understanding of the bacterial regulatory mechanisms of glutarate and l-2-HG catabolism and may help to identify regulators of l-2-HG catabolism in other species.IMPORTANCE Glutarate is an attractive dicarboxylate with various applications. Clarification of the regulatory mechanism of glutarate catabolism could help to block the glutarate catabolic pathways, thereby improving glutarate production through biotechnological routes. Glutarate is a toxic metabolite in humans, and its accumulation leads to a hereditary metabolic disorder, glutaric aciduria type I. The elucidation of the functions of CsiR and GcdR as regulators that respond to glutarate could help in the design of glutarate biosensors for the rapid detection of glutarate in patients with glutaric aciduria type I. In addition, CsiR was identified as a regulator that also regulates l-2-HG metabolism. The identification of CsiR as a regulator that responds to l-2-HG could help in the discovery and investigation of other regulatory proteins involved in l-2-HG catabolism.

Attitude measurement based on imaging ray tracking model and orthographic projection with iteration algorithm.

In the field of vision-based attitude estimation, camera model and attitude solving algorithm are the key technologies, which determine the measurement accuracy, effectiveness and applicability. Aiming at this issue, in this paper we probe into the generic imaging model and then develop a corresponding generic camera calibration method using two auxiliary calibration planes. The camera model is named as imaging ray tracking model. Based on the imaging ray tracking camera model and with the knowledge of the calibration parameters, an advanced attitude solving algorithm, imaging ray tracking model and attitude from orthographic projection with iterations algorithm, is deeply investigated, which is inspired by the classical POSIT algorithm. The initial attitude value is provided by the orthographic projection of the object on the two calibration planes and then refined by iteration to approximate the true object attitude. Experimental platform is setup to conduct the imaging ray tracking camera calibration procedure and further evaluate our attitude estimation algorithm. We show the effectiveness and superiority of our proposed attitude estimation algorithm by thorough testing on real-data and by comparison with the POSIT algorithm.

Determine turntable coordinate system considering its non-orthogonality.

In industrial measurement and laboratory research, many measured objects are placed on the three-axis turntable. In this paper, we propose a fast and practical method to determine the turntable coordinate system with the aid of spatial coordinate of point cloud data. By sphere fitting, plane fitting, and point projection, the scattered point cloud data are combined together to obtain initial direction vectors. Considering the non-orthogonality of turntable, the least two pairs of skew lines are used to compute the approximate turntable center. And the intersection angles and distances between each axis are given to judge the degree of non-orthogonality. Then, based on the approximate sphere center and the initial rotation vectors, the direction vectors are, respectively, optimized in a predefined order. An experimental system is set up to validate the proposed method. Attitude parameters computed by spatial point coordinates before and after turntable calibration are employed to give the quantitative evaluation results. And the total attitude errors in three axes motion forms all reduce, which demonstrates the effectiveness and practicality of the proposed method.

Correction of errors due to uneven intensity distribution and inaccurate phase shifts in prism-based simultaneous phase-shifting interferometry.

Simultaneous phase-shifting interferometry (SPSI) is a suitable technique for dynamic surface measurement due to its rapid measurement capability. Based on whether there are obvious multiple phase-shifting channels in the setup, it can be divided into the multi-channel type and the pixelated phase-mask type. For the former type, two important error sources are the uneven intensity distribution among different phase-shifting channels and inaccurate phase shifts in each phase-shifting channel. In order to diminish the influence of these two error sources, several methods have been proposed. However, in some of these methods, only one error source of the two was considered and corrected. In the methods where the two error sources were both considered, some assumptions such as the uniform background intensity and fringe contrast are needed, so a complete and universal suppression method for both error sources is still needed in actual applications. In this paper, for a prism-based SPSI, we proposed such an error-correction method that mainly contains the following contributions. First, the intrinsic parameters of the system, including intensity distribution coefficients and relative phase shifts among phase-shifting channels, are calibrated in advance. We also checked the uniformity of phase shifts in each phase-shifting channel. Second, based on the obtained parameters, a corrected four-step phase-shifting algorithm is deduced to recover the more accurate measured phase. Results of both simulation and experiment verify the effectiveness of the proposed method.

Seamless global positioning system/inertial navigation system navigation method based on square-root cubature Kalman filter and random forest regression.

In this paper, a seamless navigation dual-model based on Square-Root Cubature Kalman Filter (SRCKF) and Random Forest Regression (RFR) is developed to enhance the performance of the Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation system. By using the proposed method, the system can ensure seamless navigation ability even during GPS signal outages. In the proposed dual-model, sub-model 1 that directly relates the specific force of INS to the measurement of filter and sub-model 2 that directly relates the cubature points and innovation of SRCKF to the error caused by filter are established. Combined with SRCKF and RFR algorithms, the dual-model system can predict and estimate the velocity and position of the vehicle seamlessly when GPS signals are blocked. Field test data are collected to evaluate the proposed solution, and the experimental results show that the model proposed has obvious improvement in navigation accuracy by comparison. The prominent advantages of the proposed seamless navigation method include the following: (i) the proposed dual-model can effectively provide corrections to standalone INS during GPS outages, which outperforms traditional widely used single model; (ii) the proposed combination of SRCKF and RFR achieves better performance in the prediction of INS errors than other combination algorithms.

Attitude calculation method based on full-sky atmospheric polarization mode.

In the natural world, insects such as bees and sand ants can navigate with the aid of polarized skylight. Inspired by this, bio-inspired navigation systems based on polarized skylight have attracted considerable attention recently. As an important navigation parameter, attitude information is critical for control and navigation of a vehicle. In this paper, the structural characteristics of full-sky atmospheric polarization mode are studied to calculate the attitude of a vehicle relative to the observation point. The heading angle is obtained by subtraction between two angles. One is the angle between the solar meridian and the geographic north. The other is the angle between the solar meridian and the body coordinate system, which can be obtained from the polarization data detected by the sensor. And the solar position can also gain from the polarization data. Then based on the solar position and the coordinates of solar projection point, the zenith point can be determined. With the coordinates of the zenith point in the body and the reference coordinate system, the pitch angle and roll angle are solved. Simulation and practical experiments are conducted to validate the performance of the attitude calculation method. Experiment results with high accuracy show its feasibility and effectiveness.