The brain is the least explored organ in the human body. Brain functions are realized through a complex neural network composed of a vast number of neurons, and understanding the morphology of these neurons is base to brain studies. However, obtaining high-quality, large-scale data on neuron morphology remains a significant challenge. In this study, we propose a precise data-graded allocation method for neuron reconstruction, the accuracy is safeguarded by the allocation algorithm and the quantitative model. Reconstruction efficiency was improved by optimizing automated reconstruction algorithm, human-machine interaction workflow and human-task matching method. We have implemented this strategy on a web-based platform, and the results show that 92.9% of image data can be easily reconstructed, thereby reducing the skill requirements for participant. The reconstruction accuracy is 98.2%$\pm$3.1%, better than existing methods. We also provides meticulously annotated datasets that can propel significant advancements in artificial intelligence technology. In addition, we can offer a well-balance across quality, cost, and efficiency, sharing a more flexible and versatile solution for three-dimensional neuron reconstruction.

MOTIVATION: Neuroscientists have long endeavored to map brain connectivity, yet the intricate nature of brain networks often leads them to concentrate on specific regions, hindering efforts to unveil a comprehensive connectivity map. Recent advancements in imaging and text mining techniques have enabled the accumulation of a vast body of literature containing valuable insights into brain connectivity, facilitating the extraction of whole-brain connectivity relations from this corpus. However, the diverse representations of brain region names and connectivity relations pose a challenge for conventional machine learning methods and dictionary-based approaches in identifying all instances accurately. RESULTS: We propose BioSEPBERT, a biomedical pre-trained model based on start-end position pointers and BERT. In addition, our model integrates specialized identifiers with enhanced self-attention capabilities for preceding and succeeding brain regions, thereby improving the performance of named entity recognition and relation extraction in neuroscience. Our approach achieves optimal F1 scores of 85.0%, 86.6%, and 86.5% for named entity recognition, connectivity relation extraction, and directional relation extraction, respectively, surpassing state-of-the-art models by 2.6%, 1.1%, and 1.1%. Furthermore, we leverage BioSEPBERT to extract 22.6 million standardized brain regions and 165 072 directional relations from a corpus comprising 1.3 million abstracts and 193 100 full-text articles. The results demonstrate that our model facilitates researchers to rapidly acquire knowledge regarding neural circuits across various brain regions, thereby enhancing comprehension of brain connectivity in specific regions. AVAILABILITY AND IMPLEMENTATION: Data and source code are available at: http://atlas.brainsmatics.org/res/BioSEPBERT and https://github.com/Brainsmatics/BioSEPBERT.

Internal short circuit (ISC) fault diagnosis of battery packs in electric vehicles is of great significance for the effective and safe operation of battery systems. This article presents a new ISC diagnosis method based on a machine learning algorithm. In this method, the incremental capacity curves are employed to divide the voltage curves into multiple sections. The dynamic time warping (DTW) algorithm is used to describe the similarity between partial voltage curves of different cells. Furthermore, four features are selected to describe the DTW distribution and statistics characteristics, and then the ISC diagnosis model based on the gradient boosting decision tree (GBDT) algorithm is constructed. The GBDT algorithm-based method realizes the accurate detection and location of early ISC fault using only partial voltage curves under arbitrary operating conditions, rather than relying on complete charging/discharging curves under specific operating conditions, and the final detection accuracy can be up to 99.4%.

Clarifying the morphological characteristics of neurons can promote the understanding of brain function. However, traditional morphometrics fail to capture the modeling of each point in reconstructed neurons, leading to limited ability to distinguish massive nerve fibers and restricted application scenarios. To address these challenges, we propose MorphoGNN, a single neuron morphological embedding based on a graph neural network in this study. MorphoGNN learns the point-level structure information of reconstructed nerve fibers by considering their nearest neighbors on each hidden layer. This enables MorphoGNN to capture the lower-dimensional representation of a single neuron through an end-to-end model. In order to meet the requirements of various tasks, both supervised and self-supervised training strategies are designed to learn the characteristics that fit artificial semantics or the morphological patterns of neurons, respectively. We quantitatively compare our embeddings with other features in neuron classification and retrieval tasks and demonstrate cutting-edge performance. Additionally, we introduce our embeddings to the task of reconstruction quality classification and neuron clustering, where they can help detect reconstruction errors and obtain similar subtyping results to existing work. Furthermore, our method can be handily combined with other modal features, such as microscopic image features and traditional morphometrics. Ablation and robustness tests are also conducted to analyze the impact of several network components and low-quality reconstructed neurons on the performance of our method. The code is available at https://github.com/fun0515/MorphoGNN.

BACKGROUND: Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, single-neuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files. RESULTS: We propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise. CONCLUSIONS: This method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.

The performance of photoacoustic microscopy (PAM) degrades due to the turbidity of the skull that introduces attenuation and distortion of both laser and stimulated ultrasound. In this manuscript, we demonstrated that a newly developed skull optical clearing solution (SOCS) could enhance not only the transmittance of light, but also that of ultrasound in the skull in vitro. Thus the photoacoustic signal was effectively elevated, and the relative strength of the artifacts induced by the skull could be suppressed. Furthermore in vivo studies demonstrated that SOCS could drastically enhance the performance of photoacoustic microscopy for cerebral microvasculature imaging.

The Diffuse Optical Tomography (DOT) is an in vivo optical imaging technique using scattered light to detect organization function with great range of depth. The most commonly used numerical method in solving its forward problem is finite element method (FEM). However, boundary element method (BEM) is a semi-analytic method, it is of higher computation accuracy, more effective for large scale problem than FEM since it only needs to discretize boundary which reduces the dimension of the problem. In this paper, we present a scheme of boundary element method for the diffuse light propagation in the heterogeneous medium. Comparing these calculations to Monte Carlo simulations, the results show a good agreement between both methods. BEM provides an optimization scheme for image reconstruction in DOT, and lay a foundation for the coupling of finite element method and boundary element method.

A novel online method based on the symmetry property of the sum of projections (SOP) is proposed to obtain the geometric parameters in cone-beam computed tomography (CBCT). This method requires no calibration phantom and can be used in circular trajectory CBCT with arbitrary cone angles. An objective function is deduced to illustrate the dependence of the symmetry of SOP on geometric parameters, which will converge to its minimum when the geometric parameters achieve their true values. Thus, by minimizing the objective function, we can obtain the geometric parameters for image reconstruction. To validate this method, numerical phantom studies with different noise levels are simulated. The results show that our method is insensitive to the noise and can determine the skew (in-plane rotation angle of the detector), the roll (rotation angle around the projection of the rotation axis on the detector), and the rotation axis with high accuracy, while the mid-plane and source-to-detector distance will be obtained with slightly lower accuracy. However, our simulation studies validate that the errors of the latter two parameters brought by our method will hardly degrade the quality of reconstructed images. The small animal studies show that our method is able to deal with arbitrary imaging objects. In addition, the results of the reconstructed images in different slices demonstrate that we have achieved comparable image quality in the reconstructions as some offline methods.

In this paper, a new orientation-based method which operates in two-step, called in Dynamic Processing (DP) system, is proposed for the determination of fingerprint reference point. For the two-step operation, it uses different orientation features at different scales: block-based orientation certainty and pixel-based segmented direction map. A block-based orientation certainty is used to describe the change of a block curvature of a fingerprint, which is determined by two eigenvalues of the gradient covariance matrix and a pixel-based segmented direction map is used to find intersections emerging from the directional transition. The DP system is built through their cross-references to determine the position of reference point. While the proposed system has the pixel-based precision by virtue of using a pixel-based segmented direction map, it reduces many possible fault symptoms by virtue of using a block-based orientation certainty. The proposed technique shows better performance in accuracy rate than other previous techniques. The performance of the proposed one is verified through simulations and its analysis.

FY1C is a polar meteorological satellite of China, which had been worked on orbit about 5 years. In this paper, time series calibration method based on pseudo-invariant target site is applied to monitor the variance of FY1C instrument. Dunhuang test site is chose as the pseudo-invariant site and the FY1C images over this site are processed with some standard. Then the time series calibration result of FY1C seven channels at visible and near-infrared range has been calculated. In order to validate the result, apply the time series calibration coefficients to recalibrate the images of Wuwei test site from 1999 to 2003. The validation result shows that the time series calibration coefficients are efficient and can monitor the radiance status of FY1C instrument.

CBERS02B with three payloads onboard was successfully launched on September 19, 2007 in order to ensure the continuity of CBERS series and CCD is one of three payloads. Calibration of CCD is a precursor for its quantitative application because there isn't onboard calibrator for CCD. A comprehensive vicarious calibration and validation campaign of CCD was performed at Gongger test site on October 12, 2007. The reflectance-based calibration method was used in this campaign with the ground measurements of the surface reflectance and atmospheric characteristics. Then 6S, a radiative transfer code, was used to compute the top-of-atmosphere(TOA) radiance at the sensor. Calibration result was obtained for CCD showing that some change brought to the CCD after launch, especially band 1 and band 2. The in-situ field measurement at the Dunhuang test site was collected validating that the calibration result was good expect for band 4.

HJ-1A satellite has been lunched in September, 2008. It is calibration and validation that the fundamental of quantitative utilization of HJ-1A IRS imagery. HJ-1A has only one channel in thermal infrared band, compared to MODIS sensor, which has two channels accordingly. The key process of cross-calibration is band match, so this paper uses the MODIS SST product retrieval algorithm as reference for the difference of HJ-1A and MODIS thermal infrared channels characters. TIGR database were used as input parameters into radiative transfer mode Modtran4.0 to obtain band match coefficients by regression analysis. Number 711~1064 datum in TIGR database representing mid-latitude winter were chose according to the selected image's date. Research demonstrates that cross-calibration method is effective to HJ-1A thermal infrared channel 4.

This paper compared the LST retrieval algorithm between single-channel and split-window for high-resolution thermal infrared camera. MODTRAN was used to ascertain the atmospheric coefficients of the generalized split-window algorithm and JM&S single-channel algorithm based on TIGR database. Algorithm fitting precision analysis and the sensitivity analysis of the two algorithms were carried out. The total errors of land surface temperature were estimated by means of evaluating the influence of several parameters: atmospheric water vapor, land surface emissivity and noise of the sensor. The results showed that the uncertainties of several parameters above would increase the total errors. In low atmospheric water vapor, the accuracy of the single-channel algorithm is about 1K, equal to the accuracy of the split-window algorithm. In high atmospheric water vapor, the accuracy of the single-channel algorithm increase to about 2K and that of the split-window algorithm was invariant.

Light scattering properties from curved surface and cylinder are important in the area of propagation and remote sensing. The radar cross sections (RCS) of a dielectric thin curved surface and cylinder are obtained by employing a quasi-static approximation. The method is applicable to the electromagnetic (EM) scattering in general. However, the simulated results emphasize light scattering, i.e. the RCS displayed rather than the electric field. The results are complemented by numerical calculations.

A good calibration of satellite is necessary to derive reliable quantitative measurements of the surface parameters or to compare data obtain from different sensors. DCSRS (Demonstration Center for Spaceborne Remote Sensing of China National Space Administration) went to inner-Mongolia Plateau to seek fairly uniform reflectance sites as a part of Beijing multi functional test site network in May and October 2006, and four quite flat and homogenous sites were selected as potential test sites. These four sites have many good calibration site characteristics: they are large and flat; the rain is little and the evaporation is much larger than precipitation, so the water vapor content is little in atmosphere; the elevation is about 1100m and the weather is sunny in most time. In this study, more than 200 MODIS level 1 images of these four sites were obtained and the average reflectance and relative mean squared deviation of each image were calculated. In the end, the variation of reflectance with solar zenith, month and season were analyzed, and the result was consistent with in-situ investigation.

On 29 and 31 May 2006, a comprehensive vicarious calibration experiment for the Moderate Resolute Imaging Spectroradiometer(MODIS) visible and near-infrared bands was performed at Gongger test site located in Inner Mongolia, which is a flat and uniform area. The reflectance-based method was used for calibration of MODIS visible and near-infrared bands. In situ measurements of surface and atmospheric conditions were carried out. By computing the surface reflectance of the site, it was concluded that the site was appropriate for calibration because of its stable and uniform characteristics. These data were then inputted to a radiative transfer code, 6S, to compute top-of- atmosphere (TOA) radiances and TOA reflectances, which were compared with the MODIS on-board calibration results. The in situ estimated results were in good agreement with the MODIS on-board calibration results on May 31 with the variations about 2%, while vicarious calibration results on May 29 were slightly inconsistent with those of on-board calibration, whose differences were about 7%.

This Paper described that in-flight radiometric calibration for thermal channels of TERRA-MODIS sensors using a new calibration test site-Dali-lake, China. The radiance of water surface was measured by CE312, and the spectral transmittance and upward radiance of the atmosphere was calculated using radiance transfer model MODTRAN4. At the same time the spectral response of Satellite sensor and that of ground-based sensor are coupled. At last the apparent radiance of sensor spectral channels is compared to the digital count of satellite's output to give the calibration coefficient. The calibration result in May 31 showed the difference between inflight and on-board calibration was equivalent to a brightness temperature of 1.44 k for TERRA-MODIS channel 31 and 0.35 K for channel 32 respectively.