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As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

Curcumae Rhizoma (CR) and vinegar processed Curcumae Rhizoma (PCR) are common medicinal materials widely used in clinical practice in China. There are sliced CR (SCR) and two kinds of PCR products which are processed by different methods: WPCR-prepared with the whole CR root boiled in vinegar and then sliced, and SPCR-prepared with the whole CR root steamed and sliced before boiled with vinegar. In this study, the feasibility of Fourier transform near infrared spectrum (FT-NIR) used to determine the main discrepant components of SCR, WPCR and SPCR were investigated. High performance liquid chromatography (HPLC) was used to identified five discrepant compounds in the three kinds of CR products-curzerene, curcumenol, curdione, furanodienone and demethoxycurin. Pretreatment of NIR qualitative data by different methods revealed that the second derivative in combination with 9 points of Savitzky-Golay smooth (2D9S) could accurately distinguish SCR, SPCR and WPCR from each other, and the discrimination ability was improved significantly by wavebands selection. Then a model with great accuracy was established by combining with wavebands selection and partial least squares regression (PLSR). Compared with the competitive adaptive reweighted sampling (CARS) selection method, 2D9S- variable combination population analysis (VCPA)-Genetic algorithm (GA)-PLSR model was evidently more accurate in prediction of the content of curzerene, curcumenol, curdione and furanodienone, with an R2p of 0.9558, 0.9129, 0.9098 and 0.9350, as well as a ratio of performance to deviation (RPD) of 4.8454, 3.4640, 3.3020 and 4.0082, respectively. Whereas, the content of demethoxycurin failed to be well predicted. The correlation analysis revealed that the results of wavebands selection were consistent with the trend of changes in the content of these target compounds and the findings of NIR absorption analysis, and the characteristic chemical bonds of these compounds corresponded to the areas with significant correlation in the heat map. It can be concluded that the NIR system, combined with appropriate variable selection and linear regression method, can precisely distinguish SCR, SPCR and WPCR from each other, and can accurately and rapidly determine the four discrepant compounds in the three CR products, suggesting a potential of being routinely used for a more diversified analysis in medicinal herbs study., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Objective: Detecting discomfort status of infants is particularly clinically relevant. Late treatment of discomfort infants can lead to adverse problems such as abnormal brain development, central nervous system damage and changes in responsiveness of the neuroendocrine and immune systems to stress at maturity. In this study, we exploit deep convolutional neural network (CNN) algorithms to address the problem of discomfort detection for infants by analyzing their facial expressions., Approach: A dataset of 55 videos about facial expressions, recorded from 24 infants, is used in our study. Given the limited available data for training, we employ a pre-trained CNN model, which is followed by fine-tuning the networks using a public dataset with labeled facial expressions (the shoulder-pain dataset). The CNNs are further refined with our data of infants., Main Results: Using a two-fold cross-validation, we achieve an area under the curve (AUC) value of 0.96, which is substantially higher than the results without any pre-training steps (AUC  =  0.77). Our method also achieves better results than the existing method based on handcrafted features. By fusing individual frame results, the AUC is further improved from 0.96 to 0.98., Significance: The proposed system has great potential for continuous discomfort and pain monitoring in clinical practice.

Efficient and accurate extraction of the prostate, in particular its clinically meaningful sub-regions from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, we propose a novel multi-region segmentation approach to simultaneously locating the boundaries of the prostate and its two major sub-regions: the central gland and the peripheral zone. The proposed method utilizes the prior knowledge of the spatial region consistency and employs a customized prostate appearance model to simultaneously segment multiple clinically meaningful regions. We solve the resulted challenging combinatorial optimization problem by means of convex relaxation, for which we introduce a novel spatially continuous flow-maximization model and demonstrate its duality to the investigated convex relaxed optimization problem with the region consistency constraint. Moreover, the proposed continuous max-flow model naturally leads to a new and efficient continuous max-flow based algorithm, which enjoys great advantages in numerics and can be readily implemented on GPUs. Experiments using 15 T2-weighted 3D prostate MR images, by inter- and intra-operator variability, demonstrate the promising performance of the proposed approach.

We propose a novel global optimization approach to segmenting a given 3D prostate T2w magnetic resonance (MR) image, which enforces the inherent axial symmetry of the prostate shape and simultaneously performs a sequence of 2D axial slice-wise segmentations with a global 3D coherence prior. We show that the proposed challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. With this regard, we introduce a novel coupled continuous max-flow model, which is dual to the studied convex relaxed optimization formulation and leads to an efficient multiplier augmented algorithm based on the modern convex optimization theory. Moreover, the new continuous max-flow based algorithm was implemented on GPUs to achieve a substantial improvement in computation. Experimental results using public and in-house datasets demonstrate great advantages of the proposed method in terms of both accuracy and efficiency.

Objective: To investigate the anti-hypoxia active fraction of Xiangdan injection., Methods: The anti-hypoxia experiment under normal pressure with mice was adopted on the basis of orthogonal design, and a support vector machine model was established for pharmacodynamics prediction of TCM prescriptions. Moreover, the anti-hypoxia active fraction of Xiangdan injection was determined by experimental confirmation., Results: The prediction model established in this study could predict the drug actions of different formulas., Conclusion: As indicated in model prediction and experimental confirmation, the pharmacodynamic actions of several formulas are superior to that of the original formula, and ADE has the best effect.

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship's trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends. [ABSTRACT FROM AUTHOR]

In recent years, people's living standards are rising, and the demand for health is also rising. National physical exercise has become the current trend. Using personalized recommendation technology to screen appropriate information and assist people in sports training can improve the efficiency of users to obtain relevant information and improve users' physical quality. Therefore, the collaborative filtering recommendation algorithm is optimized by combining bisection, and the intelligent recommendation model of sports training resources is constructed based on the algorithm. This paper uses the improved algorithm to calculate the similarity between users. Compared with other traditional algorithms, the user algorithm in this paper has higher accuracy and certain reference value. To sum up, the intelligent recommendation model of sports training resources proposed in the research can more accurately recommend suitable sports training resources for users, so as to assist users in correct sports training and improve users' physical quality, which plays an improvement of the physical quality of the whole people in China. [ABSTRACT FROM AUTHOR]

Background: Recognizing the correlations between care problems of people with dementia could help clinicians choose treatment methods because related symptom groups might respond to the same treatment intervention.Objective: This study aimed to evaluate the prevalence of various care problems in people with dementia and to explore the core care problems and correlations between care problems of people with dementia.Methods: This cross-sectional study recruited family caregivers of people with dementia through memory clinics and WeChat groups. Care problems of people with dementia were measured using a care problems evaluation sheet, which involved three aspects: daily living care problems, behavioral and psychological symptoms, and safety risks. Clustering analysis of the care problems based on Kruskal's minimum spanning tree (MST) algorithm was performed in the Jupyter Notebook software to explore the core care problems and their correlations.Results: A total of 687 carer-patient pairs were included in the analysis. In general, the prevalence of having difficulty in language performance, agitated behavior, and incidence of falls was relatively higher than other care problems in people with dementia, which distressed their family caregivers. Through clustering analysis, the 63 care problems were clustered into 7 clusters and 7 core care problems were identified.Conclusion: The prevalence of various care problems of people living with dementia in China was relatively high. The information regarding correlations in clusters among care problems will help practitioners and policymakers to identify the core care problems and optimize more rational treatments for people with dementia. [ABSTRACT FROM AUTHOR]

The existing long term evolution networks originally designed for human-to-human communications are hard to tackle numerous and bursty random access requests from emerging machine-type communication devices (MTCDs). In this paper, we propose a novel preamble re-utilizing random access scheme for stationary MTCDs with optimal access class barring (ACB), which is based on joint ACB and timing advance (TA) model. By adopting load estimation algorithm, evolved node B (eNB) can detect the preamble collisions in the first step of random access procedure. Therefore, the eNB merely schedules physical uplink shared channel to the successful preambles according to standard random access procedure. The MTCDs that did not pass the first ACB check re-utilize the resources scheduled by the idle and collided preambles to access with a second optimal ACB. Analytical and simulation results manifest that our proposed scheme notably outperforms the conventional joint ACB and TA scheme in terms of four metrics: throughput, access delay, probability of preamble collision and resource efficiency. [ABSTRACT FROM AUTHOR]

Objective: Burkitt's lymphoma (BL) is a highly aggressive malignant lymphoma, its molecular biological mechanism has not been fully investigated. The construction of protein-protein interaction (PPI) networks and the identification of complexes through a cluster analysis are important research directions in the post-genome era. However, different cluster analysis algorithms have their own characteristics, and a single analysis has some limitations. In this study, we obtained the target and pathway information of BL using different clustering analyses. Material and Methods: First, we obtained 50 BL genes by screening the Online Mendelian Inheritance in Man (OMIM) database; their related genes were further extracted from the literature. The PPI network was constructed with the Search Tool for Retrieval of Interacting Genes/Proteins (STRING). Afterward, the interaction data were input in Cytoscape3.4.0 software and related plug-ins were used to implement topology analysis and clustering analysis. Functional analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to characterize the biological importance of the clusters. Results: We constructed a PPI network consisting of 459 nodes (proteins) and 1399 sides (interactions), 12 genes and 8 signaling pathways were found to be closely related to BL. Conclusion: In this study, the use of combined algorithms to analyse gene interactions provides a new perspective for network-based analysis. The results of this study reveal new insights into the molecular mechanisms underlying BL, which may be novel therapeutic targets for disease management and may provide a bioinformatic basis for the further understanding of BL. [ABSTRACT FROM AUTHOR]

The Kuroshio Current intrudes in the bottom layer of the East China Sea continental shelf from the northeast of Taiwan via two bottom branches named the Nearshore Kuroshio Branch Current (NKBC, along the 60 m isobath) and the Offshore Kuroshio Branch Current (OKBC, along the 100 m isobath). However, knowledge on the macrofaunal responses to these bottom branches is limited. This study examined the variations in the benthic macrofaunal community in a section of the East China Sea under the influence of the NKBC. Seven sites corresponding to three regions (the west, middle and east region) were sampled using an Agassiz trawl net at a monthly rate from February to November 2015 (except in August). A total of 270 macrofaunal species were collected in this study. Cluster analysis and nMDS ordination revealed three communities: the inshore, Kuroshio and offshore communities, roughly corresponding to the west, middle and east of NKBC route. Significant differences in the species composition (one-way PERMANOVA) and diversity indices (one-way ANOVA) among the regions and communities were observed, while no statistically significant difference among the months was detected. The indicator species also varied among the communities, with Sternaspis scutata and Odontamblyopus rubicundus dominating the inshore community, Camatopsis rubida, Schizaster lacunosus and Craspidaster hesperus dominating the Kuroshio community, and Portunus argentatus, Champsodon snyderi and Coelorinchus multispinulosus dominating the offshore community. Some rare species (e.g., Neobythites sivicola) may indicate the passage of the NKBC better than the indicator species. A redundancy analysis was used to describe the relationship between the macrofaunal species and environmental variables in this study. Water depth and turbidity played important roles in the distribution of the macrofauna. S. scutata and O. rubicundus were associated with high turbidity and shallow depth, while Plesionika izumiae and P. argentatus were associated with low turbidity and deep depth. This study outlines the impact of the NKBC on the distribution patterns of the macrofaunal community of the East China Sea. More studies are needed to understand the detailed interactions between macrofauna and the NKBC in the future. [ABSTRACT FROM AUTHOR]

A novel coupling scheme is presented to combine the discontinuous deformation analysis (DDA) and the interior penalty Galerkin (IPG) method for the modeling of contacts. The simultaneous equilibrium equations are assembled in a mixed strategy, where the entries are derived from both discontinuous Galerkin variational formulations and the strain energies of DDA contact springs. The contact algorithms of the DDA are generalized for element contacts, including contact detection criteria, open-close iteration, and contact submatrices. Three representative numerical examples on contact problems are conducted. Comparative investigations on the results obtained by our coupling scheme, ANSYS, and analytical theories demonstrate the accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, we proposed an efficient nonrigid magnetic resonance (MR) to transrectal ultrasound (TRUS) deformable registration method in order to improve the accuracy of targeting suspicious regions during a three dimensional (3-D) TRUS guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighborhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization-based algorithmic scheme was introduced to extract the deformations and align the two MIND descriptors. The registration accuracy was evaluated using 20 patient images by calculating the TRE using manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone. Additional performance metrics [Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD)] were also calculated by comparing the MR and TRUS manually segmented prostate surfaces in the registered images. Experimental results showed that the proposed method yielded an overall median TRE of 1.76 mm. The results obtained in terms of DSC showed an average of 80.8\pm7.8\\% for the apex of the prostate, 92.0\pm3.4\\% for the mid-gland, 81.7\pm6.4\\% for the base and 85.7\pm4.7\\% for the whole gland. The surface distance calculations showed an overall average of 1.84\pm0.52 mm for MAD and 6.90\pm2.07 mm for MAXD. [ABSTRACT FROM AUTHOR]

This paper develops a new model for surface soil moisture (SSM) retrieval from CBERS-02B images. The paper first analyzes the existing SSM retrieval model from Landsat TM imagery and establishes the spectral radiance relationship of each band between Landsat TM and CBERS-02B. The model associated parameters including mean reflectance, mean atmospheric transmittance, and mean sun radial brightness of each band between Landsat TM and CBERS-02B is established. The model is finally adjusted by considering the differences of response frequency and sensitivity in the two satellite sensors. Two test areas, Jili Village of Laibin county, Guangxi Province, China and Yuanjiaduan Village of Jiujiang County, JiangXi Province, China are chosen to verify the correctness of the developed model. The SSMs retrieved from Landsat TM imagery are chosen as references. The accuracy of the proposed model is evaluated through correlation coefficient and root-mean-square error (RMSE) relative to the SSMs retrieved from Landsat TM images. The verified results discover that the relative accuracy of the average SSMs retrieved by the proposed model from CBERS-02B can reach over 91.0% when compared to the SSMs retrieved from Lansat TM. In addition, six types of lands are used to further evaluate the accuracy of the proposed model. The experimental results in two areas show that the correlation coefficient and the RMSE between two SSMs from CBERS-02B and Landsat TM achieves over 0.9 and 0.011 (\bfm^3/\bfm^3), respectively, in both rocky desertification land and dry land; achieve over 0.81 and 0.09 (\bfm^3/\bfm^3), respectively, in rice field, shrub land, and woodland. These results demonstrate that the model developed in this paper can effectively calculate the SSMs for CBERS-02B satellite imagery. [ABSTRACT FROM PUBLISHER]

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a “global” 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5–6 s including 4–5 s for initialization, yielding a mean Dice similarity coefficient of 93.2\%\pm 2.0\% for 3-D TRUS images and 88.5\%\pm 3.5\% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers. [ABSTRACT FROM AUTHOR]

Studies have shown that the 2σ lightning jump (LJ) algorithm exhibits good performance for severe weather warnings but continues to produce a high false alarm rate (FAR). A method is proposed in this study for improving the 2σ LJ hail warning performance by combining the hydrometeor classification results of dual-polarization radar with the 2σ LJ algorithm. A total of 17 hail events occurred across Jiangsu, China on April 29th -30th, 2021. Dual-polarization radar and hail observation data were used to calculate five variables, including the rate, first derivative (FD), rate of rate (rate2), second derivative (SD) and the rate of FD (FD_rate), for large hail (HA), graupel and small hail (G/SH), rain and hail (RH), and ice crystal (IC) grid point numbers. The evolution of these variables was compared and analyzed. The results indicate that the positive local peaks of rate2 of the HA and G/SH grid point numbers can be used to effectively identify valid and invalid LJs. Removing the identified invalid LJs reduces the FAR without affecting the probability of detection (POD), thus improving the hail warning performance of the 2σ LJ algorithm. This method was tested for the aforementioned 17 hail events. The rejection rate of invalid LJs reached 69.6%, and the false identification rate of valid LJs was only 9/29 (31%). The proposed method for hail warning had a POD equal to that of the conventional 2σ LJ algorithm (100%) and a significantly lower FAR (29.2% versus 58.5%). Moreover, the critical success index (CSI) of the proposed method (70.8%) was considerably higher than that (41.5%) of the conventional 2σ LJ algorithm. However, the average early warning lead time of the proposed method (35.1 min) was slightly lower than that (37.9 min) of the conventional 2σ LJ algorithm. The proposed method is superior to the conventional 2σ LJ algorithm in terms of the FAR and CSI for hail warning and therefore considerably improves the ability of the 2σ LJ algorithm to produce refined early warnings of hail or even severe weather for meteorological services. • A method combining hydrometeor classification and the 2σ LJ algorithm is proposed to improve hail warning. • This method can effectively remove the invalid LJs in hail warnings in the 2σ LJ algorithm. • Compared with the conventional 2σ LJ algorithm, the POD for hail warning is the same; the FAR decreases from 58.5% to 29.2%. [ABSTRACT FROM AUTHOR]