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Čerenkov luminescence tomography (CLT) is a highly sensitive and promising technique for three-dimensional non-invasive detection of radiopharmaceuticals in living organisms. However, the severe photon scattering effect causes ill-posedness of the inverse problem, and the results of CLT reconstruction are still unsatisfactory. In this work, a multi-stage cascade neural network is proposed to improve the performance of CLT reconstruction, which is based on the attention mechanism and introduces a special constraint. The network cascades an inverse sub-network (ISN) and a forward sub-network (FSN), where the ISN extrapolates the distribution of internal Čerenkov sources from the surface photon intensity, and the FSN is used to derive the surface photon intensity from the reconstructed Čerenkov source, similar to the transmission process of photons in living organisms. In addition, the FSN further optimizes the reconstruction results of the ISN. To evaluate the performance of our proposed method, numerical simulation experiments and in vivo experiments were carried out. The results show that compared with the existing methods, this method can achieve superior performance in terms of location accuracy and shape recovery capability. [ABSTRACT FROM AUTHOR]

Objective. Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem. Approach. To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well as in vivo experiments. Main results. The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution. Siginificance. These findings indicate that the SCAD-GML method has the potential to advance the application of FMT in in vivo biological research. [ABSTRACT FROM AUTHOR]

Spatial patterns are important features for understanding regional air quality variability. Statistical analysis tools, such as empirical orthogonal function (EOF), have been extensively used to identify and classify spatial patterns. These tools, however, do not directly reveal the related weather conditions. This study used singular value decomposition (SVD) to identify spatial air pollution index (API) patterns related to meteorological conditions in China, one of world’s regions facing catastrophic air pollution. The monthly API and four meteorological variables (precipitation, surface air temperature, humidity, and wind speed) during 2001–2012 in 42 cities in China were used. The two leading SVD spatial patterns display the API anomalies with the same sign across China and opposite signs between northern and southern China, respectively. The meteorological variables have different relationships with these patterns. For the first pattern, wind speed is the most important. The key regions, where the correlations between the API field and the wind speed’s SVD time series are significant at the 99% confidence level, are found nationwide. Precipitation and air temperature are also important in the southern and northern portions of eastern China, respectively. For the second pattern, the key regions occur mainly in northern China for temperature and humidity and southern China for wind speed. Air humidity has the largest contribution to this pattern. The weather-API relationships characterized by these spatial patterns are useful for selecting factors for statistical air quality prediction models and determining the geographic regions with high prediction skills. [ABSTRACT FROM AUTHOR]

Cerenkov Luminescence Tomography (CLT) is a promising optical molecular imaging technology. It involves the three-dimensional reconstruction of the distribution of radionuclide probes inside a single object to indicate a tumor's localization and distribution. However, reconstruction using CLT suffers from severe ill-posedness, resulting in numerous artifacts within the reconstructed images. These artifacts influence the visual effect and may misguide the medical professional (diagnostician), resulting in a wrong diagnosis. Here, we proposed a deep unsupervised clustering-based post-processing framework to eliminate artifacts and facilitate high-fidelity CLT. First, an initial reconstructed image was obtained by a specific reconstruction method. Second, voxel data were generated based on the initial reconstructed result. Third, these voxels were divided into three groups, and only the group with the highest mean intensity was chosen as the final reconstructed result. A group of numerical simulation and in vivo mouse-based experiments were conducted to assess the presented framework's feasibility and potential. The results indicated that the proposed framework could reduce the number of artifacts effectively. The reconstructed image's shape and distribution were more similar to the actual light source than those obtained without the proposed framework. [ABSTRACT FROM AUTHOR]

Video Synthetic Aperture Radar (ViSAR) operating in spotlight mode has received widespread attention in recent years because of its ability to form a sequence of SAR images for a region of interest (ROI). However, due to the heavy computational burden of data processing, the application of ViSAR is limited in practice. Although back projection (BP) can avoid unnecessary repetitive processing of overlapping parts between consecutive video frames, it is still time-consuming for high-resolution video-SAR data processing. In this article, in order to achieve the same or a similar effect to BP and reduce the computational burden as much as possible, a novel time-frequency sub-aperture technology (TFST) is proposed. Firstly, based on azimuth resampling and full aperture azimuth scaling, a time domain sub-aperture (TDS) processing algorithm is proposed to process ViSAR data with large coherent integration angles to ensure the continuity of ViSAR monitoring. Furthermore, through frequency domain sub-aperture (FDS) processing, multiple high-resolution video frames can be generated efficiently without sub-aperture reconstruction. In addition, TFST is based on the range migration algorithm (RMA), which can take into account the accuracy while ensuring efficiency. The results of simulation and X-band airborne SAR experimental data verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Biomass and species richness are two important indicators of ecosystem stability. The relationship between biomass and species richness along precipitation gradient in semi-arid regions is significant for prediction of ecosystem stability under the estimated climatic drying in the future. In this study, we investigated species richness, aboveground biomass and cover of temperate steppe along a mean annual precipitation (MAP) gradient in central Inner Mongolian of China. We also measured water use efficiency (WUE) of selected species and overall communities. Our results showed that biomass almost remained unchanged in the moist half of the gradient, but species richness decreased markedly with decreasing MAP. Species richness further showed a negligible decrease, whereas a much sharper drop was detected in biomass towards the arid end with decreasing MAP. Vegetation cover shared a similar pattern with biomass and dropped sharply towards the arid end, which may create a strong light and low competition environment favoring C 4 plants. The increment of C 4 species richness could prevent a more intensive decline of species richness under severe arid conditions by raising overall community water use efficiency. Thus, plant communities experiencing water deficiency could also maintain species richness with the occurrence of C 4 plants. [ABSTRACT FROM AUTHOR]

A novel molecularly imprinted polymer silica monolith (MIPSM) was successfully designed and prepared by a two-step procedure. Silica monolith was first synthesized by a sol-gel process. Then molecularly imprinted polymer (MIP) was coated on the surface of the activated silica monolithic column by using γ-methacryloxypropyl trimethoxysilane as a silane coupling agent, methacrylic acid as a functional monomer, ethylene glycol diglycidyl ether as a cross-linker, and alpha-cypermethrin (α-CYP) as a template. The prepared MIPSM, coupled with high-performance liquid chromatography/diode array detection, was used as a solid-phase extraction material for selective enrichment and determination of α-CYP in soil samples. The recovery of MIPSM for α-CYP was 87.0 %, which was significantly higher than that of nonimprinted polymer silica monolith (59.0 %), suggesting that MIPSM has specific recognition ability for template molecule. High selectivity of MIPSM was identified by comparing with the extraction efficiency for structure analog (deltamethrin). The synthesized MIPSM could be used for five times with the recovery maintained in a desirable range (85.4-90.9 %). Combining the advantages of high specificity and selectivity of MIP with the advantages of chromatographic separation of silica monolith, this prepared MIPSM presented a promising method for the pretreatment of trace amount of synthetic pyrethroid insecticides in the soil samples prior to the chromatographic analysis. [ABSTRACT FROM AUTHOR]

Objective: To assess the clinical efficacy of Morita therapy in combination with pharmacotherapy in adults who were diagnosed with current OCDs. Methods: We searched 10 databases to identify articles written in English or Chinese that were published until 15 April 2021. Randomized controlled trials were included. Two authors of this review independently selected the studies, assessed the risk of bias, and extracted the data. Results: Twenty-one studies with a total of 1604 participants met the inclusion criteria. Morita therapy plus pharmacotherapy was significantly superior to pharmacotherapy alone in the efficiency of OCD (RR = 1.34, 95% CI: 1.26 to 1.44, I 2 = 0%), and better in reducing OCD severity symptoms (MD = −3.55, 95% CI: −4.34 to −2.75, I 2 = 80%). Conclusion: Our meta-analysis and systematic review suggest that Morita therapy may be an effective approach to improve OCDs. [ABSTRACT FROM AUTHOR]

Aims: Understanding ecological stoichiometry of plant nutrients and its relationship with vegetation succession in terrestrial ecosystems is largely limited to nitrogen (N) and phosphorus (P). Despite it being an important element for most grasses, silicon (Si) is usually ignored. We examined ecological stoichiometry of N:P:Si in grasslands. Methods: We used leaf N, P and Si concentration, climate variables and phylogenic development, life forms, ecotypes, photosynthetic pathway to determine the relationship of N:P:Si ratio distribution with environmental conditions and the succession of grassland communities. Results: The distribution of N, P and Si varied greatly among the seven types of China's grasslands as influenced by environmental conditions (e.g. mean annual temperature - MAT) and plant species groups (e.g. phylogeny). Leaf N showed a significant positive correlation with P across all species, while N and P showed significant negative correlations with Si. Conclusions: Si was less affected by grassland types than N and P. Environmental conditions such as MAT and MAP had a significant influence on the stoichiometry of N, P and Si in grasslands and that temperature had a greater influence than precipitation. The succession of grassland communities may respond to or be influenced by stoichiometry of N, P and Si. [ABSTRACT FROM AUTHOR]

Abstract: Bai Nuur (41°38.590′N, 114°30.922′E, ∼1346 m a.s.l.) and Ulan Nuur (41°44.214′N, 115°05.630′E, ∼1246 m a.s.l.) are two small lakes situated in the transition zone between semi-humid and semi-arid climate regimes that runs parallel to the present limit of the southeast monsoon along the southeastern Inner Mongolia Plateau in north China. A 215 cm-deep sediment sequence was recovered from Bai Nuur and a 244 cm-deep one from Ulan Nuur. Soils were sampled from the catchments of the two lakes and dune-materials from a sandy land lying further north and northwest. Mineral magnetic measurements were performed on the sediments, soils and dune-materials. AMS 14C dating and analyses of particle-size, TOC and C/N and pollen composition were also made for the sediments. The mineral magnetic properties of the two sediment sequences, in combination with the other proxy-climate data, reveal the palaeoenvironmental development during early- and mid-Holocene around the two lakes. Climate began to ameliorate at 10,600 or 10,500 cal. BP and maximum humidity and warmth were reached during 10,600 or 10,500–9700 or 9600 cal. BP around Bai Nuur. Environments remained generally wet and/or warm until 7700 or 7600 cal. BP. Climate deterioration commenced at 7700 or 7600 cal. BP around Bai Nuur and at 7200 or 7100 cal. BP around Ulan Nuur. This deterioration intensified from 6900 or 6800 cal. BP onwards around Bai Nuur and from 6200 or 6100 cal. BP onwards around Ulan Nuur. The differences in timing of the reconstructed environmental changes at the two short-distanced sites may be due to the differences across the narrow climatic and vegetative transition zone, errors and uncertainties of dating and/or different chronostratigraphic frames of the two sequences. The palaeoenvironments reconstructed around the two small lakes (2.3 km2 and 8.9 km2) coincide with a palaeoenvironmental record from Anguli-nuur Lake, a much larger lake (47.6 km2) in the same region. These data also agree with reconstructions at several other sites in different parts of the Inner Mongolia Plateau, although the Holocene amelioration took place earlier than in northern Mongolia. [Copyright &y& Elsevier]

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well-established for three-dimensional (3-D) information extraction of forests using the multi-baseline SAR data stacks. The multi-baseline SAR data stacks can be acquired by spaceborne and airborne SAR systems, but for forest scenarios, the data stacks acquired by the airborne SAR system are mostly used. Such a data stack has the advantages of short revisiting time and weak temporal decorrelation. However, due to the baseline errors (caused by the residual platform motion and the measurement errors of the navigation instruments), phase errors (PEs) will occur. PEs are independent of one track to the other, resulting in spreading and defocusing in tomographic imaging. In this paper, we proposed a novel phase compensation method named NC-PGA, which combines the methods of network construction (NC) and phase gradient autofocus (PGA) to estimate and compensate the PEs. The NC method uses the Delaunay triangulation network and beamforming to obtain an accurate elevation estimate of the selected permanent scatterers, which can be used as the prior information for subsequent processing to overcome the shortcomings of the PGA method in PEs estimation. The PGA method uses the spatial invariance of PEs in a limited area to compensate for the PE of each track. The applicability of the NC-PGA method is demonstrated using simulated data and real data. The real data contains two data stacks. The one is acquired by a full-polarization P-band airborne SAR system (developed independently by our project research team) over the study area in Saihanba Forest Farm in Hebei, China. The other one is acquired by ONERA SETHI airborne system over Paracou, French Guiana, in the frame of the European Space Agency's campaign TropiSAR. We select a test area in the study area and successfully retrieve the height of the forest, and use LiDAR data for results validation and evaluation. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the effectiveness of bag-of-features (BOF)-based radiomics for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) from contrast-enhanced MRI (CE-MRI).Methods: Fifty-six patients with pathologically confirmed IOI (28 patients) and OAL (28 patients) were randomly divided into training (n = 42) and testing (n = 14) groups. One hundred sixty texture features extracted from the CE-MR image were encoded into the BOF representation with fewer features. The support vector machine (SVM) with a linear kernel was used as the classifier. Data augmented was performed by cropping orbital lesions in different directions to alleviate the over-fitting problem. Student's t test and the Holm-Bonferroni method were employed to compare the performance of different analysis methods. The chi-square test was used to compare the analysis with MRI and human radiological diagnosis.Results: In the independent testing group, the differentiation by the BOF features with augmentation achieved an area under the curve (AUC) of 0.803 (95% CI: 0.725-0.880), which was significantly higher than that of the BOF features without augmentation and that of the texture features (p < 0.05). In addition, the same radiomic analysis with pre-contrast MRI obtained an AUC of 0.618 (95% CI: 0.560-0.677), which was significantly lower than that with CE-MRI. The diagnostic performance of the analysis with CE-MRI was significantly better than the radiology resident (p < 0.05) but had no significant difference with the experienced radiologist, even though there was less consistency between the radiomic analysis and the human visual diagnosis.Conclusions: The BOF-based radiomics may be helpful for the differentiation between OAL and IOI.Key Points: • It is challenging to differentiate OAL from IOI due to the similar clinical and image features. • Radiomics has great potential for the noninvasive diagnosis of orbital diseases. • The BOF representation from patch to image may help the differentiation of OAL and IOI. [ABSTRACT FROM AUTHOR]

As an optical molecular imaging modality, fluorescence molecular tomography (FMT) can monitor the activities of organisms in vivo at the molecular and cellular levels. However, the recovered image quality is affected by mesh voxel when the finite element method is utilized to recover the fluorescence probe. The target localization is likely to deviate from the actual target under the coarse mesh, but using the fine mesh will increase the number of unknowns, which makes the computational burden heavier and further aggravate the ill-posedness. To solve the problem, a reconstruction strategy using a non-uniform mesh for FMT is developed in this paper. The numerical experiment and physical experiment validated that the strategy is capable and effective for FMT. [ABSTRACT FROM AUTHOR]

• A novel PCG-logTV method is proposed for sparse reconstruction of BLT, which combines TV regularization and logarithm function. • The method reduces the ill-posedness of BLT reconstruction and avoids the staircase artifacts of TV solutions. • The method is validated by numerical simulations and in vivo experiment, and shows superior performance over other methods. • The method enhances the quality and accuracy of BLT reconstruction and facilitates various preclinical applications of BLT. Bioluminescence Tomography (BLT) is a powerful optical molecular imaging technique that enables the noninvasive investigation of dynamic biological phenomena. It aims to reconstruct the three-dimensional spatial distribution of bioluminescent sources from optical measurements collected on the surface of the imaged object. However, BLT reconstruction is a challenging ill-posed problem due to the scattering effect of light and the limitations in detecting surface photons, which makes it difficult for existing methods to achieve satisfactory reconstruction results. In this study, we propose a novel method for sparse reconstruction of BLT based on a preconditioned conjugate gradient with logarithmic total variation regularization (PCG-logTV). This PCG-logTV method incorporates the sparsity of overlapping groups and enhances the sparse structure of these groups using logarithmic functions, which can preserve edge features and achieve more stable reconstruction results in BLT. To accelerate the convergence of the algorithm solution, we use the preconditioned conjugate gradient iteration method on the objective function and obtain the reconstruction results. We demonstrate the performance of our proposed method through numerical simulations and in vivo experiment. The results show that the PCG-logTV method obtains the most accurate reconstruction results, and the minimum position error (LE) is 0.254mm, which is 26%, 31% and 34% of the FISTA (0.961), IVTCG (0.81) and L 1 -TV (0.739) methods, and the root mean square error (RMSE) and relative intensity error (RIE) are the smallest, indicating that it is closest to the real light source. In addition, compared with the other three methods, the PCG-logTV method also has the highest DICE similarity coefficient, which is 0.928, which means that this method can effectively reconstruct the three-dimensional spatial distribution of bioluminescent light sources, has higher resolution and robustness, and is beneficial to the preclinical and clinical studies of BLT. [ABSTRACT FROM AUTHOR]

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, which has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. Single-view data reconstruction as a key issue of CB-XLCT imaging promotes the effective study of dynamic XLCT imaging. However, it suffers from serious ill-posedness in the inverse problem. In this paper, a multi-spectrum strategy is adopted to relieve the ill-posedness of reconstruction. The strategy is based on the third-order simplified spherical harmonic approximation model. Then, an orthogonal Laplacianfaces-based method is proposed to reduce the large computational burden without degrading the imaging quality. Both simulated data and in vivo experimental data were used to evaluate the efficiency and robustness of the proposed method. The results are satisfactory in terms of both location and quantitative recovering with computational efficiency, indicating that the proposed method is practical and promising for single-view CB-XLCT imaging. [ABSTRACT FROM AUTHOR]

Vanadium oxides are promising cathode materials with great potential. Here, we present a simple, safe, and environmentally friendly aging treatment to change the content of V5+ in vanadium oxides. The influence of different V5+ contents in vanadium oxides cathodes on the properties of Zn-ion batteries (ZIBs) is studied. V 6 O 13 and V 3 O 7 /V 6 O 13 nanosheets are synthesized by aging VO 2 precursors for different periods of time. V 6 O 13 is found to exhibit higher specific capacity and excellent cycling performance compared to VO 2 and V 3 O 7 /V 6 O 13. It is also characterized by an extremely high capacity of 300 mA h g−1 at a high current density of 5 A g−1. At 0.3 A g−1, it maintains a capacity of 421 mA h g−1 during 100 cycles with a high capacity retention rate of 95%. Changes in the vanadium valence state, structure, and morphology of electrodes during charging and discharging are also investigated for a better understanding of the underlying electrochemical processes. • The V5+ content of the vanadium oxides is regulated by the aging process. • VO 2 , V 6 O 13 and V 3 O 7 /V 6 O 13 composite are prepared by aging method. • V 6 O 13 exhibits a better electrochemical performance compared to VO 2 and V 3 O 7 /V 6 O 13. [ABSTRACT FROM AUTHOR]

The CoCrFeNiMo eutectic high entropy alloy (EHEA) was fabricated successfully by directed energy deposition (DED). The microstructure and mechanical properties of the CoCrFeNiMo EHEA samples were investigated in this work. Interestingly, the different microstructure morphology and phase composition were shown from bottom to top along the building direction. The seaweed eutectic structure, herringbone-like hierarchical eutectic structure and irregular lamellar structure were observed at the top, middle and bottom parts, respectively, which was due to changes in temperature gradients caused by increasing deposition height. The dominant phases in the CoCrFeNiMo EHEA samples were FCC, σ and μ phases. The FCC phase alternated with the σ phase, and μ phase particles were precipitated from the fringes of the σ phase. Moreover, the CoCrFeNiMo EHEA samples by DED achieved high hardness (658.44 HV) and high wear resistance (the friction coefficient (COF) is 0.586), which were guaranteed by the intermetallic compounds and fine eutectic lamellar structure. • Fabricating the CoCrFeNiMo EHEA by directed energy deposition. • Different eutectic microstructure morphologies presented at different positions. • Intermetallic compounds σ and μ phases appearing in the FCC phase. • High hardness and high wear resistance of CoCrFeNiMo EHEA were achieved. [ABSTRACT FROM AUTHOR]

[Display omitted] • The properties of GA and PREP In718 powder and their printed parts were compared. • The powder properties of PREP were better than those of GA. • The parts printed by PREP powder have finer microstructure and less segregation. • The parts printed by PREP powder have more obvious preferential orientation. • The PREP powder could contribute to better properties of printed parts. This work reports a study of the differences between properties of gas atomized (GA) and plasma rotating electrode processed (PREP) Inconel 718 powder and their printed parts. Powder morphology, particle size distribution, chemical composition, flowability and density have been comprehensively analyzed. From that, it can be found the properties of PREP powder were better than GA powder. The densification, microstructure, mechanical properties, and oxidation resistance of GA and PREP samples prepared by direct energy deposition(DED) were also studied. The results showed that the perfect morphology of powder could contribute to a finer microstruture and a more obvious preferential orientation of the printed part. Moreover, the density, mechanical properties and oxidation resistance of the printed parts were all better. This work highlights that properties of powder are crucial to the properties of the printed parts, and emphasises the importance of powder morphology on energy absorption, microstructure and preferential orientation in DED process. [ABSTRACT FROM AUTHOR]

The equimolar AlCoCrFeNi high entropy alloy (HEA) was successfully fabricated by direct energy deposition (DED), and its phase composition, microstructure and mechanical properties were discussed systematically. The DED samples showed a duplex BCC crystal structure, which is consistent with the original powder state. The microstructure of the DED samples transferred from columnar grain structure into equiaxed dendritic structure along building direction from bottom to top. What's more, the crystallographic orientation in different positions of the DED samples showed an apparent difference. The columnar grain structure presented a strong texture in building direction at the bottom, while equiaxed dendritic structure exhibited random crystallographic orientations at the top. The equiaxed dendrites structure in the DED samples is comprised of dendrites (DR) and interdendrites (ID) regions due to elemental segregation. Meanwhile, the fine microstructure of DR and ID regions is discovered to be nano-sized spherical precipitates morphology and weave-like morphology, respectively. Furthermore, compared with AlCoCrFeNi HEA produced by conventional casting method, the mechanical properties of the DED samples display a superior combination of strength and ductility. Therefore, this research could provide an effective theoretical guidance for the preparation of high strength-ductility of HEAs. [ABSTRACT FROM AUTHOR]

• Fe 3 Al and Fe 3 Al-WC alloys were fabricated using Laser Metal Deposition. • The addition of WC particles led to the refined microstructure. • The microhardness and yield strength of Fe 3 Al-WC alloy improved with WC added. As a neat-net-shape processing technology, Laser Metal Deposition (LMD) is well-suited for the preparation of intermetallic alloys. In this study, Fe 3 Al and Fe 3 Al-WC alloys were successfully fabricated by LMD. The effects of WC on the phase composition, microstructure and mechanical properties of the as-fabricated samples were investigated. The results showed that the addition of WC particles facilitated the formation of W-containing carbides and resulted in grain refinement. Taking advantage of solid solution strengthening and fine grain strengthening, the Fe 3 Al-WC composite alloy exhibited an improved microhardness (395.50 HV 0.3) and compressive yield strength (1262 ± 13 MPa) compared with pure Fe 3 Al alloy. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes., Materials and Methods: This retrospective study included 366 breast cancer patients from two institutes, divided into training (n = 292), validation (n = 49) and testing (n = 25) sets. We first transformed the public DCE-MRI appearance to ours to alleviate small-data-size and class-imbalance issues. Second, we developed a multi-branch convolutional-neural-network (MBCNN) to perform molecular subtype prediction. Third, we assessed the MBCNN with different regions of interest (ROIs) and fusion strategies, and compared it to previous DL models. Area under the curve (AUC) and accuracy (ACC) were used to assess different models. Delong-test was used for the comparison of different groups., Results: MBCNN achieved the optimal performance under intermediate fusion and ROI size of 80 pixels with appearance transformation. It outperformed CNN and convolutional long-short-term-memory (CLSTM) in predicting luminal B, HER2-enriched and TN subtypes, but without demonstrating statistical significance except against CNN in TN subtypes, with testing AUCs of 0.8182 vs. [0.7208, 0.7922] (p=0.44, 0.80), 0.8500 vs. [0.7300, 0.8200] (p=0.36, 0.70) and 0.8900 vs. [0.7600, 0.8300] (p=0.03, 0.63), respectively. When predicting luminal A, MBCNN outperformed CNN with AUCs of 0.8571 vs. 0.7619 (p=0.08) without achieving statistical significance, and is comparable to CLSTM. For four-subtype prediction, MBCNN achieved an ACC of 0.64, better than CNN and CLSTM models with ACCs of 0.48 and 0.52, respectively., Conclusion: Developed DL model with the feature extraction and fusion of DCE-MRI from two institutes enabled preoperative prediction of breast cancer molecular subtypes with high diagnostic performance., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Fengjun Zhao reports financial support was provided by the Scientific Research Program Funded by Education Department of Shaanxi Provincial Government (22JP087) and the Key R&D Plan of Shaanxi Province (2024SF-YBXM-321). Xiaowei He reports financial support was provided by the Xi’an Science and Technology Plan (201805060ZD11CG44). If there are other authors, they 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 © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Cone beam X-ray luminescence computed tomography (CB-XLCT) is an emerging imaging technique with potential for early 3D tumor detection. However, the reconstruction challenge due to low light absorption and high scattering in tissues makes it a difficult inverse problem. In this study, the online dictionary learning (ODL) method, combined with iterative reduction FISTA (IR-FISTA), has been utilized to achieve high-quality reconstruction. Our method integrates IR-FISTA for efficient and accurate sparse coding, followed by an online stochastic approximation for dictionary updates, effectively capturing the sparse features inherent to the problem. Additionally, a re-sparse step is introduced to enhance the sparsity of the solution, making it better suited for CB-XLCT reconstruction. Numerical simulations and in vivo experiments were conducted to assess the performance of the method. The SODL-IR-FISTA achieved the smallest location error of 0.325 mm in in vivo experiments, which is 58% and 45% of the IVTCG- L 1 (0.562 mm) and OMP- L 0 (0.721 mm), respectively. Additionally, it has the highest DICE similarity coefficient, which is 0.748. The results demonstrate that our approach outperforms traditional methods in terms of localization precision, shape restoration, robustness, and practicality in live subjects., Competing Interests: The authors declare no conflicts of interest., (© 2024 Optica Publishing Group.)

Background: This study aimed to investigate the effect of 6, 12, and 24 h short-term anaerobic treatment on kiwiberry quality and antioxidant properties at 5 °C., Results: Short-term anaerobic treatment was found to delay ripening and softening in kiwiberries, evident from changes in ethylene release, total soluble solids, starch, protopectin, and fruit texture. The 24 h treatment group exhibited the lowest decay rate of 12% on day 49, a 38% reduction compared with the control group. Anaerobic treatment reduced flesh translucency and decay in the fruit. The 12 h and 24 h treatments enhanced the activities of superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase, and increased the level of total phenolics, flavonoids, anthocyanins, and ascorbic acid. Moreover, it lowered oxidative damage in cell membranes, evidenced by reduced malondialdehyde content and relative conductivity., Conclusion: These results indicate that anaerobic treatment maintains the fruit quality by stimulating its antioxidant defense system. Therefore, short-term anaerobic treatment emerges as a promising method for kiwiberry storage. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

Condyloma acuminatum (CA) is a sexually transmitted disease (STD) caused by human papillomavirus (HPV) infection. It is important to study the prevalence and distribution of HPV genotypes before implementing the HPV vaccination program. Therefore, the aim of this study was to evaluate the epidemiological characteristics of CA cases and the distribution of HPV genotypes in Shandong Province, China. One-to-one questionnaire surveys were conducted on all patients diagnosed with CA in sentinel hospitals from Shandong Province, China. HPV genotypes were determined using the polymerase chain reaction (PCR)-reverse dot blot hybridization method. The study enrolled 1185 patients (870 males and 315 females) and found that CA patients are mainly males and sexually active people between the ages of 20 and 40. Recurrence occurred in 34.7% patients. Among the 880 CA patients who underwent HPV typing, the HPV test positivity rate was 91.4%. In these cases, low-risk (LR) HPV infection was predominant, with an infection rate of 91.3%, while high-risk (HR) HPV genotypes were found in 53.5% patients. The most frequent HPV genotypes encountered were HPV6 (57.8%), HPV11 (37.2%), HPV16 (13.7%), and HPV42 (10.3%). HPV6 and/or HPV11 are the main infections in all patients, and more than half of the patients are coinfected with HR-HPV. However, unlike other regions, HPV42 has a higher prevalence rate among CA patients in Shandong Province and is a nonvaccine HPV genotype. Therefore, regular HPV typing helps to understand the characteristics of specific genotypes and the choice of the best type for vaccine coverage.

Early identification of endometrial cancer or precancerous lesions from histopathological images is crucial for precise endometrial medical care, which however is increasing hampered by the relative scarcity of pathologists. Computer-aided diagnosis (CAD) provides an automated alternative for confirming endometrial diseases with either feature-engineered machine learning or end-to-end deep learning (DL). In particular, advanced self-supervised learning alleviates the dependence of supervised learning on large-scale human-annotated data and can be used to pre-train DL models for specific classification tasks. Thereby, we develop a novel self-supervised triplet contrastive learning (SSTCL) model for classifying endometrial histopathological images. Specifically, this model consists of one online branch and two target branches. The second target branch includes a simple yet powerful augmentation module named random mosaic masking (RMM), which functions as an effective regularization by mapping the features of masked images close to those of intact ones. Moreover, we add a bottleneck Transformer (BoT) model into each branch as a self-attention module to learn the global information by considering both content information and relative distances between features at different locations. On public endometrial dataset, our model achieved four-class classification accuracies of 77.31 ± 0.84, 80.87 ± 0.48 and 83.22 ± 0.87% using 20, 50 and 100% labeled images, respectively. When transferred to the in-house dataset, our model obtained a three-class diagnostic accuracy of 96.81% with 95% confidence interval of 95.61-98.02%. On both datasets, our model outperformed state-of-the-art supervised and self-supervised methods. Our model may help pathologists to automatically diagnose endometrial diseases with high accuracy and efficiency using limited human-annotated histopathological images.

As a dual-modal imaging technology that has emerged in recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has exhibited promise as a tool for the early three-dimensional detection of tumors in small animals. However, due to the challenges imposed by the low absorption and high scattering of light in tissues, the CB-XLCT reconstruction problem is a severely ill-conditioned inverse problem, rendering it difficult to obtain satisfactory reconstruction results. In this study, a strategy that utilizes dictionary learning and group structure (DLGS) is proposed to achieve satisfactory CB-XLCT reconstruction performance. The group structure is employed to account for the clustering of nanophosphors in specific regions within the organism, which can enhance the interrelation of elements in the same group. Furthermore, the dictionary learning strategy is implemented to effectively capture sparse features. The performance of the proposed method was evaluated through numerical simulations and in vivo experiments. The experimental results demonstrate that the proposed method achieves superior reconstruction performance in terms of location accuracy, target shape, robustness, dual-source resolution, and in vivo practicability.

X-ray luminescence computed tomography (XLCT) is an emerging molecular imaging technique for biological application. However, it is still a challenge to get a stable and accurate solution of the reconstruction of XLCT. This paper presents a regularization parameter selection strategy based on incomplete variables frame for XLCT. A residual information, which is derived from Karush-Kuhn-Tucker (KKT) equivalent condition, is employed to determine the regularization parameter. This residual contains the relevant information about the solution norm and gradient norm, which improved the recovered results. Simulation and phantom experiments are designed to test the performance of the algorithm.Clinical Relevance- The results have not yet been used in clinical relevance currently, we believed that this strategy will facilitate the development of the preclinical applications in FMT.

Cerenkov luminescence tomography (CLT) is a highly sensitive and promising imaging technique that can be used to reconstruct the three-dimensional distribution of radioactive probes in living animals. However, the accuracy of CLT reconstruction is limited by the simplified radiative transfer equation and ill-conditioned inverse problem. To address this issue, we propose a model-based deep learning network that combines the neural network with a model-based approach to enhance the performance of CLT reconstruction. The Fast Iterative Shrinkage Thresholding Algorithm (FISTA), a traditional model-based approach, is expanded into a deep network (known as FISTA-NET). Each layer in the network represents an iteration of the algorithm steps, and connecting these layers can form a deep neural network. In addition, different from the traditional FISTA, the key parameters in FISTA, such as gradient step size and threshold value, can be learned through training data without manual production. To evaluate the performance of FISTA-NET, numerical simulation experiments were conducted, which demonstrate its excellent positioning and shape recovery abilities.Clinical Relevance-This indicates that FISTA-NET strategy can significantly improve the quality of CLT reconstruction, which is further beneficial to the assessment of disease activity and treatment effect based on CLT.

Accurate staging of lymph nodes provides crucial diagnostic information for breast cancer patients, where segmentation is of great importance by localizing and visualizing the breast tumor of interest. Nevertheless, current segmentation methods perform average when facing large span of tumor sizes, degraded image quality, blurred tumor boundaries, and resulting noise during manual annotation. Therefore, we develop a Multi-scale RepVGG-based Segmentation Network (MPSegNet) to segment breast tumor from MR images. In particular, we construct a consistent learning framework for the MPSegNet to alleviate the impact of noisy labels upon segmentation results. The rationale is that different views covering the same breast tumors are supposed to generate identical segmentation predictions. Then, we predict SLN metastasis given segmented breast tumors, where we evaluate the relationships between the predictive performance and tumor segmentations under different consistencies. The results show the superiority of our method over other state-of-the-art methods. A high consistency among multiple views can boost the segmentation performance during consistent learning. However, the optimal segmentation does not produce the best SLN metastatic prediction results, implying that the dependence of classification upon segmentation needs to be elaborately investigated further.Clinical Relevance- This study facilitates more accurate segmentation of breast tumors with consistent learning, and provides an initial analysis between tumor segmentation and subsequent prediction of SLN metastasis, which has potential significance for the precise medical care of breast cancer patients.

Breast cancer, the most common female malignancy, is highly heterogeneous, manifesting as different molecular subtypes. It is clinically important to distinguish between these molecular subtypes due to marked differences in prognosis, treatment and survival outcomes. In this study, we first performed convex analysis of mixtures (CAM) analysis on both intratumoral and peritumoral regions in DCE-MRI to generate multiple heterogeneous regions. Then, we developed a vision transformer (ViT)-based DL model and performed network architecture search (NAS) to evaluate all the combination of different heterogeneous regions for predicting molecular subtypes of breast cancer. Experimental results showed that the input plasma from both peritumoral and intratumoral regions, and the fast-flow kinetics from intratumoral regions were critical for predicting different molecular subtypes, achieving an area under receiver operating characteristic curve (AUROC) value of 0.66-0.68.Clinical Relevance- This study reduces the redundancy in multiple heterogeneous subregions and supports the precise prediction of molecular subtypes, which is of potential importance for the medicine care and treatment planning of patients with breast cancer.

Chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) is a promising molecular imaging tool that allows sensitive detection of endogenous metabolic changes. However, because the CEST spectrum does not display a clear peak like MR spectroscopy, its signal interpretation is challenging, especially under 3-T field strength or with a large saturation B 1 . Herein, as an alternative to conventional Z-spectral fitting approaches, a permuted random forest (PRF) method is developed to determine featured saturation frequencies for lesion identification, so-called CEST frequency importance analysis. Briefly, voxels in the CEST dataset were labeled as lesion and control according to multicontrast MR images. Then, by considering each voxel's saturation signal series as a sample, a permutation importance algorithm was employed to rank the contribution of saturation frequency offsets in the differentiation of lesion and normal tissue. Simulations demonstrated that PRF could correctly determine the frequency offsets (3.5 or -3.5 ppm) for classifying two groups of Z-spectra, under a range of B 0 , B 1 conditions and sample sizes. For ischemic rat brains, PRF only displayed high feature importance around amide frequency at 2 h postischemia, reflecting that the pH changes occurred at an early stage. By contrast, the data acquired at 24 h postischemia exhibited high feature importance at multiple frequencies (amide, water, and lipids), which suggested the complex tissue changes that occur during the later stages. Finally, PRF was assessed using 3-T CEST data from four brain tumor patients. By defining the tumor region on amide proton transfer-weighted images, PRF analysis identified different CEST frequency importance for two types of tumors (glioblastoma and metastatic tumor) (p < 0.05, with each image slice as a subject). In conclusion, the PRF method was able to rank and interpret the contribution of all acquired saturation offsets to lesion identification; this may facilitate CEST analysis in clinical applications, and open up new doors for comprehensive CEST analysis tools other than model-based approaches., (© 2022 John Wiley & Sons Ltd.)

Fluorescence molecular tomography (FMT) is an optical imaging technology with the ability of visualizing the three-dimensional distribution of fluorescently labelled probes in vivo. However, due to the light scattering effect and ill-posed inverse problems, obtaining satisfactory FMT reconstruction is still a challenging problem. In this work, to improve the performance of FMT reconstruction, we proposed a generalized conditional gradient method with adaptive regularization parameters (GCGM-ARP). In order to make a tradeoff between the sparsity and shape preservation of the reconstruction source, and to maintain its robustness, elastic-net (EN) regularization is introduced. EN regularization combines the advantages of L 1 -norm and L 2 -norm, and overcomes the shortcomings of traditional L p -norm regularization, such as over-sparsity, over-smoothness, and non-robustness. Thus, the equivalent optimization formulation of the original problem can be obtained. To further improve the performance of the reconstruction, the L-curve is adopted to adaptively adjust the regularization parameters. Then, the generalized conditional gradient method (GCGM) is used to split the minimization problem based on EN regularization into two simpler sub-problems, which are determining the direction of the gradient and the step size. These sub-problems are addressed efficiently to obtain more sparse solutions. To assess the performance of our proposed method, a series of numerical simulation experiments and in vivo experiments were implemented. The experimental results show that, compared with other mathematical reconstruction methods, GCGM-ARP method has the minimum location error (LE) and relative intensity error (RIE), and the maximum dice coefficient (Dice) in the case of different sources number or shape, or Gaussian noise of 5%-25%. This indicates that GCGM-ARP has superior reconstruction performance in source localization, dual-source resolution, morphology recovery, and robustness. In conclusion, the proposed GCGM-ARP is an effective and robust strategy for FMT reconstruction in biomedical application.

Background: Ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) are malignant and benign lesions for which radiotherapy and corticosteroids are indicated, but similar clinical manifestations make their differentiation difficult., Purpose: To develop and validate an MRI-based radiomics nomogram for individual diagnosis of OAL vs. IOI., Study Type: Retrospective., Population: A total of 103 patients (46.6% female) with mean age of 56.4 ± 16.3 years having OAL (n = 58) or IOI (n = 45) were divided into an independent training (n = 82) and a testing dataset (n = 21)., Field Strength/sequence: A 3-T, precontrast T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and postcontrast T1WI (T1 + C)., Assessment: Radiomics features were extracted and selected from segmented tumors and peritumoral regions in MRI before-and-after filtering. These features, alone or combined with clinical characteristics, were used to construct a radiomics or joint signature to differentiate OAL from IOI, respectively. A joint nomogram was built to show the impact of the radiomics signature and clinical characteristics on individual risk of developing OAL., Statistical Tests: Area under the curve (AUC) and accuracy (ACC) were used for performance evaluation. Mann-Whitney U and Chi-square tests were used to analyze continuous and categorical variables. Decision curve analysis, kappa statistics, DeLong and Hosmer-Lemeshow tests were also conducted. P < 0.05 was considered statistically significant., Results: The joint signature achieved an AUC of 0.833 (95% confidence interval [CI]: 0.806-0.870), slightly better than the radiomics signature with an AUC of 0.806 (95% CI: 0.767-0.838) (P = 0.778). The joint and radiomics signatures were comparable to experienced radiologists referencing to clinical characteristics (ACC = 0.810 vs. 0.796-0.806, P > 0.05) or not (AUC = 0.806 vs. 0.753-0.791, P > 0.05), respectively. The joint nomogram gained more net benefits than the radiomics nomogram, despite both showing good calibration and discriminatory efficiency (P > 0.05)., Data Conclusion: The developed radiomics-based analysis might help to improve the diagnostic performance and reveal the association between radiomics features and individual risk of developing OAL., Evidence Level: 3 TECHNICAL EFFICACY: 3., (© 2022 International Society for Magnetic Resonance in Medicine.)

Wildfires directly affect global ecosystem stability and severely threaten human life. The mountainous areas of Southwest China experience frequent wildfires. Mapping the susceptibility patterns and analyzing the drivers of wildfires are crucial for effective wildfire management, especially considering that the inclusion of seasonal dimensions will produce more dynamic results. Using Yunnan Province of China as a case study area, a method was attempted to distinguish dependable wildfires by season, while possible wildfire drivers were obtained and refined within seasons. The patterns of wildfire susceptibility in different seasons were modeled based on the Maxent and random forest models. Then, the spatial relationships between wildfire and potential drivers were analyzed integrating with GeoDetector to evaluate the variable importance of drivers and the marginal effect of drivers. The results showed that the two models effectively depicted each season's wildfire susceptibility. The susceptible wildfire areas in spring and winter are located throughout Yunnan Province, with anthropogenic factors being the most significant drivers. During the summer and autumn, wildfire risk areas are relatively concentrated, showing a trend of dominant drought-driven and humid conditions. The differences in wildfire drivers across seasons reflect the lagged effect of climate factors on wildfires, leading to significant discrepancies in the marginal effects of how seasonal drivers affect wildfires. The findings improve our understanding of the effects of the interseasonal variability of environmental variables on wildfires and promote the development of specific seasonal wildfire management strategies., Competing Interests: Declaration of competing interest All authors declare no potential conflicts of interest with respect to the research, author-ship, and publication of this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: The purpose of this study was to develop a deep learning-based system with a cascade feature pyramid network for the detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Methods: This retrospective study enrolled 191 consecutive patients with pathological confirmed breast lesions who underwent preoperative magnetic resonance imaging (MRI) at the Second Affiliated Hospital of Xi'an Jiaotong University. Patients were randomly divided into a training set comprising 153 patients with 126 malignant and 27 benign lesions and a validation set containing 38 patients with 31 malignant and 7 benign lesions under 5-fold cross-validation. Two radiologists annotated the location and classification of all lesions. After augmentation with pseudo-color image fusion, MRI images were fed into the developed cascade feature pyramid network system, feature pyramid network, and faster region-based convolutional neural network (CNN) for breast lesion detection and classification, respectively. The performance on large (>2 cm) and small (≤2 cm) breast lesions was further evaluated. Average precision (AP), mean AP, F1-score, sensitivity, and false positives were used to evaluate different systems. Cohen's kappa scores were calculated to assess agreement between different systems, and Student's t -test and the Holm-Bonferroni method were used to compare multiple groups., Results: The cascade feature pyramid network system outperformed the other systems with a mean AP and highest sensitivity of 0.826±0.051 and 0.970±0.014 (at 0.375 false positives), respectively. The F1-score of the cascade feature pyramid network in real detection was also superior to that of the other systems at both the slice and patient levels. The mean AP values of the cascade feature pyramid network reached 0.779±0.152 and 0.790±0.080 in detecting large and small lesions, respectively. Especially for small lesions, the cascade feature pyramid network achieved the best performance in detecting benign and malignant breast lesions at both the slice and patient levels., Conclusions: The deep learning-based system with the developed cascade feature pyramid network has the potential to detect and classify breast lesions on DCE-MRI, especially small lesions., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-323/coif). XW is an employee of GE Healthcare, China. The other authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objective. Bioluminescence tomography (BLT) is a promising non-invasive optical medical imaging technique, which can visualize and quantitatively analyze the distribution of tumor cells in living tissues. However, due to the influence of photon scattering effect and ill-conditioned inverse problem, the reconstruction result is unsatisfactory. The purpose of this study is to improve the reconstruction performance of BLT. Approach. An alternating Bregman proximity operators (ABPO) method based on TVSCAD regularization is proposed for BLT reconstruction. TVSCAD combines the anisotropic total variation (TV) regularization constraints and the non-convex smoothly clipped absolute deviation (SCAD) penalty constraints, to make a trade-off between the sparsity and edge preservation of the source. ABPO approach is used to solve the TVSCAD model (ABPO-TVSCAD for short). In addition, to accelerate the convergence speed of the ABPO, we adapt the strategy of shrinking the permission source region, which further improves the performance of ABPO-TVSCAD. Main results. The results of numerical simulations and in vivo xenograft mouse experiment show that our proposed method achieved superior accuracy in spatial localization and morphological reconstruction of bioluminescent source. Significance. ABPO-TVSCAD is an effective and robust reconstruction method for BLT, and we hope that this method can promote the development of optical molecular tomography., (© 2022 Institute of Physics and Engineering in Medicine.)

Cerenkov luminescence tomography (CLT) provides a powerful optical molecular imaging technique for non-invasive detection and visualization of radiopharmaceuticals in living objects. However, the severe photon scattering effect causes ill-posedness of the inverse problem, and the location accuracy and shape recovery of CLT reconstruction results are unsatisfactory for clinical application. Here, to improve the reconstruction spatial location accuracy and shape recovery ability, a non-negative iterative three operator splitting (NNITOS) strategy based on elastic net (EN) regularization was proposed. NNITOS formalizes the CLT reconstruction as a non-convex optimization problem and splits it into three operators, the least square, L 1/2 -norm regularization, and adaptive grouping manifold learning, then iteratively solved them. After stepwise iterations, the result of NNITOS converged progressively. Meanwhile, to speed up the convergence and ensure the sparsity of the solution, shrinking the region of interest was utilized in this strategy. To verify the effectiveness of the method, numerical simulations and in vivo experiments were performed. The result of these experiments demonstrated that, compared to several methods, NNITOS can achieve superior performance in terms of location accuracy, shape recovery capability, and robustness. We hope this work can accelerate the clinical application of CLT in the future.

Background and Objective: Endometrial hyperplasia (EH), a uterine pathology characterized by an increased gland-to-stroma ratio compared to normal endometrium (NE), may precede the development of endometrial cancer (EC). Particularly, atypical EH also known as endometrial intraepithelial neoplasia (EIN), has been proven to be a precursor of EC. Thus, diagnosing different EH (EIN, hyperplasia without atypia (HwA) and NE) and screening EIN from non-EIN are crucial for the health of female reproductive system. Computer-aided-diagnosis (CAD) was used to diagnose endometrial histological images based on machine learning and deep learning. However, these studies perform single-scale image analysis and thus can only characterize partial endometrial features. Empirically, both global (cytological changes relative to background) and local features (gland-to-stromal ratio and lesion dimension) are helpful in identifying endometrial lesions., Methods: We proposed a global-to-local multi-scale convolutional neural network (G2LNet) to diagnose different EH and to screen EIN in endometrial histological images stained by hematoxylin and eosin (H&E). The G2LNet first used a supervised model in the global part to extract contextual features of endometrial lesions, and simultaneously deployed multi-instance learning in the local part to obtain textural features from multiple image patches. The contextual and textural features were used together to diagnose different endometrial lesions after fusion by a convolutional block attention module. In addition, we visualized the salient regions on both the global image and local images to investigate the interpretability of the model in endometrial diagnosis., Results: In the five-fold cross validation on 7812 H&E images from 467 endometrial specimens, G2LNet achieved an accuracy of 97.01% for EH diagnosis and an area-under-the-curve (AUC) of 0.9902 for EIN screening, significantly higher than state-of-the-arts. In external validation on 1631 H&E images from 135 specimens, G2LNet achieved an accuracy of 95.34% for EH diagnosis, which was comparable to that of a mid-level pathologist (95.71%). Specifically, G2LNet had advantages in diagnosing EIN, while humans performed better in identifying NE and HwA., Conclusions: The developed G2LNet that integrated both the global (contextual) and local (textural) features may help pathologists diagnose endometrial lesions in clinical practices, especially to improve the accuracy and efficiency of screening for precancerous lesions., Competing Interests: Declaration of Competing Interest The authors declare that no conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients' clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted. Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86-0.96) was higher than that of RM (0.82, 95% CI: 0.75-0.89) and DLM (0.90, 95% CI: 0.85-0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77-0.93) was also higher than that of RM (0.68, 95% CI: 0.52-0.84) and DLM (0.79, 95% CI: 0.67-0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models. Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Huang, Wang, Wang, Zhang, Zhao, Li, Su, Kang and Cao.)

Detection of different classes of atherosclerotic plaques is important for early intervention of coronary artery diseases. However, previous methods focused either on the detection of a specific class of coronary plaques or on the distinction between plaques and normal arteries, neglecting the classification of different classes of plaques. Therefore, we proposed an automatic multi-class coronary atherosclerosis plaque detection and classification framework. Firstly, we retrieved the transverse cross sections along centerlines from the computed tomography angiography. Secondly, we extracted the region of interests based on coarse segmentation. Thirdly, we extracted a random radius symmetry (RRS) feature vector, which incorporates multiple descriptions into a random strategy and greatly augments the training data. Finally, we fed the RRS feature vector into the multi-class coronary plaque classifier. In experiments, we compared our proposed framework with other methods on the cross sections of Rotterdam Coronary Datasets, including 729 non-calcified plaques, 511 calcified plaques, and 546 mixed plaques. Our RRS with support vector machine outperforms the intensity feature vector and the random forest classifier, with the average precision of 92.6 ± 1.9% and average recall of 94.3 ± 2.1%. The proposed framework provides a computer-aided diagnostic method for multi-class plaque detection and classification. Graphical abstract Diagram of the proposed automatic multi-class coronary atherosclerosis plaque detection and classification framework. ᅟ.

Centerline is generally used to measure topological and morphological parameters of blood vessels, which is pivotal for the quantitative analysis of vascular diseases. However, previous centerline extraction methods have two drawbacks on complex blood vessels, represented as the failure on ring-like structures and the existing of multi-voxel width. In this paper, we propose a monocentric centerline extraction method for ring-like blood vessels, which consists of three components. First, multiple centerlines are generated from the seed points that are chosen by randomly sprinkling points on blood vessel data. Second, multi-centerline fusion is used to repair the notches of centerlines on ring-like vessels, and the local maximum of distance from oundary is employed to remedy the missing centerline points. Finally, monocentric processing is devised to keep the vascular centerline with single voxel width. We compared the proposed method with Wan et al.'s method and topological thinning on five groups of data including synthesized vascular datasets and MR brain images. The result showed the proposed method performed better than the two contrast methods both by visual inspection and by quantitative assessment, which demonstrated the performance of the proposed method on ring-like blood vessels as well as the elimination of multi-voxel width points.

Previous studies showed that all the vascular parameters from both the morphological and topological parameters were affected with the altering of imaging resolutions. However, neither the sensitivity analysis of the vascular parameters at multiple resolutions nor the distinguishability estimation of vascular parameters from different data groups has been discussed. In this paper, we proposed a quantitative analysis method of vascular parameters for vascular networks of multi-resolution, by analyzing the sensitivity of vascular parameters at multiple resolutions and estimating the distinguishability of vascular parameters from different data groups. Combining the sensitivity and distinguishability, we designed a hybrid formulation to estimate the integrated performance of vascular parameters in a multi-resolution framework. Among the vascular parameters, degree of anisotropy and junction degree were two insensitive parameters that were nearly irrelevant with resolution degradation; vascular area, connectivity density, vascular length, vascular junction and segment number were five parameters that could better distinguish the vascular networks from different groups and abide by the ground truth. Vascular area, connectivity density, vascular length and segment number not only were insensitive to multi-resolution but could also better distinguish vascular networks from different groups, which provided guidance for the quantification of the vascular networks in multi-resolution frameworks.

The two mammalian α2,8-polysialyltransferases (polyST's), ST8Sia II (STX) and ST8Sia IV (PST), catalyze synthesis of the α2-8-linked polysialic acid (polySia) glycans on neural cell adhesion molecules (NCAMs). The objective of this study was to clone the coding sequence of the piglet ST8Sia II and determine the mRNA expression levels of ST8Sia II, ST8Sia IV, NCAM and neuropilin-2 (NRP-2), also a carrier protein of polySia, during postnatal development. The amino acid sequence deduced from the coding sequence of ST8Sia II was compared with seven other mammalian species. Piglet ST8Sia II was highly conserved and shared 67.8% sequence identity with ST8Sia IV. Genes coding for ST8Sia II and IV were differentially expressed and distinctly different in neural and non-neural tissues at postnatal days 3 and 38. Unexpectedly, the cellular levels of mRNA coding for ST8Sia II and IV showed no correlation with the posttranslational level of polySia glycans in different tissues. In contrast, mRNA abundance coding for NCAM and neuropilin-2 correlated with expression of ST8Sia II and IV. These findings show that the cellular abundance of ST8Sia II and IV in postnatal piglets is regulated at the level of translation/posttranslation, and not at the level of transcription, a finding that has not been previously reported. These studies further highlight differences in the molecular mechanisms controlling polysialylation in adult rodents and neonatal piglets.

To solve the multicollinearity issue and unequal contribution of vascular parameters for the quantification of angiogenesis, we developed a quantification evaluation method of vascular parameters for angiogenesis based on in vivo micro-CT imaging of hindlimb ischemic model mice. Taking vascular volume as the ground truth parameter, nine vascular parameters were first assembled into sparse principal components (PCs) to reduce the multicolinearity issue. Aggregated boosted trees (ABTs) were then employed to analyze the importance of vascular parameters for the quantification of angiogenesis via the loadings of sparse PCs. The results demonstrated that vascular volume was mainly characterized by vascular area, vascular junction, connectivity density, segment number and vascular length, which indicated they were the key vascular parameters for the quantification of angiogenesis. The proposed quantitative evaluation method was compared with both the ABTs directly using the nine vascular parameters and Pearson correlation, which were consistent. In contrast to the ABTs directly using the vascular parameters, the proposed method can select all the key vascular parameters simultaneously, because all the key vascular parameters were assembled into the sparse PCs with the highest relative importance.