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Ratoon rice, which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop, plays an important role in both food security and agroecology while requiring minimal agricultural inputs. However, accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems (e.g., double rice). Moreover, images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather. In this study, taking Qichun County in Hubei Province, China as an example, we developed a new phenology-based ratoon rice vegetation index (PRVI) for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2 (HLS) images. The PRVI that incorporated the red, near-infrared, and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection. Based on actual field samples, the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and land surface water index (LSWI). The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice, leading to a favorable separability between ratoon rice and other land cover types. Furthermore, the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop (GHS-TS2), indicating that only several images are required to obtain an accurate ratoon rice map. Finally, the PRVI performed better than NDVI, EVI, LSWI and their combination at the GHS-TS2 stages, with producer’s accuracy and user’s accuracy of 92.22 and 89.30%, respectively. These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages, which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

IntroductionHerpes simplex keratitis (HSK) is a blinding disease caused by corneal infection of Herpes simplex virus type 1 (HSV-1). Effective clearance of HSV-1 from the infected cornea is crucial for HSK management. Macrophages play an important part in the innate immune defense against viral infections. This study investigates the immunomodulatory role of NLRP12 in macrophage immune response during HSV-1 infection.MethodsNLRP12 expression post-infection was assessed in various macrophage cell lines. Overexpression of NLRP12 was achieved by lentiviral transfection, and its effect on HSV-1 replication and immune responses were examined. Mechanistic insights into the role of NLRP12 were explored using immunofluorescence and Western Blot. For in vivo studies, ocular adoptive transfer of NLRP12-overexpressing bone marrow derived macrophages (BMDMs) was performed. HSV-1 viral loads, HSK symptoms, and macrophage-mediated immune responses were investigated.ResultsA significant decrease in NLRP12 expression post-infection was observed in various macrophage cell lines. Overexpression of NLRP12 in macrophages reduced HSV-1 replication. Mechanistically, overexpression of NLRP12 triggered early and robust pyroptosis in response to HSV-1 infection, inducing interleukin (IL)-18 production and activating downstream antiviral responses through the JAK-STAT signaling pathway. In vivo, ocular adoptive transfer of NLRP12-overexpressing BMDMs to mouse corneas alleviated HSK damage and reduced HSV-1 viral loads. NLRP12-overexpressing BMDMs improved antiviral responses in the cornea and promoted the maturation of corneal-infiltrating macrophages and dendritic cells. Additionally, NLRP12-overexpressing BMDMs amplified the adaptive immune response in the submandibular draining lymph nodes.DiscussionThese findings highlight the role of NLRP12 in macrophage-mediated immune response against HSV-1 infection and suggest its potential for possible immunotherapy for HSK.

Glioma is a common type of malignant tumor with high global incidence and limited therapeutic options. Buthus martensii Karsch (BmK) Scorpion is listed as medicinal and functional food material for centuries to relieve neurological diseases including glioma in traditional Chinese medicine. In this study, network pharmacology firstly revealed that BmK Scorpion may exert anti-tumor effect via regulating oncogenic signals like EGFR and SRC. The anti-neoplastic effect and safety of BmK Scorpion were further validated on two in vivo glioma models, subcutaneous xenograft and orthotopic transplanted glioma in C57BL/6J mice. Data from mRNA and miRNA transcriptomes of orthotopic glioma showed that BmK exerted anti-glioma as well as immune regulatory effects. Flow cytometry and immunofluorescence analysis demonstrated that BmK improved CD4 + and CD8 + T immunity and decreased myeloid-derived suppressor cells (MDSCs) infiltration in glioma. Collectively, BmK Scorpion restrained the progress of glioma possibly via improving T cell surveillance and inhibiting MDSC in tumor microenvironment.

Abstract Background Cancer-related fatigue (CRF) is an extremely common and long-term condition that affects the physical and mental health of oncology patients. While the treatment for CRF with western medicine and non-pharmacological therapy remains uncertain and challenging, traditional Chinese medicine (TCM) has become a trending option for the patients. Based on the findings from randomized controlled trials (RCTs), this study aims to identify and evaluate the evidence about the efficacy and safety of TCM for CRF. Methods A systematic literature search was conducted according to the PRISMA literature research guidelines. Seven electronic databases including PubMed, the Cochrane Library, Embase, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI) and Wanfang database were searched to identify RCTs which investigated TCM in the treatment of CRF published since inception to December 2022. RCTs comparing TCM with no treatment, placebo, or pharmacological interventions were considered eligible for this review. The Consolidated Standards of Reporting Trials Statement extensions for Chinese herbal medicine Formulas (CONSORT-CHM) and the Cochrane Collaboration’s Risk of Bias tool were used in this review to evaluate the quality and the risk of bias of all included trials. Results A total of 82 RCTs were included in this review, regardless of whether they were published in English or Chinese. After data extraction and results evaluation, 78 trials demonstrated overall efficacy in using TCM for CRF patients compared with the control group, in which 33 trials showed that the efficacy rate was statistically significant (p

Abstract Millimetre‐wave (mmWave) frequencies play a vital role in fifth‐generation (5G) wireless systems and beyond due to the vast available bandwidth of several GHz. This paper presents channel characteristics and their channel models for mmWave based on extensive channel measurements at 26 and 38 GHz conducted in 5G scenarios, such as the rooftop, the high‐speed railway, and the Industrial Internet of Things scenarios. The channel sounder receiver (Rx) uses an omnidirectional or phased array antenna to meet the requirements of channel measurement, such as recording absolute delay and obtaining 3‐dimensional angular information. We use the classical close‐in model and floating‐intercept model to model path loss. Meanwhile, channel statistics in the delay domain are derived from the measured power delay profiles. Note that the different scenarios are measured with the same channel sounder, making the measurement results of different scenarios comparable. It is shown that the shadow fading parameter σ is 0.7 dB as fewer large scatterers exist in the rooftop scenario. Moreover, due to the significant dependence of delay spread on the geometric relationship between the transmitter (Tx), Rx, and surrounding environment, the statistical data in the delay domain varies significantly in different scenarios. The channel characteristics and models will guide future air‐interface, beamforming, and transceiver designs for 5G and beyond.

Introduction: Chronic fatigue syndrome (CFS) is an increasingly common condition that is challenging to treat due to unclear etiology and a lack of consensus on clinical diagnosis and treatment guidance. Many affected people resorted to using traditional and complementary medicines (T&CMs). However, the evidence for T&CMs for CFS has been inconclusive and continues to evolve. The study aims to identify, summarize and assess the most recent evidence on the efficacy and safety of T&CMs for CFS.Methods: Randomized controlled trials (RCTs) investigating T&CMs for CFS published in English of Chinese between 1 January 2013 and 31 December 2022 were searched from 7 databases. RCTs comparing T&CMs with no treatment, placebo, or pharmacological medicine were included, irrespective of language or blinding. The Consolidated Standards of Reporting Trials Statement extensions for Chinese herbal medicine Formulas (CONSORT-CHM) and the Cochrane Collaboration’s Risk of Bias tool were used to evaluate the quality and risk of bias of included studies.Results: A total of 62 RCTs investigating 43 types of T&CMs and involving 5,231 participants with CFS were included in this review. The primary outcome measures mainly included the scoring of fatigue symptoms using the validated tool Fatigue Scale-14 (FS-14) or the TCM syndrome score. The main interventions showing overall efficacy were Chaihu Guizhi Decoction and Buzhong Yiqi combined with Xiao Chaihu Decoction, and 148 ingredients were identified, including Astragali Radix, Glycyrrhizae Radix et Rhizoma, Atractylodis Macrocephalae Rhizoma, and Bupleuri Radix. The most significant effect was the improvement of fatigue, followed by TCM-diagnosed symptoms and other psychological conditions. No serious adverse effect had been reported. However, the quality of the RCTs included RCTs were found to be suboptimal, and the risk of bias remained uncertain.Conclusion: Some evidence from RCTs supported the efficacy and safety of T&CM in CFS. However, given the methodological and quality heterogenicity of the included studies, the recommendations of T&CMs in treating CFS remain inconclusive. To develop better quality evidence about T&CMs for CFS, future studies should employ more objective diagnosis standards and outcome measurements, larger sample size, and better bias control, and ensure the compliance with the corresponding reporting guidelines.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022362268, identifier CRD42022362268.

Abstract Drought stress (DS) at different growth stages of spring wheat (Triticum aestivum L.) may affect its grain yield and end‐use quality differently. The impact of drought stress at different growth stages on yield has been widely studied, but the impact on grain quality has not been thoroughly investigated. A 2‐year field experiment was conducted to evaluate the effects of DS at different growth stages on end‐use quality and grain yield in different spring wheat classes (soft white ‘Alturas,’ hard red ‘Coho,’ and hard white ‘Dayn’). The treatments included DS (from tillering to stem elongation, booting to heading, and anthesis to soft dough) plus a fully irrigated (well‐watered) check. We found significant differences between the three wheat classes in most end‐use quality parameters. Relative to the well‐watered conditions, DS from tillering to stem elongation and from booting to heading did not affect grain yield and most end‐use quality parameters. Flour protein, lactic acid and sucrose solvent retention capacities, and mixograph peak time were more impacted by post‐anthesis DS. These changes in end‐use quality were correlated and, thus, directly or indirectly affected baking performance. Our results indicate that reduced irrigation at early growth stages can maintain grain yield and end‐use quality of spring wheat of different classes, for example, grain yield of 437 g m−2 and flour protein of 13.2–16.4 g kg−1 across different classes subject to DS at early growth stages compared to 379 g m−2 and 13.9–17.9 g kg−1 subject to post‐anthesis DS.

A thyroid nodule, which is defined as abnormal growth of thyroid cells, indicates excessive iodine intake, thyroid degeneration, inflammation, and other diseases. Although thyroid nodules are always non-malignant, the malignancy likelihood of a thyroid nodule grows steadily every year. In order to reduce the burden on doctors and avoid unnecessary fine needle aspiration (FNA) and surgical resection, various studies have been done to diagnose thyroid nodules through deep-learning-based image recognition analysis. In this study, to predict the benign and malignant thyroid nodules accurately, a novel deep learning framework is proposed. Five hundred eight ultrasound images were collected from the Third Hospital of Hebei Medical University in China for model training and validation. First, a ResNet18 model, pretrained on ImageNet, was trained by an ultrasound image dataset, and a random sampling of training dataset was applied 10 times to avoid accidental errors. The results show that our model has a good performance, the average area under curve (AUC) of 10 times is 0.997, the average accuracy is 0.984, the average recall is 0.978, the average precision is 0.939, and the average F1 score is 0.957. Second, Gradient-weighted Class Activation Mapping (Grad-CAM) was proposed to highlight sensitive regions in an ultrasound image during the learning process. Grad-CAM is able to extract the sensitive regions and analyze their shape features. Based on the results, there are obvious differences between benign and malignant thyroid nodules; therefore, shape features of the sensitive regions are helpful in diagnosis to a great extent. Overall, the proposed model demonstrated the feasibility of employing deep learning and ultrasound images to estimate benign and malignant thyroid nodules.

The millimeter-wave (mmWave) communication is a promising solution to future high-speed data transmission. To study the impact of its wireless channel, a ray-launching simulator is developed to generate deterministic channel models according to realistic scenario descriptions. For such channel modeling, the material parameters play an important role in the electromagnetic numerical computing of multi-path components (MPCs) and should be calibrated according to channel measurement results. Our contribution in this paper is three-fold: we conduct the channel measurement in an indoor scenario for extracting mmWave channel characteristics, apply the Hungarian algorithm to pair measured and simulated propagation MPCs and apply the SAGE algorithm for the parameter calibration of materials impacting on the MPCs. Note that the proposed material parameter calibration method is general for the various radio propagation environment, not limited by the indoor or outdoor scenarios. Moreover, it avoids independent measurement of specific materials in a given scenario, thus greatly reducing the difficulty of obtaining material parameters. The result shows that the space alternating generalized expectation-maximization (SAGE) algorithm achieves a good match between measurement and simulation with a reasonable computational effort.

Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.

Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and agricultural intensification assessments. However, the inherently high landscape fragmentation and irregularly shaped cropland associated with smallholder farming systems restrict the accuracy of cropland parcels extraction. In this study, we proposed an adaptive image segmentation method with the automated selection of optimal scale (MSAOS) to extract cropland parcels in heterogeneous agricultural landscapes. The MSAOS method includes three major components: (1) coarse segmentation to divide the whole images into homogenous and heterogeneous regions, (2) fine segmentation to determine the optimal segmentation scale based on average local variance function, and (3) region merging to merge and dissolve the over-segmented objects with small area. The potential cropland objects derived from MSAOS were combined with random forest to generate the final cropland parcels. The MSAOS method was evaluated over different agricultural regions in China, and derived results were assessed by benchmark cropland parcels interpreted from high-spatial resolution images. Results showed the texture features of Homogeneity and Entropy are the most important features for MSAOS to extract potential cropland parcels, with the highest separability index of 0.28 and 0.26, respectively. MSAOS-derived cropland parcels had high agreement with the reference dataset over eight tiles in Qichun county, with average F1 scores of 0.839 and 0.779 for the area-based classification evaluation (Fab) and object-based segmentation evaluation (Fob), respectively. The further evaluation of MSAOS on different tiles of four provinces exhibited the similar results (Fab = 0.857 and Fob = 0.775) with that on eight test tiles, suggesting the good transferability of the MSAOS over different agricultural regions. Furthermore, MSAOS outperformed other widely-used approaches in terms of the accuracy and integrity of the extracted cropland parcels. These results indicate the great potential of using MSAOS for image segmentation in conjunction with random forest classification to effectively extract cropland parcels in smallholder farming systems.

Calotropis gigantea is often found in mining areas with heavy metal pollution. However, little is known about the physiological and molecular response mechanism of C. gigantea to Cd stress. In the present study, Cd tolerance characteristic of C. gigantea and the potential mechanisms were explored. Seed germination test results showed that C. gigantea had a certain Cd tolerance capacity. Biochemical and transcriptomic analysis indicated that the roots and leaves of C. gigantea had different responses to early Cd stress. A total of 176 and 1618 DEGs were identified in the roots and leaves of C. gigantea treated with Cd compared to the control samples, respectively. Results indicated that oxidative stress was mainly initiated in the roots of C. gigantea, whereas the leaves activated several Cd detoxification processes to cope with Cd, including the upregulation of genes involved in Cd transport (i.e., absorption, efflux, or compartmentalization), cell wall remodeling, antioxidant system, and chelation. This study provides preliminary information to understand how C. gigantea respond to Cd stress, which is useful for evaluating the potential of C. gigantea in the remediation of Cd-contaminated soils.

CD26, acting as a costimulator of T cell activation, plays an important role in the immune system. However, the role of CD26 in the differentiation of T cell subsets, especially of new paradigms of T cells, such as Th17 and Tregs, is not fully clarified. In the present study, the role of CD26 in T cell differentiation was investigated in vitro. CD26 expression was analyzed in the different subsets of human peripheral blood T lymphocytes after solid-phase immobilized specific anti-CD3 mAb stimulation. Here, the percentage of CD4+ cells significantly increased and most of these cells were coexpressed with CD26, suggesting a close correlation of CD26 expression with the proliferation of CD4+ cells. Subsequently, after immobilized anti-CD3 mAb stimulation, CD26 high-expressing cells (CD26high) were separated from CD26 low-expressing cells (CD26low) by magnetic cell sorting. We found that the percentages of cells secreting Th1 typical cytokines (IL-2, IFN-γ) and Th17 typical cytokines (IL-6, IL-17, and IL-22) or expressing Th17 typical biomarkers (IL-23R, CD161, and CD196) in the CD26high group were markedly higher than in those in the CD26low group. In addition, a coexpression of CD26 with IL-2, IFN-γ, IL-17, IL-22, and IL-23R in lymphocytes was demonstrated by fluorescence microscopy. These results provide direct evidence that the high expression of CD26 is accompanied by the differentiation of T lymphocytes into Th1 and Th17, indicating that CD26 plays a crucial role in regulating the immune response.

The rice-crayfish field (i.e., RCF), a newly emerging rice cultivation pattern, has greatly expanded in China in the last decade due to its significant ecological and economic benefits. The spatial distribution of RCFs is an important dataset for crop planting pattern adjustment, water resource management and yield estimation. Here, an object- and topology-based analysis (OTBA) method, which considers spectral-spatial features and the topological relationship between paddy fields and their enclosed ditches, was proposed to identify RCFs. First, we employed an object-based method to extract crayfish breeding ditches using very high-resolution images. Subsequently, the paddy fields that provide fodder for crayfish were identified according to the topological relationship between the paddy field and circumjacent crayfish ditch. The extracted ditch objects together with those paddy fields were merged to derive the final RCFs. The performance of the OTBA method was carefully evaluated using the RCF and non-RCF samples. Moreover, the effects of different spatial resolutions, spectral bands and temporal information on RCF identification were comprehensively investigated. Our results suggest the OTBA method performed well in extracting RCFs, with an overall accuracy of 91.77%. Although the mapping accuracies decreased as the image spatial resolution decreased, satisfactory RCF mapping results (>80%) can be achieved at spatial resolutions greater than 2 m. Additionally, we demonstrated that the mapping accuracy can be improved by more than 10% when near-infrared (NIR) band information was involved, indicating the necessity of the NIR band when selecting images to derive reliable RCF maps. Furthermore, the images acquired in the rice growth phase are recommended to maximize the differences of spectral characteristics between paddy fields and ditches. These promising findings suggest that the OTBA approach performs well for mapping RCFs in areas with fragmented agricultural landscapes, which provides fundamental information for further agricultural land use and water resources management.

Despite desert ecosystem being crucial to our understanding of natural geography, species evolution and global climate change, there is limited information on the dynamics of their composition and the diversity of endophytic fungi communities driven by plant identity and organ differentiation. Here, an extensive investigation of endophytic fungal microbiome in root, stem, and leaf organs associated with five xerophyte shrubs in an extremely arid desert, Northwest China, were examined. The fungal community dominated by Dothideomycetes and Pleosporales. Shrub species strongly drive the niche-based processes of endophytic fungi across the root, stem and leaf compartments. The diversity and composition of endophytic fungi in stem showed higher variability among plant species than leaf and root. The fungal communities in root libraries were more diverse and exhibited a remarkable differentiation of community composition. We further demonstrated the significant host preferences and tissue specificity of desert endophytic fungi, and unique specific taxa were also observed. The co-occurrence network revealed the coexistence of fungal endophytes in arid desert, and the root fungal network harbored the highest interspecies connectivity. Members of Pleosporales were the most common keystone species in the root fungal network. This is the first report of mycobiota in both plant species and organ differentiation in an extremely arid desert ecosystem.

With the development of the vehicular network, new radio technologies have been in the spotlight for maximizing the utilization of the limited radio spectrum resource while accommodating the increasing amount of services and applications in the wireless mobile networks. New spectrum policies based on dynamic spectrum access technology such as flexible access common spectrum (FACS) have been adopted by the Korea Communications Commission (KCC). 23 GHz bands have been allocated to FACS bands by the KCC, which is expected extensively for vehicular communications. The comprehensive knowledge on the radio channel is essential to effectively support the design, simulation, and development of such radio technologies. In this paper, the characteristics of 23 GHz vehicle-to-infrastructure (V2I) channels are simulated and extracted for the urban environment in Seoul. The path loss, shadow factor, Ricean K-factor, root-mean-square (RMS) delay spread, and angular spreads are characterized from the calibrated ray-tracing simulation results, and it can help researchers have a better understanding of the propagation channel for designing vehicular radio technologies and a communication system in a similar environment.

Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.

The title molecule, C16H12ClNO, has a V-shaped conformation and the dihedral angle between the planes of the phenyl and benzene rings of 64.6 (1)°. No directional intermolecular interactions could be identified in the crystal.

The validity of the maximum capacity criterion applied to realize high-rank line-of-sight (LoS) multiple-input multiple-output (MIMO) channels is investigated for high speed railway scenarios. Performance is evaluated by ergodic capacity. Numerical results demonstrate that by simply adjusting antenna spacing according to the maximum capacity criterion, significant capacity gains are achievable. We find relatively low sensitivity of the system to displacements from the optimal point and angle in relatively short range. Thus, we present two proposals to reconfigure antenna arrays so as to maximize LoS MIMO capacity in the high speed railway scenarios

The structure of the title compound, C16H12N2O3, contains two aromatic rings bridged by a C3 chain. The dihedral angle between the rings is 67.6 (1)°. No classical hydrogen bonds are not found in the crystal structure.