Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.

MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. RESULTS: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise's true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles. AVAILABILITYAND IMPLEMENTATION: The code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional identity drives tree species richness-induced increases in litterfall production and forest floor mass in young tree communities.

Forest floor accumulation is a key process that influences ecosystem carbon cycling. Despite evidence suggesting that tree diversity and soil carbon are positively correlated, most soil carbon studies typically omit the response of the forest floor carbon to tree diversity loss. Here, we evaluated how tree species richness affects forest floor mass and how this effect is mediated by litterfall production and forest floor decay rate in a tree diversity experiment in a subtropical forest. We observed that greater tree species richness leads to higher forest floor accumulation at the soil surface through increasing litterfall production - positively linked to functional trait identity (i.e. community-weighted mean functional trait) rather than functional diversity - and unchanged forest floor decay. Interestingly, structural equation modelling revealed that this lack of overall significant tree species richness effect on forest floor decay rate was due to two indirect and opposite effects cancelling each other out. Indeed, tree species richness increased forest floor decay rate through increasing litterfall production while decreasing forest floor decay rate by increasing litter species richness. Our reports of greater organic matter accumulation in the forest floor in species-rich forests suggest that tree diversity may have long-term and important effect on ecosystem carbon cycling and services.

Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks.

BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells and tissues. In addition, due to the complex and crowded cellular environments as well as various types and sizes of proteins, a considerable number of protein images are generated every day and cannot be classified manually. Therefore, an automatic and accurate method should be designed to properly solve and analyse protein images with mixed patterns. RESULTS: In this paper, we first propose a novel customized architecture with adaptive concatenate pooling and "buffering" layers in the classifier part, which could make the networks more adaptive to training and testing datasets, and develop a novel hard sampler at the end of our network to effectively mine the samples from small classes. Furthermore, a new loss is presented to handle the label imbalance based on the effectiveness of samples. In addition, in our method, several novel and effective optimization strategies are adopted to solve the difficult training-time optimization problem and further increase the accuracy by post-processing. CONCLUSION: Our methods outperformed the SOTA method of multi-labelled protein classification on the HPA dataset, GapNet-PL, by above 2% in the F1 score. Therefore, experimental results based on the test set split from the Human Protein Atlas dataset show that our methods have good performance in automatically classifying multi-class and multi-labelled high-throughput microscopy protein images.

Analysis of codon usage patterns in citrus based on coding sequence data.

BACKGROUND: Codon usage is an important determinant of gene expression levels that can help us understand codon biology, evolution and mRNA translation of species. The majority of previous codon usage studies have focused on single species analysis, although few studies have focused on the species within the same genus. In this study, we proposed a multispecies codon usage analysis workflow to reveal the genetic features and correlation in citrus. RESULTS: Our codon usage analysis workflow was based on the GC content, GC plot, and relative synonymous codon usage value of each codon in 8 citrus species. This approach allows for the comparison of codon usage bias of different citrus species. Next, we performed cluster analysis and obtained an overview of the relationship in citrus. However, traditional methods cannot conduct quantitative analysis of the correlation. To further estimate the correlation among the citrus species, we used the frequency profile to construct feature vectors of each species. The Pearson correlation coefficient was used to quantitatively analyze the distance among the citrus species. This result was consistent with the cluster analysis. CONCLUSIONS: Our findings showed that the citrus species are conserved at the genetic level and demonstrated the existing genetic evolutionary relationship in citrus. This work provides new insights into codon biology and the evolution of citrus and other plant species.

AuTom-dualx: a toolkit for fully automatic fiducial marker-based alignment of dual-axis tilt series with simultaneous reconstruction.

Motivation: Dual-axis electron tomography is an important 3 D macro-molecular structure reconstruction technology, which can reduce artifacts and suppress the effect of missing wedge. However, the fully automatic data process for dual-axis electron tomography still remains a challenge due to three difficulties: (i) how to track the mass of fiducial markers automatically; (ii) how to integrate the information from the two different tilt series; and (iii) how to cope with the inconsistency between the two different tilt series. Results: Here we develop a toolkit for fully automatic alignment of dual-axis electron tomography, with a simultaneous reconstruction procedure. The proposed toolkit and its workflow carries out the following solutions: (i) fully automatic detection and tracking of fiducial markers under large-field datasets; (ii) automatic combination of two different tilt series and global calibration of projection parameters; and (iii) inconsistency correction based on distortion correction parameters and the consequently simultaneous reconstruction. With all of these features, the presented toolkit can achieve accurate alignment and reconstruction simultaneously and conveniently under a single global coordinate system. Availability and implementation: The toolkit AuTom-dualx (alignment module dualxmauto and reconstruction module volrec_mltm) are accessible for general application at http://ear.ict.ac.cn, and the key source code is freely available under request. Supplementary information: Supplementary data are available at Bioinformatics online.

AuTom: A novel automatic platform for electron tomography reconstruction.

We have developed a software package towards automatic electron tomography (ET): Automatic Tomography (AuTom). The presented package has the following characteristics: accurate alignment modules for marker-free datasets containing substantial biological structures; fully automatic alignment modules for datasets with fiducial markers; wide coverage of reconstruction methods including a new iterative method based on the compressed-sensing theory that suppresses the "missing wedge" effect; and multi-platform acceleration solutions that support faster iterative algebraic reconstruction. AuTom aims to achieve fully automatic alignment and reconstruction for electron tomography and has already been successful for a variety of datasets. AuTom also offers user-friendly interface and auxiliary designs for file management and workflow management, in which fiducial marker-based datasets and marker-free datasets are addressed with totally different subprocesses. With all of these features, AuTom can serve as a convenient and effective tool for processing in electron tomography.

Electron tomography simulator with realistic 3D phantom for evaluation of acquisition, alignment and reconstruction methods.

Because of the significance of electron microscope tomography in the investigation of biological structure at nanometer scales, ongoing improvement efforts have been continuous over recent years. This is particularly true in the case of software developments. Nevertheless, verification of improvements delivered by new algorithms and software remains difficult. Current analysis tools do not provide adaptable and consistent methods for quality assessment. This is particularly true with images of biological samples, due to image complexity, variability, low contrast and noise. We report an electron tomography (ET) simulator with accurate ray optics modeling of image formation that includes curvilinear trajectories through the sample, warping of the sample and noise. As a demonstration of the utility of our approach, we have concentrated on providing verification of the class of reconstruction methods applicable to wide field images of stained plastic-embedded samples. Accordingly, we have also constructed digital phantoms derived from serial block face scanning electron microscope images. These phantoms are also easily modified to include alignment features to test alignment algorithms. The combination of more realistic phantoms with more faithful simulations facilitates objective comparison of acquisition parameters, alignment and reconstruction algorithms and their range of applicability. With proper phantoms, this approach can also be modified to include more complex optical models, including distance-dependent blurring and phase contrast functions, such as may occur in cryotomography.

A marker-free automatic alignment method based on scale-invariant features.

In electron tomography, alignment accuracy is critical for high-resolution reconstruction. However, the automatic alignment of a tilt series without fiducial markers remains a challenge. Here, we propose a new alignment method based on Scale-Invariant Feature Transform (SIFT) for marker-free alignment. The method covers the detection and localization of interest points (features), feature matching, feature tracking and optimization of projection parameters. The proposed method implements a highly reliable matching strategy and tracking model to detect a huge number of feature tracks. Furthermore, an incremental bundle adjustment method is devised to tolerate noise data and ensure the accurate estimation of projection parameters. Our method was evaluated with a number of experimental data, and the results exhibit an improved alignment accuracy comparable with current fiducial marker alignment and subsequent higher resolution of tomography.

Markerauto2: A fast and robust fully automatic fiducial marker-based tilt series alignment software for electron tomography.

Cryoelectron tomography (cryo-ET) has become an indispensable technology for visualizing in situ biological ultrastructures, where the tilt series alignment is the key step to obtain a high-resolution three-dimensional reconstruction. Specifically, with the advent of high-throughput cryo-ET data collection, there is an increasing demand for high-accuracy and fully automatic tilt series alignment, to enable efficient data processing. Here, we propose Markerauto2, a fast and robust fully automatic software that enables accurate fiducial marker-based tilt series alignment. Markerauto2 implements the following novel pipelines: (1) an accelerated high-precision fiducial marker detection with wavelet multiscale template, (2) an ultra-fast and robust fiducial marker tracking supported by hashed geometric features, (3) a high-angle fiducial marker supplementation strategy to produce more complete tracks, and (4) a precise and robust calibration of projection parameters with group-weighted parameter optimization. Comprehensive experiments conducted on both simulated and real-world datasets demonstrate the robustness, efficiency, and effectiveness of the proposed software.

Human endogenous retroviral K element encodes fusogenic activity in melanoma cells.

INTRODUCTION AND HYPOTHESIS: Nuclear atypia with features of multi nuclei have been detected in human melanoma specimens. We found that the K type human endogenous retroviral element (HERV K) is expressed in such cells. Since cellular syncytia can form when cells are infected with retroviruses, we hypothesized that HERV K expressed in melanoma cells may contribute to the formation of multinuclear atypia cells in melanoma. EXPERIMENTS AND RESULTS: We specifically inhibited HERV K expression using RNA interference (RNAi) and monoclonal antibodies and observed dramatic reduction of intercellular fusion of cultured melanoma cells. Importantly, we identified loss of heterozygosity (LOH)of D19S433 in a cell clone that survived and proliferated after cell fusion. CONCLUSION: Our results support the notion that proteins encoded by HERV K can mediate intercellular fusion of melanoma cells, which may generate multinuclear cells and drive the evolution of genetic changes that provide growth and survival advantages.

Determining protein structures in cellular lamella at pseudo-atomic resolution by GisSPA.

Cryo-electron tomography is a major tool used to study the structure of protein complexes in situ. However, the throughput of tilt-series image data collection is still quite low. Here, we show that GisSPA, a GPU accelerated program, can translationally and rotationally localize the target protein complex in cellular lamellae, as prepared with a focused ion beam, using single cryo-electron microscopy images without tilt-series, and reconstruct the protein complex at near-atomic resolution. GisSPA allows high-throughput data collection without the acquisition of tilt-series images and reconstruction of the tomogram, which is essential for high-resolution reconstruction of asymmetric or low-symmetry protein complexes. We demonstrate the power of GisSPA with 3.4-Å and 3.9-Å resolutions of resolving phycobilisome and tetrameric photosystem II complex structures in cellular lamellae, respectively. In this work, we present GisSPA as a practical tool that facilitates high-resolution in situ protein structure determination.

Litter decomposition and nutrient release are faster under secondary forests than under Chinese fir plantations with forest development.

In terrestrial ecosystems, leaf litter is the main source of nutrients returning to the soil. Understanding how litter decomposition responds to stand age is critical for improving predictions of the effects of forest age structure on nutrient availability and cycling in ecosystems. However, the changes in this critical process with stand age remain poorly understood due to the complexity and diversity of litter decomposition patterns and drivers among different stand ages. In this study, we examined the effects of stand age on litter decomposition with two well-replicated age sequences of naturally occurring secondary forests and Chinese fir (Cunninghamia lanceolata) plantations in southern China. Our results showed that the litter decomposition rates in the secondary forests were significantly higher than those in the Chinese fir plantations of the same age, except for 40-year-old forests. The litter decomposition rate of the Chinese fir initially increased and then decreased with stand age, while that of secondary forests gradually decreased. The results of a structural equation model indicated that stand age, litter quality and microbial community were the primary factors driving nutrient litter loss. Overall, these findings are helpful for understanding the effects of stand age on the litter decomposition process and nutrient cycling in plantation and secondary forest ecosystems.

Linkages of soil CO2 emission with plant functional traits in young subtropical plantations.

Soil respiration is a key process in forest biogeochemical cycling. Exploring the relationship between plant functional traits and soil respiration can help understand the effects of tree species conversion on soil carbon cycling. In this study, we selected 15 common subtropical tree species planted in the logging site of second-generation Chinese fir forest to measure soil CO2 emission fluxes, soil physicochemical properties, leaf and root functional traits of each species, and explored the effects of plant functional traits on soil respiration. The results showed that the annual flux of soil CO2 emissions varied from 7.93 to 22.52 Mg CO2·hm-2, with the highest value under Castanopsis carlesii (22.52 Mg CO2·hm-2) and the lowest value under Taxus wallichiana (7.93 Mg CO2·hm-2). Results of stepwise regression analysis showed that the annual flux of soil CO2 emission decreased with the increases of leaf nitrogen content and fine root diameter, and increased with increasing leaf non-structural carbohydrate. In the structural equation model, leaf non-structural carbohydrate had a direct and significant positive effect on soil CO2 emission fluxes, while leaf nitrogen content and fine root diameter had a direct negative effect by decreasing soil pH and soluble organic nitrogen content. Plantations of different tree species would affect soil CO2 emission directly by changing functional traits related to water and nutrient acquisition or indirectly through soil properties. When creating plantations, we should select tree species based on the relationship between plant functional traits and ecosystem functions, with a view to improving forest productivity and soil carbon sequestration potential.

Phosphorus limitation induced by nitrogen addition changed soil microbial community structure in a subtropical Pinus taiwanensis forest.

Soil microorganisms play an important role in the biogeochemical cycles of terrestrial ecosystems. How-ever, it is still unclear how the amount and duration of nitrogen (N) addition affect soil microbial community structure and whether there is a correlation between the changes in microbial community structure and their nutrient limi-tation status. In this study, we conducted an N addition experiment in a subtropical Pinus taiwanensis forest to simulate N deposition with three treatments: control (CK, 0 kg N·hm-2·a-1), low N (LN, 40 kg N·hm-2·a-1), and high N (HN, 80 kg N·hm-2·a-1). Basic soil physicochemical properties, phospholipid fatty acids content, and carbon (C), N and phosphorus (P) acquisition enzyme activities were measured after one and three years of N addition. The relative nutrient limitation status of soil microorganisms was analyzed using ecological enzyme stoichiometry. The results showed that one-year N addition did not affect soil microbial community structure. Three-year LN treatment significantly increased the contents of Gram-positive bacteria (G+), Gram-negative bacteria (G-), actinomycetes (ACT), and total phospholipid fatty acids (TPLFA), whereas three-year HN treatment did not significantly affect soil microbial community, indicating that bacteria and ACT might be more sensitive to N addition. Nitrogen addition exacerbated soil C and P limitation. Phosphorus limitation was the optimal explanatory factor for the changes in soil microbial community structure. It suggested that P limitation induced by N addition might be more beneficial for the growth of certain oligotrophic bacteria (e.g. G+) and the microorganisms participating in the P cycling (e.g. ACT), with consequences on soil microbial community structure of subtropical Pinus taiwanensis forest.

High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs.

BACKGROUND: Three-dimensional (3D) reconstruction in electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction in ET, but demand huge computational costs. Multiple graphic processing units (multi-GPUs) offer an affordable platform to meet these demands. However, a synchronous communication scheme between multi-GPUs leads to idle GPU time, and a weighted matrix involved in iterative methods cannot be loaded into GPUs especially for large images due to the limited available memory of GPUs. RESULTS: In this paper we propose a multilevel parallel strategy combined with an asynchronous communication scheme and a blob-ELLR data structure to efficiently perform blob-based iterative reconstructions on multi-GPUs. The asynchronous communication scheme is used to minimize the idle GPU time so as to asynchronously overlap communications with computations. The blob-ELLR data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELLR) data structure and yields significant acceleration. CONCLUSIONS: Experimental results indicate that the multilevel parallel scheme combined with the asynchronous communication scheme and the blob-ELLR data structure allows efficient implementations of 3D reconstruction in ET on multi-GPUs.

The analysis of second-trimester triple screening for Down syndrome in Chinese normal singleton pregnancies.

BACKGROUND AND OBJECTIVES: The study aimed to compare the α fetoprotein (AFP), total ß-human chorionic gonadotropin (hCG), and unconjugated estriol (uE3) levels in the second-trimester triple screening for Down syndrome with different regions, and to analyse the related factors that influenced the screening performance. MATERIALS AND METHODS: The study was conducted between February 2007 and November 2010 in Beijing Tongren Hospital, P. R. China. Data derived from 7,647 normal singleton pregnancies between 14 and 21 weeks of gestation were examined. The regressed median values in different gestational ages were compared with earlier published data from other regions. The distribution of median values and multiples of median (MoM) of AFP, hCG and uE3 according to maternal age and weight in normal pregnancies were described. Statistic parameters for AFP, hCG, and uE3 (based on log(10) MoM values) were compared with earlier published data from other studies. RESULTS: There were significantly increasing trends for AFP (p < 0.001) and uE3 (p < 0.001), and a significantly decreasing trend for hCG (p < 0.001) in the second trimester. There were significantly decreasing trends with increasing maternal weight for all the markers and their MoMs (p < 0.001). The distribution of the log(10) MoM marker values were Gaussian for the three parameters. CONCLUSIONS: Ethnic and laboratory variance should be taken into account in the second trimester triple screening for Down syndrome. The parameters of maternal serum markers should be calculated using local data, and the algorithm modified to match the screening achievable for the local population.

VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms.

BACKGROUND AND OBJECTIVE: Cryo-electron tomography (cryo-ET) with subtomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments. Due to the low signal-to-noise ratio, the missing wedge artifacts in tomographic reconstructions, and multiple macromolecules of varied shapes and sizes, macromolecule localization and classification remain challenging. To tackle this bottleneck problem for structural determination by STA, we design an accurate macromolecule localization and classification method named voxelwise particle detector (VP-Detector). METHODS: VP-Detector is a two-stage particle detection method based on a 3D multiscale dense convolutional neural network (3D MSDNet). The proposed network uses 3D hybrid dilated convolution (3D HDC) to avoid the resolution loss caused by scaling operations. Meanwhile, it uses 3D dense connectivity to encourage the reuse of feature maps to reduce trainable parameters. In addition, the weighted focal loss is proposed to focus more attention on difficult samples and rare classes, which relieves the class imbalance caused by multiple particles of various sizes. The performance of VP-Detector is evaluated on both simulated and real-world tomograms, and it shows that VP-Detector outperforms state-of-the-art methods. RESULTS: The experiments show that VP-Detector outperforms the state-of-the-art methods on particle localization with an F1-score of 0.951 and a precision of 0.978. In addition, VP-Detector can replace manual particle picking in experiment on the real-world tomograms. Furthermore, it performs well in classifying large-, medium-, and small-weight proteins with accuracies of 1, 0.95, and 0.82, respectively. Finally, ablation studies demonstrate the effectiveness of 3D HDC, 3D dense connectivity, weighted focal loss, and training on small training sets. CONCLUSIONS: VP-Detector can achieve high accuracy in particle detection with few trainable parameters and support training on small datasets. It can also relieve the class imbalance caused by multiple particles with various shapes and sizes.

A Transfer Learning-Based Classification Model for Particle Pruning in Cryo-Electron Microscopy.

The cryo-electron microscopy (cryo-EM) single-particle analysis requires tens of thousands of particle projections to reveal structural information of macromolecular complexes. However, due to the low signal-to-noise ratio and the presence of high contrast artifacts and contaminants in the micrographs, the semiautomatic and fully automatic particle picking algorithms tend to suffer from high false-positive rates, which degrades the confidence of structure determination. In this study, we introduce PickerOptimizer (PO), a transfer learning-based classification neural network for particle pruning in cryo-EM, as an additional strategy to complement the current automated particle picking algorithms. To achieve high classification performance with minimal human intervention, we adopted two key strategies: (1) utilizing the transfer learning techniques to train the convolutional neural network, where the knowledge gained from public classification datasets is applied to the field of cryo-EM. (2) Designing a multiloss strategy, a combination of multiple loss functions, to guide the optimization of the network parameters. To reduce the domain shift between cryo-EM images and natural images for pretraining, we build the first image classification dataset for cryo-EM, which contains positive and negative samples collected from EMPIAR entries. The PO is tested on 14 public experimental datasets, achieving accuracy and F1 scores above 95% in most cases. Furthermore, three case studies are provided to verify the model performance by applying PO on problematic particle selections, showing that our algorithm achieved better or comparable performance compared with other particle pruning strategies.

Three-dimensional reconstruction using an adaptive simultaneous algebraic reconstruction technique in electron tomography.

Three-dimensional (3D) reconstruction of electron tomography (ET) has emerged as an important technique in analyzing structures of complex biological samples. However most of existing reconstruction methods are not suitable for extremely noisy and incomplete data conditions. We present an adaptive simultaneous algebraic reconstruction technique (ASART) in which a modified multilevel access scheme and an adaptive relaxation parameter adjustment method are developed to improve the quality of the reconstructed 3D structure. The reconstruction process is facilitated by using a column-sum substitution approach. This modified multilevel access scheme is adopted to arrange the order of projections so as to minimize the correlations between consecutive views within a limited angle range. In the adaptive relaxation parameter adjustment method, not only the weight matrix (as in the existing methods) but the gray levels of the pixels are employed to adjust the relaxation parameters so that the quality of the reconstruction is improved while the convergence process of the reconstruction is accelerated. In the column-sum substitution approach, the computation to obtain the reciprocal of the sum for the columns in each view is avoided so that the needed computations for each iteration can be reduced. Experimental results show that the proposed technique ASART is better based on objective quality measures than other methods, especially when data is noisy and limited in tilt angles. At the same time, the reconstruction by ASART outperforms that of simultaneous algebraic reconstruction technique (SART) in speed.

Golgi protein 73(GP73), a useful serum marker in liver diseases.

BACKGROUND: This study was performed to quantify the expression of Golgi protein-73 (GP73) in healthy controls and in patients with liver disease, and to evaluate the correlations between GP73 and other serum markers in different liver diseases. METHODS: Serum GP73 was measured in 478 healthy controls and 296 patients with different types of liver disease. Quantitative hepatitis B virus (HBV) DNA was determined in two chronic hepatitis B (CHB) groups. Other serum liver fibrosis markers were measured in the liver fibrosis group and α-fetoprotein (AFP) was measured in hepatocellular carcinoma (HCC) group. The correlations between GP73 and these markers were evaluated. RESULTS: The GP73 value in hepatitis B e antigen (HBeAg)-positive CHB group, HBeAg-negative CHB group, liver fibrosis group and HCC group was significantly higher (p<0.001) than that in healthy controls. GP73 showed significant correlation with other markers in the liver fibrosis group and with AFP in the HCC group. CONCLUSIONS: Compared with healthy controls, GP73 in patients with liver disease was significantly increased. With the progression of liver disease, GP73 showed a significantly increasing trend. These results suggest that GP73 might be used as a serum marker for the diagnosis of liver diseases and for monitoring disease progression.