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.

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.

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.

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.

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.

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.

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.

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.

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.

Differences in Peak Oxygen Uptake in Bicycle Exercise Test Caused by Body Positions: A Meta-Analysis.

Background: As demand for cardiopulmonary exercise test using a supine position has increased, so have the testing options. However, it remains uncertain whether the existing evaluation criteria for the upright position are suitable for the supine position. The purpose of this meta-analysis is to compare the differences in peak oxygen uptake (VO2peak) between upright and supine lower extremity bicycle exercise. Methods: We searched PubMed, Web Of Science and Embase from inception to March 27, 2021. Self-control studies comparing VO2peak between upright and supine were included. The quality of the included studies was assessed using a checklist adapted from published papers in this field. The effect of posture on VO2peak was pooled using random/fixed effects model. Results: This meta-analysis included 32 self-control studies, involving 546 participants (63% were male). 21 studies included only healthy people, 9 studies included patients with cardiopulmonary disease, and 2 studies included both the healthy and cardiopulmonary patients. In terms of study quality, most of the studies (n = 21, 66%) describe the exercise protocol, and we judged theVO2peak to be valid in 26 (81%) studies. Meta-analysis showed that the upright VO2peak exceeded the supine VO2peak [relative VO2peak: mean difference (MD) 2.63 ml/kg/min, 95% confidence interval (CI) 1.66-3.59, I 2 = 56%, p < 0.05; absolute VO2peak: MD 0.18 L/min, 95% CI 0.10-0.26, I 2 = 63%, p < 0.05). Moreover, subgroup analysis showed there was more pooled difference in healthy people (4.04 ml/kg/min or 0.22 L/min) than in cardiopulmonary patients (1.03 ml/kg/min or 0.12 L/min). Conclusion: VO2peak in the upright position is higher than that in supine position. However, whether this difference has clinical significance needs further verification. Systematic Review Registration: identifier, CRD42021233468.

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.

[Effects of root growth on leaf litter decomposition and enzyme activity in litter layer]. / æ ¹ç³»åœ¨å‡‹è½ç‰©å±‚ç”Ÿé•¿å¯¹å‡‹è½å¶åˆ†è§£åŠé ¶æ´»æ€§çš„å½±å“.

The growth of roots towards aboveground litter layer is a common phenomenon in forest ecosystems. It is of great significance to examine the effects of root presence on litter decomposition for understanding nutrient cycling in forest ecosystems. We explored the effects of root growth on leaf litter decomposition, nutrient release and enzyme activities by establishing treatments with and without root with a one year field decomposition experiment in Phoebe zhennan and Castanopsis kawada-mii forests at Sanming, Fujian. The results showed that after 360 days decomposition, leaf litter mass remaining ratio in the treatment with root was 8.4% and 19.7% lower than control, respectively. The presence of root exhibited significant effect on litter decomposition during the 90-180 days. Compared with the control, the remaining ratio of leaf litter carbon, nitrogen and phosphorus were 6.0%, 19.1% and 20.6% lower in the treatment with root in the P. zhennan forest, and were 21.3%, 23.2% and 20.5% lower in the C. kawadamii forest, respectively. During the whole decomposition process, root presence did not affect the hydrolytic enzyme activity. After 180 days decomposition, the peroxidase activities in the treatment with root were 111.4% and 92.4% higher than control in the P. zhennan and C. kawadamii forests, respectively. The remaining ratio of leaf litter carbon, nitrogen and phosphorus were negatively correlated with the activities of cellobiohydrolase, ß-glucosidase, acid phosphatase, and peroxidase. Root presence in litter layer could accelerate litter decomposition and nutrient release through nutrient uptake and stimulation of oxidase activity.

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.

[Soil C:N:P stoichiometry and nutrient dynamics in Cunninghamia lanceolata plantations during different growth stages]. / 不同生长阶段杉木人工林土壤C∶N∶PåŒ–å­¦è®¡é‡ç‰¹å¾ä¸Žå »åˆ†åŠ¨æ€.

We investigated soil C:N:P stoichiometry and nutrient dynamics of Cunninghamia lanceolata plantations at different stand ages (5, 8, 21, 27 and 40 years old) in Fujian Baisha Fores-try Farm. We measured the concentrations of soil total carbon (TC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), total calcium (Ca), total magnesium (Mg), and soil C:N:P stoichiometry at 0-10, 10-20, and 20-40 cm soil layers during different growth stages. The results showed that soil TC and TN concentrations and C:N remained unchanged during stand development. Soil TP content showed an increase-decrease-increase trend with increasing stand ages. Soil TP content was lowest, whereas C:P and N:P were highest at the mature stage of C. lanceolate plantation in the 0-10 and 10-20 cm soil layers. However, soil TP content showed no significant differences in all stand ages at the 20-40 cm soil layer. The contents of Ca and Mg were lowest at the mature stage of C. lanceolata stand. The TC was positively correlated with soil C:N, C:P and N:P. The TP was significantly and negatively correlated with soil C:P and N:P. Soil TP was a key factor regulating soil C:P and N:P stoichiometry. The development of mature plantation was mainly limited by soil P availability. To sustain the development of C. lanceolata plantations and improve nutrient cycling, phosphorus fertilizer could be applied during the rapid growth period of C. lanceolata. In addition, an appropriate extension of the rotation period of C. lanceolata plantation could facilitate soil nutrient restoration.

[Effects of broadleaved tree species on soil microbial stoichiometry in clear-cut patches of Cunninghamia lanceolata plantation]. / æ‰æœ¨é‡‡ä¼è¿¹åœ°è¥é€ é˜”å¶æ ‘ç§å¯¹åœŸå£¤å¾®ç”Ÿç‰©ç”Ÿæ€åŒ–å­¦è®¡é‡ç‰¹å¾çš„å½±å“.

Investigating the response of soil microbial biomass and ecological stoichiometry to tree species transition is of great significance for understanding soil nutrient cycling and availability in forest ecosystems. We measured soil microbial biomass carbon (MBC), nitrogen (MBN), phosphorus (MBP) and their stoichiometry across 0-40 cm soil depth between Mytilaria laosensis and Cunninghamia lanceolata plantations by the chloroform fumigation extraction method, which were replanted after the harvest of C. lanceolata plantation. The results showed that soil MBC in the 0-10 cm layer and soil MBN and MBP in the 0-20 cm layer under the M. laosensis were significantly higher than those under the C. lanceolata. The MBC/MBP in the 0-20 cm layer and MBN/MBP in the 10-20 cm layer were significantly lower under the M. laosensis plantation. The MBC/MBN showed no significant differences between the two forests. Soil moisture, organic carbon, total nitrogen, total phosphorus, available phosphorus were positively correlated with MBC, MBN and MBP, but negatively correlated with MBC/MBP and MBN/MBP. Results of stepwise linear regression analysis showed that MBN and MBP were mainly affected by soil total nitrogen and available phosphorus, while MBC/MBP and MBN/MBP were mainly driven by available phosphorus and organic carbon, respectively. Our results indicated that tree species transition from C. lanceolata to M. laosensis could increase soil microbial biomass in the surface layers, accelerate soil nutrients turnover and enhance soil nutrient supply. The increases of MBP under M. laosensis indicate alleviation of soil phosphorus limitation for tree growth.

A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field.

A complete and detailed cerebrovascular image segmented from time-of-flight magnetic resonance angiography (TOF-MRA) data is essential for the diagnosis and therapy of the cerebrovascular diseases. In recent years, three-dimensional cerebrovascular segmentation algorithms based on statistical models have been widely used, but the existed methods always perform poorly on stenotic vessels and are not robust enough. In this paper, we propose a parallel cerebrovascular segmentation algorithm based on focused multi-Gaussians model and heterogeneous Markov random field. Specifically, we present a focused multi-Gaussians (FMG) model with local fitting region to model the vascular tissue more accurately and introduce the chaotic oscillation particle swarm optimization (CO-PSO) algorithm to improve the global optimization capability in the parameter estimation. Furthermore, we design a heterogeneous Markov Random Field (MRF) in the three-dimensional neighborhood system to incorporate precise local character of image. Finally, the algorithm has been performed parallel optimization based on GPUs and obtain about 60 times speedup compared to serial execution. The experiments show that the proposed algorithm can produce more detailed segmentation result in shorter time and performs well on the stenotic vessels robustly.

[Effects of broadleaved tree plantation on soil phosphorus fractions and availability in diffe-rent soil layers in a logged Cunninghamia lanceolata woodland]. / æ‰æœ¨é‡‡ä¼è¿¹åœ°è¥é€ é˜”å¶æ ‘å¯¹ä¸åŒå±‚æ¬¡åœŸå£¤ç£·ç»„åˆ†å’Œæœ‰æ•ˆæ€§çš„å½±å“.

Phosphorus (P) limitation is one of the major issues for the management of subtropical plantations. Understanding the effects of tree species transition from conifer to broadleaved trees on soil P fraction and availability in different soil layers are of great significance for the sustainable development of subtropical forests. We compared changes in soil chemical properties, P fraction and availability across 0-100 cm soil profile between Mytilaria laosensis and Cunninghamia lanceolata plantations, which were initially reforested from C. lanceolata plantation in the spring of 1993. The results showed that soil organic P content in both plantations decreased significantly with soil depth. Compared with C. lanceolata, the M. laosensis plantation significantly increased soil available P content by 35.7% and 86.2% in the 0-10 and 10-20 cm, respectively. The contents of soil labile P and moderately labile P decreased significantly with soil depth in both plantations. The contents of labile P and moderately labile P were significantly higher in the surface soil (0-20 cm), while the non-labile P in the 80-100 cm was increased by 13.6%, and the free iron content in the 20-80 cm significantly decreased. Results of redundancy analysis showed that dissolved organic carbon and free iron were the most important factors influencing P fraction in those plantations. Tree species transition from C. lanceolata to M. laosensis could change the pattern of soil P fraction in soil profile, and greatly enhance soil P availability.

A Consensus Framework of Distributed Multiple-Tilt Reconstruction in Electron Tomography.

The "missing wedge" of a single tilt in electron tomography introduces severe artifacts into the reconstructed results. To reduce the "missing wedge" effect, a widely used method is "multiple-tilt reconstruction," which collects projections using multiple axes. However, as the number of tilt series increases, the computing and memory costs also rise. The degree of parallelism is limited by the sample thickness, and a large memory requirement cannot be met by most multicore computers. In our study, we present a new fully distributed multiple-tilt simultaneous iterative reconstruction technique (DM-SIRT). To improve the parallelism of the reconstruction process and reduce the memory requirements of each process, we formulate the multiple-tilt reconstruction as a consensus optimization problem and design a DM-SIRT algorithm. Experiments show that in addition to slightly better resolution, DM-SIRT can obtain a 13.9 × accelerated ratio compared with the full multiple-tilt reconstruction version. It also has a 97% decrease in memory overhead and is 16 times more scalable than the full reconstruction version.