Jasmonate mimic modulates cell elongation by regulating antagonistic bHLH transcription factors via brassinosteroid signaling.

Lodging restricts growth, development, and yield formation in maize (Zea mays L.). Shorter internode length is beneficial for lodging tolerance. However, although brassinosteroids (BRs) and jasmonic acid (JA) are known to antagonistically regulate internode growth, the underlying molecular mechanism is still unclear. In this study, application of the JA mimic coronatine (COR) inhibited basal internode elongation at the jointing stage and repressed expression of the cell wall-related gene XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE 1 (ZmXTH1), whose overexpression in maize plants promoted internode elongation. We demonstrated that the basic helix-loop-helix (bHLH) transcription factor ZmbHLH154 directly binds to the ZmXTH1 promoter and induces its expression, whereas the bHLH transcription factor ILI1 BINDING BHLH 1 (ZmIBH1) inhibits this transcriptional activation by forming a heterodimer with ZmbHLH154. Overexpressing ZmbHLH154 led to longer internodes, whereas zmbhlh154 mutants had shorter internodes than the wild type. The core JA-dependent transcription factors ZmMYC2-4 and ZmMYC2-6 interacted with BRASSINAZOLE RESISTANT 1 (ZmBZR1), a key factor in BR signaling, and these interactions eliminated the inhibitory effect of ZmBZR1 on its downstream gene ZmIBH1. Collectively, these results reveal a signaling module in which JA regulates a bHLH network by attenuating BR signaling to inhibit ZmXTH1 expression, thereby regulating cell elongation in maize.

Color image guided depth image reconstruction based on a total variation network.

A representative method to improve a depth image is to use an aligned high-quality color image to guide the depth image by migrating the color details to the depth image. In the process of color-guided depth reconstruction, there often is a misalignment of the edge of the color image used to guide the depth image reconstruction and the depth discontinuity of the depth image. This makes the results suffer from texture copy artifacts and blurring depth discontinuities. In this paper, we use a total variation deep network founded on deep learning and high-resolution color images. The experimental result indicates that under the guidance of high-resolution colors, the depth image recovered is closest to the ground truth in the edge contour, the PSNR and FSIM index are suboptimal for 64×, and the contour and position information recovered from the reconstructed depth image can be retained in the very low-resolution depth image.

Medical image segmentation of gastric adenocarcinoma based on dense connection of residuals.

BACKGROUND AND OBJECTIVE: Accurate segmentation of gastric cancer based on CT images of gastric adenocarcinoma is crucial for physicians to screen gastric diseases, clinical diagnosis, preoperative prediction, and postoperative evaluation plans. To address the issue of the inability of the segmentation algorithm to depict the correct boundaries due to unclear gastric contours in the lesion area and the visible irregular band-like dense shadow extending to the perigastric region, a 3D medical image segmentation model 3D UNet based on residual dense jumping method is proposed. METHODS: In the method we proposed, Residual Dense Block, which is applied to the image super-resolution module to remove CT artifacts, and Residual Block in ResNet are further fused. The quality of CT images is improved by Residual Dense Skip Block, which removes banded dense shadows, preserves image details and edge information, captures features, and improves the segmentation performance of gastric adenocarcinoma. The Instance Normalization layer position is modified to select the best result. Different loss functions are also combined in order to obtain the best gastric adenocarcinoma segmentation performance. RESULTS: We tested the model on a hospital-provided gastric adenocarcinoma dataset. The experimental results show that our model outperforms the existing methods in CT gastric adenocarcinoma segmentation, in which the method combining the hybrid loss function of Dice and CE obtains an average dice score of 82.3%, which is improved by 5.3% and 3.8% compared to TransUNet and Hiformer, respectively, and improves the cross-merge rate to 70.8%, compared to nnFormer, nnUNet by 1% and 0.9%, respectively. CONCLUSIONS: The residual jump connection structure indeed improves segmentation performance. The proposed method has the potential to be used as a screen for gastric diseases and to assist physicians in diagnosis.

Single-photon 3D imaging with a multi-stage network.

Active single-photon 3D imaging technology has been applied to 3D imaging of complex scenes in many frontier fields such as biomedicine, remote sensing mapping, etc. However, single-photon 3D imaging with strong background noise is still a major challenge. Several classical algorithms and machine learning methods have been proposed to solve the problem. In this paper, we propose a novel multi-stage synergistic recovery network to reconstruct an accurate depth map. In the model, we first extract multi-scale feature information using encoder and decoder architectures, then combine them with an original resolution network that retains complete spatial location information. Through this way, we can compensate the deficiencies of the original resolution network for multi-scale local feature extraction. Moreover, a self-supervised attention module (SAM) is constructed to weight local features between different stages, optimizing the feature exchange between different stages of the multi-stage architecture network. Our method currently performs the best of all the tested methods.

Construction and application of star polycation nanocarrier-based microRNA delivery system in Arabidopsis and maize.

BACKGROUND: MicroRNA (miRNA) plays vital roles in the regulation of both plant architecture and stress resistance through cleavage or translation inhibition of the target messenger RNAs (mRNAs). However, miRNA-induced gene silencing remains a major challenge in vivo due to the low delivery efficiency and instability of miRNA, thus an efficient and simple method is urgently needed for miRNA transformation. Previous researches have constructed a star polycation (SPc)-mediated transdermal double-stranded RNA (dsRNA) delivery system, achieving efficient dsRNA delivery and gene silencing in insect pests. RESULTS: Here, we tested SPc-based platform for direct delivery of double-stranded precursor miRNA (ds-MIRNA) into protoplasts and plants. The results showed that SPc could assemble with ds-MIRNA through electrostatic interaction to form nano-sized ds-MIRNA/SPc complex. The complex could penetrate the root cortex and be systematically transported through the vascular tissue in seedlings of Arabidopsis and maize. Meanwhile, the complex could up-regulate the expression of endocytosis-related genes in both protoplasts and plants to promote the cellular uptake. Furthermore, the SPc-delivered ds-MIRNA could efficiently increase mature miRNA amount to suppress the target gene expression, and the similar phenotypes of Arabidopsis and maize were observed compared to the transgenic plants overexpressing miRNA. CONCLUSION: To our knowledge, we report the first construction and application of star polycation nanocarrier-based platform for miRNA delivery in plants, which explores a new enable approach of plant biotechnology with efficient transformation for agricultural application.

Complex I mutations synergize to worsen the phenotypic expression of Leber's hereditary optic neuropathy.

Leber's hereditary optic neuropathy (LHON) is a maternal inheritance of eye disease because of the mitochondrial DNA (mtDNA) mutations. We previously discovered a 3866T>C mutation within the gene for the ND1 subunit of complex I as possibly amplifying disease progression for patients bearing the disease-causing 11778G>A mutation within the gene for the ND4 subunit of complex I. However, whether and how the ND1 mutation exacerbates the ND4 mutation were unknown. In this report, we showed that four Chinese families bearing both m.3866T>C and m.11778G>A mutations exhibited higher penetrances of LHON than 6 Chinese pedigrees carrying only the m.3866T>C mutation or families harboring only the m.11778G>A mutation. The protein structure analysis revealed that the m.3866T>C (I187T) and m.11778G>A (R340H) mutations destabilized the specific interactions with other residues of ND1 and ND4, thereby altering the structure and function of complex I. Cellular data obtained using cybrids, constructed by transferring mitochondria from the Chinese families into mtDNA-less (ρ°) cells, demonstrated that the mutations perturbed the stability, assembly, and activity of complex I, leading to changes in mitochondrial ATP levels and membrane potential and increasing the production of reactive oxygen species. These mitochondrial dysfunctions promoted the apoptotic sensitivity of cells and decreased mitophagy. Cybrids bearing only the m.3866T>C mutation displayed mild mitochondrial dysfunctions, whereas those harboring both m.3866T>C and m.11778G>A mutations exhibited greater mitochondrial dysfunctions. These suggested that the m.3866T>C mutation acted in synergy with the m.11778G>A mutation, aggravating mitochondrial dysfunctions and contributing to higher penetrance of LHON in these families carrying both mtDNA mutations.

Contextual loss based artifact removal method on CBCT image.

PURPOSE: Cone beam computed tomography (CBCT) offers advantages such as high ray utilization rate, the same spatial resolution within and between slices, and high precision. It is one of the most actively studied topics in international computed tomography (CT) research. However, its application is hindered owing to scatter artifacts. This paper proposes a novel scatter artifact removal algorithm that is based on a convolutional neural network (CNN), where contextual loss is employed as the loss function. METHODS: In the proposed method, contextual loss is added to a simple CNN network to correct the CBCT artifacts in the pelvic region. The algorithm aims to learn the mapping from CBCT images to planning CT images. The 627 CBCT-CT pairs of 11 patients were used to train the network, and the proposed algorithm was evaluated in terms of the mean absolute error (MAE), average peak signal-to-noise ratio (PSNR) and so on. The proposed method was compared with other methods to illustrate its effectiveness. RESULTS: The proposed method can remove artifacts (including streaking, shadowing, and cupping) in the CBCT image. Furthermore, key details such as the internal contours and texture information of the pelvic region are well preserved. Analysis of the average CT number, average MAE, and average PSNR indicated that the proposed method improved the image quality. The test results obtained with the chest data also indicated that the proposed method could be applied to other anatomies. CONCLUSIONS: Although the CBCT-CT image pairs are not completely matched at the pixel level, the method proposed in this paper can effectively correct the artifacts in the CBCT slices and improve the image quality. The average CT number of the regions of interest (including bones, skin) also exhibited a significant improvement. Furthermore, the proposed method can be applied to enhance the performance on such applications as dose estimation and segmentation.

An energy minimization method for the correction of cupping artifacts in cone-beam CT.

The purpose of this study was to reduce cupping artifacts and improve quantitative accuracy of the images in cone-beam CT (CBCT). An energy minimization method (EMM) is proposed to reduce cupping artifacts in reconstructed image of the CBCT. The cupping artifacts are iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. Moreover, the energy in our formulation is convex in each of its variables, which brings the robustness of the proposed energy minimization algorithm. The cupping artifacts are estimated as a result of minimizing this energy. The results indicate that proposed algorithm is effective for reducing the cupping artifacts and preserving the quality of the reconstructed image. The proposed method focuses on the reconstructed image without requiring any additional physical equipment; it is easily implemented and provides cupping correction using a single scan acquisition. The experimental results demonstrate that this method can successfully reduce the magnitude of cupping artifacts. The correction algorithm reported here may improve the uniformity of the reconstructed images, thus assisting the development of perfect volume visualization and threshold-based visualization techniques for reconstructed images.

Spectrum of rhodopsin gene mutations in Chinese patients with retinitis pigmentosa.

PURPOSE: This study was to analyze the spectrum and frequency of rhodopsin gene (RHO) mutations in Chinese patients with retinitis pigmentosa (RP). METHODS: Patients were given physical examinations, and blood samples were collected for DNA extraction. The RHO mutations were screened with direct sequencing. RESULTS: Eight heterozygous nucleotide changes were detected in eight of 300 probands with RP, including six novel mutations and two known mutations. p.R21C, p.C110S, p.G182V, p.C187G, c.409-426delGTGGTGGTGTGTAAGCCC, and p.P347L were found in six autosomal dominant families. p.T92I and p.Y178C were found in two isolated cases. CONCLUSIONS: The results reveal the spectrum and frequency of RHO mutations in Chinese patients with different forms of RP and demonstrate that RHO mutations account for a high proportion of autosomal dominant RP (adRP) cases.

EBC-Net: 3D semi-supervised segmentation of pancreas based on edge-biased consistency regularization in dual perturbation space.

BACKGROUND: Deep learning technology has made remarkable progress in pancreatic image segmentation tasks. However, annotating 3D medical images is time-consuming and requires expertise, and existing semi-supervised segmentation methods perform poorly in the segmentation task of organs with blurred edges in enhanced CT such as the pancreas. PURPOSE: To address the challenges of limited labeled data and indistinct boundaries of regions of interest (ROI). METHODS: We propose Edge-Biased Consistency Regularization (EBC-Net). 3D edge detection is employed to construct edge perturbations and integrate edge prior information into limited data, aiding the network in learning from unlabeled data. Additionally, due to the one-sidedness of a single perturbation space, we expand the dual-level perturbation space of both images and features to more efficiently focus the model's attention on the edges of the ROI. Finally, inspired by the clinical habits of doctors, we propose a 3D Anatomical Invariance Extraction Module and Anatomical Attention to capture anatomy-invariant features. RESULTS: Extensive experiments have demonstrated that our method outperforms state-of-the-art methods in semi-supervised pancreas image segmentation. Moreover, it can better preserve the morphology of pancreatic organs and excel at edges region accuracy. CONCLUSIONS: Incorporated with edge prior knowledge, our method mixes disturbances in dual-perturbation space, which shifts the network's attention to the fuzzy edge region using a few labeled samples. These ideas have been verified on the pancreas segmentation dataset.

Unveiling the Regulatory Role of miRNAs in Internode Elongation: Integrated Analysis of MicroRNA and mRNA Expression Profiles across Diverse Dwarfing Treatments in Maize (Zea mays L.).

MicroRNAs are crucial regulators of gene expression in maize. However, the mechanisms through which miRNAs control internode elongation remain poorly understood. This study engineered varying levels of internode elongation inhibition, revealing that dwarfing treatments diminished gibberellin levels, curtailed cell longitudinal growth, and slowed the rate of internode elongation. Comprehensive transcriptome and miRNA profiling of the internode elongation zone showed gene expression changes that paralleled the extent of the internode length reduction. We identified 543 genes and 29 miRNAs with significant correlations to internode length, predominantly within families, including miR164 and miR396. By incorporating target gene expression levels, we pinpointed nine miRNA-mRNA pairs that are significantly associated with the regulation of the internode elongation. The inhibitory effects of these miRNAs on their target genes were confirmed through dual-luciferase reporter assays. Overexpression of miR164h in maize resulted in increased internode and cell length, suggesting a novel genetic avenue for manipulating plant stature. These miRNAs may also serve as precise spatiotemporal regulators for in vitro plant development.

Ethylene enhanced waterlogging tolerance by changing root architecture and inducing aerenchyma formation in maize seedlings.

Waterlogging negatively affects maize growth and yield. In this study, we found that ethylene played a vital role in plant adaptation to waterlogging. ET promotes better growth in seedlings under waterlogging conditions by altering root architecture and increasing lateral root formation by 42.1%. What's more, plants with high endogenous ethylene levels exhibited reduced sensitivity to waterlogging stress. ET also induced the formation of aerenchyma, a specialized tissue that facilitates gas exchange, in a different pattern compared to aerenchyma formed under waterlogging. Aerenchyma induced by ET was mainly located in the medial cortex of the roots and was not prone to decay. ethylene inhibited root elongation under normal conditions, but this inhibition was not alleviated under waterlogging stress. Upon activation of the ET signaling pathway, the transcription factor EREB90 promoted aerenchyma formation by enhancing the programmed cell death process. Overexpression of EREB90 resulted in increased waterlogging tolerance compared to wild type plants. Our findings suggest that pre-treatment of maize seedlings with ET before waterlogging stress can trigger the programmed cell death process and induce aerenchyma formation, thus improving waterlogging resistance.

Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks.

PURPOSE: Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head and neck, thorax, abdomen, pelvis, and extremities. In this study, we present a new solution to trim automatically the given axial image stack into image volumes satisfying the given body region definition. METHODS: The proposed approach consists of the following steps. First, a set of reference objects is selected and roughly segmented. Virtual landmarks (VLs) for the objects are then identified by using principal component analysis and recursive subdivision of the object via the principal axes system. The VLs can be defined based on just the binary objects or objects with gray values also considered. The VLs may lie anywhere with respect to the object, inside or outside, and rarely on the object surface, and are tethered to the object. Second, a classic neural network regressor is configured to learn the geometric mapping relationship between the VLs and the boundary locations of each body region. The trained network is then used to predict the locations of the body region boundaries. In this study, we focus on three body regions - thorax, abdomen, and pelvis, and predict their superior and inferior axial locations denoted by TS(I), TI(I), AS(I), AI(I), PS(I), and PI(I), respectively, for any given volume image I. Two kinds of reference objects - the skeleton and the lungs and airways, are employed to test the localization performance of the proposed approach. RESULTS: Our method is tested by using low-dose unenhanced computed tomography (CT) images of 180 near whole-body 18 F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) scans (including 34 whole-body scans) which are randomly divided into training and testing sets with a ratio of 85%:15%. The procedure is repeated six times and three times for the case of lungs and skeleton, respectively, with different divisions of the entire data set at this proportion. For the case of using skeleton as a reference object, the overall mean localization error for the six locations expressed as number of slices (nS) and distance (dS) in mm, is found to be nS: 3.4, 4.7, 4.1, 5.2, 5.2, and 3.9; dS: 13.4, 18.9, 16.5, 20.8, 20.8, and 15.5 mm for binary objects; nS: 4.1, 5.7, 4.3, 5.9, 5.9, and 4.0; dS: 16.2, 22.7, 17.2, 23.7, 23.7, and 16.1 mm for gray objects, respectively. For the case of using lungs and airways as a reference object, the corresponding results are, nS: 4.0, 5.3, 4.1, 6.9, 6.9, and 7.4; dS: 15.0, 19.7, 15.3, 26.2, 26.2, and 27.9 mm for binary objects; nS: 3.9, 5.4, 3.6, 7.2, 7.2, and 7.6; dS: 14.6, 20.1, 13.7, 27.3, 27.3, and 28.6 mm for gray objects, respectively. CONCLUSIONS: Precise body region identification automatically in whole-body or body region tomographic images is vital for numerous medical image analysis and analytics applications. Despite its importance, this issue has received very little attention in the literature. We present a solution to this problem in this study using the concept of virtual landmarks. The method achieves localization accuracy within 2-3 slices, which is roughly comparable to the variation found in localization by experts. As long as the reference objects can be roughly segmented, the method with its learned VLs-to-boundary location relationship and predictive ability is transferable from one image modality to another.

Alcoholism Identification Based on an AlexNet Transfer Learning Model.

Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10-4, and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set. Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images.

Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction.

Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.

Automatic thoracic body region localization.

Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head & neck, thorax, abdomen, pelvis, and extremities. For automating image analysis and consistency of results, standardizing definitions of body regions and the various anatomic objects, tissue regions, and zones in them becomes essential. Assuming that a standardized definition of body regions is available, a fundamental early step needed in automated image and object analytics is to automatically trim the given image stack into image volumes exactly satisfying the body region definition. This paper presents a solution to this problem based on the concept of virtual landmarks and evaluates it on whole-body positron emission tomography/computed tomography (PET/CT) scans. The method first selects a (set of) reference object(s), segments it (them) roughly, and identifies virtual landmarks for the object(s). The geometric relationship between these landmarks and the boundary locations of body regions in the cranio-caudal direction is then learned through a neural network regressor, and the locations are predicted. Based on low-dose unenhanced CT images of 180 near whole-body PET/CT scans (which includes 34 whole-body PET/CT scans), the mean localization error for the boundaries of superior of thorax (TS) and inferior of thorax (TI), expressed as number of slices (slice spacing ≈ 4mm)), and using either the skeleton or the pleural spaces as reference objects, is found to be 3,2 (using skeleton) and 3, 5 (using pleural spaces) respectively, or in mm 13, 10 mm (using skeleton) and 10.5, 20 mm (using pleural spaces), respectively. Improvements of this performance via optimal selection of objects and virtual landmarks and other object analytics applications are currently being pursued. and the skeleton and pleural spaces used as a reference objects.

Genetic and phenotypic characteristics of four Chinese families with fundus albipunctatus.

Fundus albipunctatus (FA) is a rare autosomal recessive form of stationary night blindness characterized by the presence of white or white-yellow dots in the perimacular area and the periphery of the retina, with or without macular involvement. In this study, we examined four Chinese families with FA. Patients were given complete ophthalmic examinations, and blood samples were collected for DNA extraction. Three genes, RDH5, RLBP1 and RPE65, were screened by direct sequencing. Mutations in RDH5 were identified in three families and mutations in RPE65 were identified in one family. This is the second reported case of FA caused by mutations in RPE65.

Mitochondrial haplogroup D4j specific variant m.11696G > a(MT-ND4) may increase the penetrance and expressivity of the LHON-associated m.11778G > a mutation in Chinese pedigrees.

Leber's hereditary optic neuropathy (LHON) is one of the most common mitochondrial disorders. We report here the clinical, genetic and molecular analysis of mitochondrial DNA (mtDNA) in eight Han Chinese families carrying the known mitochondrial 11778G > A(MT-ND4) mutation. Thirty-seven (26 males/11 females) of 77 matrilineal relatives in these families exhibited the variable severity and age-at-onset of optic neuropathy. The penetrances were from 25% to 75%, with the average of 42%, and the age-at-onset for visual impairment varied from 10 to 25 years, with the average of 17 in these Chinese pedigrees. Molecular analysis of their mtDNA identified distinct sets of variants belonging to the Eastern Asian haplogroupD4j. Except the known m.11778G > A mutation, the m.11696G > A(MT-ND4) mutation caused the substitution of an isoleucine for valineat amino acid position 313, located in a predicted transmembrane region of ND4. And, it is reported that the m.11696G > A mutation was associated with LHON, and appeared to contribute to higher penetrance in these nine Chinese families than other Chinese families carrying only the m.11778G > A mutation. Therefore, the mitochondrial haplogroup D4j specific m.11696G > A mutation may act in synergy with the primary LHON-associated m.11778G > A mutation, thereby increasing the penetrance and expressivity of visual loss in these Chinese families.

A level set method for cupping artifact correction in cone-beam CT.

PURPOSE: To reduce cupping artifacts and improve the contrast-to-noise ratio in cone-beam computed tomography (CBCT). METHODS: A level set method is proposed to reduce cupping artifacts in the reconstructed image of CBCT. The authors derive a local intensity clustering property of the CBCT image and define a local clustering criterion function of the image intensities in a neighborhood of each point. This criterion function defines an energy in terms of the level set functions, which represent a segmentation result and the cupping artifacts. The cupping artifacts are estimated as a result of minimizing this energy. RESULTS: The cupping artifacts in CBCT are reduced by an average of 90%. The results indicate that the level set-based algorithm is practical and effective for reducing the cupping artifacts and preserving the quality of the reconstructed image. CONCLUSIONS: The proposed method focuses on the reconstructed image without requiring any additional physical equipment, is easily implemented, and provides cupping correction through a single-scan acquisition. The experimental results demonstrate that the proposed method successfully reduces the cupping artifacts.

Leber's hereditary optic neuropathy caused by the homoplasmic ND1 m.3635G>A mutation in nine Han Chinese families.

In this report, we investigated the molecular mechanism underlying Leber's hereditary optic neuropathy (LHON)-associated mitochondrial m.3635G>A (p.S110N, ND1) mutation. A mutational screening of ND1 gene in a cohort of 1070 Han Chinese subjects LHON identified the m.3635G>A mutation in nine Chinese families with suggestively maternally transmitted LHON. Thirty-eight (22 males/16 females) of 162 matrilineal relatives in these families exhibited the variable severity and age-at-onset of optic neuropathy. Molecular analysis of their mitochondrial genomes identified the homoplasmic m.3635G>A mutation and distinct sets of polymorphisms belonging to the Asian haplogroups G2a1, R11a, D4, R11a, M7b2, G1a, F1a1, B4, and N9a3, respectively. Using cybrids constructed by transferring mitochondria from lymphoblastoid cell lines derived from one Chinese family into mtDNA-less (ρ(0)) cells, we showed ~27% decrease in the activity of NADH:ubiquinone oxidoreductase (complex I) in mutant cybrids carrying the m.3635G>A mutation, compared with control cybrids. The respiratory deficiency caused by the m.3635G>A mutation results in decreased efficiency of mitochondrial ATP synthesis. These mitochondrial dysfunctions caused an increase in the production of reactive oxygen species in the mutant cybrids. The data provide the direct evidence for the m.3635G>A mutation leading to LHON. Our findings may provide new insights into the understanding of pathophysiology of LHON.