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Vortex beams (VBs) carrying orbital angular momentum (OAM) have shown promising potential in enhancing communication capacity through the possession of multiple multiplexing dimensions involving the OAM mode, polarization, and wavelength. Although many research works on multidimensional multiplexing have been conducted, the (de)multiplexer compatible with these dimensions remains elusive. Following the expanded concept of the Pancharatnam–Berry (PB) phase, we designed a polarization-dependent phase-modulation metasurface to phase-modulate the two orthogonal linearly polarized components of light, and two Dammann vortex gratings with orthogonal polarization responses were loaded to simultaneously (de)multiplex OAM mode and polarization channels. As a proof of concept, we constructed a 16-channel multidimensional multiplexing communication system (including two OAM modes, two polarization states, and four wavelengths), and 400 Gbit/s quadrature-phase shift-keying (QPSK) signals were transmitted. The results demonstrate that the OAM mode and polarization channels are successfully (de)multiplexed, and the bit-error-rates (BERs) are below 1.67 × 10−6 at the received power of −15 dBm.

Climate change has an extremely important impact on the geographic distribution of plants. The genus Millettia is an important plant resource in China and is widely used in medicine and ornamental industries. Due to the continuous changes of climate and the development and utilization of plant resources of the genus Millettia, it is of great significance to systematically investigate the geographic distribution of plants of the Millettia and their potential distribution under climate change. DIVA-GIS software was used to analyze 3492 plant specimens of 35 species of genus Millettia in the herbarium, and the ecological geographic distribution and richness of Millettia were analyzed, and the MaxEnt model was used to analyze the current and potential distribution in the future. The results show that the genus Millettia is distributed in 30 provinces in China, among which Yunnan and Guangdong provinces are the most distributed. Our model determines that precipitation in the driest month and annual temperature range are the most important bioclimatic variables. Future climate changes will increase the suitable habitat area of M. congestiflora by 16.75%, but other cliff beans Suitable habitats for vines will decrease significantly: M. cinereal by 47.66%, M. oosperma by 39.16%, M. pulchra by 36.04%, M. oraria by - 29.32%, M. nitida by 22.88%, M. dielsiana by 22.72%, M. sericosema by 19.53%, M. championii by 7.77%, M. pachycarpa by 7.72%, M. speciose by 2.05%, M. reticulata by 1.32%. Therefore, targeted measures should be taken to protect and develop these precious plant resources.

Vortex beams (VBs), characterized by helical phase front and orbital angular momentum (OAM), have shown perspective potential in improving communication capacity density for providing an additional multiplexing dimension. Here, we propose a diffractive deep neural network (D2NN) method for OAM mode multiplexing and demultiplexing. By designing the D2NN model and simulating light propagation through multiple diffractive screens, the phase and amplitude values can be automatically adjusted to manipulate the wavefront of light beams. Training the D2NN model with mode coupler and separator functions, we convert VBs into target light fields with the diffraction efficiency exceeds 97%, and the mode purities are over 97%. Constructing an OAM multiplexing link, we successfully multiplex and demultiplex two OAM channels that carry 16-QAM signals in simulation, and the demodulated bit-error-rates are below 1×10-4. It is anticipated that the D2NN can perform flexible modulation of multiple OAM modes, which may open a new avenue for high-capacity OAM communication and all-optical information processing, etc.

Here we propose a polarization-dependent gradient phase modulation strategy and fabricate a local polarization-matched metasurface to add/drop polarization multiplexed cylindrical vector beams (CVBs). The two orthogonal linear polarization states in CVB multiplexing will represent as radial- and azimuthal-polarized CVBs, which means that we must introduce independent wave vectors to them for adding/dropping the polarization channels. By designing the rotation angle and geometric sizes of a meta-atom, a local polarization-matched propagation phase plasmonic metasurface is constructed, and the polarization-dependent gradient phases were loaded to perform this operation. As a proof of concept, the polarization multiplexed CVBs, carrying 150-Gbit/s quadrature phase shift keying signals, are successfully added and dropped, and the bit error rates approach 1 × 10−6. In addition to representing a route for adding/dropping polarization multiplexed CVBs, other functional phase modulation of arbitrary orthogonal linear polarization bases is expected, which might find potential applications in polarization encryption imaging, spatial polarization shaping, etc.

Conventional microsatellite (simple sequence repeat, SSR) genotyping methods cannot accurately identify polyploid genotypes leading to allele dosage uncertainty, introducing biases in population genetic analysis. Here, a new SSR genotyping method was developed to directly infer accurate polyploid genotypes. The frequency distribution of SSR sequences was obtained based on deep-coverage high-throughput sequencing data. Corrections were performed accounting for the "stutter peak" and amplification efficiency of SSR sequences. Perl scripts and an online SSR genotyping tool "SSRSeq" were provided to process the sequencing data and output genotypes with corrected allele dosages. Hexaploid Camellia oleifera is the dominant woody oilseed crop in China. Understanding the geographical pattern of genetic variation in wild C. oleifera is essential for the conservation and utilization of genetic resources. Six wild C. oleifera populations were sampled across geographical ranges in subtropical evergreen broadleaf forests of China. Using 35 SSR markers, the high-throughput sequencing-based SSRSeq method was applied to obtain accurate hexaploid genotypes of wild C. oleifera. The results demonstrated that the new method could resolve allele dosage uncertainty and considerably improve genetic diversity, structure and differentiation analyses for polyploids. The genetic variation patterns of wild C. oleifera across geographical ranges agree with the "central-marginal hypothesis", stating that genetic diversity is high in the central population and declines from the central to the peripheral populations, and genetic differentiation increases from the centre to the periphery. This method and findings can facilitate the utilization of wild C. oleifera genetic resources for the breeding of cultivated C. oleifera.

The rapid and accurate identification of large-scale orbital angular momentum (OAM) modes is crucial for expanding the application of vortex beams (VBs). In this paper, an OAM mode recognition method based on convolutional neural networks (CNNs) is proposed and investigated. We construct an 8-layer CNN possesses complex feature extraction capability and train it to own powerful anti-turbulence competence by feeding the intensity patterns of VBs interfered by Gaussian beam. After supervised training of a large sample set, the CNN model takes on excellent network generalization ability and can well detect VBs with the mode range of [−50,50]. The simulation results indicate that under the influence of weak and medium turbulences, the average recognition accuracy exceeds 99%. Even under strong turbulence, the accuracy also reaches 98.54%. Meanwhile, the identification time is only 1.55ms per OAM mode with Intel(R) Xeon(R) Gold 6148 CPU. Moreover, the influence of different Gaussian beam waists, VB orders, input training sets, and CNN structures on OAM mode recognition performance, is fully studied. These results demonstrate that our proposed method can achieve higher accuracy and higher order OAM mode detection at a fast speed, which contributes a more effective method for the recognition of VBs.

Vortex beams have application potential in multiplexing communication because of their orthogonal orbital angular momentum (OAM) modes. OAM add–drop multiplexing remains a challenge owing to the lack of mode selective coupling and separation technologies. We proposed an OAM add–drop multiplexer (OADM) using an optical diffractive deep neural network (ODNN). By exploiting the effective data-fitting capability of deep neural networks and the complex light-field manipulation ability of multilayer diffraction screens, we constructed a five-layer ODNN to manipulate the spatial location of vortex beams, which can selectively couple and separate OAM modes. Both the diffraction efficiency and mode purity exceeded 95% in simulations and four OAM channels carrying 16-quadrature-amplitude-modulation signals were successfully downloaded and uploaded with optical signal-to-noise ratio penalties of ∼1 dB at a bit error rate of 3.8 × 10−3. This method can break through the constraints of conventional OADM, such as single function and poor flexibility, which may create new opportunities for OAM multiplexing and all-optical interconnection.

Fibroblast growth factor receptor 2 (FGFR2) is a membrane‐spanning tyrosine kinase that mediates FGF signaling. Various FGFR2 alterations are detected in breast cancer, yet it remains unclear if activation of FGFR2 signaling initiates tumor formation. In an attempt to answer this question, a mouse model berrying an activation mutation of FGFR2 (FGFR2‐S252W) in the mammary gland is generated. It is found that FGF/FGFR2 signaling drives the development of triple‐negative breast cancer accompanied by epithelial‐mesenchymal transition that is regulated by FGFR2‐STAT3 signaling. It is demonstrated that FGFR2 suppresses BRCA1 via the ERK‐YY1 axis and promotes tumor progression. BRCA1 knockout in the mammary gland of the FGFR2‐S252W mice significantly accelerated tumorigenesis. It is also shown that FGFR2 positively regulates PD‐L1 and that a combination of FGFR2 inhibition and immune checkpoint blockade kills cancer cells. These data suggest that the mouse models mimic human breast cancers and can be used to identify actionable therapeutic targets., In this study, it is demonstrated that activation of FGFR2 promotes triple‐negative breast cancer formation through regulating expression of BRCA1 negatively and PD‐L1 positively. Inhibition of FGFR2, which is widely expressed in human breast cancers, can be combined preclinically with immune checkpoint antibodies to enhance anticancer immunotherapy, warranting clinical evaluation of personalized targeted therapy.

Several Amomum species are commonly used in food as flavoring agents and traditional Chinese medicine to treat inflammation-related diseases.This study aims to investigate the protective effects of Chinese herbal medicines, including six Amomum Roxb. essential oils (AEOs), against acute lung injury (ALI) induced by lipopolysaccharide (LPS) in mice.The compositions of AEOs were analyzed using gas chromatography - mass spectrometry. RAW264.7 cells were treated with AEOS (0-100 μg/mL) and stimulated with LPS. C57 mice received AEOs (100 mg/kg) via atomization system for seven consecutive days, and then, intratracheal instillation of LPS was applied to establish an in vivo model of acute lung injury.We identified three AEOs demonstrating anti-inflammatory effects and amelioration of LPS-induced lung tissue pathological damage. Furthermore, we found that these AEOs reduced lung wet/dry weight ratios and protein concentrations in the bronchoalveolar lavage fluid of mice with LPS-induced ALI. Additionally, AEOs reduced the levels of malondialdehyde, TNF-α, IL-6, and IL-1β but increased the levels of superoxide dismutase and catalase in lung tissue, alveolar lavage fluid, and serum samples. We also found that these three AEOs affected proteins related to the TLR4/Myd88/NF-κB pathway.In summary, our findings revealed that AEOs ameliorate inflammatory and oxidative stress in mice with ALI through the TLR4/Myd88/NF-κB pathway.

Vortex beams (VBs), possessing a helical phase front and carrying orbital angular momentum (OAM), have attracted considerable attention in optical communications for their mode orthogonality. A platform for achieving all-optical signal processing of VBs, however, remains elusive due to the limited light-field-manipulation capability. We introduce diffractive deep neural networks (${\mathrm{D}}^{2}$NNs) and their applications to process VBs. Exploiting the multiple-light-field-modulation ability of multilayer diffraction structures and the strong data-processing capability of deep neural networks, we reveal that ${\mathrm{D}}^{2}$NNs can manipulate multiple VBs by configuring the phase and amplitude distribution of diffractive screens. The diffraction efficiency and converted-mode purity are greater than 96%. After being trained, ${\mathrm{D}}^{2}$NNs with functions of hybrid-OAM-mode generation, identification, and conversion are obtained, and three typical types of all-optical signal-processing communication, (OAM-shift keying (OAM-SK), OAM multiplexing and demultiplexing, and OAM-mode switching) are successfully achieved. Our simulation results provide an approach that breaks the limitations of poor functionality and complex design in processing VBs, introducing the ${\mathrm{D}}^{2}$NN as a universal light-field-modulation platform.

The research aimed at discussing the analytic function of convolutional neural network (CNN) algorithm-based magnetic resonance images (MRI) in the correlation between lumbar disc herniation (LDH) and angle and irregular variation of joint (IVJ) of lumbar facet-joint (LFJ). First, CNN-based MRI (CNNM) algorithm was constructed, and Markov random field (MRF) and fuzzy C-means (FCM) algorithms were introduced for comparison. Meanwhile, all patients received MRI examination of lumbar, and CNNM algorithm was adopted in MRI images. The results showed that the sensitivity, specificity, accuracy, and precision (98.53%, 93.65%, 99.56%, and 98.74%, respectively) of the CNNM algorithm were all superior to those of MRF algorithm (90.41%, 81.11%, 91.18%, and 91.13%, respectively) and of FCM algorithm (93.14%, 82.86%, 93.23%, and 93.08%, respectively) ( P < 0.05 ). Besides, the lumbar spine angles of L3-L4, L4-L5, and L5-S1 (6.03 ± 1.34°, 7.14 ± 1.18°, and 8.96 ± 3.26°, respectively) in the experimental group was obviously less than those in the control group (6.84 ± 1.15°, 9.85 ± 1.25°, and 17.34 ± 4.79°, respectively) ( P < 0.05 ). In the experimental group, there was irregular mutation of LFJ in 78 cases, while 8 cases suffered from irregular mutation of LFJ in the control group. The proportions of protrusion in L3/4, L4/5, and L5/S1 segments (11 cases, 53 cases, and 14 cases, respectively) was higher than that in the control group (1 case, 5 cases, and 2 cases, respectively) ( P < 0.05 ). In short, the constructed CNNM algorithm had excellent performance in diagnosing lumbar MRI images and had clinical research and promotion value. Moreover, the IVJ of patients with LDH was notably increased, most of the physiological angle of the lumbar spine changed, and facet joint was correlated with the occurrence of LDH.

Single-subject oriented analyses have capacity to discover the individual neurocognitive model in neuroscience researches. However, individual discrepancies and spatial instability impede the group level inference. In this paper, we focus on transfer learning methods to cope with magnetoencephalographic (MEG) decoding across subjects within which the individual differences exist. First, a general and effective framework allows us to extract features in Riemannian manifold. Then, an improved subspace alignment technique is proposed to adapt two different domains. We test our method on MEG decoding challenge and find that it outperforms most state of the art methods both in cross-subject validation and new testing data.

In brain-computer interface, information decoded from brain can be used as control signal. However, classifier trained from previous subjects suffer from performance drop due to some factors, including environmental, physiological and instrumental changes. Concretely speaking, statical distribution alters across subjects, as well as sessions. In this paper, we focus on across-subject problem. Thus, we propose a framework for decoding MEG and matching data distribution across subjects. First, features from Riemannian tangent space were extracted to build common feature space between source domain and target domain. Second, taking advantages of clustering in target domain, supervised subspace learning in source domain and matching the class centroid between two domains, we proposed an improved transfer learning method named class centroid matching (CCM). Several experiments had been conducted on a MEG dataset, which shows that our proposed method is effective to reduce discrepancy across subjects and can achieve a promising performance than other comparable methods.

Brain-computer interface is a technology that is helpful for these people with dyspraxia or strokes to obtain the ability to communicate with others or control devices again. However, due to the brain signal collected by the system has terrible quilty, the error decision is often made by the BCI system, which hinders the development of the technology. Therefore, detecting the error from the BCI system holds a great significance by error-related potential (ErrP) generated when erroneous feedback from the system is found by the subject. In this paper, we propose an integrated transfer learning based on Group Sparse Bayesian Linear Discriminant Analysis (ITL_GSBLDA) to detect ErrPs. In this way, the Group Sparse Bayesian Linear Discriminant (GSBLDA) has better performance with the help of transfer learning. The experiment has been finished with the dataset of Kaggle competition. In the experiment, sensitivity, specificity, and Area Under Curve (AUC) are used to evaluate the performance of the decoder. Finality, the results are 71.49% sensitivity, 66.49% specificity, and 0.7624 AUC when using the signal features and meta-feature. And in this condition, our decoder surpasses the 5th place in the competition.

Orbital angular momentum (OAM) mode multiplexing provides a new strategy for reconstructing multiple holograms, which is compatible with other physical dimensions involving wavelength and polarization to enlarge information capacity. Conventional OAM multiplexing holography usually relies on the independence of physical dimensions, and the deep holography involving spatial depth is always limited for the lack of spatiotemporal evolution modulation technologies. Herein, we introduce a depth-controllable imaging technology in OAM deep multiplexing holography via designing a prototype of five-layer optical diffractive neural network (ODNN). Since the optical propagation with dimensional-independent spatiotemporal evolution offers a unique linear modulation to light, it is possible to combine OAM modes with spatial depths to realize OAM deep multiplexing holography. Exploiting the multi-plane light conversion and in-situ optical propagation principles, we simultaneously modulate both the OAM mode and spatial depth of incident light via unitary transformation and linear modulations, where OAM modes are encoded independently for conversions among holograms. Results show that the ODNN realized light field conversion and evolution of five multiplexed OAM modes in deep multiplexing holography, where the mean square error and structural similarity index measure are 0.03 and 86%, respectively. Our demonstration explores a depth-controllable spatiotemporal evolution technology in OAM deep multiplexing holography, which is expected to promote the development of OAM mode-based optical holography and storage.

Vortex beam (VB) possessing spatially helical phase–front has attracted widespread attention in free-space optical communication, etc. However, the spiral phase of VB is susceptible to atmospheric turbulence, and effective retrieval of the distorted conjugate phase is crucial for its practical applications. Herein, a convolutional neural network (CNN) approach to retrieve the phase distribution of VB is experimentally demonstrated. We adopt a spherical wave to interfere with VB for converting its phase information into intensity changes, and construct a CNN model with excellent image processing capabilities to directly extract phase–front features from the interferogram. Since the interference intensity is correlated with the phase–front, the CNN model can effectively reconstruct the wavefront of conjugate VB carrying different initial phases from a single interferogram. The results show that the CNN-based phase retrieval method has a loss of 0.1418 in the simulation and a loss of 0.2344 for the experimental data, and remains robust even in turbulence environments. This approach can improve the information acquisition capability for recovering the distorted wavefront and reducing the reliance on traditional inverse retrieval algorithms, which may provide a promising tool to retrieve the spatial phase distributions of VBs.

The identification of hybrid orbital angular momentum (OAM) modes with high-accuracy and -speed is always a challenge in practically applying optical vortex beams (VBs). In this paper, we propose and investigate a deep feedforward neural network (FNN) method for identifying the hybrid OAM modes of VBs. The FNN model with 15 input neurons and 7 hidden layers was constructed. And an improved1-dimension fork grating (1-D FG) was designed to diffract VBs and produce feature parameters which are used as the input of the FNN. After supervised training, the deep FNN model can identify arbitrarily combined hybrid OAM modes with a wide detection range owing to its non-linear operations to neurons and massive iterations. Besides, this FNN model has better robustness to atmospheric turbulence. The results show that the identification accuracy reaches 97% with five superimposed modes. Under the influence of atmospheric turbulence with Cn2=1×10-15m-2/3, the accuracy still exceeds 98% at the transmission distance of 1000 m. With an Intel Core i5-4590 CPU and NVIDIA GeForce GTX 750 GPU, this identification process takes only 0.09 ms. Furthermore, we constructed a 120-ary OAM shift-keying communication link, and the signals were demodulated by the FNN model with only 4.3×10-3 pixels error rate. It is anticipated that the FNN might pave an effective way to detect hybrid OAM modes, which may have great potentials in increasing communication capacity and modulation ability. Keywords: Vortex beams, Feedforward neural network, Hybrid orbital angular momentum modes, 1-dimension fork grating

Breast cancer associated gene 1 (BRCA1) function has been shown to be regulated by phosphorylation but the role of acetylation has not been determined. Therefore, we tested whether BRCA1 can be acetylated by the acetyltransferases P300/CBP-associated factor (pCAF), GCN5, and p300. p300 exhibited the highest level of BRCA1 acetylation; however, there was also a decrease in the total level of BRCA1. Therefore, we focused on pCAF and GCN5 because they both acetylated BRCA1 without affecting BRCA1 expression. Further analysis indicated that the acetylated form of BRCA1 is deacetylated by wild-type (WT) SIRT1, but not deacetylase mutant SIRT1, suggesting that SIRT1 is a specific deacetylase of BRCA1. We demonstrated that lysine 830 of BRCA1 is a preferential acetylation site by pCAF and tested its function in embryonic stem (ES) cells by changing lysine 830 to arginine using a transcription activator-like effector nuclease (TALEN) system. After exposure to DNA damage-inducing UV radiation, the viability of BRCA1 K830R mutant cells is greater than the WT ES cells. Further analysis using additional cell lines indicated that the BRCA1 K830R mutation impairs the intra-S checkpoint. Also, checkpoint kinase 1 (CHK1) phosphorylation was less in K830R cells as compared with WT cells after UV exposure. These data suggest that acetylation of BRCA1 on lysine 830 activates BRCA1 function at the intra-S checkpoint after DNA damage.

Optical logical operations demonstrate the key role of optical digital computing, which can perform general-purpose calculations and possess fast processing speed, low crosstalk, and high throughput. The logic states usually refer to linear momentums that are distinguished by intensity distributions, which blur the discrimination boundary and limit its sustainable applications. Here, we introduce orbital angular momentum (OAM) mode logical operations performed by optical diffractive neural networks (ODNNs). Using the OAM mode as a logic state not only can improve the parallel processing ability but also enhance the logic distinction and robustness of logical gates owing to the mode infinity and orthogonality. ODNN combining scalar diffraction theory and deep learning technology is designed to independently manipulate the mode and spatial position of multiple OAM modes, which allows for complex multilight modulation functions to respond to logic inputs. We show that few-layer ODNNs successfully implement the logical operations of AND, OR, NOT, NAND, and NOR in simulations. The logic units of XNOR and XOR are obtained by cascading the basic logical gates of AND, OR, and NOT, which can further constitute logical half-adder gates. Our demonstrations may provide a new avenue for optical logical operations and are expected to promote the practical application of optical digital computing.

Fractional vortex beam (FVB) possessing helical phase can be applied in the shift-keying communication due to its fractional orbital angular momentum (FOAM) mode, which theoretically allows an infinite increase of the transmitted capacity. However, the discontinuity of spiral phase makes FVB more likely to be disturbed in turbulence environment, and the precise measurement of distorted FOAM modes is crucial for practical FOAM-based communication application. Here, we proposed a FOAM mode recognition method with feedforward neural network (FNN). Employing the diffraction preprocessing of a two-dimensional fork grating, the original optical features of FVBs can be extended along the far-field diffraction order, endowing FNN more feature information and saving calculation time, and enlarging the detection range to conjugate FOAM modes. The simulation results show that the 9-layer FNN can identify FOAM mode with interval of 0.1 with an accuracy of 99.1% under the turbulences of C n 2 = 1 × 10 - 14 m - 2 / 3 and Δ z = 10 m . Furthermore, we experimentally constructed a 102-ary FOAM shift-keying communication link to transmit gray image, and the signals are successfully demodulated by the FNN model with the pixel-error-rate of 0.07160. It is anticipated that the proposed FNN-based FOAM recognition method will break the limitation of precision measurement under turbulence environment in practical FOAM applications.

The aim of this study was to evaluate the direct photoacoustic (PA) effect on bone marrow mesenchymal stem cells (BMSCs) which is a key cell source for osteogenesis. As scaffold is also an indispensable element for tissue regeneration, here we firstly fabricated a composited sheet using polylactic-co-glycolic acid (PLGA) mixing with graphene oxide (GO). BMSCs were seeded on the PLGA-GO sheets and received PA treatment in vitro for 3, 9 and 15 days, respectively. Then the BMSCs were harvested and subjected to assess alkaline phosphatase (ALP) activity, calcium content and osteopontin (OPN) on 3, 9 and 15 days. For in vivo study, PLGA-GO sheet seeded with BMSCs after in vitro PA stimulation for 9 days were implanted to repair the bone defect established in the femoral mid-shaft of Sprague-Dawley rat. PLGA-GO group with PA pretreatment showed promising outcomes in terms of the expression of ALP, OPN, and calcium content, thus enhanced the repair of bone defect. In conclusion, we have developed an alternative approach to enhance the repair of bone defect by making good use of the beneficial effect of PA.

Traditional MEG-based brain decoding approaches require to manual design and extract various features from raw MEG data and often ignore the subtle spatial information contained in the MEG signal. Motivated by this fact, we present a 3D-CNN method to tackle these obstacles. In this approach, a 3-Dimensional Convolutional Neural Networks (3D-CNN) is applied to classify magnetoencephalography states by effectively learning spatial-temporal representation of raw MEG data. And the 3D data representation, which is used as the data input for the proposed 3D-CNN model, is converted from the multi-channel MEG signal to retain the spatial correlations between physically neighbouring MEG signals. An improved self-training phase is developed to enhance the cross-subject performance of the proposed 3D-CNN approach. Experiments on an MEG dataset of face vs. scramble decoding task demonstrate that the proposed method can achieve promising performance.

CRISPR/Cas9 is a powerful technology widely used for genome editing, with the potential to be used for correcting a wide variety of deleterious disease-causing mutations. However, the technique tends to generate more indels (insertions and deletions) than precise modifications at the target sites, which might not resolve the mutation and could instead exacerbate the initial genetic disruption. We sought to develop an improved protocol for CRISPR/Cas9 that would correct mutations without unintended consequences. As a case study, we focused on achondroplasia, a common genetic form of dwarfism defined by missense mutation in the Fgfr3 gene that results in glycine to arginine substitution at position 374 in mice in fibroblast growth factor receptor 3 (Fgfr3-G374R), which corresponds to G380R in humans. First, we designed a GFP reporter system that can evaluate the cutting efficiency and specificity of single guide RNAs (sgRNAs). Using the sgRNA selected based on our GFP reporter system, we conducted targeted therapy of achondroplasia in mice. We found that we achieved higher frequency of precise correction of the Fgfr3-G374R mutation using Cas9 protein rather than Cas9 mRNA. We further demonstrated that targeting oligos of 100 and 200 nucleotides precisely corrected the mutation at equal efficiency. We showed that our strategy completely suppressed phenotypes of achondroplasia and whole genome sequencing detected no off-target effects. These data indicate that improved protocols can enable the precise CRISPR/Cas9-mediated correction of individual mutations with high fidelity.

The ROC (Receiver Operating characteristics) curve for the subgroup analyzes. A-H represent the ROC curve for the young, old, male, female, smoked, non-smoked, alcohol, and non-alcohol subgroups, respectively. A-H each represent the overall ROC curve for the subgroup, which was calculated through a logistic regression model, incorporating the mean methylation percentage of the five genomic regions as the variables and without the adjustment for gender, age, and smoking status and alcohol status. (PDF 446 kb)