Measurement plane of the cross-sectional area of the masseter muscle in patients with skeletal Class III malocclusion: An artificial intelligence model.

INTRODUCTION: This study aimed to determine a measurement plane that could represent the maximum cross-sectional area (MCSA) of masseter muscle using an artificial intelligence model for patients with skeletal Class III malocclusion. METHODS: The study included 197 patients, divided into subgroups according to sex, mandibular symmetry, and mandibular plane angle. The volume, MCSA, and the cross-sectional area (CSA) at different levels were calculated automatically. The vertical distance between MCSA and mandibular foramen, along with the ratio of the masseter CSA at different levels to the MCSA (R), were also calculated. RESULTS: The MCSA and volume showed a strong correlation in the total sample and each subgroup (P <0.001). The correlation between the CSA at each level and MCSA was statistically significant (P <0.001). The peak of the r and the correlation coefficient between the CSA at different levels and MCSA were mostly present 5-10 mm above the mandibular foramen for the total sample and the subgroups. The mean of RA5 to RA10 was >0.93, whereas the corresponding correlation coefficient was >0.96, both for the entire sample and for the subgroups. CONCLUSIONS: MCSA could be used as an indicator for masseter muscle size. For patients with skeletal Class III malocclusion, the CSA 5-10 mm above the mandibular foramen, parallel to the Frankfort plane, could be used to estimate the masseter muscle MCSA.

Changes in masseter muscle morphology after surgical-orthodontic treatment in patients with skeletal Class III malocclusion with mandibular asymmetry: The automatic masseter muscle segmentation model.

INTRODUCTION: This study evaluated the masseter muscle changes after surgical-orthodontic treatment in patients with a skeletal Class III malocclusion using automatic segmentation. METHODS: Images of 120 patients with skeletal Class III malocclusion were obtained and reconstructed at T0 (pretreatment), T1 (presurgery), and T2 (6-12-month postsurgery). The patients were divided into symmetrical and asymmetrical groups. The volume, major axis length, maximum cross-sectional area, horizontal cross-sectional area 5 mm above the mandibular foramen (CSAF), and orientation were calculated automatically. RESULTS: In the asymmetrical group, the volume and major axis length on the deviated side were lower than on the nondeviated side at T0, T1, and T2 (P <0.05). There were no significant differences in maximum cross-sectional area and CSAF bilaterally. The orientation was coronally more vertical and sagittally more forward on the deviated side (both P <0.001). In the symmetrical group, there were no significant bilateral differences at T0, T1, and T2. The volume, major axis length, and CSAF decreased, and the coronal orientation was more vertical on the nondeviated side at T2 than at T0 in both groups (P <0.05). The coronal plane orientation was more inclined on the deviated side at T2 than at T0 in the asymmetrical group (P <0.05). CONCLUSIONS: The smaller volume on the deviated side at T2 indicates the need for myofunctional training after surgery. The masseter muscle volume and the cross-sectional area did not recover to the preorthodontic levels. Studies with longer follow-up durations are needed to confirm these findings.

Synthesis, X-ray Crystal Structure, and Antimicrobial Studies of New Quinazolin-4(3H)-one Derivatives Containing the 1,2,4-Triazolo[3,4-b][1,3,4]thiadiazole Moiety and 4-Piperidinyl Linker.

A total of 35 new quinazolinone derivatives bearing the 1,2,4-triazolo[3,4-b][1,3,4]thiadiazole scaffold and the 4-piperidinyl linker were designed, prepared, and assessed for their antibacterial and antifungal activities. Among these derivatives, the chemical structure of compound F5 was clearly verified via single-crystal X-ray diffraction analysis. The experimental results revealed that some of the compounds displayed good even excellent inhibitory effects toward the tested phytopathogenic bacteria. For instance, compound F33 was capable of strongly inhibiting Xanthomonas oryzae pv. oryzae (Xoo) in vitro with an EC50 (half-maximal effective concentration) value of 4.1 µg/mL, about 16-fold more effective than the commercialized bactericide bismerthiazol. Significantly, this compound also effectively suppressed the proliferation of Xoo in the potted rice plants, showing a good in vivo protection efficacy of 47.6% at 200 µg/mL. Subsequently, the antibacterial mechanisms of compound F33 were explored by means of different biophysical and biochemical methods. Last, some of the compounds were found to possess relatively good antifungal activities in vitro, like compound F19 against Phytophthora nicotianae (with an inhibition rate of 67.2% at 50 µg/mL). In a word, the current experimental results imply that the 4-piperidinyl-bridged quinazolinone-1,2,4-triazolo[3,4-b][1,3,4]thiadiazole derivatives possess potential as lead compounds for developing more efficient anti-Xoo bactericides.

Design, synthesis, and antimicrobial evaluation of 2-aminothiazole derivatives bearing the 4-aminoquinazoline moiety against plant pathogenic bacteria and fungi.

BACKGROUND: To find more effective agricultural antibiotics, a class of new 2-aminothiazole derivatives containing the 4-aminoquinazoline moiety were synthesized and evaluated for their antimicrobial properties against phytopathogenic bacteria and fungi of agricultural importance. RESULTS: All the target compounds were fully characterized by 1 H NMR, 13 C NMR, and high-resolution mass spectrometry. The bioassay results showed that compound F29 with a 2-pyridinyl substituent exhibited an outstanding antibacterial effect against Xanthomonas oryzae pv. oryzicola (Xoc) in vitro, having an half-maximal effective concentration (EC50 ) value as low as 2.0 µg/mL (over 30-fold more effective than the commercialized agrobactericide bismerthiazol, with an EC50 value of 64.3 µg/mL). In addition, compound F8 with a 2-fluorophenyl group demonstrated a good inhibitory activity toward the bacterium Xanthomonas axonopodis pv. citri (Xac), around twofold more active than bismerthiazol in terms of their EC50 values (22.8 versus 71.5 µg/mL). Interestingly, this compound also demonstrated a notable fungicidal effect against Phytophthora parasitica var. nicotianae, with an EC50 value largely comparable with that of the commercialized fungicide carbendazim. Finally, mechanistic studies revealed that compound F29 exerted its antibacterial effects by increasing the permeability of bacterial membranes, reducing the release of extracellular polysaccharides, and triggering morphological changes of bacterial cells. CONCLUSION: Compound F29 has promising potential as a lead compound for developing more efficient bactericides to fight against Xoc. © 2023 Society of Chemical Industry.

Design, synthesis, crystal structure, and antimicrobial activities of new quinazoline derivatives containing both the sulfonate ester and piperidinylamide moieties.

BACKGROUND: To discover more efficient antimicrobial agents in agriculture, a series of new quinazoline derivatives bearing both sulfonate ester and piperidine-4-carboxamide moieties were synthesized and assessed for their antimicrobial effects. RESULTS: All of the target compounds were fully characterized by proton (1 H) nuclear magnetic resonance (NMR), carbon-13 (13 C) NMR, and high-resolution mass spectroscopy (HRMS), and compound III-6 containing a 3-bromophenyl substituent was clearly confirmed via single-crystal X-ray diffraction analysis. The bioassay results indicated that some compounds displayed noticeable inhibitory effects in vitro against Xanthomonas oryzae pv. oryzicola (Xoc). Further measurements of median effective concentration (EC50 ) values showed that compound III-17 bearing a 4-methoxyphenyl group had the best anti-Xoc efficacy (EC50 = 12.4 µg mL-1 ), far better than the commercialized bismerthiazol (77.5 µg mL-1 ). Moreover, this compound also demonstrated good protection and curative activities in vivo against rice bacterial leaf streak caused by Xoc. CONCLUSION: Compound III-17 had a good potential for further development as a new bactericide for controlling Xoc. © 2023 Society of Chemical Industry.

Design, synthesis, crystal structure, and in vitro antibacterial activities of sulfonamide derivatives bearing the 4-aminoquinazoline moiety.

A total of 66 sulfonamide derivatives bearing the 4-aminoquinazoline moiety were designed and synthesized, and their structures were fully characterized by 1H NMR, 13C NMR, and HRMS techniques. Among them, the structures of compounds 5A10 and 5B11 were further confirmed through X-ray single-crystal diffraction analyses. The bioassay results indicated that some of the target compounds displayed higher inhibition activities in vitro against the tested phytopathogenic bacteria. For example, compound 5A26 exhibited a strong anti-Xanthomonas oryzae pv. oryzicola (Xoc) efficacy with an EC50 (half-maximal effective concentration) value of 30.6 µg/mL, over twofold more active than control agent bismerthiazol (BMT). Additionally, compound 5B14 had a good antibacterial effect against the phytopathogen Xanthomonas axonopodis pv. citric (Xac) with EC50 = 34.5 µg/mL, significantly better than control agent BMT (71.5 µg/mL). The anti-Xoc mechanistic studies showed that compound 5A26 exerted its antibacterial efficacy by increasing the permeability of bacterial membrane, decreasing the content of extracellular polysaccharides, and triggering morphological changes of bacterial cells.

Discovery of Novel Quinazoline-2-Aminothiazole Hybrids Containing a 4-Piperidinylamide Linker as Potential Fungicides against the Phytopathogenic Fungus Rhizoctonia solani.

A total of 29 novel quinazoline-2-aminothiazole hybrids containing a 4-piperidinylamide linker were designed, synthesized, and evaluated for their anti-microbial properties against phytopathogenic fungi and bacteria of agricultural importance. The anti-fungal assays indicated that some of the target compounds exhibited excellent inhibitory effects in vitro against Rhizoctonia solani. For example, 11 compounds within this series (including 4a, 4g, 4h, 4j, 4o, 4s, 4t, 4u, 4v, 4y, and 4b') were found to possess EC50 values (effective concentration for 50% activity) ranging from 0.42 to 2.05 µg/mL against this pathogen. In particular, compound 4y with a 2-chloro-6-fluorophenyl substituent displayed a potent anti-R. solani efficacy with EC50 = 0.42 µg/mL, nearly threefold more effective than the commercialized fungicide Chlorothalonil (EC50 = 1.20 µg/mL) and also slightly superior to the other fungicide Carbendazim (EC50 = 0.53 µg/mL). Moreover, compound 4y could efficiently inhibit the growth of R. solani in vivo on the potted rice plants, displaying an impressive protection efficacy of 82.3% at 200 µg/mL, better than those of the fungicides Carbendazim (69.8%) and Chlorothalonil (48.9%). Finally, the mechanistic studies showed that compound 4y exerted its anti-fungal effects by altering the mycelial morphology, increasing the cell membrane permeability, and destroying the cell membrane integrity. On the other hand, some compounds demonstrated good anti-bacterial effects in vitro against Xanthomonas oryzae pv. oryzae (Xoo). Overall, the presented results implied that 4-piperidinylamide-bridged quinazoline-2-aminothiazole hybrids held the promise of acting as lead compounds for developing more efficient fungicides to control R. solani.

A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.

Purpose: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results: There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods: We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions: Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance: These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.

The Chinese pine genome and methylome unveil key features of conifer evolution.

Conifers dominate the world's forest ecosystems and are the most widely planted tree species. Their giant and complex genomes present great challenges for assembling a complete reference genome for evolutionary and genomic studies. We present a 25.4-Gb chromosome-level assembly of Chinese pine (Pinus tabuliformis) and revealed that its genome size is mostly attributable to huge intergenic regions and long introns with high transposable element (TE) content. Large genes with long introns exhibited higher expressions levels. Despite a lack of recent whole-genome duplication, 91.2% of genes were duplicated through dispersed duplication, and expanded gene families are mainly related to stress responses, which may underpin conifers' adaptation, particularly in cold and/or arid conditions. The reproductive regulation network is distinct compared with angiosperms. Slow removal of TEs with high-level methylation may have contributed to genomic expansion. This study provides insights into conifer evolution and resources for advancing research on conifer adaptation and development.

Robust Collaborative Learning of Patch-Level and Image-Level Annotations for Diabetic Retinopathy Grading From Fundus Image.

Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in both academic and industrial communities. Most convolutional neural network-based algorithms treat DR grading as a classification task via image-level annotations. However, these algorithms have not fully explored the valuable information in the DR-related lesions. In this article, we present a robust framework, which collaboratively utilizes patch-level and image-level annotations, for DR severity grading. By an end-to-end optimization, this framework can bidirectionally exchange the fine-grained lesion and image-level grade information. As a result, it exploits more discriminative features for DR grading. The proposed framework shows better performance than the recent state-of-the-art algorithms and three clinical ophthalmologists with over nine years of experience. By testing on datasets of different distributions (such as label and camera), we prove that our algorithm is robust when facing image quality and distribution variations that commonly exist in real-world practice. We inspect the proposed framework through extensive ablation studies to indicate the effectiveness and necessity of each motivation. The code and some valuable annotations are now publicly available.

Spherical Interpolated Convolutional Network With Distance-Feature Density for 3-D Semantic Segmentation of Point Clouds.

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3-D convolution kernel to extract features from raw 3-D point clouds because of the unstructured property of point clouds. In this article, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3-D convolution operator. In addition, this article analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of the spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3-D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. The comparison experiments with the traditional grid-shaped 3-D convolution operator demonstrated that the newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. The source codes will be released on https://github.com/IRMVLab/SIConv.

Automatic Masseter Muscle Accurate Segmentation from CBCT Using Deep Learning-Based Model.

Segmentation of the masseter muscle (MM) on cone-beam computed tomography (CBCT) is challenging due to the lack of sufficient soft-tissue contrast. Moreover, manual segmentation is laborious and time-consuming. The purpose of this study was to propose a deep learning-based automatic approach to accurately segment the MM from CBCT under the refinement of high-quality paired computed tomography (CT). Fifty independent CBCT and 42 clinically hard-to-obtain paired CBCT and CT were manually annotated by two observers. A 3D U-shape network was carefully designed to segment the MM effectively. Manual annotations on CT were set as the ground truth. Additionally, an extra five CT and five CBCT auto-segmentation results were revised by one oral and maxillofacial anatomy expert to evaluate their clinical suitability. CBCT auto-segmentation results were comparable to the CT counterparts and significantly improved the similarity with the ground truth compared with manual annotations on CBCT. The automatic approach was more than 332 times shorter than that of a human operation. Only 0.52% of the manual revision fraction was required. This automatic model could simultaneously and accurately segment the MM structures on CBCT and CT, which can improve clinical efficiency and efficacy, and provide critical information for personalized treatment and long-term follow-up.

Classification of brain disease in magnetic resonance images using two-stage local feature fusion.

BACKGROUND: Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. METHODS: Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. RESULTS: We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. DISCUSSION: This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.

Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning.

An effective representation model, which aims to mine the most meaningful information in the data, plays an important role in visual tracking. Some recent particle-filter-based trackers achieve promising results by introducing the low-rank assumption into the representation model. However, their assumed low-rank structure of candidates limits the robustness when facing severe challenges such as abrupt motion. To avoid the above limitation, we propose a temporal restricted reverse-low-rank learning algorithm for visual tracking with the following advantages: 1) the reverse-low-rank model jointly represents target and background templates via candidates, which exploits the low-rank structure among consecutive target observations and enforces the temporal consistency of target in a global level; 2) the appearance consistency may be broken when target suffers from sudden changes. To overcome this issue, we propose a local constraint via l1,2 mixed-norm, which can not only ensures the local consistency of target appearance, but also tolerates the sudden changes between two adjacent frames; and 3) to alleviate the inference of unreasonable representation values due to outlier candidates, an adaptive weighted scheme is designed to improve the robustness of the tracker. By evaluating on 26 challenge video sequences, the experiments show the effectiveness and favorable performance of the proposed algorithm against 12 state-of-the-art visual trackers.

The Twist Tensor Nuclear Norm for Video Completion.

In this paper, we propose a new low-rank tensor model based on the circulant algebra, namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor representation to laterally store 2-D data slices in order. On one hand, t-TNN convexly relaxes the tensor multirank of the twist tensor in the Fourier domain, which allows an efficient computation using fast Fourier transform. On the other, t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain, which extends the traditional matrix nuclear norm in a block circulant way. We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness, especially when dealing with video recorded by a nonstationary panning camera. The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance. This representation, after transformation, exploits the horizontal translation relationship between the frames in a video, and endows the t-TNN model with a more powerful ability to reconstruct panning videos than the existing state-of-the-art low-rank models.