Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies.

Owing to their limited accuracy and narrow applicability, current antimicrobial peptide (AMP) prediction models face obstacles in industrial application. To address these limitations, we developed and improved an AMP prediction model using Comparing and Optimizing Multiple DEep Learning (COMDEL) algorithms, coupled with high-throughput AMP screening method, finally reaching an accuracy of 94.8% in test and 88% in experiment verification, surpassing other state-of-the-art models. In conjunction with COMDEL, we employed the phage-assisted evolution method to screen Sortase in vivo and developed a cell-free AMP synthesis system in vitro, ultimately increasing AMPs yields to a range of 0.5-2.1 g/L within hours. Moreover, by multi-omics analysis using COMDEL, we identified Lactobacillus plantarum as the most promising candidate for AMP generation among 35 edible probiotics. Following this, we developed a microdroplet sorting approach and successfully screened three L. plantarum mutants, each showing a twofold increase in antimicrobial ability, underscoring their substantial industrial application values.

Full-length radiograph based automatic musculoskeletal modeling using convolutional neural network.

Full-length radiographs contain information from which many anatomical parameters of the pelvis, femur, and tibia may be derived, but only a few anatomical parameters are used for musculoskeletal modeling. This study aimed to develop a fully automatic algorithm to extract anatomical parameters from full-length radiograph to generate a musculoskeletal model that is more accurate than linear scaled one. A U-Net convolutional neural network was trained to segment the pelvis, femur, and tibia from the full-length radiograph. Eight anatomic parameters (six for length and width, two for angles) were automatically extracted from the bone segmentation masks and used to generate the musculoskeletal model. Sørensen-Dice coefficient was used to quantify the consistency of automatic bone segmentation masks with manually segmented labels. Maximum distance error, root mean square (RMS) distance error and Jaccard index (JI) were used to evaluate the geometric accuracy of the automatically generated pelvis, femur and tibia models versus CT bone models. Mean Sørensen-Dice coefficients for the pelvis, femur and tibia 2D segmentation masks were 0.9898, 0.9822 and 0.9786, respectively. The algorithm-driven bone models were closer to the 3D CT bone models than the scaled generic models in geometry, with significantly lower maximum distance error (28.3 % average decrease from 24.35 mm) and RMS distance error (28.9 % average decrease from 9.55 mm) and higher JI (17.2 % average increase from 0.46) (P < 0.001). The algorithm-driven musculoskeletal modeling (107.15 ± 10.24 s) was faster than the manual process (870.07 ± 44.79 s) for the same full-length radiograph. This algorithm provides a fully automatic way to generate a musculoskeletal model from full-length radiograph that achieves an approximately 30 % reduction in distance errors, which could enable personalized musculoskeletal simulation based on full-length radiograph for large scale OA populations.

METTL3/YTHDC1-medicated m6A modification of circRNA3634 regulates the proliferation and differentiation of antler chondrocytes by miR-124486-5-MAPK1 axis.

BACKGROUND: The deer antler, a remarkable mammalian appendage, has a growth rate surpassing that of any other known osseous organ. Emerging evidence indicates that circRNA and MAPK1 play critical roles in chondrocytes. Thus, exploration of their functions in antler chondrocytes will help us to understand the mechanism regulating the rapid antler growth. METHODS: qRT-PCR, western blot, and immunohistochemistry were used to assess the expression of mRNAs and proteins. CCK-8, EdU, Cell migration, ALP activity detection, and ALP staining examined the effects of MAPK1 in antler chondrocytes. FISH, RIP, and luciferase assays were performed to evaluate the interactions among circRNA3634/MAPK1 and miR-124486-5. RIP and RAP assays proved the binding interaction between circRNA3634 and RBPs. Me-RIP was used to determine the m6A methylation modification of circRNA3634. RESULTS: This study revealed high MAPK1 expression in antler cartilage tissue. Overexpression of MAPK1 promoted the proliferation, migration, and differentiation of antler chondrocytes and increased the expression of MAPK3, RAF1, MEK1, RUNX2, and SOX9. The silencing of MAPK1 had the opposite effect. CircRNA3634 was found to act as a molecular sponge for miR-124486-5, leading to increased MAPK1 expression and enhanced proliferation and migration of antler chondrocytes through competitive miR-124486-5 binding. We discovered that METTL3 mediates m6A modification near the splicing site of circRNA3634 and is involved in the proliferation and differentiation of antler chondrocytes. The m6A reader YTHDC1 facilitated the nuclear export of circRNA3634 in an m6A-dependent manner. Our results indicate that m6A-modified circRNA3634 promotes the proliferation of antler chondrocytes by targeting MAPK1 and show that the nuclear export of circRNA3634 is related to the expression of YTHDC1, suggesting that circRNA3634 could represent a critical regeneration marker for the antler. CONCLUSIONS: Our results revealed a novel m6A-modified circRNA3634 promoted the proliferation and differentiation of antler chondrocytes by regulating MAPK1. The nuclear export of circRNA3634 was related to the expression of YTHDC1.

Photosynthetic Living Fiber Fabrication from Algal-Bacterial Consortia with Controlled Spatial Distribution.

Living materials that combine living cells and synthetic matrix materials have become promising research fields in recent years. While multicellular systems present exclusive benefits in developing living materials over single-cell systems, creating artificial multicellular systems can be challenging due to the difficulty in controlling the multicellular assemblies and the complexity of cell-to-cell interactions. Here, we propose a coculture platform capable of isolating and controlling the spatial distribution of algal-bacterial consortia, which can be utilized to construct photosynthetic living fibers. Through coaxial extrusion-based 3D printing, hydrogel fibers containing bacteria or algae can be deposited into designated structures and further processed into materials with precise geometries. In addition, the photosynthetic living fibers demonstrate a significant synergistic catalytic effect resulting from the immobilization of both bacteria and algae, which effectively optimizes sewage treatment for bioremediation purposes. The integration of microbial consortia and 3D printing yields functional living materials with promising applications in biocatalysis, biosensing, and biomedicine. Our approach provides an optimized solution for constructing efficient multicellular systems and opens a new avenue for the development of advanced materials.

BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation.

Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robustness of skin image segmentation algorithms are still a challenging subject. For this reason, we proposed a bi-directional feedback dense connection network framework (called BiDFDC-Net), which can perform skin lesions accurately. Firstly, under the framework of U-Net, we integrated the edge modules into each layer of the encoder which can solve the problem of gradient vanishing and network information loss caused by network deepening. Then, each layer of our model takes input from the previous layer and passes its feature map to the densely connected network of subsequent layers to achieve information interaction and enhance feature propagation and reuse. Finally, in the decoder stage, a two-branch module was used to feed the dense feedback branch and the ordinary feedback branch back to the same layer of coding, to realize the fusion of multi-scale features and multi-level context information. By testing on the two datasets of ISIC-2018 and PH2, the accuracy on the two datasets was given by 93.51% and 94.58%, respectively.

Whole-Transcriptome Sequencing of Antler Tissue Reveals That circRNA2829 Regulates Chondrocyte Proliferation and Differentiation via the miR-4286-R+1/FOXO4 Axis.

The antler is the unique mammalian organ found to be able to regenerate completely and periodically after loss, and the continuous proliferation and differentiation of mesenchymal cells and chondrocytes together complete the regeneration of the antler. Circular non-coding RNAs (circRNAs) are considered to be important non-coding RNAs that regulate body development and growth. However, there are no reports on circRNAs regulating the antler regeneration process. In this study, full-transcriptome high-throughput sequencing was performed on sika deer antler interstitial and cartilage tissues, and the sequencing results were verified and analyzed. The competing endogenous RNA (ceRNA) network related to antler growth and regeneration was further constructed, and the differentially expressed circRNA2829 was screened out from the network to study its effect on chondrocyte proliferation and differentiation. The results indicated that circRNA2829 promoted cell proliferation and increased the level of intracellular ALP. The analysis of RT-qPCR and Western blot demonstrated that the mRNA and protein expression levels of genes involved in differentiation rose. These data revealed that circRNAs play a crucial regulatory role in deer antler regeneration and development. CircRNA2829 might regulate the antler regeneration process through miR-4286-R+1/FOXO4.

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.

Introduction: Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application. Methods: In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied. Results: The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%. Discussion: The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

Notch4 affects the proliferation and differentiation of deer antler chondrocytes through the Smad3/lncRNA27785.1 axis.

Long non-coding RNA play an importantr role in the differentiation of chondrocytes. This study aims to explore the role of long non-coding RNA in the transcriptional regulation of Notch4. In previous studies, it has been found that Notch signal can be used as the downstream of TGF-ß signal to affect the proliferation and differentiation of deer antler chondrocytes, but the specific mechanism remains unclear. Here we found that lncRNA27785.1 was involved in the regulation of TGF-ß/ Smad3 signal and Notch4 gene. The overexpression lncRNA27785.1 can negatively regulate the expression of Notch4 to inhibit cell proliferation and differentiation, while interference with lncRNA27785.1 can promote the expression of Notch4 gene to promote the proliferation and differentiation of chondrocytes. Subsequently, through luciferase experiment and CHIP experiment, we found that lncRNA27785.1 is regulated by Smad3 transcription, and Smad3 inhibited the expression of lncRNA27785.1. In addition, activated TGF-ß signaling can reduce the inhibitory effect of lncRNA27785.1 on Notch4 signaling. In summary, we found that lncRNA27785.1 and TGF-ß/Smad3 play an important role in Notch4 signaling. Our findings provided evidence to explain how TGF-ß signaling regulate the Notch signaling pathway to influence chondrocyte proliferation and differentiation by a specific lncRNA27785.1.