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Accurate positioning of fruit is a key issue that has attracted much attention in the field of harvest robots. The complex environment and close proximity make the perception of dense crops in greenhouses a challenging problem. Different from various solutions proposed involving special equipment or other auxiliary information, we propose a novel positioning approach based on instance segmentation using a monocular RGB camera. To achieve high position accuracy, we first design a deep convolutional neural network (CNN) in a multi-task framework to export a binary segmentation map and an embedded feature map. To solve the problem of performance degradation in the intersection-over-union (IoU) for the binary segmentation task caused by multi-task optimisation, the encoder part of our network is redesigned on the basis of a Visual Geometry Group network with 16 convolutional layers (VGG-16). Then, mean-shift clustering is used to achieve instance segmentation. Finally, a contour-finding algorithm is presented for outlining fruit without the help of any contextual information. Based on these contours, the five fruits with the largest contour areas are selected as the targets for positioning. We verify our method on a public sweet pepper dataset and achieve competitive results. Divided by the radius of the fruit, the average position error for the first target in the harvesting order is 0.18, which shows that our method outperforms the semantic segmentation method. For the first five targets in the harvesting order, this index is less than 0.3 on average, similar to that of the semantic segmentation method with only one target output.

Myocardial infarction (MI) is the most common cause of cardiac injury, resulting in widespread and irreversible damage to the heart. The incidence of MI gives rise to the excessive production of inflammatory cytokines that further promotes myocardial dysfunction. Wnt/β-catenin signaling pathway is adversely activated during MI and plays an important role in the modulation of the inflammatory response following tissue injury. Huoxin pill (HXP) is a Traditional Chinese Medicine formulation that has been long used in the treatment of cardiovascular diseases, however its mechanisms of cardioprotection remain unclear.We performed murine models of MI in order to model myocardial ischemic damage and examine the effect and underlying mechanism of HXP in protecting against myocardial ischemic injury. We further constructed conditional cardiomyocyte-specific β-catenin knockout mice and induced surgical MI in order to better understand the role of Wnt/β-catenin signaling following myocardial infarction in the adult heart.HXP administration strongly protected against cardiac ischemic injury, improved cardiac function, and markedly decreased the expression of pro-inflammatory cytokines following MI. Nuclear activation of β‑catenin resulted in significantly increased nuclear translocation and activation of NF-κB. In contrast, cardiomyocyte-specific deletion of β-catenin decreased NF-κB activation and exhibited beneficial effects following ischemic injury. Hence, HXP protected against MI-induced ischemic injury and excessive inflammatory response via inhibiting Wnt/β‑catenin signaling.Our study elucidated the role of HXP in protecting against ischemic myocardial injury via preventing MI-induced inflammatory response, which was mediated by its ability to inhibit adverse Wnt/β‑catenin signaling activation. Thus, our study provides the basis for the implementation of HXP as an effective therapeutic strategy in protecting against myocardial ischemic diseases.

An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification.

Laser etching process of aluminum thin film on polyimide substrate was simulated by the finite element analysis software ANSYS to study the etching results. Theoretical etching profiles of aluminum thin film with different laser parameters were obtained from the simulation, which is in good agreement with experimental results observed by SEM, it shows that a best etching result can be obtained and it corresponded to a set of reasonable parameters. By optimizing such laser etching parameters as laser power and beam scanning velocity, etc., aluminum thin film could be etched ideally without damaging the polyimide substrate. Therefore, the simulation results can be taken as a guide for determination of practical laser etching parameters in order to obtain ideal etching patterns.

Daptomycin, a novel cyclic lipopeptide antibiotic against Gram-positive bacteria, is produced by Streptomyces roseosporus. Though its biosynthetic mechanism, structural shuffling and fermentation optimization have been extensively studied, little is understood about its production regulation at the transcriptional levels. Here we reported that dptR2, encoding a DeoR-type regulator located close to the daptomycin biosynthesis gene cluster in S. roseosporus SW0702, is required for daptomycin production, but not for the expression of daptomycin gene cluster, suggesting that DptR2 was not a pathway-specific regulator. Furthermore, EMSA and qRT-PCR analysis suggested that DptR2 was positively auto-regulated by binding to its own promoter. Meanwhile, the binding sites on the dptR2 promoter were determined by a DNase I footprinting assay, and the essentiality of the inverted complementary sequences in the protected region for DptR2 binding was assessed. Our results for the first time reported the regulation of daptomycin production at the transcriptional level in S. roseosporus.

Canonical Wnt/β-catenin signaling pathway plays important roles in multiple aspects of cellular responses in development and diseases. It is currently thought that Wnt receptor Frizzled (Frz) exists separately to Wnt coreceptors LRP5 and LRP6 (LRP5/6), and that Wnt-Frz-LRP5/6 triple complex formation bridged by Wnt ligand is needed for canonical pathway activation. We recently showed that Frz and LRP5/6 interact with each other in the absence of Wnt ligand binding and this interaction maintains the Frz-LRP5/6 complex in an inactive state. Here, we further show that Wnt ligand stimulation induces conformational change of the Frz-LRP6 complex and leads to hexamer formation containing the core LDLR domain-mediated LRP6 homodimer that is stabilized by two pairs of Wnt3a and Frz8, that is, Wnt3a-Frz8-LRP6-LRP6-Frz8-Wnt3a. This LDLR-mediated LRP6 dimerization is essential for robust canonical Wnt pathway activation. Our study thus suggests a previously unrecognized mode of receptor interaction in Wnt signal initiation.