The egg ribonuclease SjCP1412 accelerates liver fibrosis caused by Schistosoma japonicum infection involving damage-associated molecular patterns (DAMPs).

Schistosomiasis, a parasite infectious disease caused by Schistosoma japonicum, often leads to egg granuloma and fibrosis due to the inflammatory reaction triggered by egg antigens released in the host liver. This study focuses on the role of the egg antigens CP1412 protein of S. japonicum (SjCP1412) with RNase activity in promoting liver fibrosis. In this study, the recombinant egg ribonuclease SjCP1412, which had RNase activity, was successfully prepared. By analysing the serum of the population, it has been proven that the anti-SjCP1412 IgG in the serum of patients with advanced schistosomiasis was moderately correlated with liver fibrosis, and SjCP1412 may be an important antigen associated with liver fibrosis in schistosomiasis. In vitro, the rSjCP1412 protein induced the human liver cancer cell line Hep G2 and liver sinusoidal endothelial cells apoptosis and necrosis and the release of proinflammatory damage-associated molecular patterns (DAMPs). In mice infected with schistosomes, rSjCP1412 immunization or antibody neutralization of SjCP1412 activity significantly reduced cell apoptosis and necroptosis in liver tissue, thereby reducing inflammation and liver fibrosis. In summary, the SjCP1412 protein plays a crucial role in promoting liver fibrosis during schistosomiasis through mediating the liver cells apoptosis and necroptosis to release DAMPs inducing an inflammatory reaction. Blocking SjCP1412 activity could inhibit its proapoptotic and necrotic effects and alleviate hepatic fibrosis. These findings suggest that SjCP1412 may be served as a promising drug target for managing liver fibrosis in schistosomiasis japonica.

Characterization of a novel VenusX orthogonal dual-layer multileaf collimator.

PURPOSE: To investigate and characterize the performance of a novel orthogonal dual-layer alpha multileaf collimator (αMLC) mounted on the LinaTech VenusX linac. METHODS: We evaluated leaf positioning accuracy and reproducibility using an electronic portal imaging device through the picket fence test. The average, interleaf, intraleaf, and leaf tip transmissions of the single and dual layers were measured using an ionization chamber. Square and rhombus fields were used to evaluate the leaf penumbra of αMLC. To investigate the advantages of the orthogonal dual-layer multileaf collimator (MLC) in field shaping, right triangular and circular pattern fields were formed using both the dual layers and single layers of the αMLC. RESULTS: The average maximum positioning deviations of the upper and lower αMLC over 1 year were 0.76 ± 0.09 mm and 0.62 ± 0.07 mm, respectively. The average transmissions were 1.87%, 1.83%, and 0.03% for the upper-, lower- and dual-layer αMLC, respectively. The maximum interleaf transmissions of the lower- and dual-layer were 2.43% and 0.17%, respectively. The leaf tip transmissions were 9.34% and 0.25%, respectively. The penumbra of the square field was 6.2 mm in the X direction and 8.0 mm in the Y direction. The average penumbras of the rhombus fields with side lengths of 5 and 10 cm were 3.6 and 4.9 mm, respectively. For the right triangular and circular fields, the fields shaped by the dual-layer leaves were much closer to the set field than those shaped by single-layer leaves. The dose undulation amplitude of the 50% isodose lines and leaf stepping angle change of the dual-layer leaves were smaller than those of the single-layer leaves. CONCLUSIONS: The αMLC benefits from its orthogonal dual-layer design. Leaf transmission, dose undulations at the field edge, and MLC field dependence of the leaf stepping angle of the dual-layer αMLC were remarkably reduced.

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask.

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion.

To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey.

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks.

IMPORTANCE: The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

B Lymphocytes Balance Th1/Th2 to Participate in Fibrotic Hypersensitivity Pneumonia Induced by Pigeon Shedding.

INTRODUCTION: We investigated the molecular mechanism by which B lymphocytes regulate Th1/Th2 imbalance to participate in the pulmonary fibrosis in hypersensitivity pneumonia induced by pigeon shedding in rats. METHODS: CD19+ rats and CD19- rats were used to construct animal models of fibrotic hypersensitivity pneumonia. DAPT was used to inhibit the Notch signaling pathway. The pathological changes were assessed with HE and Masson staining. Protein level was detected with Western blot. Th1/Th2 ratio was analyzed with flow cytometry. Cytokine levels were measured with ELISA. RESULTS: The pathological changes of pulmonary fibrosis were not obvious in the CD19- rats and after DAPT treatment. Notch signaling pathway proteins, including Notch1, Notch2, Jag1, Jag2, DLL1, and DLL4, in lung tissues of model rats were all significantly upregulated than those in control rats. However, these proteins in CD19- rats were lower in CD19+ rats, suggesting that B cells play a key role in inducing pneumonia. Besides, the Th1/Th2 ratio in the BALF of model rats decreased, which was further reversed by DAPT. However, we found that in CD19- rats, the regulation of the Th1/Th2 ratio by the Notch signaling pathway was lost. CONCLUSION: Deleting B lymphocytes or blocking the Notch pathway both reversed the Th1/Th2 imbalance in fibrotic hypersensitivity pneumonia and inhibited pulmonary fibrosis.

Infrared spectra of the SARS-CoV-2 spike receptor-binding domain: Molecular dynamics simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread around the world rapidly, which seriously threatens to human health and safety. The rapid detection of the virus in the early stage is very important to prevent the cross infection and transmission. It is also a key link in the post-treatment examination. This paper has explored the infrared (IR) spectra of spike protein receptor-binding domain (RBD) for SARS-CoV-2 using molecular dynamics simulations, and the absorption bands are assigned. The calculated IR spectra of water and insulin are compared with that measured in the related literatures. The results showed that O-H stretching vibration generated a strong absorption band located around 3591 cm-1, the oscillator strength of 310 K is slightly higher than that at 298 K. The absorption peaks have a small red shift or blue shift with the change of temperature. As a theoretical basis for the optical detection of SARS-CoV-2 virus, this work will play a positive role in promoting the development of new virus detection technology.

Nitrogen-rich based conjugated microporous polymers for highly efficient adsorption and removal of COVID-19 antiviral drug chloroquine phosphate from environmental waters.

Chloroquine phosphate (CQP) has been suggested as an important and effective clinical reliever medication for the 2019 coronavirus (COVID-19). Nevertheless, its excessive use will inevitably cause irreparable damage to the entire ecosystem, thereby posing a considerable environmental safety concern. Hence, the development of highly-efficient methods of removing CQP from water pollution sources, e.g., effluents from hospitals and pharmaceutical factories is significant. This study reported the fabrication of novel C-N bond linked conjugated microporous polymers (CMPs) (BPT-DMB-CMP) with multiple nitrogen-rich anchoring sites for the quick and efficient removal of CQP from aqueous solutions. The irreversible covalent C-N bond linked in the internal framework of BPT-DMB-CMP endowed it with good chemical stability and excellent adsorbent regeneration. With its predesigned functional groups (i.e., rich N-H bonds, triazine rings, and benzene rings) and large area surface (1,019.89 m2·g-1), BPT-DMB-CMP demonstrated rapid adsorption kinetics (25 min) and an extraordinary adsorption capacity (334.70 mg·g-1) for CQP, which is relatively higher than that of other adsorbents. The adsorption behavior of CQP on BPT-DMB-CMP corresponded with Liu model and mixed-order model. Based on the density functional theory (DFT) calculations, X-ray photoelectron spectroscopy (XPS), and adsorption comparisons test, the halogen bonding, and hydrogen bonding cooperates with π - π, C - H···π interactions and size-matching effect in the CQP adsorption system on BPT-DMB-CMP. The excellent practicability for the removal of CQP from real wastewater samples verified the prospect of practical application of BPT-DMB-CMP. BPT-DMB-CMP exhibited the application potentials for the adsorption of other antiviral drugs. This work opens up an efficient, simple, and high adsorption capacity way for removal CQP.

Infrared and Visible Image Fusion Technology and Application: A Review.

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.

Conditional Generative Adversarial Network for Spectral Recovery to Accelerate Single-Cell Raman Spectroscopic Analysis.

Raman spectroscopy is a powerful tool to investigate cellular heterogeneity. However, Raman spectra for single-cell analysis are hindered by a low signal-to-noise ratio (SNR). Here, we demonstrate a simple and reliable spectral recovery conditional generative adversarial network (SRGAN). SRGAN reduced the data acquisition time by 1 order of magnitude (i.e., 30 vs 3 s) by improving the SNR by a factor of ∼6. We classified five major foodborne bacteria based on single-cell Raman spectra to further evaluate the performance of SRGAN. Spectra processed using SRGAN achieved an identification accuracy of 94.9%, compared to 60.5% using unprocessed Raman spectra. SRGAN can accelerate spectral collection to improve the throughput of Raman spectroscopy and enable real-time monitoring of single living cells.

Insights into the conformation changes of SARS-CoV-2 spike receptor-binding domain on graphene.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely spread in the world, causing more than two million deaths and seriously threatening human life. Effective protection measures are important to prevent the infection and spreading of the virus. To explore the effects of graphene on the virus adsorption and its biological properties, the adsorption process of the receptor binding domain (RBD) of SARS-CoV-2 on graphene has been investigated by molecular dynamics simulations in this paper. The results show that RBD can be quickly adsorbed onto the surface of graphene due to π - π stacking and hydrophobic interactions. Residue PHE486 with benzene ring has stronger adsorption force and the maximum contact area with graphene. Graphene significantly affects the secondary structure of RBD area, especially on the three key sites of binding with human ACE2, GLY476, PHE486 and ASN487. The binding free energy of RBD and graphene shows that the adsorption is irreversible. Undoubtedly, these changes will inevitably affect the pathogenicity of the virus. Therefore, this study provides a theoretical basis for the application of graphene in the protection of SARS-CoV-2, and also provides a reference for the potential application of graphene in the biomedical field.

Learning to Reason on Tree Structures for Knowledge-Based Visual Question Answering.

Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism or use handcrafted rules to generate a layout for performing compositional reasoning, which lacks the process of visual reasoning and introduces a large number of parameters for predicting the correct answer. For conducting visual reasoning on all kinds of image-question pairs, in this paper, we propose a novel reasoning model of a question-guided tree structure with a knowledge base (QGTSKB) for addressing these problems. In addition, our model consists of four neural module networks: the attention model that locates attended regions based on the image features and question embeddings by attention mechanism, the gated reasoning model that forgets and updates the fused features, the fusion reasoning model that mines high-level semantics of the attended visual features and knowledge base and knowledge-based fact model that makes up for the lack of visual and textual information with external knowledge. Therefore, our model performs visual analysis and reasoning based on tree structures, knowledge base and four neural module networks. Experimental results show that our model achieves superior performance over existing methods on the VQA v2.0 and CLVER dataset, and visual reasoning experiments prove the interpretability of the model.

Polarization-Dependent Ultrasensitive Dynamic Wrinkling on Floating Films Induced by Photo-Orientation of Azopolymer.

Ubiquitous surface wrinkling has been well-studied theoretically and experimentally. How to modulate the stress state of a liquid-supported system for the unexploited wrinkling capabilities remains a challenge. Here we report a simple linearly-polarized-light illumination to spatiotemporally trigger ultrasensitive in situ dynamic wrinkling on a floating azo-film. The smart combination of the liquid substrate with photoresponsive azo-moieties leads to the light-induced ultrafast wrinkling evolution, accompanied by unprecedented sequential wrinkling orientation conversion (from polarization-parallel to polarization-perpendicular). The involved different polarization-dependent sequential photo-orientation for azo side chains and azo-grafted main chains of azopolymers is disclosed experimentally for the first time. Meanwhile, programmable dynamic wrinkling with all-optical switchable surface topographies is available, which has wide application potentials in photoresponsive soft photonics.

On-chip monolithic Fourier transform spectrometers assisted by cGAN spectral prediction.

Silicon photonic spatial heterodyne Fourier transform spectrometers (SH-FTSs) are attractive with chip-scale monolithic arrays of imbalanced Mach-Zehnder interferometers; however, there exist optical path difference (OPD) errors from the inevitable fabrication imperfection, which will severely distort the retrieved spectra. In this Letter, we propose that a predictive model can be created for rapid and accurate spectral recovery based on the conditional generative adversarial network (cGAN) featuring strong input-on-output supervision, instead of both complicated physical OPD modification and time-consuming iterative spectral calculation. As a demonstration, cGAN spectral prediction was performed for our previously presented dual-polarized SH-FTS with large OPD errors [Opt. Lett.44, 2923 (2019)OPLEDP0146-959210.1364/OL.44.002923]. Due to the strong noise-resistant capability, the cGAN-predicted spectra can stay reliable, even though the signal-to-noise ratio of acquired interferograms dramatically drops from 1000 to 100, implying a lower limit of detection.

Adversarial Learning with Bidirectional Attention for Visual Question Answering.

In this paper, we provide external image features and use the internal attention mechanism to solve the VQA problem given a dataset of textual questions and related images. Most previous models for VQA use a pair of images and questions as input. In addition, the model adopts a question-oriented attention mechanism to extract the features of the entire image and then perform feature fusion. However, the shortcoming of these models is that they cannot effectively eliminate the irrelevant features of the image. In addition, the problem-oriented attention mechanism lacks in the mining of image features, which will bring in redundant image features. In this paper, we propose a VQA model based on adversarial learning and bidirectional attention. We exploit external image features that are not related to the question to form an adversarial mechanism to boost the accuracy of the model. Target detection is performed on the image-that is, the image-oriented attention mechanism. The bidirectional attention mechanism is conducive to promoting model attention and eliminating interference. Experimental results are evaluated on benchmark datasets, and our model performs better than other models based on attention methods. In addition, the qualitative results show the attention maps on the images and leads to predicting correct answers.

On-chip polarization-insensitive Fourier transform spectrometer.

Chip-scale monolithic Fourier transform spectrometers (FTSs) offer great potential for inexpensive, high-resolution, and robust spectroscopic applications in a wide variety of scenarios. Having attracted considerable attention, spatial heterodyne FTSs (SH-FTSs) are featured with a simple and stable configuration composed of an array of Mach-Zehnder interferometers (MZIs) with linearly increasing optical path differences. Owing to the strong waveguide birefringence, MZIs on the popular silicon-on-insulator platform are polarization-sensitive, raising the challenge of polarization control of incident light. We propose and demonstrate a polarization-insensitive SH-FTS using a two-dimensional grating coupler to split an arbitrary state of polarization into two orthogonal polarization components that are both coupled into the TE mode but propagate in opposite directions in the arrayed MZIs. The two orthogonal polarization components are finally recombined in photodetection without polarization-dependent losses. An edge-coupling configuration using a polarization splitter-rotator is also proposed.

Path-Guided Hierarchical Surface Relief Gratings on Azo-Films Induced by Polarized Light Illumination through Surface-Wrinkling Phase Mask.

Surface relief gratings (SRGs) with hierarchical microstructures are highly needed owing to their diverse applications in various fields. Here, we introduce surface-wrinkling templates as novel nonlithographic phase masks to direct the generation of hierarchical well-prescribed SRGs on nonconformally contacted azo-films by a simple single-beam illumination. The light-induced SRGs have controlled microstructures including single/double/triple wavelengths and single/double orientations as well as their organizations. These microstructures can be well tailored by the wavelength of the surface-wrinkling phase masks and the polarization direction of incident light relative to the wrinkling patterns in the phase masks. Interestingly, we find that the larger wavelength is induced prior to the smaller ones, offering another new strategy to tailor the microstructures of SRGs through simple manipulation of the illumination duration. In particular, path-guided SRGs with unprecedented well-organized hierarchical microstructures have been available in the case of controlled moving of the light illumination through the surface-wrinkling phase mask. As demonstrated, the obtained hierarchical SRGs with the capability of multiple optical inscription/erasure have great application potentials in fields such as confidential information (or pattern) records and encryption/decryption.

Carbogenic Nanozyme with Ultrahigh Reactive Nitrogen Species Selectivity for Traumatic Brain Injury.

Reactive oxygen and nitrogen species (RONS), especially reactive nitrogen species (RNS) are intermediate products during incidence of nervous system diseases, showing continuous damage for traumatic brain injury (TBI). Here, we developed a carbogenic nanozyme, which shows an antioxidant activity 12 times higher than ascorbic acid (AA) and behaves as multienzyme mimetics. Importantly, the nanozyme exhibits an ultrahigh scavenging efficiency (∼16 times higher than AA) toward highly active RNS, such as •NO and ONOO- as well as traditional reactive oxygen species (ROS) including O2•-, H2O2, and •OH. In vitro experiments show that neuron cells injured by H2O2 or lipopolysaccharide can be significantly recovered after carbogenic nanozyme treatment via scavenging all kinds of RONS. Moreover, the carbogenic nanozyme can serve as various enzyme mimetics and eliminate the harmful peroxide and glutathione disulfide from injured mice, demonstrating its potential as a therapeutic for acute TBI.

Bone Fracture Enhanced Blood-Brain Barrier Breakdown in the Hippocampus and White Matter Damage of Stroke Mice.

BACKGROUND: Tibia fracture (BF) before stroke shortly causes long-term post-stroke memory dysfunction in mice. The mechanism is unclear. We hypothesize that BF enhances neuroinflammation and blood brain barrier (BBB) breakdown in the hippocampus and white matter (WM) damage. METHODS: Mice were assigned to groups: BF, stroke, BF+stroke (BF 6 h before stroke) and sham. BBB integrity was analyzed 3 days after the surgeries and WM injury was analyzed 3 days and 8 weeks after the surgeries. RESULTS: Stroke and BF+stroke groups had more activated microglia/macrophages and lower levels of claudin-5 in the ipsilateral hippocampi than the BF group. BF+stroke group had the highest number microglia/macrophages and the lowest level of claudin-5 among all groups and had fewer pericytes than BF group. Stroke and BF+stroke groups had smaller WM areas in the ipsilateral basal ganglia than the sham group 8 weeks after the injuries. The BF+stroke group also had smaller WM areas in the ipsilateral than sham and BF groups 3 days after the injuries and in the contralateral basal ganglia than stroke and BF groups 8 weeks after the injuries. CONCLUSIONS: BF exacerbates neuroinflammation and BBB leakage in the hippocampus and WM damage in basal ganglia, which could contribute to the long-lasting memory dysfunction in BF+stroke mice.