TLT-1 Promotes Platelet-Monocyte Aggregate Formation to Induce IL-10-Producing B Cells in Tuberculosis.

The immunoregulation of platelets and platelet-monocyte aggregates (PMAs) is increasingly recognized, but it roles in tuberculosis (TB) remain to be elucidated. In this study, we found that CD14+CD41+ PMAs were increased in peripheral blood of patients with active TB. CD14+CD41+ PMAs highly expressed triggering receptors expressed on myeloid cells (TREMs)-like transcript-1 (TLT-1), P-selectin (CD62P), and CD40L. Our in vitro study found that platelets from patients with active TB aggregate with monocytes to induce IL-1ß and IL-6 production by monocytes. Importantly, we identified that TLT-1 was required for formation of PMAs. The potential TLT-1 ligand was expressed and increased on CD14+ monocytes of patients with TB determined by using TLT-1 fusion protein (TLT-1 Fc). Blocking of ligand-TLT-1 interaction with TLT-1 Fc reduced PMA formation and IL-1ß and IL-6 production by monocytes. Further results demonstrated that PMAs induced IL-10 production by B cells (B10) dependent on IL-1ß, IL-6, and CD40L signals in a coculture system. Moreover, TLT-1 Fc treatment suppressed B10 polarization via blocking PMA formation. Taking all of these data together, we elucidated that TLT-1 promoted PMA-mediated B10 polarization through enhancing IL-1ß, IL-6, and CD40L origin from PMAs, which may provide potential targeting strategies for TB disease treatment.

Simulation analysis and freshness prediction of eggs laid at room temperature.

BACKGROUND: Respiration is an important physiological activity of eggs and is closely related to their freshness. To further observe the diffusion of carbon dioxide released by egg respiration, we used a respirometer to measure the respiration parameters of eggs stored at room temperature and performed a respiration simulation using Fluent software. This paper also explores the relationship between respiratory intensity, freshness, and storage period. RESULTS: The results demonstrate that the diffusion of carbon dioxide released from the respiration of eggs is related to the characteristics of heavy gas diffusion. By comparison, the simulated value (0.0199 m s-1 ) is close to the experimental value (0.0208 m s-1 ), which indicates that the simulation and analysis results are valid. In addition, the logarithmic model was used to assess the relationship between respiration intensity, Haugh unit, and the yolk index (R2 values 0.89 and 0.87). The R2 of the relationship between the real and the predicted Haugh unit value and the yolk index are 0.9 and 0.84 respectively, indicating that the model is a good fit. The equivalent egg age model was established using nonlinear regression, where the correlation coefficient R was 0.888 and P < 0.01, indicating it was both stable and reliable. CONCLUSION: The standard k-ε model is suitable for egg respiration simulation analysis. Respiratory intensity can be used as a potential index for nondestructive testing of egg freshness, which is a new method for nondestructive testing of egg freshness and storage period. © 2022 Society of Chemical Industry.

The significance of serum IgG4 and CA19-9, autoantibodies in diagnosis and differential diagnosis of IgG4-related sclerosing cholangitis.

OBJECTIVE: To investigate the value of serum levels of IgG4 and CA19-9, and autoantibodies in the diagnosis of IgG4-related sclerosing cholangitis (IgG4-SC). METHODS: We detected the serum IgG4 and CA19-9 of 45 IgG4-SC patients, 173 non-IgG4-SC patients and 48 healthy controls by immunoassay and chemiluminescence, respectively, with antinuclear antibody (ANA), anti-neutrophil antibody (ANCA), anti-smooth muscle antibody (SMA) and anti-mitochondrial antibody (AMA) level detected by indirect immunofluorescence. Then analyze the detection results. RESULTS: (1) The positive rates of ANA, ANCA, SMA and AMA in patients with IgG4-SC were 40%, 6.67%, 0 and 2.22%. Among them, the positive rate of ANA was significantly higher than that of the healthy control group (p < .01), and the positive rate of ANA, ANCA, SMA and AMA were significantly different from that of the non-IgG4-SC group (p < .05). (2) Serum levels of IgG4 and CA19-9 increased significantly in patients with IgG4-SC compared with the healthy controls (p < .01). The areas under the ROC curve (AUC) of IgG4 and CA19-9 were 0.9750 and 0.6498, respectively (p < .05). CONCLUSION: The high levels of serum IgG4 and CA19-9, and autoantibodies detections are of great important clinical value in diagnosis and differential diagnosis of IgG4-SC.

[Egg Freshness Detection Based on Hyper-Spectra].

This research collected the transmission hyper-spectral data of eggs with hyper-spectral imager. Haugh unit value was used as freshness norm. With the help of MATLAB and SAS software combined with stechiometry method, the hyper-spectral data of sample eggs was analyzed and processed. The prediction model of egg freshness was established based on hyper-spectral technology. The research chose the band range from 500 to 1 000 nm as sensitive band. The hyper-spectral data of abnormal samples were removed by using mahalanobis distance. Differential correction was done on hyper-spectral data. After the comparison, there was a high linearity between the second-order differential data of hyper-spectra and haugh unit value. Therefore, this paper conducted a further research on the second-order differential data of hyper-spectra. And it was treated with wavelet denoising, smoothing and standardizing. This paper chose the newly proposed CARS variable selection method to do dimensionality reduction on hyper-spectral data. And thirty-two characteristic parameters were extracted. They were used to establish partial least square prediction model based on all band and multiple regression model based on characteristic parameters on white shell eggs. The correlation coefficients of white shell eggs were 0.88 and 0.93 respectively, and the corresponding mean square errors being 7.565 and 6.44. Inspections were conducted on PLS prediction model based on all band hyper-spectral second-order differential and multiple regression model based on characteristic parameters by using eggs of validation set. The accuracy rates of these two models to discriminate white shell eggs' freshness and non-freshness were 100% and 88% respectively.

[Study on Non-Destructive Detection Method for Egg Freshness Based on LLE-SVR and Visible/Near-Infrared Spectrum].

The freshness of egg is an important index to reflect the internal quality. In order to achieve non-destructive detection of freshness, micro fiber spectrometer was used to sample 550～950 nm transmittance spectra of eggs which performed quantitative analysis with haugh unit of eggs. Different pretreatment was combined with partial least squares regression(PLS) and support vector regression(SVR) respectively to find that first derivative combined with SVR predicted better than others through comparison, and it was better to model by SVR than by PLS. In order to improve efficiency and decrease adverse effects of useless information for modeling, the linear dimensionality reduction with principal component analysis (PCA) and the nonlinear dimensionality reduction with locally linear embedding(LLE) were used for the data of first derivative respectively. It indicated that LLE was better than PCA after comparison, and the correlation coefficient of calibration and prediction were 92.2%, 91.1%, and the root mean square error were 7.21, 8.80. The root mean square error of cross validation decreased 0.79.The experimental result illustrated that the nonlinear model of LLE combined with SVR improved predictive performance of egg freshness. It is feasible for the detection of visible/near-infrared spectrum of egg freshness to apply this method.

[Quantitative Approach to Melamine Detection in Egg White with Surface-Enhanced Raman Spectroscopy].

Due to the harmfulness of melamine to human, the quantitative detection of melamine in egg is very necessary. In the present study, the surface enhanced Raman spectra technology combined with chemometric analysis method was used to conduct melamine quantitative detection in egg white. Firstly, the melamine egg sample could be got by the method of artificial feeding hens usingdifferent feeding formulation. Then the surface enhanced Raman spectra of egg white was determined using portable Raman spectroscopy (Opto Trace RamTracer-200) and Raman enhancement reagents, and the melamine content within the white eggs was measured with gas chromatography mass spectrometry technology. The software of Raman Analyzer was used for baseline correction of Raman spectra. The correlation coefficient method was used to choose 320 spectral variables from the surface enhanced Raman spectroscopy as input variables to establish partial least squares quantitative calibration model . And the peaks-decomposition method was used to establish peaks-decomposition quantitative calibration model. Both models selected 90 and 44 samples respectively as calibration sets and validation sets during model establishment, and both models achieved good prediction effect. The determination coefficient between predicted values of partial least squares quantitative calibration model and measured values of gas chromatography mass spectrometry was 0.856, and root mean square error of prediction was 1.547. The determination coefficient was 0.947 and RMSEP was 0.893 for the peaks-decomposition quantitative calibration model. This study demonstrated that the method can effectively quantitatively detect melamine in eggs. Testing a sample only takes 15 minutes, which can provide a new way for the melamine egg detection.

Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy.

Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection.

Galectin-3 induces pathogenic immunosuppressive macrophages through interaction with TREM2 in lung cancer.

BACKGROUND: High infiltration of tumor-associated macrophages (TAMs) is associated with tumor promotion and immunosuppression. The triggering receptor expressed on myeloid cells 2 (TREM2) is emerged as a key immunosuppressive regulator for TAMs, however, how TREM2-expressing TAMs are recruited and what ligands TREM2 interacts with to mediate immunosuppression is unknown. METHODS: Flow cytometry and single-cell RNA sequencing were used to analyze TREM2 expression. Mechanistically, mass spectrometry and immunoprecipitation were employed to identify proteins binding to TREM2. Phagocytosis and co-culture experiments were used to explore the in vitro functions of galectin3-TREM2 pair. Establishment of TREM2f/f-Lyz2-cre mice to validate the role of TREM2 signaling pathway in lung carcinogenesis. GB1107 were further supplemented to validate the therapeutic effect of Galectin3 based on TREM2 signaling regulation. RESULTS: This study identified that abundant TREM2+ macrophages were recruited at the intra-tumor site through the CCL2-CCR2 chemotactic axis. Galectin-3 impaired TREM2-mediated phagocytosis and promoted the conversion of TREM2+ macrophages to immunosuppressive TAMs with attenuated antigen presentation and co-stimulatory functions both in vitro both in vivo, and galectin-3 is a potential ligand for TREM2. Genetic and pharmacological blockade of TREM2 and galectin-3 significantly inhibited lung cancer progression in subcutaneous and orthotopic cancer models by remodeling the tumor immune microenvironment. CONCLUSION: Our findings revealed a previously unknown association between galectin-3 and TREM2 in TAMs of lung cancer, and suggested simultaneous inhibition of galectin3 and TREM2 as potent therapeutic approach for lung cancer therapy.

CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy.

Unfertilized duck eggs not removed prior to incubation will deteriorate quickly, posing a risk of contaminating the normally fertilized duck eggs. Thus, detecting the fertilization status of breeding duck eggs as early as possible is a meaningful and challenging task. Most existing work usually focus on the characteristics of chicken eggs during mid-term hatching. However, little attention has been paid to the detection for duck eggs prior to incubation. In this paper, we present a novel hybrid deep learning detection framework for the fertilization status of pre-incubation duck eggs, termed CVAE-DF, based on visible/near-infrared (VIS/NIR) transmittance spectroscopy. The framework comprises the encoder of a convolutional variational autoencoder (CVAE) and an improved deep forest (DF) model. More specifically, we first collected transmittance spectral data (400-1000 nm) of 255 duck eggs before hatching. The multiplicative scatter correction (MSC) method was then used to eliminate noise and extraneous information of the raw spectral data. Two efficient data augmentation methods were adopted to provide sufficient data. After that, CVAE was applied to extract representative features and reduce the feature dimension for the detection task. Finally, an improved DF model was employed to build the classification model on the enhanced feature set. The CVAE-DF model achieved an overall accuracy of 95.94 % on the test dataset. These experimental results in terms of four metrics demonstrate that our CVAE-DF method outperforms the traditional methods by a significant margin. Furthermore, the results also indicate that CVAE holds great promise as a novel feature extraction method for the VIS/NIR spectral analysis of other agricultural products. It is extremely beneficial to practical engineering.

Robust Detection of Cracked Eggs Using a Multi-Domain Training Method for Practical Egg Production.

The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown test egg data. Firstly, multi-domain egg data are built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust cracked egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 (You Only Look Once v5) model trained by the proposed multi-domain training method with NSFE-MMD has a detection mAP of 86.6% on the unknown test Domain 4, and the YOLOV8 (You Only Look Once v8) model has a detection mAP of 88.8% on Domain 4, which is an increase of 8% and 4.4% compared to the best performance of models trained on a single domain, and an increase of 4.7% and 3.7% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain training method has a detection mAP of 87.9% on egg data of the unknown testing Domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain training method, which can be more suitable for the large quantity and variety of egg detection production.

A Non-Destructive Detection and Grading Method of the Internal Quality of Preserved Eggs Based on an Improved ConvNext.

As a traditional delicacy in China, preserved eggs inevitably experience instances of substandard quality during the production process. Chinese preserved egg production facilities can only rely on experienced workers to select the preserved eggs. However, the manual selection of preserved eggs presents challenges such as a low efficiency, subjective judgments, high costs, and hindered industrial production processes. In response to these challenges, this study procured the transmitted imagery of preserved eggs and refined the ConvNeXt network across four pivotal dimensions: the dimensionality reduction of model feature maps, the integration of multi-scale feature fusion (MSFF), the incorporation of a global attention mechanism (GAM) module, and the amalgamation of the cross-entropy loss function with focal loss. The resultant refined model, ConvNeXt_PEgg, attained proficiency in classifying and grading preserved eggs. Notably, the improved model achieved a classification accuracy of 92.6% across the five categories of preserved eggs, with a grading accuracy of 95.9% spanning three levels. Moreover, in contrast to its predecessor, the refined model witnessed a 24.5% reduction in the parameter volume, alongside a 3.2 percentage point augmentation in the classification accuracy and a 2.8 percentage point boost in the grading accuracy. Through meticulous comparative analysis, each enhancement exhibited varying degrees of performance elevation. Evidently, the refined model outshone a plethora of classical models, underscoring its efficacy in discerning the internal quality of preserved eggs. With its potential for real-world implementation, this technology portends to heighten the economic viability of manufacturing facilities.

Non-destructive detection of egg white and yolk morphology transformation and salt content of salted duck eggs in salting by hyperspectral imaging.

Salt content is a crucial indicator of the maturity and internal quality of salted duck eggs (SDEs) during the pickling process. However, there is currently no valid and rapid method available for accurately detecting salt content. In the present study, we utilized hyperspectral imaging to no-destructively determine the salt content in egg yolks, egg whites, and whole eggs during the curing period. Firstly, principal component analysis was applied to explain the characteristics of egg yolk and white morphology transformation of SDEs with different maturities during curing. Secondly, sensitive spectral factors representative of changes in the salt content of SDEs were extracted by three spectral transformations (Savitzky-Golay SG, continuum removal CR, and first-order derivation FD) and three approaches of selecting characteristic wavelengths (successive projection algorithm SPA, uninformative variables elimination UVE and competitive adaptive reweighting sampling algorithm CARS). The results of the PLSR model suggested that the optimal models for predicting salt content in egg yolks, whites, and whole eggs were SG-UVE-PLSR (predicted coefficient of determination Rp2=0.912, predicted standard deviation SEp=0.151, residual prediction deviation RPD = 3.371), CR-CARS-PLSR (Rp2=0.873, SEp=0.862, RPD = 2.806), and CR-UVE-PLSR (Rp2=0.877, SEp=0.680, RPD = 2.851), respectively. Eventually, the optimal prediction model for the salt content of the whole egg was employed to a pixel spectral matrix to calculate the salt content values of pixel points on the hyperspectral image of SDEs. Additionally, pseudo-color techniques were employed to visualize the spatial distribution of predicted salt content. This work will provide a theoretical foundation for rapidly detecting maturity and enabling high-throughput quality sorting of SDEs.

Nondestructive testing of runny salted egg yolk based on improved ConvNeXt-T.

Salted egg yolks from salted duck eggs are widely utilized in the domestic and international food industry as both raw materials and ingredients. When salted egg yolks are not fully cured and matured, they exist in a fluid state, with a mixture of solid and liquid internally. Due to this composition, they are susceptible to deterioration during storage and usage, necessitating their detection and classification. In this study, a dataset specifically for salted egg yolks was established, and the ConvNeXt-T model, employed as the benchmark model, underwent two notable improvements. First, a lightweight location-aware circular convolution (ParC) was introduced, utilizing a ParC-block to replace a portion of the original ConvNeXt-T block. This enhancement aimed to overcome the limitations of convolution in extracting global feature information while integrating the global sensing capability of vision transformer and the localization capability of convolution. Additionally, the activation function was modified through substitution. These improvements resulted in the final model. Experimental results indicate that the enhanced model exhibits faster convergence on the custom salted egg yolk dataset compared to the baseline model. Furthermore, a significant reduction of model parameters by a factor of 4 led to a 2.167 percentage point improvement in the accuracy of the test set. The ParC-ConvNeXt-SMU-T model achieved an accuracy of 96.833% with 26.8 million parameters. Notably, the improved model demonstrates exceptional effectiveness in recognizing salted egg yolks. PRACTICAL APPLICATION: This study can be widely applied in the process of salted egg yolk production and quality inspection, which can improve the actual sorting efficiency of salted egg yolks and reduce the labor cost at the same time. It can also be used for nondestructive testing of salted egg yolks by governmental enterprises and other regulatory authorities.

HSP70 contributes to pathogenesis of fulminant hepatitis induced by coronavirus.

Fulminant viral hepatitis (FH) represents a significant clinical challenge, with its pathogenesis not yet fully elucidated. Heat shock protein (HSP)70, a molecular chaperone protein with a broad range of cytoprotective functions, is upregulated in response to stress. However, the role of HSP70 in FH remains to be investigated. Notably, HSP70 expression is upregulated in the livers of coronavirus-infected mice and patients. Therefore, we investigated the mechanistic role of HSP70 in coronavirus-associated FH pathogenesis. FH was induced in HSP70-deficient (HSP70 KO) mice or in WT mice treated with the HSP70 inhibitor VER155008 when infected with the mouse hepatitis virus strain A59 (MHV-A59). MHV-A59-infected HSP70 KO mice exhibited significantly reduced liver damage and mortality. This effect was attributed to decreased infiltration of monocyte-macrophages and neutrophils in the liver of HSP70 KO mice, resulting in lower levels of inflammatory cytokines such as IL-1ß, TNFα, and IL-6, and a reduced viral load. Moreover, treatment with the HSP70 inhibitor VER155008 protected mice from MHV-A59-induced liver damage and FH mortality. In summary, HSP70 promotes coronavirus-induced FH pathogenesis by enhancing the infiltration of monocyte-macrophages and neutrophils and promoting the secretion of inflammatory cytokines. Therefore, HSP70 is a potential therapeutic target in viral FH intervention.

[Investigation of chigger mites on small mammals in a flatland area of Menghan, Xishuangbanna, Yunnan Province].

OBJECTIVE: To investigate the species composition and distribution of chigger mites on small mammals in flatland area in Menghan, Xishuangbanna of Yunnan Province. METHODS: The field investigation was made in a flatland area near Lancangjiang River in Menghan, Xishuangbanna of Yunnan Province. Small mammals were captured with mouse cages and traps. All mites on the hosts were collected and preserved in 70% ethanol. Hoyer's solution was used to mount the chiggers on glass slides. The specimens of the chigger mites on the slides were finally identified into species under microscope. The constituent ratio, infestation rate, mean abundance and mean intensity of chigger mites in different habitats or on different hosts were used to measure the community structure. The species richness and community diversity were analyzed. RESULTS: A total of 233 small mammal hosts were captured (belonging to 2 families, 3 genera and 5 species). 5 763 individuals of chigger mites were identified as 2 subfamilies, 7 genera, and 45 species. Rattus tanezumi (R. flavipectus) was the dominant species among the captured hosts, accounting for 97.4% (227/233). The mite infestation rate, average ectoparasite abundance, and mean mite intensity on R. tanezumi was 56.4% (128/227), 24.7 (5 600/227) and 43.8 (5 600/128), respectively. Leptotrombidium deliense was dominant chigger mite species and account for 57.9% (3 337/5 763), mainly infested R. tanezumi. Compared with indoor and cultivated field habitats, the species richness and community diversity of chigger mites in shrub habitat were higher, and 41 species of chigger mites were collected. CONCLUSION: The species composition and community structure is relatively simple in the flatland area in Xishuangbanna. L. deliense is the most dominant species of chigger mites and its main host is R. tanezumi.

Research on Online Nondestructive Detection Technology of Duck Egg Origin Based on Visible/Near-Infrared Spectroscopy.

As living standards rise, people have higher requirements for the quality of duck eggs. The quality of duck eggs is related to their origin. Thus, the origin traceability and identification of duck eggs are crucial for protecting the rights and interests of consumers and preserving food safety. As the world's largest producer and consumer of duck eggs, China's duck egg market suffers from a severe lack of duck egg traceability and rapid origin identification technology. As a result, a large number of duck eggs from other regions are sold as products from well-known brands, which seriously undermines the rights and interests of consumers and is not conducive to the sound development of the duck egg industry. To address the above issues, this study collected visible/near-infrared spectral data online from duck eggs of three distinct origins. To reduce noise in the spectral data, various pre-processing algorithms, including MSC, SNV, and SG, were employed to process the spectral data of duck eggs in the range of 400-1100 nm. Meanwhile, CARS and SPA were used to select feature variables that reflect the origin of duck eggs. Finally, classification models of duck egg origin were developed based on RF, SVM, and CNN, achieving the highest accuracy of 97.47%, 98.73%, and 100.00%, respectively. To promote the technology's implementation in the duck egg industry, an online sorting device was built for duck eggs, which mainly consists of a mechanical drive device, spectral software, and a control system. The online detection performance of the machine was validated using 90 duck eggs, and the final detection accuracy of the RF, SVM, and CNN models was 90%, 91.11%, and 94.44%, with a detection speed of 0.1 s, 0.3 s, and 0.5 s, respectively. These results indicate that visible/near-infrared spectroscopy can be exploited to realize rapid online detection of the origin of duck eggs, and the methodologies used in this study can be immediately implemented in production practice.

Natural Exponential and Three-Dimensional Chaotic System.

Existing chaotic system exhibits unpredictability and nonrepeatability in a deterministic nonlinear architecture, presented as a combination of definiteness and stochasticity. However, traditional two-dimensional chaotic systems cannot provide sufficient information in the dynamic motion and usually feature low sensitivity to initial system input, which makes them computationally prohibitive in accurate time series prediction and weak periodic component detection. Here, a natural exponential and three-dimensional chaotic system with higher sensitivity to initial system input conditions showing astonishing extensibility in time series prediction and image processing is proposed. The chaotic performance evaluated theoretically and experimentally by Poincare mapping, bifurcation diagram, phase space reconstruction, Lyapunov exponent, and correlation dimension provides a new perspective of nonlinear physical modeling and validation. The complexity, robustness, and consistency are studied by recursive and entropy analysis and comparison. The method improves the efficiency of time series prediction, nonlinear dynamics-related problem solving and expands the potential scope of multi-dimensional chaotic systems.

Single-View Measurement Method for Egg Size Based on Small-Batch Images.

Egg size is a crucial indicator for consumer evaluation and quality grading. The main goal of this study is to measure eggs' major and minor axes based on deep learning and single-view metrology. In this paper, we designed an egg-carrying component to obtain the actual outline of eggs. The Segformer algorithm was used to segment egg images in small batches. This study proposes a single-view measurement method suitable for eggs. Experimental results verified that the Segformer could obtain high segmentation accuracy for egg images in small batches. The mean intersection over union of the segmentation model was 96.15%, and the mean pixel accuracy was 97.17%. The R-squared was 0.969 (for the long axis) and 0.926 (for the short axis), obtained through the egg single-view measurement method proposed in this paper.

HSPA13 modulates type I interferon antiviral pathway and NLRP3 inflammasome to restrict dengue virus infection in macrophages.

Dengue virus (DENV) is a type of arthropod-borne Flavivirus, which leads to a series of serious diseases like dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). DENV has a devastating health and economic impact worldwide. However, there are no suitable drugs to combat the virus. Here we reported that HSPA13, also known as stress chaperone (STCH), is a member of the HSP70 family and is a key regulator of type I interferon (IFN-I) and pro-inflammatory responses during DENV infection. HSPA13 expression was increased in macrophages infected with DENV or other Flaviviruses like Zika virus (ZIKV), Yellow fever virus (YFV) and Japanese encephalitis virus (JEV). Further, HSPA13 suppressed the replication of DENV and other Flaviviruses (ZIKV, JEV, YFV), which exhibited broad-spectrum antiviral effects. On the one hand, HSPA13 promoted production of IFN-ß and interferon-stimulated genes (ISGs, such as ISG15, OAS and IFIT3) by interacting with RIG-I and up-regulating RIG-I expression during DENV infection. On the other hand, HSPA13 enhanced NLRP3 inflammasome activation and IL-1ß secretion by interacting with ASC in DENV infection. We identified HSPA13 as a potential anti-DENV target. Our results provide clues for the development of antiviral drugs against DENV based on HSPA13 and reveal novel drug target against Flaviviruses.

SLAMF7 regulates the inflammatory response in macrophages during polymicrobial sepsis.

Uncontrolled inflammation occurred in sepsis results in multiple organ injuries and shock, which contributes to the death of patients with sepsis. However, the regulatory mechanisms that restrict excessive inflammation are still elusive. Here, we identified an Ig-like receptor called signaling lymphocyte activation molecular family 7 (SLAMF7) as a key suppressor of inflammation during sepsis. We found that the expression of SLAMF7 on monocytes/macrophages was significantly elevated in patients with sepsis and in septic mice. SLAMF7 attenuated TLR-dependent MAPK and NF-κB signaling activation in macrophages by cooperating with Src homology 2-containing inositol-5'­phosphatase 1 (SHIP1). Furthermore, SLAMF7 interacted with SHIP1 and TNF receptor-associated factor 6 (TRAF6) to inhibit K63 ubiquitination of TRAF6. In addition, we found that tyrosine phosphorylation sites within the intracellular domain of SLAMF7 and the phosphatase domain of SHIP1 were indispensable for the interaction between SLAMF7, SHIP1, and TRAF6 and SLAMF7-mediated modulation of cytokine production. Finally, we demonstrated that SLAMF7 protected against lethal sepsis and endotoxemia by downregulating macrophage proinflammatory cytokines and suppressing inflammation-induced organ damage. Taken together, our findings reveal a negative regulatory role of SLAMF7 in polymicrobial sepsis, thus providing sights into the treatment of sepsis.