Targeting the TRIM14/USP14 Axis Enhances Immunotherapy Efficacy by Inducing Autophagic Degradation of PD-L1.

Immunotherapy has greatly improved cancer treatment in recent years by harnessing the immune system to target cancer cells. The first immunotherapeutic agent approved by the US Food and Drug Administration (FDA) was interferon a (IFNa). Treatment with IFNa can lead to effective immune activation and attenuate tumor immune evasion, but persistent treatment has been shown to elicit immune suppressive effects. Here, we identified an autophagy-dependent mechanism by which IFNa triggers tumor immune evasion by upregulating PD-L1 to suppress the anti-tumor activity of CD8+ T cells. Mechanistically, IFNa increased transcription of TRIM14, which recruited the deubiquitinase USP14 to inhibit the autophagic degradation of PD-L1. USP14 removed K63-linked ubiquitin chains from PD-L1, impairing its recognition by the cargo receptor p62 (also known as SQSTM1) for subsequent autophagic degradation. Combining the USP14 inhibitor IU1 with IFNa and anti-CTLA4 treatment effectively suppressed tumor growth without significant toxicity. This work suggests a strategy for targeting selective autophagy to abolish PD-L1-mediated cancer immune evasion.

Genetic variants in UNC93B1 predispose to childhood-onset systemic lupus erythematosus.

Rare genetic variants in toll-like receptor 7 (TLR7) are known to cause lupus in humans and mice. UNC93B1 is a transmembrane protein that regulates TLR7 localization into endosomes. In the present study, we identify two new variants in UNC93B1 (T314A, located proximally to the TLR7 transmembrane domain, and V117L) in a cohort of east Asian patients with childhood-onset systemic lupus erythematosus. The V117L variant was associated with increased expression of type I interferons and NF-κB-dependent cytokines in patient plasma and immortalized B cells. THP-1 cells expressing the variant UNC93B1 alleles exhibited exaggerated responses to stimulation of TLR7/-8, but not TLR3 or TLR9, which could be inhibited by targeting the downstream signaling molecules, IRAK1/-4. Heterozygous mice expressing the orthologous Unc93b1V117L variant developed a spontaneous lupus-like disease that was more severe in homozygotes and again hyperresponsive to TLR7 stimulation. Together, this work formally identifies genetic variants in UNC93B1 that can predispose to childhood-onset systemic lupus erythematosus.

A rare case of Baker's cysts with hematoma of the lower calf treated with arthroscopic internal drainage combined with intramuscular dissection.

Popliteal cysts, also termed Baker's cysts, are clinically common cystic lesions in the popliteal fossa. Typically, the contents of a ruptured cyst tend to spread into the myofascial interfaces in any direction, most commonly inferomedially or into a palpable superficial position. However, to our knowledge, reports of Baker's cysts dissecting into the deep intermuscular septum of the lower calf are extremely rare. We present here the details of the successful treatment through arthroscopy combined with lower calf incision of a patient who sustained hematoma of the knee and lower calf secondary to Baker's cyst rupture. Given the rarity of this disease in China, we present this case report to improve our understanding of the disease and avoid misdiagnosis and provide evidence for its clinical treatment, management, and prognosis.

Tibial tubercle avulsion fracture following preexisting Osgood-Schlatter disease in an adolescent: a case report.

Tibial tubercle avulsion fractures (TTAFs) are rare but typical in children and adolescents and Osgood-Schlatter disease (OSD) may be involved in their pathogenesis. However, few publications have reported the relationship between OSD and TTAF. A 16-year-old healthy male adolescent presented with pain, swelling and limited range of motion of the right knee following sudden acceleration while running. Based on the radiographic evidence, the patient was diagnosed with an avulsion fracture of the right tibial tubercle and OSD. Open reduction and internal fixation were performed using two cannulated screws and two Kirschner wires. The patient returned to preinjury activity levels at the 12-month follow-up postoperatively. This case report aimed to highlight this unique injury pattern. For patients with TTAFs, not only should the fracture be treated, but the cause of the fracture, such as OSD, should also be given appropriate treatment.

[Progress on study of factors influencing the prognosis and complications of surgery for discoid meniscus injury in children].

The discoid meniscus is a common congenital meniscal malformation that is prevalent mainly in Asians and often occurs in the lateral discoid meniscus. Patients with asymptomatic discoid meniscus are usually treated by conservative methods such as observation and injury avoidance, while patients with symptoms and tears need to be treated surgically. Arthroscopic saucerization combined with partial meniscectomy and meniscus repair is the most common surgical approach., and early to mid-term reports are good. The prognostic factors are the patient's age at surgery、follow-up time and type of surgery. Some patients experience complications such as prolonged postoperative knee pain, early osteoarthritis, retears and Osteochondritis dissecans. The incidence of prolonged postoperative knee pain was higher and the incidence of Osteochondritis dissecans was the lowest. Retears of the lateral meniscus is the main reason for reoperation.

Spatio-temporal data prediction of multiple air pollutants in multi-cities based on 4D digraph convolutional neural network.

In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data's temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities.

Lake eutrophication prediction based on improved MIMO-DD-3Q Learning.

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction.

P21-activated kinase 1 (PAK1)-mediated cytoskeleton rearrangement promotes SARS-CoV-2 entry and ACE2 autophagic degradation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has had a significant impact on healthcare systems and economies worldwide. The continuous emergence of new viral strains presents a major challenge in the development of effective antiviral agents. Strategies that possess broad-spectrum antiviral activities are desirable to control SARS-CoV-2 infection. ACE2, an angiotensin-containing enzyme that prevents the overactivation of the renin angiotensin system, is the receptor for SARS-CoV-2. ACE2 interacts with the spike protein and facilitates viral attachment and entry into host cells. Yet, SARS-CoV-2 infection also promotes ACE2 degradation. Whether restoring ACE2 surface expression has an impact on SARS-CoV-2 infection is yet to be determined. Here, we show that the ACE2-spike complex is endocytosed and degraded via autophagy in a manner that depends on clathrin-mediated endocytosis and PAK1-mediated cytoskeleton rearrangement. In contrast, free cellular spike protein is selectively cleaved into S1 and S2 subunits in a lysosomal-dependent manner. Importantly, we show that the pan-PAK inhibitor FRAX-486 restores ACE2 surface expression and suppresses infection by different SARS-CoV-2 strains. FRAX-486-treated Syrian hamsters exhibit significantly decreased lung viral load and alleviated pulmonary inflammation compared with untreated hamsters. In summary, our findings have identified novel pathways regulating viral entry, as well as therapeutic targets and candidate compounds for controlling the emerging strains of SARS-CoV-2 infection.

Artificial neural network-based shelf life prediction approach in the food storage process: A review.

The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, fast and accurate method for modeling, simulating and predicting shelf life in food. ANNs are capable of tackling nonlinear, complex and ill-defined problems between the variables without prior knowledge. ANN model exhibited excellent fit performance evidenced by low root mean squared error and high correlation coefficient. The low relative error between actual values and predicted values from the ANN model demonstrates its high accuracy. This paper describes the modeling of ANN in food quality prediction, encompassing commonly used ANN architectures, ANN simulation techniques, and criteria for evaluating ANN model performance. The review focuses on the application of ANN for modeling nonlinear food quality during storage, including dairy, meat, aquatic, fruits, and vegetables products. The future prospects of ANN development mainly focus on optimal models and learning algorithm selection, multiple model fusion, self-learning and self-correcting shelf-life prediction model development, and the potential utilization of deep learning techniques.

ANN-based food shelf life prediction methods are reviewed.This paper discusses application of ANN in the food storage process.BPNN is the mainstream ANN architecture used for the prediction of food quality.ANNs are useful for prediction of outputs with high accuracy.Future trends of ANN in the agri-supply chain are evaluated.

Establishing a Berry Sensory Evaluation Model Based on Machine Learning.

In recent years, people's quality of life has increased, and the requirements for fruits have also become higher; blueberries are particularly popular because of their rich nutrients. In the blueberry industry chain, sensory evaluation is an important link in determining the quality of blueberries. Therefore, to make a more objective scientific evaluation of blueberry quality and reduce the influence of human factors, on the basis of traditional sensory evaluation methods, machine learning is introduced to establish a support vector regression prediction model optimized by the particle swarm algorithm. Ten physical and chemical flavor indices of blueberries (such as catalase, flavonoids, and soluble solids) were used as input data, and sensory evaluation scores were used as output data. Three different predictive models were applied and compared: a particle swarm optimization support vector machine, a convolutional neural network, and a long short-term memory network model. To ensure reliability, the experiments with each of the three models were repeated 20 times, and the mean of each index was calculated. The experimental results showed that the root mean square error and mean absolute error of the particle swarm optimization support vector machine were 0.45 and 0.40, respectively; these values were lower than those of the convolutional neural network (0.96 and 0.78, respectively) and the long short-term memory network (1.22 and 0.97, respectively). Hence, these results highlighted the superiority of the proposed model when sample data are limited.

A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles.

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target's path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.

Analysis of Incentive Policies and Initiatives on Orphan Drug Development in China: Challenges, Reforms and Implications.

OBJECTIVES: Rare diseases are a global public health issue with a more pressing situation in China. Unfortunately, the relevant research and development in this country are still in its infancy, leading to limited drug accessibility. In view of this, the Chinese government has taken a series of countermeasures to promote orphan drug R&D in recent years, which has presented encouraging results. This paper aims to review incentive policies and funding initiatives formulated by the Chinese government and examine their implications on orphan drug R&D. METHODS: Policies targeting orphan drug R&D during 2012-2022 were retrieved from the relevant official websites, categorized into different themes and analyzed for the contents. Data on government funding, drug approval, clinical trial approval and orphan drug designation were collected through internet search to analyze the implications of those incentive policies and initiatives on orphan drug R&D in China. RESULTS: A total of 20 relevant policy documents were identified and five major themes were revealed through content analysis, including national strategy, expedited approval, safety and efficacy requirements, data protection and technical support. The government input in orphan drug R&D has witnessed a steady annual increase. Driven by those incentives, the numbers of orphan drugs approved for marketing and drug candidates entering clinical studies are increasing year by year, and more domestic pharmaceutical companies are actively involved in the R&D of orphan drugs. CONCLUSIONS: Orphan drug development in China is growing rapidly under the stimulation of incentive regulatory policies and more investment in researches. China is working toward a more standardized and comprehensive rare disease ecosystem. However, there are still some challenges, such as the lack of sufficient financial support and the call for systematic legislation on rare diseases, to be addressed for future success.

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer.

Air pollution is a serious problem that affects economic development and people's health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively.

Closed-loop seizure modulation via extreme learning machine based extended state observer.

Neuromodulation is a promising way in clinical treatment of epilepsy, but the existing methods cannot adjust stimulations according to patients' real-time reactions. Therefore, it is necessary to acquire a systematic and a scientific regulation method based on patients' real-time reactions. The linear active disturbance rejection control can adapt to complex epileptic dynamics and improve the epilepsy regulation, even if little model information is available, and various uncertainties and external disturbances exist. However, a linear extended state observer estimates the time-varying total disturbance with a steady-state error. To improve regulation, it is crucial to estimate the total disturbance in a more accurate manner. An extreme learning machine is capable of approximating any nonlinear function. Its initial parameter generation is more convenient, adjustable parameters are fewer, and learning speed is faster. Thus, a nonlinear time-varying function can be estimated more timely and accurately. Then, an extreme learning machine based extended state observer is proposed to get a more satisfactory total disturbance estimation and more desired closed-loop regulation. The convergence of the extreme learning machine based extended state observer is verified and the stability of the closed-loop system is analyzed. Numerical results show that the proposed extended state observer is much better than a linear extended state observer in estimating the total disturbance. It guarantees a more satisfied closed-loop neuromodulation.

Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof.

As the main food source of the world's population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

Data-Driven Analysis of Risk-Assessment Methods for Cold Food Chains.

The problem of cold-chain food safety is becoming increasingly prominent. Cold food chain risk assessment is an important way to ensure cold-chain food safety. Using CiteSpace, this study analyzes the knowledge map of research hotspots in the field of cold-chain food safety over the past 18 years, identifies the research keywords, presents the centrality statistics, and calculates the cluster values and average cluster contour values. Adopting a data-driven perspective, risk-assessment methods for cold food chains are summarized based on qualitative risk assessment, quantitative risk assessment, and comprehensive qualitative and quantitative risk assessment. The advantages and disadvantages of each are summarized. Finally, the problems and challenges in current cold food chain risk-assessment research are summarized in three aspects: the data credibility of cold food chain traceability systems, cold-chain food safety audit methods, and nontraditional cold food chain risk assessment. Suggestions are given for strengthening the cold food chain risk-assessment system to provide a decision-making reference to help regulatory authorities take risk prevention and control measures.

A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model.

Rice is common in the human diet, making rice safety issues important. Moreover, rice processing safety is key for rice security, so rice processing chain risk assessment is critical. However, methods proposed to assess the rice processing chain risk have issues, such as the use of unreasonable thresholds for the rice processing chain and fixed weight. To solve these problems, we propose a risk assessment method for the rice processing chain based on a multidimensional trapezoidal cloud model. First, an evaluation model based on a multidimensional trapezoidal cloud model was established. Based on the historical evaluation results, Atanassov's interval-valued intuition language numbers (AIVILNs) were introduced to determine the cloud model's parameters. Second, the concept of dynamic weight was introduced to integrate the static and dynamic weights. An exponential function was used to construct dynamic weighting mechanisms, and the analytic hierarchy stage (AHP) was used to construct a static weight. The proposed method was validated by 104 sets of rice processing chain data, and the results show that the method could evaluate the risk level of the rice processing chain more accurately and reasonably than other methods, indicating that it can provide a sound decision-making basis for food safety supervision authorities.

Metabolic enzyme UAP1 mediates IRF3 pyrophosphorylation to facilitate innate immune response.

Post-translational modifications (PTMs) of proteins are crucial to guarantee the proper biological functions in immune responses. Although protein phosphorylation has been extensively studied, our current knowledge of protein pyrophosphorylation, which occurs based on phosphorylation, is very limited. Protein pyrophosphorylation is originally considered to be a non-enzymatic process, and its function in immune signaling is unknown. Here, we identify a metabolic enzyme, UDP-N-acetylglucosamine pyrophosphorylase 1 (UAP1), as a pyrophosphorylase for protein serine pyrophosphorylation, by catalyzing the pyrophosphorylation of interferon regulatory factor 3 (IRF3) at serine (Ser) 386 to promote robust type I interferon (IFN) responses. Uap1 deficiency significantly impairs the activation of both DNA- and RNA-viruse-induced type I IFN pathways, and the Uap1-deficient mice are highly susceptible to lethal viral infection. Our findings demonstrate the function of protein pyrophosphorylation in the regulation of antiviral responses and provide insights into the crosstalk between metabolism and innate immunity.

Construction of rice supply chain supervision model driven by blockchain smart contract.

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.