Recent advances of machine learning in the geographical origin traceability of food and agro-products: A review.

The geographical origin traceability of food and agro-products has been attracted worldwide. Especially with the rise of machine learning (ML) technology, it provides cutting-edge solutions to erstwhile intractable issues to identify the origin of food and agro-products. By utilizing advanced algorithms, ML can extract feature information of food and agro-products that is closely related to origin and, more accurately, identify and trace their origins, which is of great significance to the entire food and agriculture industry.

Ultrasound-Guided Needling Combined With High- or Low-Energy Extracorporeal Shock Wave Therapy Versus Needling Alone for Calcified Rotator Cuff: A Retrospective Study.

Background: Rotator cuff calcific tendinitis is a common shoulder joint disorder. Nonsurgical treatment methods, including multiple needling and extracorporeal shock wave therapy (ESWT), can effectively treat calcific tendinitis.

Purpose: To evaluate the clinical results and radiological outcomes of treatment with ultrasound-guided needling (UGN) alone versus UGN with high-energy ESWT (UGN-H) or UGN with low-energy ESWT (UGN-L) in patients with calcific tendinitis of the rotator cuff.

An Innovative, Cost-Effective, and Flexible Traction Alternative for Shoulder Arthroscopy in Lateral Decubitus Position.

During shoulder arthroscopic surgery in the lateral decubitus position, effective and stable continuous traction is a basic requirement for the smooth progression of the surgery. Herein, we describe a safe, reliable, and cost-effective lateral decubitus traction assembly.

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 FDA was IFNα. Treatment with IFNα can lead to effective immune activation and attenuate tumor immune evasion, but persistent treatment has been shown to elicit immunosuppressive effects.

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.

[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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Research on Grain Food Blockchain Traceability Information Management Model Based on Master-Slave Multichain.

Aiming at the problems such as slow traceability efficiency, poor sharing, and the difficulty of matching the throughput of a blockchain single chain structure due to the complexity of the grain food supply chain links, the large number of participants, and the large amount of data information, this paper proposes a grain food blockchain traceability information management model based on the master-slave multichain structure by analyzing the processes and data characteristics of each link in the grain food supply chain; on this basis, the PLEW consensus algorithm based on Raft + improved PoW algorithm is designed for the master chain, and the CI-PBFT consensus algorithm based on trusted information degree is designed for the slave chain. The master chain and slave chain are anchored to each other through hash locking, and the data is uploaded and queried through smart contracts. In order to verify the effectiveness of the model, the blockchain traceability system is designed and implemented based on Hyperledger Fabric2.

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.

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.