Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach.

Individual detection techniques cannot guarantee accurate and reliable results when combatting the presence of adulterated lamb meat in the market. Here, we propose an approach combining the electronic nose and near-infrared spectroscopy fusion data with machine learning methods to effectively detect adulterated lamb meat (mixed with duck meat). To comprehensively analyse the data from both techniques, the F1-score-based Model Reliability Estimation (F1-score-MRE) data fusion method was introduced.

M6A modification promotes blood-brain barrier breakdown during cerebral ischemia/reperfusion injury through increasing matrix metalloproteinase 3 expression.

Blood-brain barrier (BBB) breakdown is a critical event in cerebral ischemia-reperfusion (I/R) injury, and matrix metalloproteinases (MMPs), which are proteolytic enzymes, play essential roles in BBB breakdown through degrading the extracellular matrix. N6-Methyladenosine (m6A), the most common and reversible mRNA modification, has an important role in the progression of cerebral I/R injury. However, whether m6A is related to BBB breakdown and MMPs expression in cerebral I/R injury is still not clear.

An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm.

To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization-back-propagation neural network (PSO-BPNN), a gray wolf optimization-backward propagation neural network (GWO-BPNN), and a sparrow search algorithm-backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.

COP9 Signalosome Subunit 3 Restricts Neuroinflammatory Responses During Cerebral Ischemia/Reperfusion Injury Through Stabilizing Suppressor of Cytokine Signaling 3 Protein.

Background: The suppressor of cytokine signaling 3 (SOCS3) is a specific negative regulator of signal transducer and activator of transcription 3 (STAT3) signaling, which is predominantly activated to induce neuroinflammatory response in microglia and functions essential roles during cerebral ischemia-reperfusion (I/R) injury. Constitutive photomorphogenesis 9 (COP9) signalosome (CSN) is a signaling platform controlling protein stability by remodeling of cullin-RING ubiquitin ligases, which is recently reported to specifically recognize proteins with SOCS-box domains. However, whether SOCS3 is related to COP9 signalosome in neuroinflammation during cerebral I/R injury is completely unclear.