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Neuroctenus taiwanicus Kormilev, 1955 is a flat-bodied and enigmatic bug that was first discovered on the island of Taiwan, China. In this study, the whole mitochondrial genome of N. taiwanicus was sequenced and annotated for the first time, and its genomic data were uploaded to Genbank feedback number OR675057. The mitochondrial genome of N. taiwanicus is 15,340 bp in length, a typical circular DNA encoding 37 genes and a control region with 68.4% A + T content. The phylogeny reveals the taxonomic status of N. taiwanicus, which is most closely related to N. yunnanensis, and demonstrates the sister relationship among Neuroctenus, Mezira, and Brachyrhynchus. In addition, the results also confirm that Aradinae and Calisiinae are the earliest branching and more primitive in the family Aradidae, which is consistent with the analysis of the traditional classification.

AbstractThe complete Neuroctenus yunnanensis Hsiao, 1964, mitogenome was sequenced using an Illumina NovaSeq platform and submitted to GenBank (accession number: ON507991). The mitochondrial genome is a typical circular DNA molecule of 15,283 bp with 37 genes, including 22 tRNA genes, 13 protein-coding genes (PCGs), two rRNA genes, and a control region. Phylogenetic reconstruction validated the taxonomic status of N. yunnanensis, which was placed in the Mezirinae subfamily (Aradidae) and most closely related to N. parus. The mitochondrial genome data of N. yunnanensis provides a basis for genetic research.

The global energy is increasingly tight, and the global temperature is gradually rising. Energy efficiency assessment and carbon emission accounting can provide theoretical tools and practical support for the formulation of energy conservation and emission reduction strategies for the food cold chain, and is also a prerequisite for the sustainable development of the food cold chain. In this paper, the relationship and differences between energy consumption and carbon emissions in the general food cold chain are first described, and the principle, advantages and disadvantages of three energy consumption conversion standards of solar emergy value, standard coal and equivalent electricity are discussed. Besides, the possibilities of applying these three energy consumption conversion standards to energy consumption analysis and energy efficiency evaluation of food cold chain are explored. Then, for a batch of fresh agricultural products, the energy consumption of six links of the food cold chain, including the first transportation, the manufacturer, the second transportation, the distribution center, the third transportation, and the retailer, are systematically and comprehensively analyzed from the product level, and the comprehensive energy consumption level of the food cold chain are obtained. On this basis, ten energy efficiency indicators from five aspects of macro energy efficiency are proposed, including micro energy efficiency, energy economy, environmental energy efficiency and comprehensive energy efficiency, and constructs the energy efficiency evaluation index system of food cold chain. At the same time, other energy efficiency evaluation indicators and methods are also summarized. In addition, the standard of carbon emission conversion of food cold chain, namely carbon dioxide equivalent is introduce, the boundary of carbon emission accounting is determined, and the carbon emission factors of China's electricity is mainly discussed. Furthermore, the origin, principle, advantages and disadvantages of the emission factor method, the life cycle assessment method, the input-output analysis method and the hybrid life cycle assessment method, and the basic process of life cycle assessment method in the calculation of food cold chain carbon footprint are also reviewed. In order to improve the energy efficiency level of the food cold chain and reduce the carbon emissions of each link of the food cold chain, energy conservation and emission reduction methods for food cold chain are proposed from five aspects: refrigerant, distribution path, energy, phase change cool storage technology and digital twin technology. Finally, the energy efficiency assessment and carbon emission accounting of the food cold chain are briefly prospected in order to provide reference for promoting the sustainable development of China's food cold chain.

Environmental and physiological fluctuations in the live oyster cold chain can result in reduced survival and quality. In this study, a flexible wireless sensor network (F-WSN) monitoring system combined with knowledge engineering was designed and developed to monitor environmental information and physiological fluctuations in the live oyster cold chain. Based on the Hazard Analysis and Critical Control Point (HACCP) plan to identify the critical control points (CCPs) in the live oyster cold chain, the F-WSN was utilized to conduct tracking and collection experiments in real scenarios from Yantai, Shandong Province, to Beijing. The knowledge model for shelf-life and quality prediction based on environmental information and physiological fluctuations was established, and the prediction accuracies of TVB-N, TVC, and pH were 96%, 85%, and 97%, respectively, and the prediction accuracy of viability was 96%. Relevant managers, workers, and experts were invited to participate in the efficiency and applicability assessment of the established system. The results indicated that combining F-WSN monitoring with knowledge-based HACCP modeling is an effective approach to improving the transparency of cold chain management, reducing quality and safety risks in the oyster industry, and promoting the sharing and reuse of HACCP knowledge in the oyster cold chain.

The complete mitochondrial genome of Brachyrhynchus triangulus Bergroth, 1889 was sequenced and annotated in the present study. It was a typical circular DNA molecule of 15,170 bp, with 37 genes and a control region. The percentages of A, C, G, and T nucleotides in the genome of B. triangulus were 41.1%, 17.4%, 11.9%, and 29.4%, respectively. Thirteen protein-coding genes (PCGs) start with a ATN codon or a TTG codon and terminate with a TAA codon or a TAG codon or a single T residue. With the exception of tRNASer(AGN), each of the 22 tRNA genes had a clover-leaf structure and ranged in length from 62 to 69 bp. The length of lrRNA and srRNA was 1241 bp and 828 bp, respectively. The control region had a length of 708 bp and an A + T content of 74.6%. The sister relationship between B. triangulus and Brachyrhynchus hsiaoi is supported by the phylogenetic tree. Additionally, it proved the sister relationship between Mezirinae and Aneurinae, supporting the classical taxonomy of the Aradidae.

Non-invasive blood glucose level (BGL) evaluation technology in skin mucus is a wearable stress-detection means to indicate the health status of live fish for compensating the drawbacks using traditional invasive biochemical inspection. Nevertheless, the commonly used methods cannot accurately obtain the BGL variations owing to the influence of an uncertain glucose exudation rate, ambient effects, and individualized differences. Our study proposes a non-invasive multi-sensor-fusion-based method to evaluate the dynamic BGL variations using the enhanced gray wolf-optimized backpropagation network (EGWO-BP) to continuously acquire more accurate trends. Furthermore, the K-means++ (KMPP) algorithm is utilized to further improve the accuracy of BGL acquisition by clustering fish with full consideration of its size features. In the verification test, turbot (Scophthalmus Maximus) was selected as an experimental subject to perform the continuous BGL monitoring in waterless keep-alive transportation by acquiring comprehensive biomarker information from different parts of fish skin mucus, such as fins, body, and tails. The comparison of results indicates that the KMPP-EGWO-BP can effectively acquire more accurate BGL variation than the traditional gray wolf-optimized backpropagation network (GWO-BP), particle swarm-optimized backpropagation network (PSO-BP), backpropagation network (BP), and support vector regression (SVR) by mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Finally, the proposed BGL fusion evaluation model can precisely acquire the live fish's physiological stress states to substantially reduce the potential mortality for the live fish circulation industry.

Abstract Background Automated breast ultrasound (ABUS) is a useful choice in breast disease diagnosis. The axillary lymph node (ALN) status is crucial for predicting the clinical classification and deciding on the treatment of early-stage breast cancer (EBC) and could be the primary indicator of locoregional recurrence. We aimed to establish a prediction model using ABUS features of primary breast cancer to predict ALN status. Methods A total of 469 lesions were divided into the axillary lymph node metastasis (ALNM) group and the no ALNM (NALNM) group. Univariate analysis and multivariate analysis were used to analyze the difference of clinical factors and ABUS features between the two groups, and a predictive model of ALNM was established. Pathological results were as the gold standard. Results Ki-67, maximum diameter (MD), posterior feature shadowing or enhancement and hyperechoic halo were significant risk factors for ALNM in multivariate logistic regression analysis (P

(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection.

Abstract Introduction The inflammation mediated by microglial cells plays an important role in the process of neurodegenerative diseases. Recent evidence indicates that semaphorin 7A (SEMA7A) is implicated in various neurodegenerative diseases, but whether it plays a role in Parkinson's disease (PD) remains unclear. Methods In this study, 1.0 mmol/L 1‐methyl‐4‐phenylpyridinium (MPP+)‐stimulated mouse microglia (BV2) cells were used as an in vitro model of PD. The expression of SEMA7A was detected by quantitative polymerase chain reaction. Cell Counting Kit‐8 and apoptosis kits were used to analyze the viability and apoptosis of BV‐2 cells. The content of IL‐6, IL‐β, and tumor necrosis factor‐α was determined by ELISA (enzyme‐linked immunosorbent assay) kit. Western blot was used to detect the protein expression level of the inducible NO synthase and cyclooxygenase‐2. Results Our findings indicated that SEMA7A expression in BV2 cells was upregulated after MPP+ stimulation. Knockdown of SEMA7A promoted cell viability while it inhibited apoptosis and the expression of proinflammatory enzymes and proinflammatory cytokines. Silencing SEMA7A‐induced peroxisome proliferator‐activated receptor‐gamma (PPAR‐γ) activation and mitogen‐activated protein kinase (MAPK) signaling pathway inactivation. Furthermore, a PPAR‐γ inhibitor and an MAPK activator promoted the effect of MPP+ on cell viability, apoptosis, and inflammation of BV2 cells; what is more, the PPAR‐γ inhibitor and MAPK activator blocked the inhibitory effect of SEMA7A downregulation on MPP+‐induced injury. Conclusion In general, knockdown of SEMA7A inhibits MPP+‐induced BV2 cell apoptosis and inflammation via PPAR‐γ activation and MAPK inactivation, which may provide a new therapy target for PD.

Flexible sensors for food quality control are experiencing rapid development. The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs. Practical applications: The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs. [ABSTRACT FROM AUTHOR]

Shell‐closing strength (SCS) is commonly used to assess oyster vigor and quality as an important physiological stress indicator of the oyster organism. In this study, a quality decision support system based on flexible wireless sensor network and web services was designed and developed to explore the application of SCS in the rapid assessment of vitality and quality. Based on physiological and environmental information collected by flexible wireless sensor networks, this study proposes a novel quantitative vitality assessment method to identify oyster life decline cycles and processes. Meanwhile, TVB‐N, TVC, and pH were selected as quality indicators, and a back‐propagation artificial neural network (BP‐ANN) was used to predict and evaluate oyster quality indicators. The results showed that the root mean square errors (RMSE) for TVB‐N, TVC, and pH were 0.4624, 0.8827, and 0.1941; the coefficients of determination (R2) were 0.8919, 0.8578, and 0.6249. Therefore, the rapid assessment of oyster vitality and quality based on a flexible wireless sensor network is a reliable and effective means. Practical applications: This study aims to explore the application of live oyster SCS for rapid quality evaluation. In this article, a quality decision support system based on a flexible wireless sensor network (WSN) and web services is developed to improve the quality and health of live oysters in the supply chain. The use of the multi‐sensor system enables the monitoring and collection of environmental and physiological signals of live oysters throughout the supply chain process. The evaluation of the quality of live oysters using physiological and environmental information collected by flexible wireless sensor networks is a reliable and efficient method. This will provide an effective and reliable quality evaluation and management for the oyster industry. [ABSTRACT FROM AUTHOR]