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The purpose of this study was to compare the differences in intramuscular fat content, melanin content, amino acid content and electronic sensory bionic evaluation of the pectoral muscles of five species of black-boned chicken (Taihe black-boned chicken, Yanjin black-boned chicken, Muchuan black-boned chicken, Jiuyuan black-boned chicken and Lueyang black-boned chicken), in order to provide data support for further research on the quality and flavor of local black-boned chickens. The results showed that there were differences between the groups in the intramuscular fat content and melanin content of the breast muscle of the five species of black-boned chicken, showing different gradient gap. Moreover, the relative contents of 14 amino acids, namely, aspartic acid, threonine, serine, glutamic acid, alanine, methionine, isoleucine, leucine, tyrosine, phenylalanine, lysine, histidine, arginine and proline were different among groups, among which the contents of amino acids were relatively higher in Lueyang black-boned chickens and Muchuan black-boned chickens. Furthermore, the electronic tongue analysis revealed that the taste of black-boned chicken was mainly fresh, fresh aftertaste and bitter, and there were significant differences in umami aftertaste among different varieties. The Taihe black-boned chicken and Yanjin black-boned chicken had a certain astringent aftertaste. The results of electronic nose detection showed that there were great differences in odor profiles among different breeds of black-boned chickens, and the gap in the odor profiles was the greatest between Yanjin black-boned chicken and the other four varieties, its flavor was the smallest in terms of the sensor response. In summary, the breast muscle of Lueyang black-boned chicken had better quality, it had higher intramuscular fat, melanin content and amino acid ratio, and all of the electronic sensory bionic evaluation response values were relatively higher compared to the other groups. Meanwhile, the different varieties of black-boned chicken had their own outstanding characteristics, and the results of this study would provide the theoretical reference for consumers to purchase black-boned chicken products.

The quality of meat is important to the consumer. Color is a primary indicator of meat quality and is characterized mainly into lightness, redness, and yellowness. Here, we used the genome-wide association study (GWAS) and gene-based association analysis with whole-genome resequencing of 230 fast-growing white-feathered chickens to map genes related to meat lightness and redness to a 6.24 kb QTL region (GGA15: 6298.34-6304.58 kb). This analysis revealed that only the protein phosphatase 1 catalytic subunit gamma (PPP1CC) was associated with meat color (P = 8.65E-08). The causal relationships between PPP1CC expression and meat lightness/redness were further validated through Mendelian randomization analyses (P < 2.9E-12). Inducible skeletal muscle-specific PPP1CC knockout (PPP1CC-SSKO) mice were generated and these mice showed increased lightness and decreased myoglobin content in the limb muscles. In addition, the predominant myofiber shifted from slow-twitch to fast-twitch myofibers. Through transcriptome and targeted metabolome evidence, we found that inhibition of PPP1CC decreased the expression of typical slow-twitch myofiber and myofiber-type specification genes and enhanced the glycolysis pathway. Functional validation through a plasmid reporter assay revealed that a SNP (rs315520807, C > T) located in the intron of PPP1CC could regulate the gene transcription activity. The differences in meat color phenotypes, myoglobin content, frequency of rs315520807 variant, expression of PPP1CC and fast-twitch fiber marker genes were detected between fast-growing white-feathered chickens and local chickens. In this study, PPP1CC was identified as the causative gene for meat color, and the novel target gene and variant that can aid in the innovation of meat improvement technology were detected.

Real-time estimation of fish biomass plays a crucial role in real-world fishery production, as it helps formulate feeding strategies and other management decisions. In this paper, a dense fish counting network called Swin-CSRNet is proposed. Specifically, the VGG16 layer in the front-end is replaced with the Swin transformer to extract image features more efficiently. Additionally, a squeeze-and-excitation (SE) module is introduced to enhance feature representation by dynamically adjusting the importance of each channel through “squeeze” and “excitation”, making the extracted features more focused and effective. Finally, a multi-scale fusion (MSF) module is added after the back-end to fully utilize the multi-scale feature information, enhancing the model’s ability to capture multi-scale details. The experiment demonstrates that Swin-CSRNet achieved excellent results with MAE, RMSE, and MAPE and a correlation coefficient R2 of 11.22, 15.32, 5.18%, and 0.954, respectively. Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. Therefore, the proposed method not only counts the number of fish with higher speed and accuracy but also contributes to advancing the automation of aquaculture.

The real-time classification of fish feeding behavior plays a crucial role in aquaculture, which is closely related to feeding cost and environmental preservation. In this paper, a Fish Feeding Intensity classification model based on the improved Vision Transformer (CFFI-Vit) is proposed, which is capable of quantifying the feeding behaviors of rainbow trout (Oncorhynchus mykiss) into three intensities: strong, moderate, and weak. The process is outlined as follows: firstly, we obtained 2685 raw feeding images of rainbow trout from recorded videos and classified them into three categories: strong, moderate, and weak. Secondly, the number of transformer encoder blocks in the internal structure of the ViT was reduced from 12 to 4, which can greatly reduce the computational load of the model, facilitating its deployment on mobile devices. And finally, a residual module was added to the head of the ViT, enhancing the model’s ability to extract features. The proposed CFFI-Vit has a computational load of 5.81 G (Giga) Floating Point Operations per Second (FLOPs). Compared to the original ViT model, it reduces computational demands by 65.54% and improves classification accuracy on the validation set by 5.4 percentage points. On the test set, the model achieves precision, recall, and F1 score of 93.47%, 93.44%, and 93.42%, respectively. Additionally, compared to state-of-the-art models such as ResNet34, MobileNetv2, VGG16, and GoogLeNet, the CFFI-Vit model’s classification accuracy is higher by 6.87, 8.43, 7.03, and 5.65 percentage points, respectively. Therefore, the proposed CFFI-Vit can achieve higher classification accuracy while significantly reducing computational demands. This provides a foundation for deploying lightweight deep network models on edge devices with limited hardware capabilities.

In intelligent feeding recirculating aquaculture system, accurately estimating fish population and density is pivotal for management practices and survival rate assessments. However, challenges arise due to mutual occlusion among fish, rapid movement, and complex breeding environments. Traditional object detection methods based on convolutional neural networks (CNN) often fall short in fully addressing the detection demands for fish schools, especially for distant and small targets. In this regard, we introduce a detection framework dubbed FCFormer (Fish Count Transformer). Specifically, the Twins-SVT backbone network is employed first to extract global features of fish schools. To further enhance feature extraction, especially in the fusion of features at different levels, a Bi-FPN aggregation network model with a CAM Count module is incorporated (BiCC). The CAM module aids in focusing more on critical region features, thus rendering feature fusion more cohesive and effective. Furthermore, to precisely predict density maps and elevate the accuracy of fish counting, we devised an adaptive feature fusion regression head: CRMHead. This approach not only optimizes the feature fusion process but also ensures superior counting precision. Experimental results shown that the proposed FCFormer network achieves an accuracy of 97.06%, with a mean absolute error (MAE) of 6.37 and a root mean square error (MSE) of 8.69. Compared to the Twins transformer, there's a 2.02% improvement, outperforming other transformer-based architectures like CCTrans and DM_Count. The presented FCFormer algorithm can be effectively applied to fish density detection in intelligent feeding recirculating aquaculture system, offering valuable input for the development of intelligent breeding management systems.

Automated recognition of insect category, which currently is performed mainly by agriculture experts, is a challenging problem that has received increasing attention in recent years. The goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile terminals. State-of-the-art lightweight convolutional neural networks (such as SqueezeNet and ShuffleNet) have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters, thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited memory. In this research, we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational cost. In addition, we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the time-delay problems in the field. Experiments demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64% with less training time relative to other classical convolutional neural networks. We have also verified the results that the improved SqueezeNet model has a 2.3% higher than of the original model in the open insect data IP102.

Abstract Ensuring the traceability of Pu-erh tea products is crucial in the production and sale of tea, as it is a key means to ensure their quality and safety. The common approach used in traceability systems is the utilization of bound Quick Response (QR) codes or Near Field Communication (NFC) chips to track every link in the supply chain. However, counterfeiting risks still persist, as QR codes or NFC chips can be copied and inexpensive products can be fitted into the original packaging. To address this issue, this paper proposes a tea face verification model called TeaFaceNet for traceability verification. The aim of this model is to improve the traceability of Pu-erh tea products by quickly identifying counterfeit products and enhancing the credibility of Pu-erh tea. The proposed method utilizes an improved MobileNetV3 combined with Triplet Loss to verify the similarity between two input tea face images with different texture features. The recognition accuracy of the raw tea face dataset, ripe tea face dataset and mixed tea face dataset of the TeaFaceNet network were 97.58%, 98.08% and 98.20%, respectively. Accurate verification of tea face was achieved using the optimal threshold. In conclusion, the proposed TeaFaceNet model presents a promising approach to enhance the traceability of Pu-erh tea products and combat counterfeit products. The robustness and generalization ability of the model, as evidenced by the experimental results, highlight its potential for improving the accuracy of Pu-erh tea face recognition and enhancing the credibility of Pu-erh tea in the market. Further research in this area is warranted to advance the traceability of Pu-erh tea products and ensure their quality and safety.

Silkie chicken, an important chicken breed with high medicinal and nutritional value, has a long history of being used as a dietary supplement in China. However, the compounds with health-promoting effects in Silkie chickens remain unclear. In the present study, we conducted a comprehensive analysis of metabolic and lipidomic profiles to identify the characteristic bioactive compounds in Silkie chickens, using a common chicken breed as control. The results showed that the levels of 13 metabolites including estradiol, four lipid subclasses including cardiolipin (CL), eight lipid molecules, and three fatty acids including docosahexaenoic acid (C22:6) were significantly increased in Silkie chickens, which have physiological activities such as resisting chronic diseases and improving cognition. These characteristic bioactive compounds have effects on meat quality characteristics, including improving its water-holding capacity and umami taste and increasing the content of aromatic compounds and phenols. The differentially expressed genes (DEGs) between the two chicken breeds revealed the regulatory network for these characteristic bioactive compounds. Fifteen DEGs, including HSD17B1, are involved in the synthesis of characteristic metabolites. Eleven DEGs, including ELOVL2, were involved in the synthesis and transport of characteristic lipids and fatty acids. In summary, we identified characteristic bioactive compounds in Silkie chickens, and analyzed their effects on meat quality characteristics. This study provided important insight into Silkie chicken meat as a functional food.

Abstract Background To monitor dypsnea and mortality at 5 and 10 years, respectively, after surgical treatment of tuberculosis-destroyed lung (TDL) patients. Methods TDL patients treated surgically at Beijing Chest Hospital from November 2007 to June 2019 were monitored in this observational study. Follow-up assessments of respiratory function indicators and survival conducted 5 and 10 years post-surgery led to patient grouping based on mMRC score into a dyspnea group (mMRC ≥ 1) and a non-dyspnea group (mMRC = 0). Cox regression analysis detected effects of patient demographics, clinical characteristics, surgical factors and respiratory function on 5 year post-surgical survival. Results By study completion (June 30, 2020), 32 of 104 patients were lost and 72 completed follow-up for a study total of 258.9 person-years. 45 patients (62.5%, 45/72) had mMRC scores of 0, while 12 (16.7%, 12/72), 21 (36.2%, 21/58) and 27 (60.0%, 27/45) patients exhibited dyspnea by 1, 3 and 5 years post-surgery, respectively. Low lung carbon monoxide diffusion score (DLCO% pred) and scoliosis contributed to dyspnea occurrence. Conclusions Most TDL patients lacked subjective dyspnea signs post-surgery, while dyspnea rates increased with time. Preoperative low lung diffusion function and Scoliosis were associated with factors for postoperative dyspnea. Surgical treatment increased TDL patient survival overall.

Abstract Meat is among the most consumed foods worldwide and has a unique flavor and high nutrient density in the human diet. However, the genetic and biochemical bases of meat nutrition and flavor are poorly understood. Here, 3431 metabolites and 702 volatiles in 423 skeletal muscle samples are profiled from a gradient consanguinity segregating population generated by Pekin duck × Liancheng duck crosses using metabolomic approaches. The authors identified 2862 metabolome‐based genome‐wide association studies (mGWAS) signals and 48 candidate genes potentially modulating metabolite and volatile levels, 79.2% of which are regulated by cis‐regulatory elements. The level of plasmalogen is significantly associated with TMEM189 encoding plasmanylethanolamine desaturase 1. The levels of 2‐pyrrolidone and glycerophospholipids are regulated by the gene expression of AOX1 and ACBD5, which further affects the levels of volatiles, 2‐pyrrolidone and decanal, respectively. Genetic variations in GADL1 and CARNMT2 determine the levels of 49 metabolites including L‐carnosine and anserine. This study provides novel insights into the genetic and biochemical basis of skeletal muscle metabolism and constitutes a valuable resource for the precise improvement of meat nutrition and flavor.

BackgroundTuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) and remains a major health threat worldwide. However, a detailed understanding of the immune cells and inflammatory mediators in Mtb-infected tissues is still lacking. Tuberculous pleural effusion (TPE), which is characterized by an influx of immune cells to the pleural space, is thus a suitable platform for dissecting complex tissue responses to Mtb infection.MethodsWe employed singe-cell RNA sequencing to 10 pleural fluid (PF) samples from 6 patients with TPE and 4 non-TPEs including 2 samples from patients with TSPE (transudative pleural effusion) and 2 samples with MPE (malignant pleural effusion).ResultCompared to TSPE and MPE, TPE displayed obvious difference in the abundance of major cell types (e.g., NK, CD4+T, Macrophages), which showed notable associations with disease type. Further analyses revealed that the CD4 lymphocyte population in TPE favored a Th1 and Th17 response. Tumor necrosis factors (TNF)-, and XIAP related factor 1 (XAF1)-pathways induced T cell apoptosis in patients with TPE. Immune exhaustion in NK cells was an important feature in TPE. Myeloid cells in TPE displayed stronger functional capacity for phagocytosis, antigen presentation and IFN-γ response, than TSPE and MPE. Systemic elevation of inflammatory response genes and pro-inflammatory cytokines were mainly driven by macrophages in patients with TPE.ConclusionWe provide a tissue immune landscape of PF immune cells, and revealed a distinct local immune response in TPE and non-TPE (TSPE and MPE). These findings will improve our understanding of local TB immunopathogenesis and provide potential targets for TB therapy.

Melanin as a natural pigment widely exists in animals, plants, fungi and bacteria. In vivo and in vitro tests have proven that melanin has a variety of health-promoting effects, such as antioxidant and immunomodulatory activities, hypoglycemic and hypolipidemic effect, liver and gastrointestinal tract protection effect. Melanin can be extracted from animal and plant by-products or produced by microbial fermentation, involving five extraction and purification methods, with their own positive and negative aspects. Melanin can be added to a variety of foods as natural colorant and preservative, with high safety and almost no toxicity. This article focuses on melanin’s source classification, health benefits, extraction technology and applications in food products to explore the potential of it as a functional food ingredient.

This study established back-propagation neural networks (BPNNs) for evaluating the freshness of bighead carp (Hypophthalmichthys nobilis) heads during chilled storage via fluorescence spectroscopy using an excitation-emission matrix (EEM). The total volatile basic nitrogen (TVB-N) and total aerobic count (TAC) of fish increased obviously during storage at 0, 4, 8, 12, and 16 °C, while sensory scores decreased with increasing storage time. The EEM fluorescence intensity was measured, and its change was correlated with the freshness indicators of the samples. Three characteristic components of EEM data were extracted by parallel factor analysis, and two freshness indicators were used to construct the EEM-BPNNs model. The results demonstrated that the relative errors of the EEM-BPNNs model for TVB-N and TAC were less than 14%. This result indicated that the EEM-BPNNs model could determine the freshness of fish in cold chains in a rapid and nondestructive way.

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Abstract Aim To assess the efficacy of extraperitoneal colostomy (EPC) in preventing stoma-related complications. Background Transperitoneal colostomy (TPC) is a widely used surgical approach. However, TPCs have been reported to have increased risks of stoma-related complications, such as parastomal hernias, stomal retraction, and stomal prolapse. The purpose of EPC is to reduce these complications. However, there is still a lack of evidence-based studies. Materials and methods MEDLINE, EMBASE, Web of Science, Scopus, MOOSE, PubMed, Google Scholar, Baidu Scholar, and the Cochrane Library were searched to conduct a systematic review and meta-analysis with RCTs. The meta-analysis was performed with RevMan 5.4 software. Results This study included 5 eligible RCTs. Compared with the TPC group, the EPC group had lower incidence rates of parastomal hernias (RR, 0.14; 95% CI, 0.04–0.52, P = 0.003, I 2 = 0%) and stomatal prolapse (RR, 0.27; 95% CI, 0.08–0.95, P = 0.04, I 2 = 0%), but a higher rate of defecation sensation (RR, 3.51; 95% CI, 2.47–5.0, P

Accurately recognizing insect pest in their larva phase is significant to take the early treatment on the infected crops, thus helping timely reduce the yield loss in agricultural products. The convolutional neural networks (CNNs)-based classification methods have become the most competitive methods to address many technical challenges related to image recognition in the field. Focusing on accurate and small models carried on mobile devices, this study proposed a novel pest classification method PCNet (Pest Classification Network) based on lightweight CNNs embedded attention mechanism. PCNet was designed with EfficientNet V2 as the backbone, and the coordinate attention mechanism (CA) was incorporated in this architecture to learn the inter-channel pest information and pest positional information of input images. Moreover, combining the feature maps output by mobile inverted bottleneck (MBConv) with the feature maps output by average pooling to develop the feature fusion module, which implements the feature fusion between shallow layers and deep layers to address the loss of insect pest features in the down-sampling procedures. In addition, a stochastic, pipeline-based data augmentation approach was adopted to randomly enhance data diversity and thus avoid model overfitting. The experimental results show that the PCNet model achieved recognition accuracy of 98.4 % on the self-built dataset consisting of 30 classes of larvae, which outperforms three classic CNN models (AlexNet, VGG16, and ResNet101), and four lightweight CNN models (ShuffleNet V2, MobileNet V3, EfficientNet V1 and V2). To further verify the robustness on different datasets, the proposed model was also tested on two other public datasets: IP102 and miniImageNet. The recognition accuracy of PCNet is 73.7 % on the IP102 dataset, outperforming other models and 94.0 % on miniImageNet dataset, which is only lower than that of ResNet101 and MobileNet V3. The number of PCNet parameters is 20.7 M, which is less than those of traditional classic CNN models. The satisfactory accuracy and small size of this model makes it suitable for real-time pest recognition in the field with resource constrained mobile devices. Our code will be available at https://github.com/pby521/PCNet/tree/master.

ABSTRACT The diagnosis of pleural tuberculosis (TB) remains difficult due to the paucity of Mycobacterium tuberculosis in pleural fluid (PF). This study aimed to improve pleural TB diagnosis using highly sensitive digital PCR (dPCR) technique. A total of 310 patients with evidence of PF were consecutively enrolled, 183 of whom suffered from pleural TB and 127 from non-TB. PF samples were prospectively collected and total DNA was extracted. The copy numbers of M. tuberculosis insertion sequence (IS) 6110 and IS1081 in DNA were quantified using dPCR. The overall area under the curve of IS6110-dPCR was greater than that of IS1081-dPCR (0.85 versus 0.79). PF IS6110 OR IS1081-dPCR (according to their cut-off values, “positive” was defined as either of them was positive, while “negative” was defined as both of them were negative) had higher sensitivity and equal specificity compared with single target-dPCR. The sensitivity of PF IS6110 OR IS1081-dPCR for total, definite, and probable pleural TB was 59.0% (95% CI = 51.5% to 66.2%), 72.8% (95% CI = 62.6% to 81.6%), and 45.1% (95% CI = 34.6% to 55.8%), respectively. Its specificity was 100% (95% CI = 97.1% to 100.0%). PF IS6110 OR IS1081-dPCR showed a higher sensitivity than smear microscopy (57.4% versus 7.1%), mycobacterial culture (55.3% versus 31.8%), and Xpert MTB/RIF (57.6% versus 23.0%). Long antituberculosis treatment time (>1 month) was found to be associated with negative dPCR results in pleural TB patients. This study indicates that PF IS6110 OR IS1081-dPCR is an accurate molecular assay, which is more sensitive than routine etiological tests and has the potential to enhance the definite diagnosis of pleural TB. IMPORTANCE Pleural TB is one of the most frequent causes of pleural effusion, especially in areas with high burden of TB. Due to the paucibacillary nature of the disease, the diagnostic sensitivities of all available bacteriological and molecular tests remain poor. There is an urgent need to develop new efficient methods. Digital PCR (dPCR) is the third generation of PCR that enables the exact quantification of trace nucleic acids in samples. This study evaluates the diagnostic performance of pleural fluid (PF) dPCR analysis for pleural TB, and shows that PF IS6110 OR IS1081-dPCR has a higher sensitivity than routine etiological tests such as smear microscopy, mycobacterial culture, and Xpert MTB/RIF. This work provides a new choice for improving the definite diagnosis of pleural TB.

Traceability systems have changed the way food safety is managed and data is stored. Blockchain tracking services now provide customers with an infrastructure that allows them to easily access data online. However, there are limitations to these new capabilities, such as a lack of transparency and the existence of privacy and security challenges. Additionally, as the need for more agile, private, and traceability secure data solutions continues to grow exponentially, rethinking the current structure of blockchain agricultural traceability is mission-critical for a country. By leveraging and building upon blockchain's unique attributes, including tamper-evident, security hash crypto-data, and distributed ledger, we have proposed a prototype that allows traceability data to be reliably stored via blockchain while simultaneously being secured, with completeness auditing to enhance credibility. The result, the trusted auditing chain (TA chain), is a flexible solution that assures data security and solves challenges such as scalability and privacy-preserving. The TA chain works through Schnorr-style non-interactive Zero-knowledge proof to support security automatical choose privacy augmented. In addition, The TA chain can audit more than 1000 transactions within 1ms, and its error stabilizes below the 250 μs, which proves a security and fair traceability system to assure that data is distributed and reliably, and provably audited.

Tuberculous pleurisy (TP) is one of the most common forms of extrapulmonary tuberculosis, but its diagnosis is challenging. Lipoarabinomannan (LAM) antigen is a biomarker for Mycobacterium tuberculosis (Mtb) infection. LAM detection has potential as an auxiliary diagnostic method for TP. We have successfully generated five rabbit anti-LAM monoclonal antibodies (BJRbL01, BJRbL03, BJRbL20, BJRbL52, and BJRbL76). Here, anti-LAM antibodies were tested to detect LAM in the pleural fluid and plasma of patients with TP by sandwich enzyme-linked immunosorbent assays (ELISAs). The results revealed that all of the anti-LAM antibodies were successfully used as capture and detection antibodies in sandwich ELISAs. The BJRbL01/BJRbL01-Bio pair showed better performance than the other antibody pairs for detecting mycobacterial clinical isolates and had a limit of detection of 62.5 pg/mL for purified LAM. LAM levels were significantly higher in the pleural fluid and plasma of patients with TP than in those of patients with malignant pleural effusion or the plasma of non-TB, and LAM levels in the pleural fluid and plasma were positively correlated. Moreover, LAM levels in the pleural fluid sample were significantly higher in confirmed TP patients than in clinically diagnosed TP patients. Our studies provide novel LAM detection choices in the pleural fluid and plasma of TP patients and indicate that LAM detection assay has an auxiliary diagnostic value for TP, which may help to improve the diagnosis of TP.

Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments. First, a dataset containing nine common maize pests is constructed after an image augmentation based on image cropping. Then, a linear transformation method is proposed to optimize the anchors generated by the k-means clustering algorithm, which can improve the matching accuracy between anchors and ground truths. In addition, two residual units are added to the second residual block of the original YOLOv3 network to obtain more information about the location of the underlying small targets, and one ResNet unit is used in the feature pyramid network structure to replace two DBL(Conv+BN+LeakyReLU) structures to enhance the reuse of pest features. Experiment results show that the mAP and mRecall of our proposed method are improved by 6.3% and 4.61%, respectively, compared with the original YOLOv3. The proposed method outperforms other state-of-the-art methods (SSD, Faster-rcnn, and YOLOv4), indicating that the proposed method achieves the best detection performance, which can provide an effective model for the realization of intelligent monitoring of maize pests.

Abstract Background Tuberculous pleural effusion (TPE) is the most common extrapulmonary manifestation and may have lasting effect on lung function. However conventional diagnostic tests for TPE register multiple limitations. This study estimates diagnostic efficacy of the interferon gamma release assay (IGRA: T-SPOT.TB) in TPE patients of different characteristics. Methods We performed a prospective, single-centre study including all suspected pleural effusion patients consecutively enrolled from June 2015 to October 2018. Through receiver operating characteristic (ROC) curves, technical cut-offs and the utility of T-SPOT on pleural fluid (PF) were determined and analysed. Logistic regression analysis was performed to obtain the independent risk factors for TPE, and evaluated the performance of the T-SPOT assay stratified by risk factors in comparison to ADA. Results A total of 601 individuals were consecutively recruited. The maximum spot-forming cells (SFCs) of early secretory antigenic target-6 (ESAT-6) and culture filtrate protein-10 (CFP-10) in the PF T-SPOT assay had the best diagnostic efficiency in our study, which was equal to ADA (0.885 vs 0.887, P = 0.957) and superior to peripheral blood (PB), with a sensitivity of 83.0% and a specificity of 83.1% (The cut-off value was 466 SFCs/106 mononuclear cells). Among the TPE patients with low ADA (

Traditional traceability system has problems of centralized management, opaque information, untrustworthy data, and easy generation of information islands. To solve the above problems, this paper designs a traceability system based on blockchain technology for storage and query of product information in supply chain of agricultural products. Leveraging the characteristics of decentralization, tamper-proof and traceability of blockchain technology, the transparency and credibility of traceability information increased. A dual storage structure of “database + blockchain” on-chain and off-chain traceability information is constructed to reduce load pressure of the chain and realize efficient information query. Blockchain technology combined with cryptography is proposed to realize the safe sharing of private information in the blockchain network. In addition, we design a reputation-based smart contract to incentivize network nodes to upload traceability data. Furthermore, we provide performance analysis and practical application, the results show that our system improves the query efficiency and the security of private information, guarantees the authenticity and reliability of data in supply chain management, and meets actual application requirements.

Large scale pest recognition is one of crucial components in pest management in outdoor conditions, which is much more difficult than common object recognition because of the variational image acquisition direction, location, pest size and complex image background. To overcome the challenges, this study proposes a CNN model by combining spatial attention mechanism and channel attention mechanism to realize accurate pest location and recognition in field images. The proposed model consists of two major parts. Firstly, the module Spatial Transformer Networks (STN) is incorporated into a Convolutional Neural Network (CNN) architecture to provide image cropping out and scale-normalization of the appropriate region, which can simplify the subsequent classification task. The second one is called Improved Split-Attention Networks that is used to enable feature-map attention across feature-map groups. The proposed model is evaluated on three different datasets: Li’s dataset (10 species), proposed dataset (58 species) and IP102 dataset (102 species), achieving the classification accuracies of 96.78%, 96.50% and 73.29%, respectively. Comparisons with five traditional CNN models and three attention-related state-of-the-art deep learning models show that the current method outperforms these previous models. Besides, to verify the robustness of this proposed model on different image resolutions, six datasets with different image resolutions are constructed and all accuracies exceed 92% with the image resolution of $400\times 267$ pixels reaching the optimal performance. All results show that the proposed method provides a reliable solution to recognize insect pest in field and support precision plant protection in agriculture production.

Abstract The mechanisms governing community assembly is fundamental to ecological restoration and clarification of the assembly processes associated with severe disturbances (characterized by no biological legacy and serious environmental problems) is essential. However, a systematic understanding of community assembly in the context of severe anthropogenic disturbance remains lacking. Here, we explored community assembly processes after metal mining, which is considered to be a highly destructive activity to provide insight into the assembly rules associated with severe anthropogenic disturbance. Using a chronosequence approach, we selected vegetation patches representing different successional stages and collected data on eight plant functional traits from each stage. The traits were classified as establishment and regenerative traits. Based on these traits, null models were constructed to identify the processes driving assembly at various successional stages. Comparison of our observations with the null models indicated that establishment and regenerative traits converged in the primary stage of succession. As succession progressed, establishment traits shifted to neutral assembly, whereas regeneration traits alternately converged and diverged. The observed establishment traits were equal to expected values, whereas regenerative traits diverged significantly after more than 20 years of succession. Furthermore, the available Cr content was linked strongly to species' ecological strategies. In the initial stages of vegetation succession in an abandoned metal mine, the plant community was mainly affected by the available metal content and dispersal limitation. It was probably further affected by strong interspecific interaction after the environmental conditions had improved, and stochastic processes became dominant during the stage with a successional age of more than 20 years.

Abstract Background The development of skeletal muscle is closely related to the efficiency of meat production and meat quality. Chicken skeletal muscle development depends on myogenesis and adipogenesis and occurs in two phases—hyperplasia and hypertrophy. However, cell profiles corresponding to the two-phase muscle development have yet to be determined. Single-cell RNA-sequencing (scRNA-seq) can elucidate the cell subpopulations in tissue and capture the gene expression of individual cells, which can provide new insights into the myogenesis and intramuscular adipogenesis. Results Ten cell clusters at the post-hatching developmental stage at Day 5 and seven cell clusters at the late developmental stage at Day 100 were identified in chicken breast muscles by scRNA-seq. Five myocyte-related clusters and two adipocyte clusters were identified at Day 5, and one myocyte cluster and one adipocyte cluster were identified at Day 100. The pattern of cell clustering varied between the two stages. The cell clusters showed clear boundaries at the terminal differentiation stage at Day 100; by contrast, cell differentiation was not complete at Day 5. APOA1 and COL1A1 were selected from up-regulated genes in the adipocyte cluster and found to be co-expressed with the ADIPOQ adipocyte marker gene in breast muscles by RNA in situ hybridization. Conclusions This study is the first to describe the heterogeneity of chicken skeletal muscle at two developmental stages. The genes APOA1 and COL1A1 were identified as biomarkers for chicken intramuscular fat cells.

Design: A prospective multicentre study was conducted to compare the diagnostic accuracy of the GeneXpert MTB/RIF Ultra (Xpert Ultra) and cell-free DNA (cfDNA) assay for tuberculous meningitis (TBM) in China. Methods: A prospective cohort study was conducted among individuals with symptoms suggestive of TBM registered in three TB specialised hospitals in China between June 2018 and January 2019. Results: Overall, 84 patients suggestive of TBM were included in this analysis between June 2018 and January 2019. Using microbiological evidence as reference, the sensitivity/specificity for the diagnostic tests were Xpert Ultra 93.3%/100%, cfDNA 93.3%/92.6% and mycobacteria growth indicator tube (MGIT) culture 13.3%/100%. In addition, the sensitivity of culture was 6.7% when using clinical diagnosis criteria as the gold standard. Xpert Ultra correctly identified 28 cases as TBM, indicating a sensitivity of 46.7%. Notably, four additional TBM cases were detected by cfDNA compared with Xpert Ultra, yielding an overall sensitivity of 53.3%. Statistical analysis revealed that the sensitivity of Xpert Ultra and cfDNA was significantly higher than that of culture. Conclusions: The data demonstrate that Xpert Ultra and cfDNA assay showed optimal sensitivity compared with MGIT culture. In addition, there was no significant correlation between bacterial load and TBM severity in the participants.

In aquaculture, the accurate prediction of feed intake for group fish is considered to be crucial to any feeding system. Previous studies mainly used mathematical statistics to establish the mapping relationship between feed intake and influencing factors. The result was easily influenced by subjective experience. To solve the above issues, this paper proposed a feed intake prediction model for group fish using the back-propagation neural network (BPNN) and mind evolutionary algorithm (MEA). Firstly, four factors, including water temperature, dissolved oxygen, the average fish weight and the number of fish were selected as the input of the BPNN model. Secondly, the initial weight and threshold of the BPNN were optimized by the MEA to improve the matching precision. Finally, the prediction model was achieved after training. Experimental results showed that the correlation coefficient between the predicted and measured values reached 0.96. And the root mean squared error, mean square error, mean absolute error, mean absolute percent error of the model was 6.89, 47.53, 6.17 and 0.04, respectively. In addition, the proposed method also had the better nonlinear fitting ability than BPNN and GA-BP. By using an intelligent optimization algorithm, the mapping relationship between fish intake and environmental factors was automatically established, thus avoiding the subjectivity of traditional methods. Therefore, it can lay a theoretical foundation for the development of intelligent feeding equipment and meet the needs of the smart fishery.

In recent years, residual learning has shown excellent performance on convolutional neural network (CNN)-based single-image super-resolution (SISR) tasks. However, CNN-based SISR approaches have focused mainly on the design of deep architectures, and the rectified linear units (ReLUs) used in these networks hinder shallow-to-deep information transfer. As a result, these methods are unable to utilize some shallow information, and improving model performance is difficult. To solve the above issues, this paper proposes an image SR reconstruction method based on a generative adversarial network with a residual dense architecture. First, before ReLU activation, the number of feature channels is expanded by a factor of 6~9 using a 1 × 1 convolutional layer, which improves the utilization of shallow information. Next, the original discriminator is replaced with a relativistic average discriminator, thereby improving the authenticity of the discriminative network. Finally, preactivation features are used to improve the perceptual loss, thus providing stronger monitoring for brightness consistency and texture restoration. Experimental results show that the proposed algorithm improves the utilization of shallow information in a deep network. Structural similarity (SSIM) index evaluations show that the overall utilization of shallow information is increased by 105.52%. In addition, the average runtime is 0.42 sec/frame, nearly 3.6 times faster than those of traditional methods. Moreover, the recovered images have an average natural image quality evaluator value of 3.4 and high perceptual quality, showing that the proposed method is suitable for image reconstruction applications in fields such as agriculture and medicine.

This paper proposes an optimal generation scheduling approach based on linear relaxation and mixed integer programming, which is used to solve the generation dispatch problem. The quadratic transmission loss constraint of each transmission line is converted into linear constraints by using the linear relaxation and mixed integer programming technique. Consequently, the original optimal generation scheduling problem is formulated as a quadratic programming or mixed integer quadratic programming problem that can be solved by commercial optimization solver. In order to improve the efficiency of algorithm, this paper further analyses the generation scheduling model and deletes the redundant variables and constraints. Three test systems, including IEEE 30-node system, IEEE 118-node system, and Polish 2746-node system, are employed to examine the effectiveness of the proposed method. The comparative results obtained by the proposed method, quadratically constrained quadratic programming method (QCQP), and solving constraint integer programs solver (SCIP) verify the effectiveness of the proposed method in solving the optimal generation scheduling problem.

Objective To investigate the diagnostic value of the T cell spot (T-SPOT.TB) test, oxidation-related factors (ORF), and antimicrobial peptide LL-37 in patients with pulmonary tuberculosis (PTB) with type 2 diabetes. Methods A total of 560 patients with PTB admitted to our hospital from January 2019 to April 2021 were retrospectively included in this study. Of these, 232 patients with PTB and type 2 diabetes were assigned to the combined group, and 328 patients without complications were assigned to the PTB group. Results Areas under the curve (AUCs) for the number of spot-forming cells in CFP10 and ESAT-6 test panels detecting PTB with type 2 diabetes were 0.892 (95% confidence interval [CI] 0.831–0.921) and 0.893 (95% CI 0.841–0.935), respectively. CFP10 combined with ESAT-6 had the highest diagnostic value, with sensitivity and specificity levels and an AUC of 87.73%, 88.93%, and 0.942 (95% CI 0.907–0.967), respectively. The levels of total antioxidant capacity, superoxide dismutase, and catalase in the combined group were lower than in PTB and control groups. Conclusion The combination of T-SPOT.TB, ORF, and LL-37 in the diagnosis of pulmonary tuberculosis with type 2 diabetes mellitus has a high diagnostic value and clinical application value.

For the rapid, reliable, and cost-effective methods of tuberculosis (TB) auxiliary diagnosis, antibody (Ab) detection to multiple antigens of Mycobacterium tuberculosis (Mtb) has great potential; however, this methodology requires optimization. We constructed 38KD-MPT32-MPT64, CFP10-Mtb81-EspC, and Ag85B-HBHA fusion proteins and evaluated the serum Ab response to these fusion proteins and to lipoarabinomannan (LAM) by ELISA in 50 TB patients and 17 non-TB subjects. IgG responses to the three fusion proteins and to LAM were significantly higher in TB patients, especially in Xpert Mtb-positive TB patients (TB-Xpert+), than in non-TB subjects. Only the anti-38KD-MPT32-MPT64 Ab showed higher levels in the Xpert Mtb-negative TB patients (TB-Xpert−) than in the non-TB, and only the anti-LAM Ab showed higher levels in the TB-Xpert+ group than in the TB-Xpert− group. Anti-Ag85B-HBHA Ab-positive samples could be accurately identified using 38KD-MPT32-MPT64. The combination of 38KD-MPT32-MPT64, CFP10-Mtb81-EspC, and LAM conferred definite complementarity for the serum IgG detection of TB, with relatively high sensitivity (74.0%) and specificity (88.2%). These data suggest that the combination of 38KD-MPT32-MPT64, CFP10-Mtb81-EspC, and LAM antigens provided a basis for IgG detection and for evaluation of the humoral immune response in patients with TB.

Abstract Background This retrospective study evaluated the performance of a lipoarabinomannan (LAM)-based immunological method for diagnosing pleural tuberculosis (TB) from pleural effusion samples. Results were compared to those obtained using conventional culture and molecular testing methods. Methods Suspected pleural TB patients who visited Beijing Chest Hospital for medical care between January 2016 and June 2017 were retrospectively analysed in the study. Pleural effusion samples were tested for Mycobacterium tuberculosis (MTB) using the BACTEC MGIT 960 System, GeneXpert, and an anti-LAM antibody assay (LAM assay). Results Pleural effusion samples were collected from a total of 219 retrospectively recruited participants suspected of having pleural TB. Thirteen of 155 confirmed pleural TB cases tested positive for MTB via MGIT culture, for a sensitivity of 8.4% [95% confidence interval (CI): 4.0–12.8%]. In addition, GeneXpert and LAM testing identified 22 and 55 pleural TB cases, for sensitivities of 14.2% (95% CI: 8.7–19.7%) and 35.5% (95% CI: 28.1–43.6%), respectively. The specificities of these two assays were 100.0% (95% CI: 92.9–100.0%) and 96.9% (95% CI: 88.2–99.5%), respectively. Combined application of culture and LAM testing identified 60 positive cases, for a sensitivity of 38.7% (95% CI: 31.0–46.4%) that was significantly higher than that of MGIT culture alone (P

This study presents a novel composite thin film based on rhodamine B encapsulated into MOF-5 (Metal Organic Frameworks) as a fluorescence sensor for the real-time detection of the freshness of chilled pork. The composite film can adsorb and respond to the volatile amines produced by the quality deterioration of pork during storage at 4 °C, with the fluorescence intensity of RhB decreasing over time. The quantitative model used for predicting the freshness indicator (total volatile base nitrogen) of pork was built using the fluorescence spectra (excited at 340 nm) of the RhB@MOF-5 composite film combined with the partial least squares (PLS) algorithm, providing Rc2 and Rp2 values of 0.908 and 0.821 and RMSEC (root mean square error of calibration) and RMSEP (root mean square error of prediction) values of 3.435 mg/100 g and 3.647 mg/100 g, respectively. The qualitative model established by the partial least squares discriminant analysis (PLS-DA) algorithm was able to accurately classify pork samples as fresh, acceptable or spoiled, and the accuracy was 86.67%.

BackgroundCarcass traits are crucial characteristics of broilers. However, the underlying genetic mechanisms are not well understood. In the current study, significant loci and major-effect candidate genes affecting nine carcass traits related to meat production were analyzed in 873 purebred broilers using an imputation-based genome-wide association study.ResultsThe heritability estimates of nine carcass traits, including carcass weight, thigh muscle weight, and thigh muscle percentage, were moderate to high and ranged from 0.21 to 0.39. Twelve genome-wide significant SNPs and 118 suggestively significant SNPs of 546,656 autosomal variants were associated with carcass traits. All SNPs for six weight traits (body weight at 42 days of age, carcass weight, eviscerated weight, whole thigh weight, thigh weight, and thigh muscle weight) were clustered around the 24.08 Kb region (GGA24: 5.73–5.75 Mb) and contained only one candidate gene (DRD2). The most significant SNP, rs15226023, accounted for 4.85–7.71% of the estimated genetic variance of the six weight traits. The remaining SNPs for carcass composition traits (whole thigh percentage and thigh percentage) were clustered around the 42.52 Kb region (GGA3: 53.03–53.08 Mb) and contained only one candidate gene (ADGRG6). The most significant SNP in this region, rs13571431, accounted for 11.89–13.56% of the estimated genetic variance of two carcass composition traits. Some degree of genetic differentiation in ADGRG6 between large and small breeds was observed.ConclusionWe identified one 24.08 Kb region for weight traits and one 42.52 Kb region for thigh-related carcass traits. DRD2 was the major-effect candidate gene for weight traits, and ADGRG6 was the major-effect candidate gene for carcass composition traits. Our results supply essential information for causative mutation identification of carcass traits in broilers.

Fat traits are important in the chicken industry where there is a desire for high intramuscular fat (IMF) and low abdominal fat. However, there is limited knowledge on the relationship between the dynamic status of gene expression and the body fat deposition in chicken. Transcriptome data were obtained from breast muscle and abdominal fat of female chickens from nine developmental stages (from embryonic day 12 to hatched day 180). In total, 8,545 genes in breast muscle and 6,824 genes in abdominal fat were identified as developmentally dynamic genes. Weighted correlation network analysis was used to identify gene modules and the hub genes. Twenty-one hub genes were identified, e.g., ENSGALG00000041996, which represents a candidate for high IMF, and CREB3L1, which relates to low abdominal fat weight. The transcript factor L3MBTL1 and the transcript factor cofactors TNIP1, HAT1, and BEND6 related to both high breast muscle IMF and low abdominal fat weight. Our results provide a resource of developmental transcriptome profiles in chicken breast muscle and abdominal fat. The candidate genes can be used in the selection for increased IMF content and/or a decrease in abdominal fat weight which would contribute to the improvement of these traits.

The occurrence of cucumber downy mildew in solar greenhouses directly affects the yield and quality of cucumber. Chemical control methods may cause excessive pesticide residues, endanger food quality and safety, pollute the ecological environment, etc. Therefore, it is very important to predict the disease before its occurrence. To provide farmers with better and effective guidance for the prevention and control work, minimize the loss of disease damage, this article took cucumber ‘Lyujingling No. 2′ as the experimental material and acquired greenhouse environmental factors data by wireless sensors, including Temp (Temperature), RH (Relative Humidity), ST (Soil Temperature) and SR (Solar Radiation). LSTM (Long Short-Term Memory) neural network structure was constructed based on Keras deep learning framework to develop a prediction model with time-series environmental factors. Combined with the occurrence of downy mildew from manual investigation and statistics, through debugging the parameters, this article developed an occurrence prediction model for cucumber downy mildew and compared it with KNN (K-Nearest Neighbors Classification) and ANN (Artificial Neural Network). In the prediction model, the forecasted results of the four environmental factors were consistent with the true value distributions, and R2 (R-Squared) were all above 0.95. Among them, the ST variable predicted the best results, e.g., R2 = 0.9982, RMSE (Root Mean Square Error) = 0.08 °C, and MAE (Mean Absolute Error) = 0.05 °C. In the disease occurrence prediction model, the training accuracy was 95.99%, the Loss value was 0.0159, the disease occurrence prediction Accuracy was 90%, Precision was 94%, Recall was 89%, F1-score was 91%, the AUC (Area Under Curve) value was 90.15%, and Kappa coefficient was 0.80. It also had obvious advantages over other different models. In summary, the model had a high classification accuracy and performance, and it can provide a reference for the occurrence prediction of cucumber downy mildew in actual production.

Detection of copper (II) ions (Cu2+) in water is important for preventing them from entering the human body to preserve human health. Here, a highly sensitive and selective fluorescence probe that uses mercaptopropionic acid (MPA)-capped InP/ZnS quantum dots (MPA-InP/ZnS QDs) was proposed for the detection of trace amounts of Cu2+ in water. The fluorescence of MPA-InP/ZnS QDs can be quenched significantly in the presence of Cu2+, and the fluorescence intensity shows excellent linearity when the concentration of Cu2+ varies from 0–1000 nM; this probe also exhibits an extremely low limit of detection of 0.22 nM. Furthermore, a possible fluorescence-quenching mechanism was proposed. The MPA-InP/ZnS QDs probes were further applied to the detection of trace Cu2+ in real water samples and drink samples, showing good feasibility.

Abstract Due to the low and uneven illumination that is typical of a recirculating aquaculture system (RAS), visible and near infrared (NIR) images collected from RASs always have low brightness and contrast. To resolve this issue, this paper proposes an image enhancement method based on the Multi-Scale Retinex (MSR) algorithm and a greyscale nonlinear transformation. First, the images are processed using the MSR algorithm to eliminate the influence of low and uneven illumination. Then, the normalized incomplete Beta function is used to perform a greyscale nonlinear transformation. The function’s optimal parameters (α and β) are automatically selected by the particle swarm optimization (PSO) algorithm based on an image contrast measurement function. This adaptive image enhancement method is compared with other classic enhancement methods. The results show that the proposed method greatly improves the image contrast and highlights dark areas, which is helpful during further analysis of these images.

In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.

In apple cultivation, simulation models may be used to monitor fruit size during the growth and development process to predict production levels and to optimize fruit quality. Here, Fuji apples cultivated in spindle-type systems were used as the model crop. Apple size was measured during the growing period at an interval of about 20 days after full bloom, with three weather stations being used to collect orchard temperature and solar radiation data at different sites. Furthermore, a 2-year dataset (2011 and 2012) of apple fruit size measurements were integrated according to the weather station deployment sites, in addition to the top two most important environment factors, thermal and sunshine hours, into the model. The apple fruit diameter and length were simulated using physiological development time (PDT), an indicator that combines important environment factors, such as temperature and photoperiod, as the driving variable. Compared to the model of calendar-based development time (CDT), an indicator counting the days that elapse after full bloom, we confirmed that the PDT model improved the estimation accuracy to within 0.2 cm for fruit diameter and 0.1 cm for fruit length in independent years using a similar data collection method in 2013. The PDT model was implemented to realize a web-based management information system for a digital orchard, and the digital system had been applied in Shandong Province, China since 2013. This system may be used to compute the dynamic curve of apple fruit size based on data obtained from a nearby weather station. This system may provide an important decision support for farmers using the website and short message service to optimize crop production and, hence, economic benefit.

BACKGROUND: The diagnosis of pleural tuberculosis (TB) remains to be difficult. Interferon-gamma release assay (IGRA) is a promising method for diagnosing TB in low TB burden countries. The release of interferon-gamma (IFN-γ) by T lymphocytes increases at a localized site of infection with Mycobacterium tuberculosis antigen. This study aimed to examine the clinical accuracy of T-SPOT.TB on pleural fluid and peripheral blood for the diagnosis of pleural TB in high TB burden country. METHODS: 168 subjects with pleural effusion were enrolled prospectively and examined with T-SPOT.TB on pleural fluid and peripheral blood samples simultaneously. RESULTS: The receiver operating characteristic (ROC) curve and cut-off value of pleural fluid T-SPOT.TB was established according to spot forming cells (SFC) between culture/biopsy-confirmed pleural TB group and no pleural TB group. The sensitivity of pleural fluid T-SPOT.TB and peripheral blood T-SPOT.TB was similar (96.3% and 92.7%, respectively) (P= 0.691). In contrast, the specificity of pleural fluid T-SPOT.TB (94.5%) was significantly higher than that of peripheral blood T-SPOT.TB (76.1%) (P=0.002). 2% (2/98) of pleural fluid T-SPOT.TB results were indeterminate. CONCLUSION: The diagnostic accuracy of peripheral blood T-SPOT.TB is low in high TB burden countries due to latent tuberculosis infection. Pleural fluid T-SPOT.TB is a relatively useful and supplementary test to explore pleural TB in high TB burden countries, but its diagnostic accuracy needs to be validated in further large scale research.