Abstract
Neoseiulus californicus
is a predatory mite with a wide global distribution that can effectively control a variety of pest mites. In this study, MaxEnt was used to analyse the potential ...distribution of
N. californicus
in China and the BCC-CSM2-MR model was used to predict changes in the suitable areas for the mite from 2021 to 2100 under the scenarios of SSP126, SSP245 and SSP585. The results showed that (1) the average of area under curve value of the model was over 0.95, which demonstrated an excellent model accuracy. (2) Annual mean temperature (Bio1), precipitation of coldest quarter (Bio19), and precipitation of driest quarter (Bio17) were the main climatic variables that affected and controlled the potential distribution of
N. californicus
, with suitable ranges of 6.97–23.27 °C, 71.36–3924.8 mm, and 41.94–585.08 mm, respectively. (3) The suitable areas for
N. californicus
were mainly distributed in the southern half of China, with a total suitable area of 226.22 × 10
4
km
2
in current. Under the future climate scenario, compared with the current scenario, lowly and moderately suitable areas of
N. californicus
increased, while highly suitable areas decreased. Therefore, it may be necessary to cultivate high-temperature resistant strains of
N. californicus
to adapt to future environmental changes.
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The frequency of thyroid cancer has rapidly increased in recent years globally. Thus, more papillary thyroid microcarcinoma (PTMC) patients are being diagnosed, including clinical lymph node-negative ...(cN0) patients. Our study attempted to develop a prediction model for assessing the probability of central lymph node metastasis (CLNM) in cN0 PTMC patients.
A total of 595 patients from the Affiliated Hospital of Qingdao University (training cohort: 456 patients) and the Affiliated Hospital of Jining Medical University (verification cohort: 139 patients) who underwent thyroid surgery between January 2020 and May 2022 were enrolled in this study. Their clinical and molecular pathology data were analyzed with multivariate logistic regression to identify independent factors, and then we established a prediction model to assess the risk of CLNM in cN0 PTMC patients.
Multivariate logistic regression analysis revealed that sex, Hashimoto's thyroiditis (HT), tumor size, extrathyroidal extension, TERT promoter mutations and NRAS mutation were independent factors of CLNM. The prediction model demonstrated good discrimination ability (C-index: 0.757 and 0.753 in the derivation and validation cohorts, respectively). The calibration curve of the model was near the optimum diagonal line, and decision curve analysis (DCA) showed a noticeably better benefit.
CLNM in cN0 PTMC patients is associated with male sex, tumor size, extrathyroidal extension, HT, TERT promoter mutations and NRAS mutation. The prediction model exhibits good discrimination, calibration and clinical usefulness. This model will help to assess CLNM risk and make clinical decisions in cN0 PTMC patients.
Peptides from oyster hydrolysate (OPs) have a variety of biological activities. However, its protective effect and exact mechanism on testicular injury remain poorly understood. This study aimed to ...evaluate the protective effect of OPs on triptolide (TP)-induced testis damage and spermatogenesis dysfunction and investigate its underlying mechanism. In this work, the TP-induced testis injury model was created while OPs were gavaged in mice for 4 weeks. The results showed that OPs significantly improved the sperm count and motility of mice, and alleviated the seminiferous tubule injury. Further study showed that OPs decreased malonaldehyde (MDA) level and increased antioxidant enzyme (SOD and GPH-Px) activities, attenuating oxidative stress and thereby reducing the number of apoptotic cells in the testis. In addition, OPs improved the activities of enzymes (LDH, ALP and ACP) related to energy metabolism in the testis and restored the serum hormone level of mice to normal. Furthermore, OPs promoted the expression of Nrf2 protein, and then increased the expression of antioxidant enzyme regulatory protein (HO-1 and NQO1) in the testis. OPs inhibited JNK phosphorylation and Bcl-2/Bax-mediated apoptosis. In conclusion, OPs have a protective effect on testicular injury and spermatogenesis disorders caused by TP, suggesting the potential protection of OPs on male reproduction.
•MCBLP: A novel model extending MCLP for billboard location selection challenges.•ReCovNet: Attention model and Encoder-Decoder used for solving MCBLP through DRL.•Comparative Analysis: Achieving a ...balance between time and accuracy while addressing MCBLP.•Real-World Application: Practical application in guiding billboard deployment in New York City.
Maximizing billboard coverage with limited resources and different objective goals plays a vital role in social activities. The Maximal Coverage Billboard Location Problem (MCBLP) is complex, especially for multi-objective functions. A multi-objective spatial optimization model was developed using mixed-integer linear programming based on MCBLP to formulate the spatial optimization problem of determining billboard locations. Combining the distinctive features of location problems, we have developed a new approach called ReCovNet that utilizes Deep Reinforcement Learning (DRL) to solve the MCBLP. We applied the ReCovNet to address a real-world billboard location problem in New York City. To assess its performance, we implemented various algorithms such as Gurobi solver, Genetic Algorithm (GA) and a deep learning baseline called Attention Model (AM). The Gurobi reports the optimal solutions, while GA and AM serve as benchmark algorithms. Our proposed approach achieves a good balance between efficiency and accuracy and effectively solves MCBLP. The ReCovNet introduced in our study has potential to improve advertising effectiveness, and our proposed approach offers novel insights for addressing the MCBLP.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Herein, a chiral thiourea Schiff base derived from (1 R ,2 R )-1,2-cyclohexanediamine and tetraphenylethylene (TPE) was applied as a highly effective chiral sensor for the enantioselective ...discrimination of various acids and amines via ion-pair and hydrogen-bond interaction. Compared to the case of the sensors 5 and 6 , the additional thiourea and hydrogen groups of sensor 4 were essential and greatly enhanced the enantioselectivity of chiral guests. In addition, for amino acids, the sensor 4 showed high enantioselectivity, and precipitates visible by the naked eye appeared; however, for chiral amines, the enantioselectivity decreased. This was attributed to weaker hydrogen bond interaction between the amino groups of chiral amines and the sensor. Both the thiourea and acidic hydroxyl groups are essential for chiral TPE thiourea to provide an appropriate chiral environment for highly efficient enantioselective discrimination of chiral substrates. Our findings will be of great value in the design of new chiral sensors.
Measuring Chinese character recognition ability is essential in research on character learning among learners of Chinese as a second language (CSL). Three methods are typically used to evaluate ...character recognition competence by investigating the following properties of a given character: (a) pronunciation (phonological method), (b) meaning (semantic method), and (c) pronunciation and meaning (phonological and semantic or PS method). However, no study has explored the similar or dissimilar outcomes that these three measurements might yield. The current study examined this issue by testing 162 CSL learners with various L1 backgrounds and Chinese proficiency levels. Participants' performance in character recognition measured using a phonological method, a semantic method, and a PS method was compared, which led to two major findings. In terms of similarity, participants' performance in character recognition and the influence of L1 background and Chinese proficiency level on character recognition was similar across the three methods. As for differences, the semantic method could yield a character recognition test with better quality than the other two methods, and the three methods yielded different best fitting models and showed different predictions for Chinese proficiency across different L1 groups. Theoretical and practical implications of these findings are proposed.
Novel ratiometric fluorescent probe for real-time detection of alkaline phosphatase and its application in living cells.
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•A novel ratiometric probe was constructed to detect alkaline ...phosphatase.•The introduction of spontaneously degradable linker is conducive to the construction of ratio fluorescent probes.•The detection limit is as low as 0.16 U/L with high selectivity and accuracy;•This probe possessing excellent biocompatibility.
A novel ratiometric fluorescent probe has been developed through a simple synthetic route for the detection of alkaline phosphatase(ALP) in aqueous media and for fluorescence imaging in living cells. The introduction of a spontaneous-degradation spacer in the design of the fluorescent probe is beneficial for the ratio detection method and allows the selection of a fluorophore with an amino group. Under catalysis by ALP, the phosphate monoester bond breaks; this is followed by 1,4-elimination, decomposition of the carbamate moiety, and subsequent formation of the 4-amine-1,8-naphthalimide fluorophore. The probe APN shows a significant fluorescence colour change from blue to green in response to ALP, and the fluorescence intensity ratio of the probe solution (F550/F480) has a good linear relationship with the ALP concentration in the range of 0 to 100 U L-1. Our studies have demonstrated that APN exhibits high accuracy in recognising ALP, with a detection limit as low as 0.16 U L-1. Furthermore, the probe shows very good biocompatibility, which is beneficial for its application in biological systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Learn to Branch: A new approach is proposed to optimize the branch strategy in Branch-and-bound.•Deep Learning Model: The new model Bipartite Graph Neural Network with Attention Mechanism is ...proposed to learn the branch strategies.•Comparative Analysis: The models outperform other machine learning algorithms.•Real-World Application: The method is used to solve the Surat Road network data.
The multiple traveling salesman problems (MTSP), which arise from real world problems, are essential in urban logistics. Variations such as MinMax-MTSP and Bounded-MTSP aim to distribute workload evenly among salesmen and impose constraints on visited cities, respectively. Branch-and-bound (B&B) provides an exact algorithm solution for these problems. The Learn to Branch (L2B) approach guides branch node selection through deep learning. In this study, we utilize mathematical modeling of Bipartite Graph Neural Network (BiGNN) and an attention mechanism to support B&B in exploring solution spaces through imitation learning. The problems are framed to formulate mixed integer linear programming, which is different from conventional algorithms. It is proposed that a bipartite graph network approach makes a feature representation by setting a structure of constraints and variables. Experimental results showed that our model can generate more accurate solutions than three benchmark models. The BiGNN model can effectively learn the strong branch strategy, which reduces solution time by replacing complex calculations with fast approximations. Additionally, the small-scale instances model can be applied to larger-scale ones.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, ...this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model's efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model's effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model's generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model's commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance.
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Non-grain production has emerged as a potential threat to grain production capacity and security in China. Agricultural products with higher economic returns are beginning to replace traditional ...grain crops, which have relatively low economic returns on a large scale. In this study, we proposed and verified an identification method utilizing an unmanned aerial vehicle and a U-net algorithm to distinguish peach trees in cultivated land; the overall accuracy for verification and prediction were 0.90 and 0.92, respectively. Additionally, a non-grain production index was developed to assess the degree of non-grain production in target plots. The index was 76.90% and 91.38% in the projected plots, representing a high degree of non-grain production. This combination of an identification method and non-grain production index could provide efficient tools for agricultural management to inspect peach trees in cultivated land, thus replacing field measurements to achieve significant labor savings. Furthermore, this method can provide a reference for creating high-standard farmland, sustainable development of cultivated land, and policymaking.
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