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Nowadays, the evaluation of students’ learning effect in colleges and universities mainly rely on manual management and supervision, which is inefficient and inaccurate. What’s worse, it completely depends on the subjective judgment of the supervisors, many potential teaching quality data could be ignored. Using an unsupervised classroom monitoring system based on deep learning can master the learner’s learning result. This paper mainly researched a learner’s behavior evaluation method based on deep learning. Firstly, MTCNN is being used to detected learner’s facial feature in order to confirm their identification. Then, collected the data set in learning environment and a Mosaic data enhancement had been done. Later, an improved method, is proposed. Identifying the students’ actions of playing the mobile phone, moving to classroom, eating, reading, writing, this method has more advantages on classroom behavior detecting. In addition, by building a quantitative evaluation method–CFIndex (class focus index), the time of students' behaviors in classroom has been calculated. Therefore, CFIndex evaluation method can well reflect the real performance of students in the classroom. In this paper, compared with Fast R-CNN and classical algorithm on the same data set, the proposed method has better performance in classroom behavior detection, and it can better reflect the students’ real actions in the classroom, it has certain meaning of theoretical guidance.

This paper explores whether it is feasible to use the RGB color information in images of wheat canopies that were exposed to low temperatures during the growth season to achieve fast, non-destructive, and accurate determination of the severity of any freeze injury it may have incurred. For the study presented in this paper, we compared the accuracy of a number of algorithmic classification models using either meteorological data reported by weather services or the color gradation skewness-distribution from high-definition digital canopy images acquired in situ as inputs against a reference obtained by manually assessing the severity of the freeze injury inflicted upon wheat populations at three experimental stations in Shandong, China. The algorithms we used to construct the models included in our study were based on either K-means clustering, systematic clustering, or naïve Bayesian classification. When analyzing the reliability of our models, we found that, at more than 85%, the accuracy of the Bayesian model, which used the color information as inputs and involved the use of prior data in the form of the reference data we had obtained through manual classification, was significantly higher than that of the models based on systematic or the K-means clustering, which did not involve the use of prior data. It was interesting to note that the determination accuracy of algorithms using meteorological factors as inputs was significantly lower than that of those using color information. We also noted that the determination accuracy of the Bayesian model had some potential for optimization, which prompted us to subject the inputs of the model to a factor analysis in order to identify the key independent leaf color distribution parameters characterizing wheat freeze injury severity. This optimization allowed us to improve the determination accuracy of the model to over 90%, even in environments comprising several different ecological zones, as was the case at one of our experimental sites. In conclusion, our naïve Bayesian classification algorithm, which uses six key color gradation skewness-distribution parameters as inputs and involves the use of prior data in the form of manual assessments, qualifies as a contender for the development of commercial-grade wheat freeze injury severity monitoring systems supporting post-freeze management measures aimed at ensuring food security.

Zhengdan 958 (ZD958) is the summer maize variety with the widest planting area in Huang-Huai-Hai plain in the past 20 years. Understanding the agronomic characteristics of maize and its adaptability to climatic factors is of great significance for breeding maize varieties with high yield and stability. In this study, the experimental data of 33 experimental stations from 2005 to 2015 were analyzed to clarify the effects of different agronomic traits on yield and the correlation between agronomic traits, and to understand the effects of different climatic factors on summer maize yield and agronomic traits. The results showed that the average yield of ZD958 was 9.20 t ha -1 , and the yield variation coefficient was 13.41%. There was a certainly negative correlation between high yield and high stability. Plant heights, ear heights, double ear rate, ear length, ear rows, line grain number, grain number per ear, ear diameter, cob diameter, and 1000 grains weight were significantly positive correlation with maize yield. Solar radiation before and after silking were significantly positive correlation with maize yield. Path analysis showed that changes in agronomic traits accounted for 54% of the yield variation, and changes in climate factors accounted for 26% of the yield variation. Our study showed that higher plant height, ear height, grain number per ear and 1000-grain weight, lower lodging rate, pour the discount rate and shorter bald tip long were the main reasons for high yield. Among the climatic factors, solar radiation and the lowest temperature have significant effects on the yield.

BackgroundPrimary pineal malignant melanoma (PPMM) is a rare entity of primary central nervous system melanomas, with only 26 cases reported in the literature to date.Case presentationWe report the case of a 65-year-old male with a PPMM who has prolonged survival of more than 104 weeks after combined microsurgical and endoscopic total resection. This is the first report: combined microscope and endoscopy total resection; PPMM in China; PPMM with total resection alone.ConclusionCombined microscope and endoscopy total resection is beneficial to prolong the survival of patients. But the best approach to treatment needs verification from more clinical cases in future.

The YEATS domain has been identified as a reader of histone acylation and more recently emerged as a promising anti-cancer therapeutic target. Here, we detail the structural mechanisms for π-π-π stacking involving the YEATS domains of yeast Taf14 and human AF9 and acylated histone H3 peptides and explore DNA-binding activities of these domains. Taf14-YEATS selects for crotonyllysine, forming π stacking with both the crotonyl amide and the alkene moiety, whereas AF9-YEATS exhibits comparable affinities to saturated and unsaturated acyllysines, engaging them through π stacking with the acyl amide. Importantly, AF9-YEATS is capable of binding to DNA, whereas Taf14-YEATS is not. Using a structure-guided approach, we engineered a mutant of Taf14-YEATS that engages crotonyllysine through the aromatic-aliphatic-aromatic π stacking and shows high selectivity for the crotonyl H3K9 modification. Our findings shed light on the molecular principles underlying recognition of acyllysine marks and reveal a previously unidentified DNA-binding activity of AF9-YEATS. YEATS domains are histone acylation readers that recognize crotonyllysine and acetyllysine. Here the authors provide structural insights into how YEATS domains recognize acetyllysines and further show that the human AF9 YEATS domain also binds DNA.

Background Peritumoral brain edema (PTBE) in refractory brain metastases significantly reduces the quality of life and even threatens the lives of patients with brain metastases. Although the molecular mechanism of PTBE remains unclear, the important role of vascular endothelial growth factor (VEGF) expression and angiogenesis in PTBE has been confirmed. Therefore, tyrosine kinase inhibitors (TKIs) that block VEGF/VEGFR signaling and inhibit angiogenesis may have therapeutic effects. Case presentation In this study, we report four cases of refractory peritumoral brain edema in brain metastases treated with anlotinib, a novel small-molecule multi-target TKI. All patients were diagnosed through core needle biopsy, with pathological results indicating non-small cell lung cancer, nasal melanoma, skin cancer, and breast cancer, respectively. They received standard multiline treatment during which multiple brain metastases occurred. Following chemotherapy resistance, anlotinib was used to treat the tumors and control refractory brain edema. After two cycles of treatment, brain edema and related symptoms improved, and anlotinib monotherapy was continued until disease progression. The main adverse reactions observed during treatment were hypertension and fatigue, which were tolerable. Conclusion In this small case series, anlotinib was associated with marked radiological reduction of peritumoral brain edema and symptom relief in patients with refractory brain metastases, suggesting it may be a potential therapeutic option that warrants further investigation in larger studies.

To analyze the phytogenic components of honey of Amorpha fruticosa L. (AFH) and establish a targeted quantitative method, the liquid chromatography-mass spectrometry (LC-MS) based metabolomic technology was used in this study. Firstly, high performance liquid chromatography—quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) untargeted metabolomics technology was used to screen candidate markers by comparing AFH metabolites with plant chemicals of Amorpha fruticosa L. Afterward, high performance liquid chromatography—triple quadrupole tandem mass spectrometry (HPLC-QQQ-MS/MS) was used to verify and identify the candidate markers, confirming ononin as the characteristic phytogenic marker of AFH, and determining its content range in AFH as 76.84–93.27 μg/kg (absent in acacia, rape, jujube, and Galla chinensis honey). Then, network pharmacology and molecular docking techniques were adopted to explore the gastric protective mechanism of ononin, and the results showed that ononin strongly binds AKT1 (binding free energy −8.0677 kcal/mol). Using the established method, the LC-MS analytical method for ononin in honey established in this study may be used for the authenticity identification of the characteristic phytogenic markers of AFH.

This study examined the response of color information in digital wheat canopy images from Shandong Province, China, to meteorological indicators during extreme cold spells. Analysis revealed that low-temperature stress altered pixel color and grayscale values, with shifts captured by skewness and kurtosis parameters of color gradation distributions. The kurtosis and skewness of color gradient distributions showed the strongest sensitivity to cold stress. Daily minimum temperature was significantly correlated with kurtosis values for R (0.661), G (0.744), B (0.694), and grayscale (0.744) channels. Models relating these parameters to meteorological factors were developed, with polynomial functions outperforming multilinear approaches. All models demonstrated satisfactory fit, as evidenced by determination coefficients exceeding 0.480. The kurtosis model for green values achieved exceptional prediction accuracy, surpassing 90%. Findings demonstrate quantifiable cold-induced changes in canopy color gradient distribution, establishing a foundation for enhancing freeze damage monitoring systems through image-based metrics. These models enable efficient early warning by linking meteorological data to visible canopy responses, offering practical tools for mitigating agricultural cold stress impacts.

In China, the most common grain crop is maize ( ). The increasing pressure to meet the food demands of its growing population has pushed Chinese maize farmers toward an excessive use of chemical fertilizers, a practice which ultimately leads to a massive waste of resources and widespread environmental pollution. As a result, increasing the yield and improving the nitrogen (N) use efficiency of maize has become a critical issue for agriculture in China. This study, which analyzes the combined data from a simulation carried out using the Decision Support System for Agrotechnology Transfer (DSSAT), a field experiment, and a household survey, explored the effectiveness of several approaches aimed at narrowing the maize yield gap and improving the N utilization efficiency in the Huang-Huai-Hai Plain (HHHP), the most important area for the production of summer maize in China. The various approaches we studied deploy different methods for the integrated management of N fertilizer input and the planting density. The study produced the following results: (1) For the simulated and actual maize yields, the root mean square error (RMSE), the normalized root mean squared errors (NRMSE) and the index of agreement (d) were 1,171 (kg ha ), 12% and 0.84, respectively. These results show that the model is viable for the experiment included in the study; (2) The potential yield was 15.58 t ha , and the yields achieved by the super-high-yield cultivation pattern (SH), the optimized nutrient and density management pattern (ONM), the simulated farmer's practice cultivation pattern (FP) and actual farmer's practice (AFP) were 11.43, 11.06, 10.33, and 7.95 t ha , respectively. The yield gaps associated with the different yield levels were large; (3) For summer maize, the high yield and a high N partial factor productivity (NPFP) was found when applying a planting density of 9 plants m and an N application amount of 246 kg ha . These results suggest that the maximum yield that can actually be achieved by optimizing the N application and planting density is less than 73% of the potential yield. This implies in turn that in order to further narrow the observed yield gaps, other factors, such as irrigation, sowing dates and pest control need to be considered.

Background To summarize the safety and effectiveness of high flow extracranial to intracranial saphenous vein bypass grafting in the treatment of complex intracranial aneurysms. Methods The data of complex intracranial aneurysms patients for high flow extracranial to intracranial saphenous vein bypass grafting from January 2008 to January 2020 were retrospectively collected and analyzed. Eighty-two patients (31 men and 51 women) with 89 aneurysms underwent 82 saphenous vein bypass grafts followed by immediate parent vessel occlusion. The aneurysm was located at the internal carotid artery, middle cerebral artery, and basilar artery in 75, 11, and 3 cases, respectively. Results The patency rate of bypass grafting was 100, 100, 96.3 and 92.4% on intraoperation, on the first postoperative day, at discharge and 6 months follow-up, respectively. At discharge and 6 months follow-up, 3 and 6 patients had graft occlusions. The main postoperative complications were transient hemiparesis and hemianopsia. 3 patients died due to bypass complications and poor physical condition. Conclusions High flow extracranial to intracranial saphenous vein bypass grafting is safe and effective in the treatment of complex intracranial aneurysms and the saphenous vein can meet the requirements of brain blood supply. A high rate of graft patency and adequate cerebral blood flow can be achieved. Highlights A single-centre long-term retrospective study was conducted to assess the safety and effectiveness of high flow EC-IC saphenous vein bypass grafting in the treatment of complex intracranial aneurysms. The data of 82 patients from January 2008 to January 2020 were retrospectively collected and analysed. We found the patency rate of bypass grafting was 100, 100, 96.3 and 92.4% on intraoperation, on the first postoperative day, at discharge and 6 months follow-up, respectively. At discharge and 6 months follow-up, 3 and 6 patients had graft occlusions. Finally, we conclude that high flow extracranial to intracranial saphenous vein bypass grafting is safe and effective in the treatment of complex intracranial aneurysms and the selected blood supply vessels can meet the requirements of blood supply. As far as we know, this study is one of the maximum number of cases in the treatment of complex intracranial aneurysms with saphenous vein bypass.