Calculation of Carbon Sink of Bamboo Forest in Zhejiang Province and Its Value Realization Path

The biomass method was applied to measure the carbon sequestration status of bamboo forests in Zhejiang province, and the carbon emissions of Zhejiang province from 1989 to 2018 were estimated by using the energy activity CO2 emission measurement method, and the carbon sink contribution of bamboo forests was analyzed by comparison. The results show that the total carbon sequestration in bamboo forests increased from 18,206,400 t to 34,604,000 t during the ninth national forest inventory, with a net increase of 16,397,600 t and a growth rate of 90.07%, showing an overall increasing trend, among which moso bamboo forests are the main carbon sequestration species; the carbon emissions in Zhejiang province showed a stable growth trend, but the growth rate has decreased in recent periods; the amount of carbon sequestered by bamboo forests and carbon emissions show a convergent growth trend, but the amount of carbon sequestered by bamboo forests is relatively small for the overall carbon emissions, and the contribution of carbon sequestration is small. In order to effectively contribute to the process of carbon peaking and carbon neutrality in Zhejiang province, corresponding measures should be taken to effectively play the function of bamboo forest carbon sink and realize its value

Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://github.com/liamheng/Non-IID_Medical_Image_Segmentation

CuI catalyzed sulfonylation of organozinc reagents with sulfonyl halides

In this study, a facile CuI catalyzed synthesis of sulfones involving a nucleophilic addition of functionalized organozinc reagents to organic sulfonyl chlorides is realized. This reaction proceeds efficiently at room temperature, giving rise to various functional group substituted sulfones, generally in moderate to high yields. The method provides a novel, simple, and promising strategy for functionalized sulfone synthesis in the research field of sulfur chemistry

Learning image quality assessment by reinforcing task amenable data selection

In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation. We propose to train simultaneously two neural networks for image selection and a target task using reinforcement learning. A controller network learns an image selection policy by maximising an accumulated reward based on the target task performance on the controller-selected validation set, whilst the target task predictor is optimised using the training set. The trained controller is therefore able to reject those images that lead to poor accuracy in the target task. In this work, we show that the controller-predicted image quality can be significantly different from the task-specific image quality labels that are manually defined by humans. Furthermore, we demonstrate that it is possible to learn effective image quality assessment without using a ``clean'' validation set, thereby avoiding the requirement for human labelling of images with respect to their amenability for the task. Using $6712$, labelled and segmented, clinical ultrasound images from $259$ patients, experimental results on holdout data show that the proposed image quality assessment achieved a mean classification accuracy of $0.94\pm0.01$ and a mean segmentation Dice of $0.89\pm0.02$, by discarding $5\%$ and $15\%$ of the acquired images, respectively. The significantly improved performance was observed for both tested tasks, compared with the respective $0.90\pm0.01$ and $0.82\pm0.02$ from networks without considering task amenability. This enables image quality feedback during real-time ultrasound acquisition among many other medical imaging applications

糖尿病培训手册在培养糖尿病护士的应用

Objective: To explore the nursing service mode for specialized nurses in diabetes and its application effect. Methods:In view of the actual situation of our hospital, a special training manual named with Diabetes training manual was prior composed. 40 specialized nurses in diabetes were trained with the special training manual. Training lasted for 3 months. After the training, the specialized nurses were assessed with the specialty knowledge of Diabetes Mellitus, the skill of clinical procedures, and the knowledge about patients' health education. Results: The skill of the trained nurses was improved. The satisfaction of patients was enhanced.Conclusion: Diabetes training manual can be used for specialized nurses in diabetes.目的  探讨糖尿病护士培训方式。方法  对2013年1—10月本院内分泌科的40名糖尿病护士培训,根据以往医院的培训，结合本院实际，应用自编的糖尿病培训手册，最后考核糖尿病专科理论、操作及宣教能力考核，培训时间为3个月。结果  培训前与培训后比较差别有统计学意义（P＜0.05），理论、操作及健康宣教能力较培训前提高；提高了患者及新护士的满意度。结论  糖尿病培训手册可以用于培养糖尿病新护士

Mass windborne migrations extend the range of the migratory locust in East China

This is the final version. Available on open access from Wiley via the DOI in this recordMigratory insect pests pose a substantial challenge to global food security. These issues are particularly acute when pest incursions occur considerably beyond the expected range, through natural migration or human-aided transport, because the lack of species-specific control strategies and a potential absence of species-specific natural enemies in the newly-invaded area may lead to rapid establishment of a new pest. One such threat is posed by the Oriental migratory locust Locusta migratoria manilensis in China, which, historically, has been restricted to eastern China from the Bohai Gulf southwards, and now threatens to expand its range into the agriculturally important region of northeast China. We analyzed data from a recent outbreak of migratory locusts in Heilongjiang Province (extreme northeast China), > 700 km north of its current known range, and identified the source region, timing of arrival and probable migratory routes of this incursion. We further show that warming temperatures in this region will likely allow subsequent invasions to establish permanent populations in northeast China, and thus authorities in this important crop-producing region of East Asia should be vigilant to the threat posed by this species.China Agriculture Research SystemChinese Academy of SciencesNational Natural Science Foundation of ChinaNatural Science Foundation of Jiangsu ProvinceScience and Technology Facilities Council (STFC

Adaptable image quality assessment using meta-reinforcement learning of task amenability

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7 % % and 29.6 % % expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100 % % expert labels

A proportional hazards model for time-to-event data with epidemiological bias

In hepatitis C virus (HCV) epidemiological studies, the estimation of progression to cirrhosis and prognostic effects of associated risk factors is of particular importance when projecting national disease burden. However, the progression estimates obtained from conventional methods could be distorted due to a referral bias (Fu et al., 2007). In recent years, several approaches have been developed to handle this epidemiological bias in analyzing time-to-event data. This paper proposes a new estimation approach for this problem under a semiparametric proportional hazards framework. The new method uses a martingale approach based on the mean rate function, rather than the traditional hazard rate function, and develops an iterative algorithm to estimate the Cox regression parameter and baseline hazard rate simultaneously. The consistency and asymptotic properties of the proposed estimators are derived theoretically and evaluated via simulation studies. The new method is also applied to a real HCV cohort study