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A comprehensive quantification of global forest fragmentation is urgently required to guide forest protection, restoration and reforestation policies. Previous efforts focused on the static distribution patterns of forest remnants, potentially neglecting dynamic changes in forest landscapes. Here, we map global distribution of forest fragments and their temporal changes between 2000 and 2020. We find that forest landscapes in the tropics were relatively intact, yet these areas experienced the most severe fragmentation over the past two decades. In contrast, 75.1% of the world’s forests experienced a decrease in fragmentation, and forest fragmentation in most fragmented temperate and subtropical regions, mainly in northern Eurasia and South China, declined between 2000 and 2020. We also identify eight modes of fragmentation that indicate different recovery or degradation states. Our findings underscore the need to curb deforestation and increase connectivity among forest fragments, especially in tropical areas. Forest losses and gains are highly dynamic processes. Here, the authors present a forest fragmentation index to map distribution and temporal changes of forest fragments globally, revealing major trends and patterns during the first two decades of the 21st century.

This paper explores cultural nostalgia and historical memory in Taipei People , looking at how these themes are portrayed and their deeper meanings. The study discovers that cultural nostalgia in the novel not only extends a longing for one’s hometown; it also unveils the anxieties of historical rupture, political transformations, and individual identity crises. Although situated in Taipei, those characters’ emotions and memories remain deeply rooted the main land of China, reflecting a spatial dislocation between emotions and memories. Hence, historical memory in the novel is activated through the inner conflicts and spiritual pursuits, becoming a crucial factor in shaping cultural identity thus aiding characters in dealing identity crises and historical trauma. Finally, through a sophisticated narrative structure and symbolic expressions, the author presents a complex emotional relationship with traditional culture, illustrating the efforts of the diasporic community to restore and preserve their culture in a foreign land.

The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.

The electrochemical stability window of the electrolyte solution limits the energy content of non-aqueous lithium metal batteries. In particular, although electrolytes comprising fluorinated solvents show good oxidation stability against high-voltage positive electrode active materials such as LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811), the ionic conductivity is adversely affected and, thus, the battery cycling performance at high current rates and low temperatures. To address these issues, here we report the design and synthesis of a monofluoride ether as an electrolyte solvent with Li-F and Li-O tridentate coordination chemistries. The monofluoro substituent (-CH 2 F) in the solvent molecule, differently from the difluoro (-CHF 2 ) and trifluoro (-CF 3 ) counterparts, improves the electrolyte ionic conductivity without narrowing the oxidation stability. Indeed, the electrolyte solution with the monofluoride ether solvent demonstrates good compatibility with positive and negative electrodes in a wide range of temperatures (i.e., from −60 °C to +60 °C) and at high charge/discharge rates (e.g., at 17.5 mA cm −2 ). Using this electrolyte solution, we assemble and test a 320 mAh Li||NCM811 multi-layer pouch cell, which delivers a specific energy of 426 Wh kg −1 (based on the weight of the entire cell) and capacity retention of 80% after 200 cycles at 0.8/8 mA cm −2 charge/discharge rate and 30 °C. The energy content of non-aqueous lithium batteries is limited by the electrochemical stability window of the electrolyte solution. Here, the authors report a monofluoride ether-based electrolyte to stabilize high-voltage lithium metal batteries at high current rates and low temperatures.

Electrochemical CO 2 conversion to methane, powered by intermittent renewable electricity, provides an entrancing opportunity to both store renewable electric energy and utilize emitted CO 2 . Copper-based single atom catalysts are promising candidates to restrain C-C coupling, suggesting feasibility in further protonation of CO* to CHO* for methane production. In theoretical studies herein, we find that introducing boron atoms into the first coordination layer of Cu-N 4 motif facilitates the binding of CO* and CHO* intermediates, which favors the generation of methane. Accordingly, we employ a co-doping strategy to fabricate B-doped Cu-N x atomic configuration (Cu-N x B y ), where Cu-N 2 B 2 is resolved to be the dominant site. Compared with Cu-N 4 motifs, as-synthesized B-doped Cu-N x structure exhibits a superior performance towards methane production, showing a peak methane Faradaic efficiency of 73% at −1.46 V vs . RHE and a maximum methane partial current density of −462 mA cm −2 at −1.94 V vs . RHE. Extensional calculations utilizing two-dimensional reaction phase diagram analysis together with barrier calculation help to gain more insights into the reaction mechanism of Cu-N 2 B 2 coordination structure. Developing efficient electrocatalysts for selective CO2 conversion is of high interest. Here the authors investigate B doped Cu single atom catalysts with Cu-N2B2 coordination for enhanced CO2 to CH4 conversion.

With the prohibition of antibiotics in feed, plant functional substances have been widely studied as feed additives. Resveratrol, a natural stilbene, and a non-flavonoid polyphenol found in plants, possesses antioxidant, anti-inflammatory, and metabolic regulatory features. Resveratrol generated intense scientific and public interest, primarily due to its widely reported ability to prevent cancer, delay aging and alleviate related metabolic diseases. Recently, resveratrol has been studied and applied as a feed additive in animal production. This review focuses on the outline of the absorption and metabolism and biological functions of resveratrol and summarizes the application of dietary resveratrol in animal production up to the present, including pigs, poultry, and ruminants. In pigs, dietary resveratrol improved intestinal health, mitochondrial function, meat quality, and more. In poultry, studies have shown that dietary resveratrol improves growth performance and meat and egg quality and alleviates heat stress induced adverse effects. There are few studies on dietary resveratrol in ruminants; however previous studies have indicated that dietary resveratrol increases nutrient digestibility and reduces methane emissions in sheep. It is hoped that this review could provide a specific theoretical basis and research ideas for the research and application of resveratrol.

The SARS-CoV-2 Omicron variant exhibits striking immune evasion and is spreading rapidly worldwide. Understanding the structural basis of the high transmissibility and enhanced immune evasion of Omicron is of high importance. Here, using cryo-electron microscopy, we present both the closed and the open states of the Omicron spike (S) protein, which appear more compact than the counterparts of the G614 strain 1 , potentially related to enhanced inter-protomer and S1–S2 interactions induced by Omicron residue substitution. The closed state showing dominant population may indicate a conformational masking mechanism for the immune evasion of Omicron. Moreover, we captured three states for the Omicron S–ACE2 complex, revealing that the substitutions on the Omicron RBM result in new salt bridges and hydrogen bonds, more favourable electrostatic surface properties, and an overall strengthened S–ACE2 interaction, in line with the observed higher ACE2 affinity of Omicron S than of G614. Furthermore, we determined the structures of Omicron S in complex with the Fab of S3H3, an antibody that is able to cross-neutralize major variants of concern including Omicron, elucidating the structural basis for S3H3-mediated broad-spectrum neutralization. Our findings shed light on the receptor engagement and antibody neutralization or evasion of Omicron and may also inform the design of broadly effective vaccines against SARS-CoV-2. The structures of the open and closed states of the Omicron spike protein and its complex with the ACE2 receptor or a broadly neutralizing antibody are resolved and shed light on the receptor engagement and antibody neutralization of Omicron.

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods. A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenario.

The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

Achieving a “win–win” situation regarding economic growth and environmental protection has become a common goal for sustainable development in all countries around the world. As the world’s largest developing country and the second largest economy, China has been striving to maintain economic growth while improving environmental quality to achieve its sustainable development goals. Applying the decoupling approach, a model widely used to quantify the relationship between the environment and the economy, this study analyzed the relationship between the economy and the environment, examining the decoupling performance of economic growth and environmental impacts in 30 Chinese provinces, autonomous regions, and municipalities to investigate whether economic growth and environmental protection have achieved a “win–win” situation. Nighttime light (NTL) data were used to measure the performance of economic growth. In addition, an environmental pressure index (EPI) assessment framework covering 6 primary and 11 secondary indicators was constructed to measure the environmental quality of China over time. First, NTL data proved to be a valid data source for assessing decoupling performance; second, environmental pressure at both the national and provincial levels significantly decreased during the study period; third, the relationship between the economy and the environment has been further improved, and economic growth and environmental protection have achieved a “win–win” situation. These findings offer an in-depth analysis of the decoupling of the economy and the environment in China and serve as a guide for future implementation strategies for sustainable development in various regions.