December-March temperature reconstruction from tree-ring earlywood width in southeastern China during the period of 1871-2016.

Extremely cold temperatures are a significant threat to agriculture and transportation in winter in southeastern China. However, due to the shortness of instrumental records and the scarcity of long-term temperature reconstructions, more high-quality temperature reconstructions are still needed to fully examine their spatial-temporal variability over the past several centuries. In this study, we built an earlywood width (EWW) chronology, a latewood width (LWW) chronology, and a tree-ring width (TRW) chronology using tree-ring samples of Pinus taiwanensis Hayata from the western Tianmu Mountains and the Xianyu Mountains in southeastern China. The tree growth-climate relationships were analyzed, and we found the strongest correlation between December and March mean temperature and the EWW chronology. The December-March mean temperature history was then reconstructed over the period of 1871-2016 using a linear regression model, which is the first EWW-based temperature reconstruction in southeastern China. With a higher explained variance (47.0%) than that (31.7%) of a previous reconstruction using a TRW chronology, the quality of the model has largely improved. This reconstruction was also comparable with other nearby records, further demonstrating the reliability of our new model. Furthermore, our reconstruction exhibits a significantly negative relationship with the East Asian winter monsoon index (EAWMI) since the 1920s, which may be attributed to the obviously enhanced EAWMI thereafter.

Electroacupuncture protects rats from ischemic brain injury via coffilin in mice.

BACKGROUND: This study aimed to study the expression level of cofilin after electroacupuncture (EA) pretreatment, using ischemic brain injury model in mice. In addition, infarct volume and neurological functions were measured to understand whether electroacupuncture stimulation could restore the functions of the brain. METHODS: Total of 36 mice was randomly divided into three groups: sham group, middle cerebral artery occlusion model (MACO), and middle cerebral artery occlusion model pretreated with EA (MACO + EA). Mice were stimulated at "Baihui (G20)" and "Dazhui (G14)" 24 hours before focal cerebral ischemia. Infarct volume and neuronal function of brain tissue were scored among different experimental groups. The expression level of cofilin and phosphocofilin of brain tissue were evaluated by using Western blot analysis. TUNEL assay was performed to determine the degree of cell apoptosis. RESULTS: Compared with the sham group, the level of cofilin was dramatically reduced in the MACO group. EA pretreatment could reduce the protein level of cofilin, while EA therapy could also upregulate the protein level of phosphocofilin. Improved neuronal function, smaller infarct volume, and reduced neuronal apoptosis were observed among the mice underwent EA before middle artery occlusion. CONCLUSION: Our results from Western blot analysis and TUNEL assay might suggest that the upregulation of cofilin was concerned with the EA protects rats from ischemic brain injury. Cofilin might be a potential target for developing drugs against brain ischemia.

Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model.

PURPOSE: To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: A total of 287 patients with HCC from our institution and 58 patients from another individual institution were included. Among these, 119 patients with only CT data and 116 patients with only MRI data were selected for single-modality deep learning model development, after which select parameters were migrated for MDL model development with transfer learning (TL). In addition, 110 patients with simultaneous CT and MRI data were divided into a training cohort (n = 66) and a validation cohort (n = 44). We input the features extracted from DenseNet121 into an extreme learning machine (ELM) classifier to construct a classification model. RESULTS: The area under the curve (AUC) of the MDL model was 0.844, which was superior to that of the single-phase CT (AUC = 0.706-0.776, P < 0.05), single-sequence MRI (AUC = 0.706-0.717, P < 0.05), single-modality DL model (AUCall-phase CT = 0.722, AUCall-sequence MRI = 0.731; P < 0.05), clinical (AUC = 0.648, P < 0.05), but not to that of the delay phase (DP) and in-phase (IP) MRI and portal venous phase (PVP) CT models. The MDL model achieved better performance than models described above (P < 0.05). When combined with clinical features, the AUC of the MDL model increased from 0.844 to 0.871. A nomogram, combining deep learning signatures (DLS) and clinical indicators for MDL models, demonstrated a greater overall net gain than the MDL models (P < 0.05). CONCLUSION: The MDL model is a valuable noninvasive technique for preoperatively predicting MVI in HCC.

Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the receiver operating characteristic curve (AUC) values of 0.710, 0.798, 0.809, and 0.869 across four data centres. Additionally, the effectiveness of the algorithm is evaluated, and both adaptive and common features are identified through analysis.

[Reconstructing mean temperature of April-July in 1809-2018 based on tree-ring of Pinus taiwanensis in the Tianmu Mountain, East China]. / åŸºäºŽé»„å±±æ¾æ ‘è½®é‡å»ºåŽä¸œå¤©ç›®å±±åœ°åŒº1809­2018å¹´4­7月平均气温.

We established 340-year chronologies of total ring width, early wood width, and late wood width with tree-ring samples of Pinus taiwanensis at high altitude collected from the western Tianmu Mountain in northern Zhejiang Province. According to the criterion that subsample signal strength (SSS) should be larger than 0.8, the reliable period was from 1810 to 2019. Through the correlation analysis between chronologies and climatic factors, we examined the responses of tree ring growth to climate. The results showed that radial growth of P. taiwanensis was more sensitive to temperature than to precipitation. Comprehensively considering the correlation analysis results for the raw and first-order difference series, early wood width was significantly correlated with the early growing season mean and maximum temperatures of the prior year, while late wood width with prior May and current September mean and maximum temperatures. The correlation pattern of total ring width was similar to that of early wood width, although at a low level. The optimal correlation was between early wood width and prior April-July mean temperature. Based on this relationship, April-July mean temperature of the Tianmu Mountain, East China was reconstructed for the period of 1809-2018 with an explained variance of 61.5%. Both the raw and first-order difference series passed the split sample calibration-verification test. The warm periods were 1809-1833 and 1965-2018, with a cold period in 1834-1964. Temperature had risen rapidly since the 1960s. From the standpoint of low frequency, it reached an unprecedented level since the 1980s over the past 210 years. Spatial correlation analysis showed that the reconstructed temperature series could represent temperature variations of East China, which had a good agreement with a reconstructed regional temperature series from East China. Our results showed that P. taiwanensis had a great potential for paleoclimate reconstruction in East China.

Two centuries of April-July temperature change in southeastern China and its influence on grain productivity.

China is a traditional agriculture based country and one main region for crop production is southeastern China where temperature is a dominant climate variable affecting agriculture. Temperature and social disturbances both influence crop production, yet distinguishing their relative impacts is difficult due to a lack of reliable, high-resolution historical climatic records before the very recent period. Here we present the first tree-ring based warm-season temperature reconstruction for southeastern China, a core region of the East Asian monsoon, for the past 227years. The reconstruction target was April-July mean temperature, and our model explained 60.6% of the observed temperature variance during 1953-2012. Spatial correlation analysis showed that the reconstruction is representative of April-July temperature change over most of eastern China. The reconstructed temperature series agrees well with China-scale (heavily weighted in eastern China) agricultural production index values quite well at decadal timescales. The impacts of social upheavals on food production, such as those in the period 1920-1949, were confirmed after climatic influences were excluded. Our study should help distinguish the influence of social disturbance and warm-season temperature on grain productivity in the core agricultural region of China during the past two centuries.

[Growth response of Abies fargesii to climate in Shennongjia Mount of Hubei Province, Southeastern China].

A well-replicated Abies fargesii tree-ring width chronology in the Shennongjia Mount was developed to investigate its radial growth response to climate variables (e.g., monthly mean tempe- rature and total precipitation) and other growing season indicators (e.g., cumulative temperature, continuous days, initial and final dates). Correlation analyses showed that the tree-ring width was positively correlated to the mean temperatures of February, April and September, and negatively correlated to the total precipitation of September, prior September and prior December. The analyses between the chronology and other growing season parameters showed that tree growth responded positively to the cumulative temperature and continuous days of the growing season. The correlation was the highest when the growing season was defined as the days above the temperature threshold of 9.0 °C. Defined this way, the growing season typically started in late-May and ended in mid-September, lasting about 120 days. Correlation analyses were also conducted between the tree-ring growth and the initial and final dates of the growing season. Results showed that correlation was the highest for initial dates defined at 9.0 °C (with the coefficient of -0.25 and p-value close to 0.05), and for final dates defined at 9.3 °C (with the coefficient of 0.33 and p-value less than 0.05). All these results indicated that the sensitive temperature threshold for photosynthesis of A. fargesii was around 9.0 °C. The year 1978 marked an abrupt shift of climate in southeast China. We compared A. fargesii growth between pre-1978 and post-1978 periods. Results showed that as temperature rose, the growing season was lengthened with both earlier initial dates and later final dates. Longer growing season increased the A. fargesii growth in the Shennongjia Mount, southeastern China.

[High altitude Pinus taiwanensis Hayata growth response to climate in Jiulongshan and Guniujian, Southeastern China].

Two robust Pinus taiwanensis Hayata tree-ring width chronologies were developed at high elevation sites in Jiulongshan Natural Conservation Area (JLS01), southeastern Zhejiang Province and Guniujiang (GNJ01), southern Anhui Province, China. The reliable period was 1884-2010 for JLS01 and 1837-2008 for GNJ01, based on subsample signal strength (SSS) threshold value of 0.8. Meteorological data were monthly mean temperature, monthly total precipitation and monthly mean relative humidity, monthly total cloud cover, as well as monthly sunshine duration. The data from the meteorological stations around the sampling sites were averaged to represent regional climate, which were used in the correlation analyses with the tree-ring chronologies. The correlation analyses indicated that summer (prior June-July and current June) hydrothermal condition was the main limiting factor on radial tree growth at the two high elevation sites. For JLS01 chronology, significant positive correlations were found with prior June-July temperature and sunshine duration, significant negative correlations with prior June-July and current June precipitation and relative hu- midity, and total cloud cover of prior July. GNJ01 chronology was significantly positively correlated with prior July temperature as well as prior July and current June sunshine duration, negatively correlated with prior July and current June precipitation and relative humidity, as well as total cloud cover of prior June. These results showed that relatively high temperature could promote radial growth, whereas high precipitation, together with high relative humidity, high cloud cover and low sunshine duration, could limit the photosynthesis and thus restrain the radial tree growth.