Evapotranspiration and its partitioning during and following a mountain pine beetle infestation of a lodgepole pine stand in the interior of British Columbia, Canada

IntroductionMassive tree mortality events in western Canada due to widespread infestation by mountain pine beetle (MPB) are expected to impact local-to-regional evapotranspiration (ET) dynamics during and after a disturbance. How ecosystem-level ET and its components may vary with canopy-tree mortality (treefall) and subsequent understory recovery remains unclear.MethodsWe used 10 years of continuous eddy-covariance and remote-sensing data (2007–2016) and machine-learning models based on random forest and xgboost to determine forest- and climate-driven effects at temporal scales appropriate for a lodgepole pine-dominated stand following a major, five-year MPB disturbance initiated in the summer of 2006.ResultsTotal annual ET over the 10 years ranged from 207.2 to 384.6 mm, with annual plant transpiration (T) contributing to 57 ± 5.4% (mean ± standard deviation) of annual ET. Annual ET initially declined (2007–2011) and then increased (2011–2016), with ET and T/ET increasing at statistically non-significant rates of approximately 3.2 and 1.2% per year from 2007 to 2016. Air temperature (Ta) and vapor pressure deficit (VPD) were the most important predictors of seasonal variation in ET and T/ET during the 10-year period, with high Ta, VPD, and photosynthetically active radiation (PAR) causing ET and T/ET to increase. Annual ET increased with both increasing spring Ta and decreasing VPD. Annual T/ET was shown to increase with increasing VPD and decrease with increasing volumetric soil water content at a 5-cm depth (VWC5). Enhanced vegetation index (EVI, an indicator of canopy greenness) lagged T and overstory tree mortality, whereas previous- and current-year values of EVI were shown to be poor predictors of annual ET and T/ET.Discussion and conclusionsThese findings suggest that the promotion of climate factors on forest ecosystem-level water vapor fluxes may offset reductions promoted by MPB outbreaks. Climate processes affected water vapor fluxes more than biotic factors, like stand greenness, highlighting the need to include climate-regulatory mechanisms in predictive models of ET dynamics during and subsequent to stand disturbance. Climate and forest-greenness effects on water vapor fluxes need to be explored at even longer time scales, e.g., at decadal scales, to capture long-drawn-out trends associated with stand disturbance and its subsequent recovery

Genetic Heterogeneity and Mutated PreS Analysis of Duck Hepatitis B Virus Recently Isolated from Ducks and Geese in China

In this study, we detected 12 duck and 11 goose flocks that were positive for duck hepatitis B virus (DHBV) using polymerase chain reaction and isolated 23 strains between 2020 and 2022 in China. The complete genomes of goose strains E200801 and E210501 shared the highest identity (99.9%), whereas those of strains Y220217 and E210526 shared the lowest identity (91.39%). The phylogenetic tree constructed based on the genome sequences of these strains and reference strains was classified into three major clusters: the Chinese branch DHBV-I, the Chinese branch DHBV-II, and the Western branch DHBV-III. Furthermore, the duck-origin strain Y200122 was clustered into a separate branch and was predicted to be a recombinant strain derived from DHBV-M32990 (belonging to the Chinese branch DHBV-I) and Y220201 (belonging to the Chinese branch DHBV-II). Additionally, preS protein analysis of the 23 DHBV strains revealed extensive mutation sites, almost half of which were of duck origin. All goose-origin DHBV contained the mutation site G133E, which is related to increased viral pathogenicity. These data are expected to promote further research on the epidemiology and evolution of DHBV. Continuing DHBV surveillance in poultry will enhance the understanding of the evolution of HBV

Data_Sheet_1_Evapotranspiration and its partitioning during and following a mountain pine beetle infestation of a lodgepole pine stand in the interior of British Columbia, Canada.docx

IntroductionMassive tree mortality events in western Canada due to widespread infestation by mountain pine beetle (MPB) are expected to impact local-to-regional evapotranspiration (ET) dynamics during and after a disturbance. How ecosystem-level ET and its components may vary with canopy-tree mortality (treefall) and subsequent understory recovery remains unclear.MethodsWe used 10 years of continuous eddy-covariance and remote-sensing data (2007–2016) and machine-learning models based on random forest and xgboost to determine forest- and climate-driven effects at temporal scales appropriate for a lodgepole pine-dominated stand following a major, five-year MPB disturbance initiated in the summer of 2006.ResultsTotal annual ET over the 10 years ranged from 207.2 to 384.6 mm, with annual plant transpiration (T) contributing to 57 ± 5.4% (mean ± standard deviation) of annual ET. Annual ET initially declined (2007–2011) and then increased (2011–2016), with ET and T/ET increasing at statistically non-significant rates of approximately 3.2 and 1.2% per year from 2007 to 2016. Air temperature (Ta) and vapor pressure deficit (VPD) were the most important predictors of seasonal variation in ET and T/ET during the 10-year period, with high Ta, VPD, and photosynthetically active radiation (PAR) causing ET and T/ET to increase. Annual ET increased with both increasing spring Ta and decreasing VPD. Annual T/ET was shown to increase with increasing VPD and decrease with increasing volumetric soil water content at a 5-cm depth (VWC5). Enhanced vegetation index (EVI, an indicator of canopy greenness) lagged T and overstory tree mortality, whereas previous- and current-year values of EVI were shown to be poor predictors of annual ET and T/ET.Discussion and conclusionsThese findings suggest that the promotion of climate factors on forest ecosystem-level water vapor fluxes may offset reductions promoted by MPB outbreaks. Climate processes affected water vapor fluxes more than biotic factors, like stand greenness, highlighting the need to include climate-regulatory mechanisms in predictive models of ET dynamics during and subsequent to stand disturbance. Climate and forest-greenness effects on water vapor fluxes need to be explored at even longer time scales, e.g., at decadal scales, to capture long-drawn-out trends associated with stand disturbance and its subsequent recovery.</p

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM