The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method.

Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean [Formula: see text] of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.

Tree mode of death and mortality risk factors across Amazon forests.

The carbon sink capacity of tropical forests is substantially affected by tree mortality. However, the main drivers of tropical tree death remain largely unknown. Here we present a pan-Amazonian assessment of how and why trees die, analysing over 120,000 trees representing > 3800 species from 189 long-term RAINFOR forest plots. While tree mortality rates vary greatly Amazon-wide, on average trees are as likely to die standing as they are broken or uprooted-modes of death with different ecological consequences. Species-level growth rate is the single most important predictor of tree death in Amazonia, with faster-growing species being at higher risk. Within species, however, the slowest-growing trees are at greatest risk while the effect of tree size varies across the basin. In the driest Amazonian region species-level bioclimatic distributional patterns also predict the risk of death, suggesting that these forests are experiencing climatic conditions beyond their adaptative limits. These results provide not only a holistic pan-Amazonian picture of tree death but large-scale evidence for the overarching importance of the growth-survival trade-off in driving tropical tree mortality.

Detection of Albumin Expression by RNA In Situ Hybridization Is a Sensitive and Specific Method for Identification of Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas.

OBJECTIVES: Inconsistent data on detection of albumin expression by ribonucleic acid (RNA) in situ hybridization have been reported. We investigated the utility of RNAscope (Advanced Cell Diagnostics, Hayward, CA) in detection of albumin in hepatocellular carcinomas (HCCs), intrahepatic cholangiocarcinomas (ICCs), and carcinomas from various organs using manual and automated staining. METHODS: RNAscope for albumin detection was performed on 482 cases on tissue microarray sections and on 22 cases of ICC, including 14 surgical resection and eight core biopsy specimens. RESULTS: Thirty-six of 37 (97%) HCCs had detectable mRNA, whereas all non-HCC and non-ICC cases, except one lung adenocarcinoma, were negative for albumin. Fourteen of 22 ICCs (64%) were positive for albumin. CONCLUSIONS: RNAscope for albumin is highly sensitive and specific for identifying HCCs and is highly specific and moderately sensitive for detection of ICCs; however, rare carcinomas (non-HCC, non-ICC, and those with no hepatoid histomorphology) can also have aberrant expression of albumin.

The Potential Diagnostic Utility of TROP-2 in Thyroid Neoplasms.

OBJECTIVES: Human trophoblast cell-surface marker (TROP-2) has been reported to be overexpressed in various human carcinomas (CAs) and suggested to be a prognostic marker for some CAs. The diagnostic utility of TROP-2 in CAs has not been explored. METHODS: Immunohistochemical evaluation of TROP-2 expression on tissue microarray sections of 136 thyroid neoplasms, surgical specimens of 61 atypical thyroid follicular-patterned lesions [including 33 papillary thyroid carcinomas (PTCs), 17 atypical follicular neoplasms (AFNs), and 11 adenomatoid nodules with focal nuclear atypia (ANFNA)], and 20 benign thyroid lesions, as well as 10 cytology specimens of PTCs was performed. For comparison, immunoassay for Hector Battifora mesothelial-1 (HBME-1), galectin-3, and cytokeratin 19 was performed on the 61 atypical thyroid follicular-patterned lesions. RESULTS: Strong membranous staining with TROP-2 was seen in 94% (33/35) of classic PTCs and 81% (30/37) of confirmed follicular variant PTCs on tissue microarray and routine surgical sections, as well as 100% (10/10) of PTCs on cytology specimens; it was not observed in follicular adenomas (n=51) or CAs (n=37), AFNs or ANFNA (n=28), benign (n=20) or normal (n=15) thyroid tissue. In contrast, the expression of HBME-1 and galectin-3 was identified in 100% (33/33) of surgical cases of PTCs and in 57% (16/28) and 50% (14/28) of AFNs and ANFNA, respectively. CONCLUSIONS: Our findings demonstrate that a membranous TROP-2 staining pattern is highly specific for PTC, which may serve as a potential diagnostic marker aiding in the accurate classification of morphologically equivocal thyroid follicular-patterned lesions.