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Abstract Mathematical modelling performs a vital part in estimating and controlling the recent outbreak of coronavirus disease 2019 (COVID-19). In this epidemic, most countries impose severe intervention measures to contain the spread of COVID-19. The policymakers are forced to make difficult decisions to leverage between health and economic development. How and when to make clinical and public health decisions in an epidemic situation is a challenging question. The most appropriate solution is based on scientific evidence, which is mainly dependent on data and models. So one of the most critical problems during this crisis is whether we can develop reliable epidemiological models to forecast the evolution of the virus and estimate the effectiveness of various intervention measures and their impacts on the economy. There are numerous types of mathematical model for epidemiological diseases. In this paper, we present some critical reviews on mathematical models for the outbreak of COVID-19. Some elementary models are presented as an initial formulation for an epidemic. We give some basic concepts, notations, and foundation for epidemiological modelling. More related works are also introduced and evaluated by considering epidemiological features such as disease tendency, latent effects, susceptibility, basic reproduction numbers, asymptomatic infections, herd immunity, and impact of the interventions.

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO2 flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.

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CKAP4, one kind of type II trans-membrane protein, plays an important role to maintain endoplasmic reticulum structure and inhibits the proliferation of bladder cancer cells by combining its ligand anti-proliferative factor (APF). However, the biological function of CKAP4 in the progression of liver cancer has not been clearly demonstrated. In the present study, we knocked down or overexpressed CKAP4 in hepatocellular carcinoma (HCC) cells and cell proliferation, invasion, and migration capacities were investigated by CCK-8 and transwell assays. In vivo tumor model in mice was used to evaluate the role of CKAP4 on growth and metastasis of HCC. The data documented that HCC cells with high CKAP4 levels were featured by low proliferation capability as well as low invasion potential. Interestingly, we found that CKAP4 suppressed the activation of epithelial growth factor receptor (EGFR) signaling, which may partly explain the role of CKAP4 in cell biological behavior of HCC. Further study revealed that CKAP4 could associate with EGFR at basal status and the complex was reduced upon EGF stimulation, leading to release EGFR into cytoplasm. Thus, we demonstrate the novel mechanism, for the first time, expression of CKAP4 regulates progression and metastasis of HCC and it may provide therapeutic values in this tumor.

ObjectiveTo assess clinical value of fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) for differentiation of malignant from benign focal thyroid incidentaloma.Materials and MethodsThis retrospective study included 99 patients with focal thyroid incidentaloma of 5216 non-thyroid cancer patients that had undergone PET/CT. PET/CT semi-quantitative parameters, volume-based functional parameters, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of thyroid incidentaloma were assessed. Receiver-operating characteristic (ROC) analysis was conducted and areas under the curve (AUC) were compared by Hanley and McNeil test to evaluate usefulness of maximum standardized uptake value (SUVmax), MTV and TLG, as markers for differentiating malignant from benign thyroid incidentalomas.ResultsOf 99 thyroid incidentalomas, 64 (64.6%) were malignant and 35 (35.4%) were benign. Malignant thyroid incidentalomas were larger (1.8 cm vs. 1.3 cm, p = 0.006), and had higher SUVmax (11.3 vs. 4.8, p < 0.001), MTV (all p < 0.001) and TLG (all p < 0.001) than benign. TLG 4.0 had the highest performance for differentiation of malignant from benign thyroid incidentaloma in all semi-quantitative parameters with AUC 0.895 by ROC curve analysis. AUC (TLG 4.0) was significantly larger than AUC (SUVmean), AUC (MTV 2.5), AUC (MTV 3.0), AUC (MTV 3.5), AUC (TLG 2.5), and AUC (TLG 3.0), respectively (all, p < 0.05). There was no statistical difference between AUC (TLG 4.0) and AUC (SUVmax) (p > 0.05). A threshold TLG 4.0 of 2.475 had 81.3% sensitivity and 94.3% specificity for identifying malignant thyroid incidentalomas.ConclusionVolume-based PET/CT parameters could potentially have clinical value in differential diagnosis of thyroid incidentaloma along with SUVmax.

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