Autophagy modulation is a potential therapeutic strategy for tongue squamous cell carcinoma (TSCC). Melatonin possesses significant anticarcinogenic activity. However, whether melatonin induces autophagy and its roles in cell death in TSCC are unclear. Herein, we show that melatonin induced significant apoptosis in the TSCC cell line Cal27. Apart from the induction of apoptosis, we demonstrated that melatonin-induced autophagic flux in Cal27 cells as evidenced by the formation of GFP-LC3 puncta, and the upregulation of LC3-II and downregulation of SQSTM1/P62. Moreover, pharmacological or genetic blockage of autophagy enhanced melatonin-induced apoptosis, indicating a cytoprotective role of autophagy in melatonin-treated Cal27 cells. Mechanistically, melatonin induced TFE3 dephosphorylation, subsequently activated TFE3 nuclear translocation, and increased TFE3 reporter activity, which contributed to the expression of autophagy-related genes and lysosomal biogenesis. Luzindole, a melatonin membrane receptor blocker, or MT2-siRNA partially blocked the ability of melatonin to promote mTORC1/TFE3 signaling. Furthermore, we verified in a xenograft mouse model that melatonin with hydroxychloroquine or TFE3-siRNA exerted a synergistic antitumor effect by inhibiting autophagy. Importantly, TFE3 expression positively correlated with TSCC development and poor prognosis in patients. Collectively, we demonstrated that the melatonin-induced increase in TFE3-dependent autophagy is mediated through the melatonin membrane receptor in TSCC. These data also suggest that blocking melatonin membrane receptor-TFE3-dependent autophagy to enhance the activity of melatonin warrants further attention as a treatment strategy for TSCC.

Background: The severity and outcome of COVID-19 cases has been associated with the percentage of circulating lymphocytes (LYM%), levels of C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT), lactic acid (LA), and viral load (ORF1ab Ct). However, the predictive power of each of these indicators in disease classification and prognosis remains largely unclear. Methods: We retrospectively collected information on the above parameters in 142 patients with COVID-19, stratifying them by survival or disease severity. Findings: CRP, PCT, IL-6, LYM%, and ORF1ab Ct were significantly altered between survivors and non-survivors. LYM%, CRP, and IL-6 were the most sensitive and reliable factors in distinguishing between survivors and non-survivors. These indicators were significantly different between critically ill and severe/moderate patients. Only LYM% levels were significantly different between severe and moderate types. Among all the investigated indicators, LYM% was the most sensitive and reliable in discriminating between critically ill, severe, and moderate types and between survivors and non-survivors. Conclusions: CRP, PCT, IL-6, LYM%, and ORF1ab Ct, but not LA, could predict prognosis and guide classification of COVID-19 patients. LYM% was the most sensitive and reliable predictor for disease typing and prognosis. We recommend that LYM% be further investigated in the management of COVID-19.

Summary In closed-loop reservoir management, one periodically updates reservoir models by integrating production data and then solves an optimal control problem to determine optimum operating conditions to maximize hydrocarbon production or net present value (NPV) for the remaining expected life of the reservoir. The cycle of model updating and production optimization is repeated at specified times. Here, to account for geological uncertainty, we suggest using the ensemble Kalman filter for reservoir model updating and consider three different algorithms for production optimization. Two simple but representative examples indicate that the steepest ascent algorithm is the best of the optimization methods. If the required adjoint software for calculating the gradient of NPV with respect to the controls is not available, we show that iteration using an easily computed stochastic gradient can yield a good estimate of the optimal NPV if properly implemented. For the problem considered, it is shown that NPV is a nonlinear function of the controls, but the final controls from the cases with both known true geology and uncertain geology show "bang-bang" behavior. Introduction In recent years, the concept of closed-loop reservoir management has attracted intensive research interest (Brouwer et al. 2004; Jansen et al. 2005; Sarma et al. 2006). This approach enables one to adjust the reservoir production control parameters to optimize the reservoir production performance with geological uncertainty while assimilating dynamic production data in real-time. There are two optimization steps in the approach: The first step is the dynamic data assimilation (history matching), and the second step is to optimize the reservoir performance by adjusting the well controls based on the history-matched reservoir models. Studies in the literature have been focusing on one of the steps (Zakirov et al. 1996; Brouwer and Jansen 2004; Sarma et al. 2005, 2006) and only a few researchers have investigated the conjunction of the two (Brouwer et al. 2004; Sarma et al. 2006).

Summary In this paper, we develop an efficient algorithm for production optimization under linear and nonlinear constraints and an uncertain reservoir description. The linear and nonlinear constraints are incorporated into the objective function using the augmented Lagrangian method, and the bound constraints are enforced using a gradient-projection trust-region method. Robust long-term optimization maximizes the expected life-cycle net present value (NPV) over a set of geological models, which represent the uncertainty in reservoir description. Because the life-cycle optimal controls may be in conflict with the operator's objective of maximizing short-time production, the method is adapted to maximize the expectation of short-term NPV over the next 1 or 2 years subject to the constraint that the life-cycle NPV will not be substantially decreased. The technique is applied to synthetic reservoir problems to demonstrate its efficiency and robustness. Experiments show that the field cannot always achieve the optimal NPV using the optimal well controls obtained on the basis of a single but uncertain reservoir model, whereas the application of robust optimization reduces this risk significantly. Experimental results also show that robust sequential optimization on each short-term period is not able to achieve an expected life-cycle NPV as high as that obtained with robust long-term optimization.

Summary The ensemble Kalman filter (EnKF) is a subject of intensive investigation for use as a reservoir management tool. For strongly nonlinear problems, however, the EnKF can fail to achieve an acceptable data match at certain times in the data assimilation process. Here, we provide two iterative EnKF procedures to remedy this deficiency and explore the validity of these iterative methods compared to the standard EnKF by considering two examples. In both examples, we are able to obtain better data matches using iterative methods than with the standard EnKF. The simplest derivation of the EnKF analysis equation "linearizes" the objective function by adding the vector of predicted data to the original combined state vector of model parameters and dynamical variables. We show that there is no assurance that this trick for turning a nonlinear problem into a linear problem results in a correct sampling of the pdf one wishes to sample. However, we show that augmenting the state vector with the data results in a correct procedure for sampling the probability density function (pdf) if, at every data assimilation step, the predicted data vector is a linear function of the combined (unaugmented) state vector. Without this assumption, we know of no way to show EnKF samples correctly.