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Many types of thermometers have been developed to measure body temperature. Infrared thermometers (IRT) are fast, convenient and ease to use. Two types of infrared thermometers are uses to measure body temperature: tympanic and forehead. With the spread of COVID-19 coronavirus, forehead temperature measurement is used widely to screen people for the illness. The performance of this type of device and the criteria for screening are worth studying. This study evaluated the performance of two types of tympanic infrared thermometers and an industrial infrared thermometer. The results showed that these infrared thermometers provide good precision. A fixed offset between tympanic and forehead temperature were found. The measurement values for wrist temperature show significant offsets with the tympanic temperature and cannot be used to screen fevers. The standard operating procedure (SOP) for the measurement of body temperature using an infrared thermometer was proposed. The suggestion threshold for the forehead temperature is 36 °C for screening of fever. The body temperature of a person who is possibly ill is then measured using a tympanic infrared thermometer for the purpose of a double check.

Autophagy is a major degradation pathway that utilizes lysosome hydrolases to degrade cellular constituents and is often induced under cellular stress conditions to restore cell homeostasis. Another prime degradation pathway in the cells is ubiquitin-proteasome system (UPS), in which proteins tagged by certain types of polyubiquitin chains are selectively recognized and removed by proteasome. Although the two degradation pathways are operated independently with different sets of players, recent studies have revealed reciprocal cross talks between UPS and autophagy at multiple layers. In this review, we summarize the roles of protein ubiquitination and deubiquitination in controlling the initiation, execution, and termination of bulk autophagy as well as the role of ubiquitination in signaling certain types of selective autophagy. We also highlight how dysregulation of ubiquitin-mediated autophagy pathways is associated with a number of human diseases and the potential of targeting these pathways for disease intervention.

Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique’s response and the target analyte’s standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument’s response is dependent. The inverse equation is the opposite: the instrument’s response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument’s response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced.

A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established using regression analysis. An adequate calibration equation ensures the performance of these instruments. Most studies use linear and polynomial equations. This study uses data sets from previous studies. Four types of calibration equations are proposed: linear, higher-order polynomial, exponential rise to maximum and power equations. A constant variance test was performed to assess the suitability of calibration equations for this dataset. Suspected outliers in the data sets are verified. The standard error of the estimate errors, s, was used as criteria to determine the fitting performance. The Prediction Sum of Squares (PRESS) statistic is used to compare the prediction ability. Residual plots are used as quantitative criteria. Suspected outliers in the data sets are checked. The results of this study show that linear and higher order polynomial equations do not allow accurate calibration equations for many data sets. Nonlinear equations are suited to most of the data sets. Different forms of calibration equations are proposed. The logarithmic transformation of the response is used to stabilize non-constant variance in the response data. When outliers are removed, this calibration equation’s fit and prediction ability is significantly increased. The adequate calibration equations with the data sets obtained with the same equipment and laboratory indicated that the adequate calibration equations differed. No universe calibration equation could be found for these data sets. The method for this study can be used for other chemical instruments to establish an adequate calibration equation and ensure the best performance.

Particulate matter 2.5 (PM2.5) is a risk factor for lung cancer. In this study, we investigated the molecular mechanisms of PM2.5 exposure on lung cancer progression. We found that short‐term exposure to PM2.5 for 24 h activated the EGFR pathway in lung cancer cells (EGFR wild‐type and mutant), while long‐term exposure of lung cancer cells to PM2.5 for 90 days persistently promoted EGFR activation, cell proliferation, anchorage‐independent growth, and tumor growth in a xenograft mouse model in EGFR‐driven H1975 cancer cells. We showed that PM2.5 activated AhR to translocate into the nucleus and promoted EGFR activation. AhR further interacted with the promoter of TMPRSS2, thereby upregulating TMPRSS2 and IL18 expression to promote cancer progression. Depletion of TMPRSS2 in lung cancer cells suppressed anchorage‐independent growth and xenograft tumor growth in mice. The expression levels of TMPRSS2 were found to correlate with nuclear AhR expression and with cancer stage in lung cancer patient tissue. Long‐term exposure to PM2.5 could promote tumor progression in lung cancer through activation of EGFR and AhR to enhance the TMPRSS2‐IL18 pathway. Synopsis PM2.5 promotes lung cancer progression through activation of the AhR‐TMPRSS2‐IL18. Exposure to PM2.5 activates EGFR pathway and promotes lung cancer progression. Long‐term exposure to PM2.5 increases lung cancer cell proliferation, anchorage‐independent growth, and xenograft tumor growth in mice. PM2.5 activates AhR to translocate into the nucleus and upregulates the expression of TMPRSS2. Depletion of TMPRSS2 in lung cancer cells suppresses anchorage‐independent growth and xenograft tumor growth in mice. TMPRSS2 upregulates IL I8 expression and promotes lung cancer progression. Graphical Abstract PM2.5 promotes lung cancer progression through activation of the AhR‐TMPRSS2‐IL18.

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable. Quantitative phase imaging suffers from a lack of specificity in label-free imaging. Here, the authors introduce Phase Imaging with Computational Specificity (PICS), a method that combines phase imaging with machine learning techniques to provide specificity in unlabeled live cells with automatic training.

In this paper, we present an algorithmic framework for signal‐geometry‐based approaches of GNSS spoofing detection. We formulate a simple vs. simple hypothesis test independent of nuisance parameters that results in significantly reduced missed detection probability compared to prior approaches. It is highly tractable such that it can be computed online by the receiver. We employ a hypothesis iteration framework that finds spoofed subsets of satellites efficiently and accounts for the presence of weak multipath, for a provable decision behavior in safety‐of‐life applications. We support the theoretical derivations by showing results on previously published simulated and on‐air data sets. We validate the measurement model and show robustness to multipath with flight data from a Dual Polarization Antenna (DPA) mounted on a C12 aircraft. Finally, we show the algorithm's benefit on data recorded during a government‐sponsored live spoofing event.

Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investigation method performed for a variety of clinical indications and provide an ideal platform for the opportunistic identification of VF. This study uses a deep convolutional neural network (DCNN) to identify the feasibility for the screening, detection, and localization of VFs using PARs. A DCNN was pretrained using ImageNet and retrained with 1306 images from the PARs database obtained between August 2015 and December 2018. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated. The visualization algorithm gradient-weighted class activation mapping (Grad-CAM) was used for model interpretation. Only 46.6% (204/438) of the VFs were diagnosed in the original PARs reports. The algorithm achieved 73.59% accuracy, 73.81% sensitivity, 73.02% specificity, and an AUC of 0.72 in the VF identification. Computer driven solutions integrated with the DCNN have the potential to identify VFs with good accuracy when used opportunistically on PARs taken for a variety of clinical purposes. The proposed model can help clinicians become more efficient and economical in the current clinical pathway of fragile fracture treatment.

Macrophages in a tumor microenvironment have been characterized as M1- and M2-polarized subtypes. Here, we discovered the different macrophages’ impacts on lung cancer cell A549. The M2a/M2c subtypes promoted A549 invasion and xenograft tumor growth. The M1 subtype suppressed angiogenesis. M1 enhanced the sensitivity of A549 to cisplatin and decreased the tube formation activity and cell viability of A549 cells by inducing apoptosis and senescence. Different macrophage subtypes regulated genes involved in the immune response, cytoskeletal remodeling, coagulation, cell adhesion and apoptosis pathways in A549 cells, which was a pattern that correlated with the altered behaviors of the A549 cells. Furthermore, we found that the identified M1/M2 gene signatures were significantly correlated with the extended overall survival of lung cancer patients. These results suggest that M1/M2 gene expression signature may be used as a prognostic indicator for lung cancer patients and M1/M2 polarization may be a target of investigation of immune-modulating therapies for lung cancer in the future.

Experimental design is important for researchers and those in other fields to find factors affecting an experimental response. The response surface methodology (RSM) is a special experimental design used to evaluate the significant factors influencing a process and confirm the optimum conditions for different factors. RSM models represent the relationship between the response and the influencing factors established with the regression analysis. Then these equations are used to produce the contour and response surface plots for observers to determine the optimization. The influence of regression techniques on model building has not been thoroughly studied. This study collected twenty-five datasets from the literature. The backward elimination procedure and t-test value of each variable were adopted to evaluate the significant effect on the response. Modern regression techniques were used. The results of this study present some problems of RSM studies in the previous literature, including using the complete equation without checking the statistical test, using the at-once variable deletion method to delete the variables whose p-values are higher than the preset value, the inconsistency between the proposed RSM equations and the contour and response surface plots, the misuse of the ANOVA table of the sequential model to keep all variables in the linear or square term without testing for each variable, the non-normal and non-constant variance conditions of datasets, and the finding of some influential data points. The suggestions for applying RSM for researchers are training in the modern regression technique, using the backward elimination technique for sequential variable selection, and increasing the sample numbers with three replicates for each run.