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Growing Information in to Targeted Therapy-Tolerant Persister Cells in Cancer malignancy.
In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.Self-reported social network analysis studies are often complex and burdensome, both during the interview process itself, and when conducting data management following the interview. Through funding obtained from the National Institute on Drug Abuse (NIDA/NIH), our team developed the Network Canvas suite of software - a set of complementary tools that are designed to simplify the collection and storage of complex social network data, with an emphasis on usability and accessibility across platforms and devices, and guided by the practical needs of researchers. The suite consists of three applications Architect an application for researchers to design and export interview protocols; Interviewer a touch-optimized application for loading and administering interview protocols to study participants; and Server an application for researchers to manage the interview deployment process and export their data for analysis. Together, they enable researchers with minimal technological expertise to access a complete research workflow, by building their own network interview protocols, deploying these protocols widely within a variety of contexts, and immediately attaining the resulting data from a secure central location. In this paper, we outline the critical decisions taken in developing this suite of tools for the network research community. We also describe the work which guides our decision-making, including prior experiences and key discovery events. We focus on key design choices, taken for theoretical, philosophical, and pragmatic reasons, and outline their strengths and limitations.This paper constructs various measures of domestic and global uncertainty and provides a comprehensive study of their impacts on the Thai economy. Based on a small open economy VAR, global uncertainty delivers deeper and more long-lasting effects when compared to within-country ones. In addition, we find that uncertainty shocks first generate sudden and large declines for stock prices and foreign portfolio investment, before gradually affecting the real economy through investment and trade channels. There is also meaningful heterogeneity among different types of domestic uncertainty. While financial uncertainty matters most for the Thai economy overall, consumption demand largely responds to macroeconomic uncertainty, while economic policy and political uncertainty generates the most persistent effects on investment. Furthermore, fiscal policy uncertainty is a key driver of trade flows while monetary policy uncertainty plays an important role for capital markets.COVID-19 made considerable changes in the lifestyle of people, which have led to a rise in energy use in homes. So, this study investigated the relationship between COVID-19 and domestic hot water demands. LDC7559 For this purpose, a nondimensional and principal component analysis were conducted to find out the influencing factors using demand data before and after COVID-19 from our study site. Analysis showed that the COVID-19 outbreak affected the daily peak time and the amount of domestic hot water usage, the active case number of COVID-19 was a good indicator for correlating the changes in hot water demand and patterns. Based on this, a machine learning model with an artificial neural network was developed to predict hot water demand depending on the severity of COVID-19 and the relevant correlation was also derived. The model analysis showed that the increase in the number of active cases in the region affected the hot water demand increased at a certain rate and the maximum demand peak in morning during weekdays and weekends decreased. Furthermore, if the number of active cases reached more than 4000, the peak in morning moved to afternoon so that the energy use patterns of weekdays and weekends are assimilated.The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) provides breastfeeding support and free formula to low-income participating infants in the U.S. Literature has consistently documented worse breastfeeding outcomes in WIC infants and children than in non-participants, although self-selection bias poses a challenge in examining the relationship between WIC participation and breastfeeding in low-income mother-child dyads. The WIC program adopted a comprehensive food package revision in 2009, the first one in four decades. Since that time, few national studies have examined the relationship between WIC participation and breastfeeding while controlling for the endogeneity of WIC participation with the propensity score method. This paper applied an instrumental variable (IV) approach on a large, nationally representative survey sample of children, the National Immunization Surveys (NIS), to examine the relationship between WIC participation and breastfeeding among children born between 2005 and 2014. We identified state Supplemental Nutrition Assistance Program (SNAP) enrollment rates and SNAP Policy Indices as valid IVs to address WIC participation endogeneity. Without the IVs, WIC participation had a significantly negative relationship with breastfeeding. After addressing endogeneity using the IVs, the relationship became insignificant in the whole sample and in the subpopulations across race/ethnicity and child gender. The neutrality of WIC participation on breastfeeding is important for policy makers to understand in seeking to improve breastfeeding among WIC participants.The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following Does the virus spread in the same way in different countries? Or are there significant differences in the development of the epidemic? In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. In our empirical study, we use the methods to compare the outbreak patterns of the epidemic in a number of European countries.The main objective of the article is twofold. On the one hand, it aims to offer a critical analysis of the different operationalizations of the concept of social exclusion at the international level, including reflection on widely used methods such as the "At risk of poverty or social exclusion" rate. On the other hand, it offers an empirically tested proposal of indicator aggregation for the measurement of social exclusion. The debate regarding the measurement of social exclusion has been widely addressed, but there are hardly any proposals that test different systems of indicator aggregation. The multiple correspondence analysis allows the implementation of a new approach for measuring the weights of the indicators, based on the distance to the integration point, which is understood as the absence of problems. The proposed new system shows an important potential to be extrapolated to the comparative measurement of social exclusion, also allowing the comparison of different social groups. The empirical reference used for the analysis is the Survey on Social Needs and Social Integration of the FOESSA Foundation for Spain 2018.It is imperative that image-guided intervention (IGI) systems provide accurate and precise navigation information to enable the user to trust the system and not place unwarranted confidence in the guidance capabilities of the system. Unfortunately, the actual error associated with the overall targeting capabilities of an IGI system is not readily known. Here we are primarily interested in the application of image-guided surgery in the context of renal interventions. We built a simulation pipeline to study the uncertainty propagation through an optically tracked IGI system to gain insight into the overall accuracy of the system. Our simulation pipeline models several stages, including stylus calibration, tool tracking, patient tracking, and image to patient registration. In the effort to realistically estimate tracking noise and user-associated fiducial localization error (FLE), we conducted several experiments using the optical tracking system. Our simulation suggested that a wider cone angle results in a more accurate tool calibration, which improves further with the collection of additional samples. Furthermore, our simulations also suggested that the image-to-patient registration was the most significant contributor to navigation uncertainty, followed by the fiducial localization error. Lastly, we also observed a 0.72 correlation between the Target Registration Error (TRE) estimated at target fiducials and the distance between the the centroids of the registration and target fiducial landmarks. To validate the simulation predictions, we also conducted several in vitro experiments using a 3D printed patient specific kidney phantom and compared the simulation-based registration predictions with those observed experimentally in vitro. The experiments confirmed the registration metrics (Fiducial Registration Error and TRE) predicted by the simulations, given several specific combinations of fiducial landmarks used to perform the image to patient registration.In this paper, several Mn(I) complexes were applied as catalysts for the homogeneous hydrogenation of ketones. The most active precatalyst is the bench-stable alkyl bisphosphine Mn(I) complex fac-[Mn(dippe) (CO)3(CH2CH2CH3)]. The reaction proceeds at room temperature under base-free conditions with a catalyst loading of 3 mol % and a hydrogen pressure of 10 bar. A temperature-dependent selectivity for the reduction of α,β-unsaturated carbonyls was observed. At room temperature, the carbonyl group was selectively hydrogenated, while the C=C bond stayed intact. At 60 °C, fully saturated systems were obtained. A plausible mechanism based on DFT calculations which involves an inner-sphere hydride transfer is proposed.The catalytic reduction of carbon dioxide is a process of growing interest for the use of this simple and abundant molecule as a renewable building block in C1-chemical synthesis and for hydrogen storage. The well-defined, bench-stable alkylcarbonyl Mn(I) bis(phosphine) complex fac-[Mn(CH2CH2CH3)(dippe)(CO)3] [dippe = 1,2-bis(diisopropylphosphino)ethane] was tested as an efficient and selective non-precious-metal precatalyst for the hydrogenation of CO2 to formate under mild conditions (75 bar total pressure, 80 °C), in the presence of a Lewis acid co-catalyst (LiOTf) and a base (DBU). Mechanistic insight into the catalytic reaction is provided by means of density functional theory (DFT) calculations.A convenient synthetic method to obtain d-galactose-substituted acylsilanes and acylgermanes is described. These acyl group 14 compounds are easily accessible in good yields. Their structural properties were analyzed by a combination of NMR, single crystal X-ray crystallography, and UV/vis spectroscopy. A d-galactose-substituted tetraacylgermane represents a new interesting visible light photoinitiator based on its absorption properties as well as its high solubility.
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