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Augmentation deformity on testing mammography, a rare display regarding busts augmentation associated lymphoma.
Moreover, the new MTE is a bivariate function, and thus is easier to visualize than the original MTE. Finally, the redefined MTE immediately reveals treatment effect heterogeneity among individuals who are at the margin of treatment. As a result, it can be used to evaluate a wide range of policy changes with little analytical twist, and to design policy interventions that optimize the marginal benefits of treatment. We illustrate the proposed method by estimating heterogeneous economic returns to college with National Longitudinal Study of Youth 1979 (NLSY79) data.
We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern.

A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted.

The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal.

We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.
We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As of April 2021, the number of confirmed cases of COVID-19 reported to the World Health Organization has already exceeded 135 million worldwide, while the number of deaths exceeds 2.9 million. Due to the impacts of the disease, efforts in the literature have intensified in terms of studying approaches aiming to detect COVID-19, with a focus on supporting and facilitating the process of disease diagnosis. This work proposes the application of texture descriptors based on phylogenetic relationships between species to characterize segmented CT volumes, and the subsequent classification of regions into COVID-19, solid lesion or healthy tissue. To evaluate our method, we use images from three different datasets. The results are promising, with an accuracy of 99.93%, a recall of 99.93%, a precision of 99.93%, an F1-score of 99.93%, and an AUC of 0.997. We present a robust, simple, and efficient method that can be easily applied to 2D and/or 3D images without limitations on their dimensionality.In the current research landscape, there are increasing demands for research to be innovative and cutting-edge. At the same time, concerns are voiced that as a consequence of neoliberal regimes of research governance, innovative research becomes impeded. In this paper, I suggest that to gain a better understanding of these dynamics, it is indispensable to scrutinise current demands for innovativeness as a distinct way of ascribing worth to research. Drawing on interviews and focus groups produced in a close collaboration with three research groups from the crop and soil sciences, I develop the notion of a project-innovation regime of valuation that can be traced in the sphere of research. In this evaluative framework, it is considered valuable to constantly re-invent oneself and take 'first steps' instead of 'just' following up on previous findings. Subsequently, I describe how these demands for innovativeness relate to and often clash with other regimes of valuation that matter for researchers' practices. I show that valuations of innovativeness are in many ways bound to those of productivity and competitiveness, but that these two regimes are nevertheless sometimes in tension with each other, creating a complicated double bind for researchers. Moreover, I highlight that also the project-innovation regime as such is not always in line with what researchers considered as a valuable progress of knowledge, especially because it entails a de-valuation of certain kinds of long-term epistemic agendas. I show that prevailing pushes for innovativeness seem to be based on a rather short-sighted temporal imaginary of scientific progress that is hardly grounded in the complex realities of research practices, and that they can reshape epistemic practices in potentially problematic ways.Ocean uptake of carbon dioxide (CO2) is causing changes in carbonate chemistry that affect calcification in marine organisms. In coastal areas, this CO2-enriched seawater mixes with waters affected by seasonal degradation of organic material loaded externally from watersheds or produced as a response to nutrient enrichment. As a result, coastal bivalves often experience strong seasonal changes in carbonate chemistry. 3-benzyl-5-((2-nitrophenoxy) methyl)-dihydrofuran-2(3H)-one In some cases, these changes may resemble those experienced by aquacultured bivalves during translocation activities. We mimicked these changes by exposing juvenile hard clams (500 μm, Mercenaria mercenaria) to pCO2 in laboratory upwellers at levels resembling those already reported for northeastern US estuaries (mean upweller pCO2 = 773, 1274, and 1838 μatm) and then transplanting to three grow-out sites along an expected nutrient gradient in Narragansett Bay, RI (154 bags of 100 clams). Prior to the field grow-out, clams exposed to elevated pCO2 exhibited larger shells but lower dry weight per unit volume (dw/V). However, percent increase in dw/V was highest for this group during the 27-day field grow-out, suggesting that individuals with low dw/V after the laboratory treatment accelerated accumulation of dw/V when they were transferred to the bay. Treatments also appeared to affect shell mineral structure and condition of digestive diverticula. Although treatment effects diminished during the field grow-out, clams that were pre-exposed for several weeks to high pCO2 would likely have been temporarily vulnerable to predation or other factors that interact with shell integrity. This would be expected to reduce population recovery from short-term exposures to high pCO2.The "science-softCon UV/Vis+ Photochemistry Database" (www.photochemistry.org) is a large and comprehensive collection of EUV-VUV-UV-Vis-NIR spectral data and other photochemical information assembled from published peer-reviewed papers. The database contains photochemical data including absorption, fluorescence, photoelectron, and circular and linear dichroism spectra, as well as quantum yields and photolysis related data that are critically needed in many scientific disciplines. This manuscript gives an outline regarding the structure and content of the "science-softCon UV/Vis+ Photochemistry Database". The accurate and reliable molecular level information provided in this database is fundamental in nature and helps in proceeding further to understand photon, electron and ion induced chemistry of molecules of interest not only in spectroscopy, astrochemistry, astrophysics, Earth and planetary sciences, environmental chemistry, plasma physics, combustion chemistry but also in applied fields such as medical diagnostics, pharmaceutical sciences, biochemistry, agriculture, and catalysis. In order to illustrate this, we illustrate the use of the UV/Vis+ Photochemistry Database in four different fields of scientific endeavor.The recently emerged SARS-CoV2 caused a major pandemic of coronavirus disease (COVID-19). Non structural protein 1 (nsp1) is found in all beta coronavirus that cause severe respiratory disease. This protein is considered as a virulence factor and has an important role in pathogenesis. This study aims to elucidate the structural conformations of nsp1 to aid in the prediction of epitope sites and identification of important residues for targeted therapy against COVID-19. In this study, molecular modelling coupled with molecular dynamics simulations were performed to analyse the conformational landscape of nsp1 homologs of SARS-CoV1, SARS-CoV2 and MERS-CoV. Principal component analysis escorted by free energy landscape revealed that SARS-CoV2 nsp1 protein shows greater flexibility compared to SARS-CoV1 and MERS-CoV nsp1. Sequence comparison reveals that 28 mutations are present in SARS-CoV2 nsp1 protein compared to SARS-CoV1 nsp1. link2 Several B-cell and T-cell epitopes were identified by an immunoinformatics approach. link3 SARS-CoV2 nsp1 protein binds with the interface region of the palm and finger domain of POLA1 via hydrogen bonding and salt bridge interactions. Taken together, these in silico findings may help in the development of therapeutics specific against COVID-19.
To explore sexual orientation disparities in unwanted pregnancy by race/ethnicity.

Previous research has documented that sexual-minority women (SMW) are more likely to report unplanned pregnancy than heterosexual women, and that Black and Latina women are more likely to report unplanned pregnancy than White women. No research has examined how pregnancy intention varies at the intersection of these two identities.

Data come from the pregnancy roster data in Waves IV and Wave V subsample in the National Longitudinal Study of Adolescent to Adult Health. We used pregnancy as the unit of analysis (n=10,845) and multilevel logistic regression models to account for clustering of pregnancies within women. Per pregnancy, women were asked if they "wanted" to be pregnant at the time of pregnancy. We conducted models stratified by race/ethnicity, as well as models stratified by sexual identity.

Among White women, sexual-minority women were more likely to describe their pregnancy as unwanted than were their heterosexual counterparts. Conversely, among Black and Latina women, sexual-minority women were less likely to describe their pregnancy as unwanted than were their heterosexual counterparts. Results stratified by sexual identity underscore these contrasting patterns Among heterosexual women, White women were less likely to describe their pregnancies as unwanted compared to Black and Latina women; among sexual-minority women, White women were more likely to describe their pregnancy as unwanted than were Black and Latina women.

Traditional race/ethnicity trends in pregnancy intention (i.e., greater unwanted pregnancy among Black/Latina than White women) are reversed among sexual-minority women.
Traditional race/ethnicity trends in pregnancy intention (i.e., greater unwanted pregnancy among Black/Latina than White women) are reversed among sexual-minority women.
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