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Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

Chang et al. | Apr 29, 2022

Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

With monitoring of climate change and the evolving properties of the atmosphere more critical than ever, the authors of this study take sea salt aerosols into consideration. These sea salt aerosols, sourced from the bubbles found at the surface of the sea, serve as cloud condensation nuclei (CCN) and are effective for the formation of clouds, light scattering in the atmosphere, and cooling of the climate. With amines being involved in the process of CCN formation, the authors explore the effects of alkylamines on the properties of sea salt aerosols and their potential relevance to climate change.

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Combating drug resistance in cancer cells: Cooperative effect of green tea and turmeric with chemotherapeutic drug

Nair et al. | Jul 27, 2020

Combating drug resistance in cancer cells: Cooperative effect of green tea and turmeric with chemotherapeutic drug

The major drawback of chemotherapy regimens for treating cancer is that the cancerous cells acquire drug resistance and become impervious to further dose escalation. Keeping in mind the studied success of herbal formulations with regard to alternative treatments for cancer, we hypothesized that the use of a chemotherapeutic drug and proprietary herbal formulation, HF1, would combat this phenomenon when administered with common chemotherapeutic drug 5FU. Results demonstrated a cooperative effect between HF1 and 5FU on the drug resistant cell line, implying that administration of HF1 with 5FU results in cell death as measured by MTT assay.

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How does light affect the distribution of Euglena sp. and Tetrahymena pyriformis

Singh et al. | Mar 03, 2022

How does light affect the distribution of <em>Euglena sp.</em> and <em>Tetrahymena pyriformis</em>

In this article, the authors explored the locomotory movement of Euglena sp. and Tetrahymena pyriformis in response to light. Such research bears relevance to the migration and distribution patterns of both T. pyriformis and Euglena as they differ in their method of finding sustenance in their native environments. With little previous research done on the exploration of a potential response to photostimulation enacted by T. pyriformis, the authors found that T. pyriformis do not bias in distribution towards areas of light - unlike Euglena, which displayed an increased prevalence in areas of light.

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Racial and gender disparities in the portrayal of lawyers and physicians on television

Asadi et al. | Nov 18, 2022

Racial and gender disparities in the portrayal of lawyers and physicians on television

Powered by the sociological framework that exposure to television bleeds into social biases, limiting media representation of women and minority groups may lead to real-world implications and manifestations of racial and gender disparities. To address this phenomenon, the researchers in this article take a look at primetime fictional representation of minorities and women as lawyers and physicians and compare television representation to census data of the same groups within real-world legal and medical occupations. The authors maintain the hypothesis that representation of female and minority groups as television lawyers and doctors is lower than that of their white male counterparts relative to population demographics - a trend that they expect to also be reflected in actual practice. With fictional racial and gender inequalities and corresponding real-world trends highlighted within this article, the researchers call for address towards representation biases that reinforce each other in both fictional and non-fictional spheres.

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A land use regression model to predict emissions from oil and gas production using machine learning

Cao et al. | Mar 24, 2023

A land use regression model to predict emissions from oil and gas production using machine learning

Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.

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