Insect pheromones are crucial for survival and reproduction because they influence insect behavior, communication, and interactions within and outside the colony. Honey bees (Apis mellifera) have one of the most complex pheromonal communication systems. One pheromone, known as Queen Mandibular Pheromone (QMP), is released by the queen bee to regulate physiology, behavior, and gene expression in the female worker caste. The pheromone acts as a signal of queen presence that suppresses worker reproduction. In the absence of reproduction, young workers focus on taking care of the queen and larvae, known as nurse tasks, while older workers forage. In nurse bees, QMP has fundamental physiological impacts, including increasing abdominal lipid stores and increasing the protein content of hypopharyngeal glands (HPG). The HPG are worker-specific glands that can synthesize royal jelly used in colony nourishment. In workers, larger HPG signifies the ability to secrete royal jelly, while shrunken glands are characteristic of foragers that do not make jelly. While it is known that QMP increases abdominal lipid stores, the underlying mechanism is unclear: Does the pheromone simply make workers consume more pollen which provides lipids and protein, or does QMP also increase lipogenesis? In this study, I measured abdominal lipogenesis as fatty acid synthase (FAS) activity and monitored abdominal protein content and HPG size in caged, nurse-aged worker bees. In cages, workers were exposed to QMP or not, and they were provided with a lipid less diet in a full factorial design experiment. I found that QMP did not influence abdominal FAS activity or protein, but significantly increased HPG size. The data also revealed a significant positive correlation between abdominal protein and HPG size. My results do not support the idea that QMP modulates lipogenesis in worker bees, but my data can be interpreted to reflect that QMP mobilizes abdominal protein for the production of jelly in the HPG. This finding is in line with a previous study revealing a role of honey bee Brood Pheromone in mobilization of a major protein used in jelly production. Overall, my results support a fundamental role of QMP in worker metabolic processes associated with colony nourishment.

Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.

Cities in the Global South face rapid urbanization challenges and often suffer an acute lack of infrastructure and governance capacities. Smart Cities Mission, in India, launched in 2015, aims to offer a novel approach for urban renewal of 100 cities following an area‐based development approach, where the use of ICT and digital technologies is particularly emphasized. This article presents a critical review of the design and implementation framework of this new urban renewal program across selected case‐study cities. The article examines the claims of the so‐called “smart cities” against actual urban transformation on‐ground and evaluates how “inclusive” and “sustainable” these developments are. We quantify the scale and coverage of the smart city urban renewal projects in the cities to highlight who the program includes and excludes. The article also presents a statistical analysis of the sectoral focus and budgetary allocations of the projects under the Smart Cities Mission to find an inherent bias in these smart city initiatives in terms of which types of development they promote and the ones it ignores. The findings indicate that a predominant emphasis on digital urban renewal of selected precincts and enclaves, branded as “smart cities,” leads to deepening social polarization and gentrification. The article offers crucial urban planning lessons for designing ICT‐driven urban renewal projects, while addressing critical questions around inclusion and sustainability in smart city ventures.`

Attitudes and habits are extremely resistant to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring long-term, massive societal changes. During the pandemic, people are being compelled to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. Going forward, a critical question is whether these experiences will result in changed behaviors and preferences in the long term. This paper presents initial findings on the likelihood of long-term changes in telework, daily travel, restaurant patronage, and air travel based on survey data collected from adults in the United States in Spring 2020. These data suggest that a sizable fraction of the increase in telework and decreases in both business air travel and restaurant patronage are likely here to stay. As for daily travel modes, public transit may not fully recover its pre-pandemic ridership levels, but many of our respondents are planning to bike and walk more than they used to. These data reflect the responses of a sample that is higher income and more highly educated than the US population. The response of these particular groups to the COVID-19 pandemic is perhaps especially important to understand, however, because their consumption patterns give them a large influence on many sectors of the economy.