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Here, the authors explored how the sale and use of electric vehicles could reduce emissions from the transport industry in Canada. By fitting the sale of total of electric vehicles with an exponential model, the authors predicted the number of electric vehicle sales through 2030 and related that to the average emission for such vehicles. Ultimately, they found that the sale and use of electric vehicles alone would likely not meet the 45% reduction in emissions from the transport industry suggested by the Canadian government

The authors set to test a system that would help with the dehumidification and overall management of greehouses.

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

Large corporations often are known for their financial power, but what social and environmental power and conscious do they have? The more responsible corporations are in relation to environmental, social, and governance criteria the better they do fiscally.

Pillai et al. look at whether exposure to Schistosoma mansoni, a parasitic blood fluke, has any relation to peanut allergies. They found that cockroaches exposed to an antigen found in S. mansoni eggs exhibited an allergic reaction to peanuts.

The facial integument, or external skin tissues, were assessed on set of dinosaurs from the Allosauroidea clade to test whether dermal patterns served specific functions.

In this study, the authors conduct a bibliometric analysis to understand the recent growth in and current state of peripheral nerve regeneration research. They also explored potential future studies.

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

In this study, we aimed to characterize CD44-mediated regulation of the Wnt/β-catenin signaling pathway, which promotes cancer invasion and metastasis. We hypothesized that CD44 down-regulation will inhibit gastric cancer cell migration and invasion by leading to down-regulation of Wnt/β-catenin signaling. We found that CD44 up-regulation was significantly related to poor prognosis in gastric cancer patients. We demonstrated the CD44 down-regulation decreased β-catenin protein expression level. Our results suggest that CD44 down-regulation inhibits cell migration and invasion by down-regulating β-catenin expression level.

In this study, the authors develop an architecture to implement in a cloud-based database used by law firms to ensure confidentiality, availability, and integrity of attorney documents while maintaining greater efficiency than traditional encryption algorithms. They assessed whether the architecture satisfies necessary criteria and tested the overall file sizes the architecture could process. The authors found that their system was able to handle larger file sizes and fit engineering criteria. This study presents a valuable new tool that can be used to ensure law firms have adequate security as they shift to using cloud-based storage systems for their files.