The goal of this project was to see if the addition of wild orange essential oil to freshly squeezed orange juice would help to slow down the decay of ascorbic acid when exposed to various temperatures, allowing vital nutrients to be maintained and providing a natural alternative to the chemical additives in use in industry today. The authors hypothesized that the addition of wild orange essential oil to freshly squeezed orange juice would slow down the rate of oxidation when exposed to various temperatures, reducing ascorbic acid decay. On average, wild orange EO slowed down ascorbic acid decay in freshly squeezed orange juice by 15% at the three highest temperatures tested.
Chronic bad breath, or halitosis, is a problem faced by nearly 50% of the general poluation, but existing treatments such as liquid mouthwash or sugar-free gum are imperfect and temporary solutions. In this study, the authors investigate potential alternative treatments using natural ingredients such as Manuka Honey and Licorice root extract. They found that Manuka honey is almost as effective as commercial mouthwashes in reducing the growth of P gingivalis (one of the main bacteria that causes bad breath), while Licorice root extract was largely ineffective. The authors' results suggest that Manuka honey is a promising candidate in the search for new and improved halitosis treatments.
Wound-healing involves a sequence of events, such as inflammation, proliferation, and migration of different cell types like fibroblasts. Zinc Finger CCCH-type with G-Patch Domain Containing Protein (ZGPAT), encodes a protein that has its main role as a transcription repressor by binding to a specific DNA sequence. The aim of the study was to find out whether inhibiting ZGPAT will expedite the wound healing process by accelerating cell migration. This treatment strategy can provide a key to the development of wound healing strategies in medicine and cellular biology.
In our work we followed the formation of gas bubbles on the surface of the vessel walls in different carbonated liquids, over different time intervals, at different temperatures and in vessels made of different materials. Our results made it possible to identify patterns affecting the process of formation and disappearance of carbon dioxide bubbles.
Resveratrol is a type of stillbenoid, a phenolic compound produced in plants, that is known for its anti-inflammatory and anticancer effects. Many oligomers of resveratrol have recently been isolated their bioactivities remain unknown. Here, authors compared the bioactivities of resveratrol with natural dimers (ε-viniferin and gnetin H) and trimers (suffruticosol B and C). Results provide preliminary evidence that resveratrol oligomers could be potential preventive or therapeutic agents for cancers and other immune-related diseases
Nitrogen-fixing bacteria, such as the legume mutualist rhizobia, convert atmospheric nitrogen into a form that is usable by living organisms. Leguminous plants, like the model species Medicago truncatula, directly benefit from this process by forming a symbiotic relationship with rhizobia. Here, Rathod and Rowe investigate how M. truncatula responds to non-rhizobial bacterial partners.
Simon and colleagues test how exposure to microwaves affect radish seed germination, either microwaving seeds for ninety seconds or four minutes prior to planting. Surprisingly, the authors found that seeds microwaved for four minutes exhibited 150% increased germination as compared to controls. The authors hypothesize that breakdown of the radish seed coat when exposed to heat may allow seedlings to sprout more efficiently.
Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.
We conducted this research as our start-up's research that addresses the problem of biogas production in cow-dense regions like India. We hypothesized that the thermophilic temperature (45-60oC) would increase biogas production. The production process is much faster and more abundant at temperatures around 55-60oC.
Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.
