Rhizopus stolonifer is a mold commonly found growing on bread that can cause many negative health effects when consumed. Preservatives are the well-known answer to this problem; however, many preservatives are not naturally found in food, and some have negative health effects of their own. We focused on honey as a possible solution because of its natural origin and self-preservation ability. We hypothesized that honey would decrease the growth rate of R. stolonifer . We evaluated the honey with a zone of inhibition (ZOI) test on agar plates. Sabouraud dextrose agar was mixed with differing volumes of honey to generate concentrations between 10.0% and 30.0%. These plates were then inoculated with a solution of spores collected from the mold. The ZOI was measured to determine antifungal effectiveness. A statistically significant difference was found between the means of all concentrations except for 20.0% and 22.5%. Our findings support the hypothesis as we showed a positive correlation between the honey concentration and growth rate of mold. By using this data, progress could be made on an all-natural, honey-based preservative.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.

Here, recognizing the important role of bacterial biofilms in many life-threatening chronic infections, the authors investigated the effectiveness of a combination treatment on biofilms composed of up to three different common species within the lungs of cystic fibrosis patients with computational analysis. They found that a triple cocktail therapy targeting three different signaling pathways has significant potential as both a treatment and prophylaxis.

As the COVID-19 pandemic continues, the authors noticed a change in the physical activity of many people, as well as a change in the type of physical activity they practice. Here, the authors used a cross-sectional survey of 150 participants from the province of Basra in Iraq. They found an overall decrease in the number of days of physical activity for participants along with an increasing proportion of at-home exercises compared to other activities that are performed inside sports clubs during the pandemic.

Spinal degeneration has been linked to critical conditions such as osteoarthritis in adults aged 40+; while this condition is considered to be irreversible, we took interest in magnetic resonance imaging (MRI) for early detection of the condition. Ultimately, our purpose was to determine the effectiveness of a relatively novel T1rho method in the early detection of spinal degeneration, and we hypothesized that the early to mild progression of spinal degeneration would affect T1rho values following an MRI scan.

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

In this study, the authors investigate the relationship between iron/hydrogen ratio [Fe/H] of a type of variable stars commonly used as reference points RR Lyrae stars and their light curves to see if one can determine the composition of these stars solely by measuring their light curve characteristics.

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.

Amyotrophic lateral sclerosis (ALS) affects nearly 200,000 people worldwide and there is currently no cure. The purpose of the study was to determine if Helianthus annuus seeds helped reduce nerve degeneration and increase locomotion using Drosophila melanogaster as the model organism. Through this experiment, we found a general trend suggesting that H. annuus helped increase the mobility of the D. melanogaster suggesting it could be a viable supplement for patients with ALS.

Nosocomial infections acquired in hospitals pose a risk to patients, a risk compounded by resistant microorganisms. To combat this problem, researchers have turned to bioactive compounds from medicinal plants such as the widely used neem. In the present study, researchers sought to determine the effectiveness of different neem preparations against several hospital acquired human pathogens. Neem powder in water successfully inhibited microorganism growth making it a potential agent to combat these infections.