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The study of technology and the use of individual cognitive effort

Neravetla et al. | Jan 24, 2023

The study of technology and the use of individual cognitive effort
Image credit: Glenn Carstens-Peters

A trial study was performed in 2021 to investigate the link between technology and transactive memory. Transactive memory is shared knowledge in which members share the responsibility to encode, store, and retrieve certain tasks or assignments, leading to a successful and collective performance. We hypothesize that a participants’ expected access to an external source affects the recall rate and retrieval of information.

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Efficacy of Mass Spectrometry Versus 1H Nuclear Magnetic Resonance With Respect to Denaturant Dependent Hydrogen-Deuterium Exchange in Protein Studies

Chenna et al. | Jan 22, 2020

Efficacy of Mass Spectrometry Versus 1H Nuclear Magnetic Resonance With Respect to Denaturant Dependent Hydrogen-Deuterium Exchange in Protein Studies

The misfolding of proteins leads to numerous diseases including Akzheimer’s, Parkinson’s and Type II Diabetes. Understanding of exactly how proteins fold is crucial for many medical advancements. Chenna and Englander addressed this problem by measuring the rate of hydrogen-deuterium exchange within proteins exposed to deuterium oxide in order to further elucidate the process of protein folding. Here, mass spectrometry was used to measure exchange in Cytochrome c and was compared to archived 1H NMR data.

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The Effect of Poverty on Mosquito-borne Illness Across the United States

Kar et al. | Feb 25, 2021

The Effect of Poverty on Mosquito-borne Illness Across the United States

Mosquito-borne diseases are a major issue across the world, and the objective for this project was to determine the characteristics that make some communities more susceptible to these diseases than others. The authors identified and studied characteristics that make communities susceptible to mosquito-borne diseases, including water in square miles, average temperature, population, population density, and poverty rates per county. They found that the population of a county is the best indicator of the prevalence of mosquito-borne diseases.

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Effect Of SMC On The Growth Of Bean, Cherry Tomato And Roma Tomato Plant

Rao et al. | Sep 12, 2020

Effect Of SMC On The Growth Of Bean, Cherry Tomato And Roma Tomato Plant

Mushroom compost, also called Spent Mushroom Substrate or Spent Mushroom Compost (SMC), is suitable for a variety of plants. Previous research has found that the application of SMC will increase plant growth. However, it is unclear which exact proportions of SMC and soil will maximize tomato and bean plant growth. We showed that the hypothesized growth media with 30% SMC optimizes seed germination, plant height, number of leaves, and survival rate compared to other combinations of growth media. Our research suggests that SMC is a useful alternative for conventional fertilizers.

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Analyzing honey’s ability to inhibit the growth of Rhizopus stolonifer

Johnecheck et al. | Jun 06, 2023

Analyzing honey’s ability to inhibit the growth of <i>Rhizopus stolonifer</i>
Image credit: Johnecheck et al. 2023

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.

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Culturally Adapted Assessment Tool for Autism Spectrum Disorder and its Clinical Significance

Das et al. | Apr 19, 2021

Culturally Adapted Assessment Tool for Autism Spectrum Disorder and its Clinical Significance

Diagnosing of Autism Spectrum Disorder (ASD) using tools developed in the West is challenging in the Indian setting due to a huge diversity in sociocultural and economic backgrounds. Here, the authors developed a home-based, audiovisual game app (Autest) suitable for ASD risk assessment in Indian children under 10 years of age. Ratings suggested that the tool is effective and can reduce social inhibition and facilitate assessment. Further usage and development of Autest can improve risk assessment and early intervention measures for children with ASD in India.

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Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Suresh et al. | Jan 12, 2024

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

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