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A machine learning approach for abstraction and reasoning problems without large amounts of data

Isik et al. | Jun 25, 2022

A machine learning approach for abstraction and reasoning problems without large amounts of data

While remarkable in its ability to mirror human cognition, machine learning and its associated algorithms often require extensive data to prove effective in completing tasks. However, data is not always plentiful, with unpredictable events occurring throughout our daily lives that require flexibility by artificial intelligence utilized in technology such as personal assistants and self-driving vehicles. Driven by the need for AI to complete tasks without extensive training, the researchers in this article use fluid intelligence assessments to develop an algorithm capable of generalization and abstraction. By forgoing prioritization on skill-based training, this article demonstrates the potential of focusing on a more generalized cognitive ability for artificial intelligence, proving more flexible and thus human-like in solving unique tasks than skill-focused algorithms.

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Impact of simple vs complex carbohydrates under time constraint before anaerobic and aerobic exercise

Cui et al. | Oct 13, 2022

Impact of simple vs complex carbohydrates under time constraint before anaerobic and aerobic exercise

The goal of this study was to determine the if carbohydrates or complex carbohydrates are better for athlete's performance in anaerobic and aerobic exercise. Ultimately, we found that, when one’s schedule only allows for 30 minutes to eat before a workout, the best pre-workout meal for optimal glycogen levels to prompt muscle hypertrophy, strength increases, and better endurance is one that is simple carbohydrate-heavy.

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Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Bhat et al. | Dec 03, 2024

Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.

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Effects of social support on adolescent identity development

Yim et al. | Nov 12, 2024

Effects of social support on adolescent identity development

Adolescence is a critical period for self-identity formation, heavily influenced by feedback from social networks. This research examined the interplay between social support from parents and peers and self-concept development in adolescents using data from the National Longitudinal Study of Adolescent to Adult Health. While individual support from parents and peers did not directly impact self-concept, their combined interaction significantly influenced it, highlighting the importance of various social supports in fostering healthy self-concept development and overall adolescent well-being.

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Specific Transcription Factors Distinguish Umbilical Cord Mesenchymal Stem Cells From Fibroblasts

Park et al. | Aug 16, 2019

Specific Transcription Factors Distinguish Umbilical Cord Mesenchymal Stem Cells From Fibroblasts

Stem cells are at the forefront of research in regenerative medicine and cell therapy. Two essential properties of stem cells are self-renewal and potency, having the ability to specialize into different types of cells. Here, Park and Jeong took advantage of previously identified stem cell transcription factors associated with potency to differentiate umbilical cord mesenchymal stem cells (US-MSCs) from morphologically similar fibroblasts. Western blot analysis of the transcription factors Klf4, Nanog, and Sox2 revealed their expression was unique to US-MSCs providing insight for future methods of differentiating between these cell lines.

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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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