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Testing HCN1 channel dysregulation in the prefrontal cortex using a novel piezoelectric silk neuromodulator

Mathew et al. | May 05, 2021

Testing HCN1 channel dysregulation in the prefrontal cortex using a novel piezoelectric silk neuromodulator

Although no comprehensive characterization of schizophrenia exists, there is a general consensus that patients have electrical dysfunction in the prefrontal cortex. The authors designed a novel piezoelectric silk-based implant and optimized electrical output through the addition of conductive materials zinc oxide (ZnO) and aluminum nitride (AlN). With further research and compatibility studies, this implant could rectify electrical misfiring in the infralimbic prefrontal cortex.

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Testing the Effects of Salep Derived From the Tubers of Orchis mascula, Aloe vera, and Alpha-chymotrypsin on Wound Healing in Drosophila melanogaster Larvae

Halder et al. | Sep 09, 2019

Testing the Effects of Salep Derived From the Tubers of <em>Orchis mascula</em>, <em>Aloe vera</em>, and Alpha-chymotrypsin on Wound Healing in <em>Drosophila melanogaster</em> Larvae

Aloe vera and alpha-chymotrypsin have been used in are known for their various wound healing properties. Halder et al hypothesized that these treatments would enhance wound healing in Drosophila melanogaster larvae over 2 weeks by decreasing wound size more effectively compared to controls. The results of two of the treatment groups, Salep and Aloe vera, yielded wound sizes small enough to present a significant percent decrease when compared with the wound sizes of the control group. Their results show support that both Salep and Aloe vera were effective for enhancing wound healing in epithelial cells in D. melanogaster larvae.

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Reading recall: A comparison of reading comprehension

Rudins et al. | Nov 16, 2022

Reading recall: A comparison of reading comprehension

Researchers query whether reading comprehension is the same, worse, or better when using e-books as compared with standard paper texts. This study evaluated this question in the elementary school population. Our hypothesis was that information would be retained equally whether read from paper or from an electronic device. Each participant read four stories, alternating between electronic and paper media types. After each reading, the participants completed a five-question test covering the information read. The study participants correctly answered 167 out of 200 comprehension questions when reading from an electronic device. These same participants correctly answered 145 out of 200 comprehension questions when reading from paper. At a significance level of p < 0.05, the results showed that there was a statistically significant difference in reading comprehension between the two media, demonstrating better comprehension when using electronic media. The unexpected results of this study demonstrate a shift in children’s performance and desirability of using electronic media as a reading source.

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Redesigning an Experiment to Determine the Coefficient of Friction

Hu et al. | Jun 27, 2016

Redesigning an Experiment to Determine the Coefficient of Friction

In a common high school experiment to measure friction coefficients, a weighted mass attached to a spring scale is dragged across a surface at a constant velocity. While the constant velocity is necessary for an accurate measurement, it can be difficult to maintain and this can lead to large errors. Here, the authors designed a new experiment to measure friction coefficients in the classroom using only static force and show that their method has a lower standard deviation than the traditional experiment.

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The determinants and incentives of corporate greenhouse gas emission reduction

Liu et al. | Jun 04, 2021

The determinants and incentives of corporate greenhouse gas emission reduction

This study used hand-collected Greenhouse gas (GHG) emissions data from the Environmental Protection Agency (EPA) and aimed to understand the determinants and incentives of GHG emissions reduction. It explored how companies’ financials, Chief Executive Officer (CEO) compensation, and corporate governance affected GHG emissions. Results showed that companies reporting GHG emissions were wide-spread among the 48 industries represented by two-digit Standard Industrial Classification (SIC) codes.

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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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Heavy Metal Contamination of Hand-Pressed Well Water in HuNan, China

Long et al. | Oct 20, 2019

Heavy Metal Contamination of Hand-Pressed Well Water in HuNan, China

Unprocessed water from hand-pressed wells is still commonly used as a source of drinking water in Chenzhou, the “Nonferrous Metal Village” of China. Long et al. conducted a study to measure the heavy metal contamination levels and potential health effects in this area. Water samples were analyzed through Inductively Coupled Plasma Optical Emission Spectroscopy (ICPOES) and the concentrations of 20 metal elements. Results showed that although none of the samples had dangerous levels of heavy metals, the concentrations of Al, Fe, and Mn in many locations substantially exceeded those suggested in the Chinese Drinking Water Standard and the maximum contaminant levels of Environmental Protection Agency (EPA). The authors have made an important discovery regarding the water safety in HuNan and their suggestions to install water treatment systems would greatly benefit the community.

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The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Dasgupta et al. | Jul 06, 2021

The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

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