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Assaying the Formation of Beneficial Biofilms by Lactic Acid Bacteria and the Effect of Ayurvedic Plant Extracts on Their Enhancement

Rajpal et al. | Oct 12, 2017

Assaying the Formation of Beneficial Biofilms by Lactic Acid Bacteria and the Effect of Ayurvedic Plant Extracts on Their Enhancement

This study aimed to obtain an optimal non-antibiotic method to suppress the growth of pathogenic bacteria within the body. The two-fold purpose of this project was to determine which combination of bacteria would result in the most biofilm formation and then to assess the effect of ayurvedic plant extracts on the biofilm. The results show that the addition of a plant extract can affect the biofilm growth of a bacteria combination. The applications of this study can be used to design probiotic supplements with added beneficial plant extracts.

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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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Mathematical modeling of plant community composition for urban greenery plans

Fang et al. | Jul 05, 2023

Mathematical modeling of plant community composition for urban greenery plans
Image credit: CHUTTERSNAP

Here recognizing the importance of urban green space for the health of humans and other organisms, the authors investigated if mathematical modeling can be used to develop an urban greenery management plan with high eco-sustainability by calculating the composition of a plant community. They optimized and tested their model against green fields in a Beijing city park. Although the compositions predicted by their models differed somewhat from the composition of testing fields, they conclude that by using a mathematical model such as this urban green space can be finely designed to be ecologically and economically sustainable.

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Repulsion of Ants Using Non-Toxic Household Products

Ambati et al. | Sep 10, 2019

Repulsion of Ants Using Non-Toxic Household Products

Ant invasion causes damage exceeding $5 billion annually in North America. In this study, Ambati and Duvvuri aim to identify natural products with ant-repelling properties using a custom ring apparatus designed to quantify ant-repellence. They report that cinnamon and lemon were the most effective ant repellents of the tested products. These data suggest that compounds found in non-toxic household products, such as cinnamon oil and lemon juice, could be used in low-dose combinations as potent, effective, eco-friendly, and safe ant repellents.

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The relationship between macroinvertebrates, water quality, and the health of Stevens Creek

Li et al. | Aug 18, 2021

The relationship between macroinvertebrates, water quality, and the health of Stevens Creek

Stevens Creek, which flows through Santa Clara County in California, provides a crucial habitat for federally designated threatened steelhead trout, with a portion of the trout’s diet being dependent on the presence and abundance of macroinvertebrates that inhabit the creek. In this article, the authors investigate how the water chemistry within the creek was associated with the abundance and diversity of macroinvertebrates, and subsequently the creek’s health. They conduct qualitative analysis of macroinvertebrates and water quality to obtain a general understanding of the health of Stevens Creek.

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A comparison of use of the mobile electronic health record by medical providers based on clinical setting

Stover et al. | Jul 12, 2023

A comparison of use of the mobile electronic health record by medical providers based on clinical setting
Image credit: Tima Miroshnichenko

The electronic health record (EHR), along with its mobile application, has demonstrated the ability to improve the efficiency and accuracy of health care delivery. This study included data from 874 health care providers over a 12-month period regarding their usage of mobile phone (EPIC® Haiku) and tablet (EPIC® Canto) mEHR. Ambulatory and inpatient care providers had the greatest usage levels over the 12-month period. Awareness of workflow allows for optimization of mEHR design and implementation, which should increase mEHR adoption and usage, leading to better health outcomes for patients.

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High-throughput virtual screening of novel dihydropyrimidine monastrol analogs reveals robust structure-activity relationship to kinesin Eg5 binding thermodynamics

Shern et al. | Jan 20, 2021

High-throughput virtual screening of novel dihydropyrimidine monastrol analogs reveals robust structure-activity relationship to kinesin Eg5 binding thermodynamics

As cancer continues to take millions of lives worldwide, the need to create effective therapeutics for the disease persists. The kinesin Eg5 assembly motor protein is a promising target for cancer therapeutics as inhibition of this protein leads to cell cycle arrest. Monastrol, a small dihydropyrimidine-based molecule capable of inhibiting the kinesin Eg5 function, has attracted the attention of medicinal chemists with its potency, affinity, and specificity to the highly targeted loop5/α2/α3 allosteric binding pocket. In this work, we employed high-throughput virtual screening (HTVS) to identify potential small molecule Eg5 inhibitors from a designed set of novel dihydropyrimidine analogs structurally similar to monastrol.

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Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

Sikdar et al. | Jan 10, 2023

Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

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.

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