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Starts and Stops of Rhythmic and Discrete Movements: Modulation in the Excitability of the Corticomotor Tract During Transition to a Different Type of Movement

Lim et al. | Aug 27, 2018

Starts and Stops of Rhythmic and Discrete Movements: Modulation in the Excitability of the Corticomotor Tract During Transition to a Different Type of Movement

Control of voluntary and involuntary movements is one of the most important aspects of human neurological function, but the mechanisms of motor control are not completely understood. In this study, the authors use transcranial magnetic stimulation (TMS) to stimulate a portion of the motor cortex while subjects performed either discrete (e.g. throwing) or rhythmic (e.g. walking) movements. By recording electrical activity in the muscles during this process, the authors showed that motor evoked potentials (MEPs) measured in the muscles during TMS stimulation are larger in amplitude for discrete movements than for rhythmic movements. Interestingly, they also found that MEPs during transitions between rhythmic and discrete movements were nearly identical and larger in amplitude than those recorded during either rhythmic or discrete movements. This research provides important insights into the mechanisms of neurological control of movement and will serve as the foundation for future studies to learn more about temporal variability in neural activity during different movement types.

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A novel deep learning model for visibility correction of environmental factors in autonomous vehicles

Dey et al. | Oct 31, 2022

A novel deep learning model for visibility correction of environmental factors in autonomous vehicles

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

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The Development and Maximization of a Novel Photosynthetic Microbial Fuel Cell Using Rhodospirillum rubrum

Gomez et al. | Mar 02, 2014

The Development and Maximization of a Novel Photosynthetic Microbial Fuel Cell Using <em>Rhodospirillum rubrum</em>

Microbial fuel cells (MFCs) are bio-electrochemical systems that utilize bacteria and are promising forms of alternative energy. Similar to chemical fuel cells, MFCs employ both an anode (accepts electrons) and a cathode (donates electrons), but in these devices the live bacteria donate the electrons necessary for current. In this study, the authors assess the functionality of a photosynthetic MFC that utilizes a purple non-sulfur bacterium. The MFC prototype they constructed was found to function over a range of environmental conditions, suggesting its potential use in industrial models.

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Heat impact to food’s shelf life - An example of milk

Xu et al. | Dec 13, 2022

Heat impact to food’s shelf life - An example of milk

Food spoilage happens when food is not kept in a good storage condition. Qualitatively estimating the shortened shelf life of food could reduce food waste. In this study, we tested the impact of heat on milk shelf life. Our results showed that an exposure at room temperature (25°C) for 3.2 hours will decrease the shelf life of milk by one day.

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Evaluation of platelet-rich plasma vs. platelet lysate: VEGF and PDGF concentration, stability, and shelf life

Prasad et al. | Mar 30, 2022

Evaluation of platelet-rich plasma vs. platelet lysate: VEGF and PDGF concentration, stability, and shelf life

Cell-free biologicals are a novel method of treating clinical conditions which involve chronic inflammation such as tendonitis and osteoarthritis. This study compared platelet-derived growth factor (PDGF) and vascular endothelial growth factor (VEGF) in platelet-rich plasma (PRP), activated PRP (aPRP), and platelet lysate (PL). It was hypothesized that PL would contain higher concentrations of growth factors than PRP and that different storage temperatures for PL would diminish cytokine expression. Results demonstrated PL had the highest concentrations of both cytokines, with concentrations slightly diminishing at-80C. aPRP and PRP demonstrated lower concentrations of PDGF and VEGF than PL.

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The effect of neuroinflammation and oxidative stress on the recovery time of seizures

Kantipudi et al. | Jul 31, 2023

The effect of neuroinflammation and oxidative stress on the recovery time of seizures

Neuroinflammation and oxidative stress are both known to play a role in the occurrence and severity of seizures. This study tested effects of oxidative stress from seizures by evaluating the longevity, egg-laying, and electroshock resilience of C. elegans. Results revealed that oxidative stress and neuroinflammation diminish longevity and reproductivity while also increasing recovery time after seizures in C. elegans. This research can help lead to future studies and may also lead to finding new therapeutics for epilepsy.

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Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease

Kar et al. | Oct 10, 2020

Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease

Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).

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Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Sirohi et al. | Sep 25, 2022

Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

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Investigating the Role of the Novel ESCRT-III Recruitment Factor CCDC11 in HIV Budding: A Potential Target for Antiviral Therapy

Takemaru et al. | Feb 24, 2020

Investigating the Role of the Novel ESCRT-III Recruitment Factor CCDC11 in HIV Budding: A Potential Target for Antiviral Therapy

Acquired immunodeficiency syndrome (AIDS) is a life-threatening condition caused by the human immunodeficiency virus (HIV). In this work, Takemaru et al explored the role of Coiled-Coil Domain-Containing 11 (CCDC11) in HIV-1 budding. Their results suggest that CCDC11 is critical for efficient HIV-1 budding, potentially indicating CCDC11 a viable target for antiviral therapeutics without major side effects.

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