In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.
The surface of the unicellular eukaryote, Tetrahymena pyriformis, is covered with thousands of hair-like cilia. These cilia are very similar to cilia of the human olfactory and respiratory tracts making them model organisms for studying cilia function and pathology. The authors of this study investigated the effect of voltage on T. pyriformis galvanotaxis, the movement towards an electrical stimulus. They observed galvanotaxis towards the cathode at voltages over 4V which plateau, indicating opening of voltage gated-ion channels to trigger movement.
Nitrogen-fixing bacteria, such as the legume mutualist rhizobia, convert atmospheric nitrogen into a form that is usable by living organisms. Leguminous plants, like the model species Medicago truncatula, directly benefit from this process by forming a symbiotic relationship with rhizobia. Here, Rathod and Rowe investigate how M. truncatula responds to non-rhizobial bacterial partners.
In this study, the authors investigated the biological mechanism underlying the actions of a traditional medicinal plant, Astragalus membranaceus. Using C. elegans as an experimental model, they tested the effects of AM root on heat stress responses. Their results suggest that AM root extract may enhance the activity of endogenous pathways that mediate cellular responses to heat stress.
Kadıoğlu and Oğuzalp study the synergistic effects of Metformin and Captopril, two commonly prescribed drugs for type 2 diabetes and hypertension, respectively. Using C. elegans nematodes as a model system, the authors find that the nematodes decreased in average body length when exposed to Metformin or Captopril individually, but grew 11% in body length when both drugs were used together. Because C. elegans body size is regulated in part by the TGF-β signaling pathway, the authors suggest that synergistic effects of these two drugs may be modulating TGF-β activity, a previously uncharacterized phenomenon.
Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.
Here the authors hypothesized that reducing folliculin (FLCN) might affect p62 protein levels in the dorsal hippocampus of mice, given their potential functional connection and p62's role in neurodegenerative diseases. Their study, using western blots and a two-way ANOVA on young wild-type mice, found that p62 levels correlated with FLCN expression, but ultimately concluded there's no evidence of a functional connection between FLCN and p62 in this specific model.
The global issue of water quality has led to the use of machine learning models, like ANN and SVM, to predict water potability. However, these models can be complex and resource-intensive. This research aimed to find a simpler, more efficient model for water quality prediction.
Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.
Rechargeable batteries are playing an increasingly prominent role in our lives due to the ongoing transition from fossil energy sources to green energy. The purpose of this study was to investigate variables that impact the effectiveness of rechargeable batteries. Alkaline (non-rechargeable) and rechargeable batteries share common features that are critical for the operation of a battery. The positive and negative electrodes, also known as the cathode and anode, are where the energy of the battery is stored. The electrolyte is what facilitates the transfer of cations and anions in a battery to generate electricity. Due to the importance of these components, we felt that a systematic investigation examining the surface area of the cathode and anode as well the impact of electrolytes with different properties on battery performance was justified. Utilizing a copper cathode and aluminum anode coupled with a water in salt electrolyte, a model rechargeable battery system was developed to test two hypotheses: a) increasing the contact area between the electrodes and electrolyte would improve battery capacity, and b) more soluble salt-based electrolytes would improve battery capacity. After soaking in an electrolyte solution, the battery was charged and the capacity, starting voltage, and ending voltage of each battery were measured. The results of this study supported our hypothesis that larger anode/cathodes surface areas and more ionic electrolytes such as sodium chloride, potassium chloride and potassium sulfate resulted in superior battery capacity. Incorporating these findings can help maximize the efficiency of commercial rechargeable batteries.
.png)
