Evaluating the Suitability of Linear and Nonlinear Regression Approaches for the Langmuir Adsorption Model as Applied toward Biomass-Based Adsorbents: Testing Residuals and Assessing Model Validity.

Regression analysis is a powerful tool in adsorption studies. Researchers often favor linear regression for its simplicity when fitting isotherm models, such as the Langmuir equation. Validating regression assumptions is crucial to ensure that the model accurately represents the data and allows appropriate inferences. This study provides a detailed examination of assumption checking in the context of adsorption studies while simultaneously evaluating the robustness of linear regression methods for fitting the Langmuir equation to isotherm data from 2,4-dichlorophenol (DCP) adsorption onto various biomass-based adsorbents and activated carbon. Different linearized Langmuir equations (Hanes-Woolf, Lineweaver-Burk, Eadie-Hofstee, and Scatchard) were compared to nonlinear regression, and each method was validated by rigorous residual checking. This included visual plots of residuals as well as statistical tests, including the Durbin-Watson test for autocorrelation (independence), the Shapiro-Wilk test for normality, and the White test for homoscedasticity. Key findings indicate that the Hanes-Woolf (type 1) and Lineweaver-Burk (type 2) linearizations were the best for most biomass adsorbents studied and that Eadie-Hofstee (type 3) and Scatchard (type 4) were generally invalid due to the negative parameters or assumption violations. For activated carbon, all linearization methods were unsuitable due to independence violations. In the case of nonlinear regression, there were no major assumption violations for all of the adsorbents. Symbolic regression identified the Langmuir equation only for activated carbon (AC). This study revealed shortcomings in relying solely on linearized Langmuir models. A proposed workflow recommends using nonlinear or weighted nonlinear regression, starting with Hanes-Woolf or Lineweaver-Burk linearization results as initial values for parameter estimation. If assumptions remain violated with nonlinear techniques, novel methods such as symbolic regression should be employed. This advanced regression technique can improve adsorption models' accuracy and predictive behavior without the stringent need for assumption checking. Symbolic regression can also aid in understanding mechanisms of novel adsorbents.

Predicting molecular docking of per- and polyfluoroalkyl substances to blood protein using generative artificial intelligence algorithm DiffDock.

This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein-ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body.

Impacts of Diet on Reproductive Performance of Captive American Alligators (Alligator mississippiensis).

Historically, there has been little success with the captive breeding of American alligators (Alligator mississippiensis) for both commercial and conservative purposes. This study, conducted at Golden Ranch in Gheens, LA, between 2016 and 2022, utilized a newly formulated commercial feed and practical dietary supplementation (crawfish waste products) to enhance egg production, fertility, and hatch rates. The primary focus of this study was to compare the outcome of this captive breeding program at Golden Ranch with a program conducted at Rockefeller Refuge (RR) between 1979 and 1984. Notable success was achieved in terms of reproductive performance in comparison to the captive breeding program conducted at Rockefeller Refuge. In this study, 16.1 hatchlings were produced per nest on Golden Ranch from captive breeders. Additionally, when wild nests from Golden Ranch were incubated in the same controlled environmental chambers, they produced an average of 16.3 hatchlings per nest. This comparison emphasizes the similarity in egg production between captive-bred A. mississippiensis and their wild counterparts. The findings of this study suggest that a closed farming system for A. mississippiensis can be established by employing captive breeders derived from artificially incubated wild eggs. Furthermore, American alligators raised in controlled environmental chambers during their initial three years of life demonstrated adaptability to captive conditions and tolerated stocking rates associated with farming conditions and served as breeding stock.