Dynamics of Neural Microstates in the VTA-Striatal-Prefrontal Loop during Novelty Exploration in the Rat.

Neural activity at the large-scale population level has been suggested to be consistent with a sequence of brief, quasistable spatial patterns. These "microstates" and their temporal dynamics have been linked to myriad cognitive functions and brain diseases. Most of this research has been performed using EEG, leaving many questions, such as the existence, dynamics, and behavioral relevance of microstates at the level of local field potentials (LFPs), unaddressed. Here, we adapted the standard EEG microstate analysis to triple-area LFP recordings from 192 electrodes in rats to investigate the mesoscopic dynamics of neural microstates within and across brain regions during novelty exploration. We performed simultaneous recordings from the prefrontal cortex, striatum, and ventral tegmental area in male rats during awake behavior (object novelty and exploration). We found that the LFP data can be accounted for by multiple, recurring microstates that were stable for ∼60-100 ms. The simultaneous microstate activity across brain regions revealed rhythmic patterns of coactivations, which we interpret as a novel indicator of inter-regional, mesoscale synchronization. Furthermore, these rhythmic coactivation patterns across microstates were modulated by behavioral states such as movement and exploration of a novel object. These results support the existence of a functional mesoscopic organization across multiple brain areas and present a possible link of the origin of macroscopic EEG microstates to zero-lag neuronal synchronization within and between brain areas, which is of particular interest to the human research community.SIGNIFICANCE STATEMENT The coordination of neural activity across the entire brain has remained elusive. Here we combine large-scale neural recordings at fine spatial resolution with the analysis of microstates (i.e., short-lived, recurring spatial patterns of neural activity). We demonstrate that the local activity in different brain areas can be accounted for by only a few microstates per region. These microstates exhibited temporal dynamics that were correlated across regions in rhythmic patterns. We demonstrate that these microstates are linked to behavior and exhibit different properties in the frequency domain during different behavioral states. In summary, LFP microstates provide an insightful approach to studying both mesoscopic and large-scale brain activation within and across regions.

Intra- and inter-regional dynamics in cortical-striatal-tegmental networks.

It is increasingly recognized that networks of brain areas work together to accomplish computational goals. However, functional connectivity networks are not often compared between different behavioral states and across different frequencies of electrical oscillatory signals. In addition, connectivity is always defined as the strength of signal relatedness between two atlas-based anatomical locations. Here, we performed an exploratory analysis using data collected from high-density arrays in the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA) of male rats. These areas have all been implicated in a wide range of different tasks and computations including various types of memory as well as reward valuation, habit formation and execution, and skill learning. Novel intraregional clustering analyses identified patterns of spatially restricted, temporally coherent, and frequency-specific signals that were reproducible across days and were modulated by behavioral states. Multiple clusters were identified within each anatomical region, indicating a mesoscopic scale of organization. Generalized eigendecomposition (GED) was used to dimension-reduce each cluster to a single component time series. Dense intercluster connectivity was modulated by behavioral state, with connectivity becoming reduced when the animals were exposed to a novel object, compared with a baseline condition. Behavior-modulated connectivity changes were seen across the spectrum, with δ, θ, and γ all being modulated. These results demonstrate the brain's ability to reorganize functionally at both the intra- and inter-regional levels during different behavioral states.NEW & NOTEWORTHY We applied novel clustering techniques to discover functional subregional anatomical patches that changed with behavioral conditions but were frequency specific and stable across days. By taking into account these changes in intraregional signal generator location and extent, we were able to reveal a richer picture of inter-regional functional connectivity than would otherwise have been possible. These findings reveal that the brain's functional organization changes with state at multiple levels of scale.

Integrating machine learning with experimental investigation for optimizing photocatalytic degradation of Rhodamine B using neodymium-doped titanium dioxide: a comprehensive approach with toxicity assessment.

In this study, neodymium-doped titanium dioxide (Nd-TiO2) nanoparticles were synthesized via a hydrothermal method for the photocatalytic degradation of Rhodamine B (RhB) under UV and sunlight conditions. The properties of these NPs were comprehensively characterized. And optimization of RhB degradation was conducted using control-variable experiment and artificial neural networks (ANN) under various operational conditions and in the presence of competing compounds. The acute toxicity of both NPs, RhB, and the environmental impact of the photocatalytic treatment effluent on Danio rerio were evaluated. The Nd modification increased the catalyst's specific surface area and thermal stability. X-ray diffraction confirmed the tetragonal anatase phase in undoped TiO2, while Nd-doped TiO2 exhibited shifts in peaks and the presence of brookite and rutile phases. Nd (1 mol%) doped TiO2 demonstrated superior RhB photocatalytic degradation efficiency, achieving 95% degradation and 82% total organic carbon (TOC) removal within 60 min under UV irradiation. Optimization under sunlight conditions yielded 95.14% RhB removal with 0.28 g/L photocatalyst and 1% doping. Under UV light, 98.12% RhB removal was optimized with 0.97% doping, along with the presence of humic acid and CaCl2. ANN modeling achieved high precision (R2 of 0.99) in modeling environmental photocatalysis. Toxicity assessments indicated that the 96-h LC50 values were 681.59 mg L-1 for both NPs, and 23.02 mg L-1 for RhB. The treated dye solution exhibited a significant decline in toxicity, emphasizing the potential of 1% Nd-TiO2 in wastewater treatment.

Changes in Selected Organic and Inorganic Compounds in the Hydrothermal Carbonization Process Liquid While in Storage.

Although many studies have investigated the hydrothermal transformation of feedstock biomass, little is known about the stability of the compounds present in the process liquid after the carbonization process is completed. The physicochemical characteristics of hydrothermal carbonization (HTC) liquid products may change over storage time, diminishing the amount of desired products or producing unwanted contaminants. These changes may restrict the use of HTC liquid products. Here, we investigate the effect of storage temperature (20, 4, and -18 °C) and time (weeks 1-12) on structural and compositional changes of selected organic compounds and physicochemical characteristics of the process liquid from the HTC of digested cow manure. ANOVA showed that the storage time has a significant effect on the concentrations of almost all of the selected organic compounds, except acetic acid. Considerable changes in the composition of the process liquid took place at all studied temperatures, including deep freezing at -18 °C. Prominent is the polymerization of aromatic compounds with the formation of precipitates, which settle over time. This, in turn, influences the inorganic compounds present in the liquid phase by chelating or selectively adsorbing them. The implications of these results on the further processing of the process liquid for various applications are discussed.

Machine learning-based modeling of malachite green adsorption on hydrochar derived from hydrothermal fulvification of wheat straw.

This study investigated the efficiency of hydrochar derived from hydrothermal fulvification of wheat straw in adsorbing malachite green (MG) dye. The characterizations of the hydrochar samples were determined using various analytical techniques like SEM, EDX, FTIR, X-ray spectroscopy, BET surface area analysis, ICP-OES for the determination of inorganic elements, elemental analysis through ultimate analysis, and HPLC for the content of sugars, organic acids, and aromatics. Adsorption experiments demonstrated that hydrochar exhibited superior removal efficiency compared to feedstock. The removal efficiency of 91 % was obtained when a hydrochar dosage of 2 g L-1 was used for 20 mg L-1 of dye concentration in a period of 90 min. The results showed that the study data followed the Freundlich isotherms as well as the pseudo-second order kinetic model. Moreover, the determined activation energy of 7.9 kJ mol-1 indicated that the MG adsorption was a physical and endothermic process that increased at elevated temperatures. The study also employed an artificial neural network (ANN), a machine learning approach that achieved remarkable R2 (0.98 and 0.99) for training and validation dataset, indicating high accuracy in simulating MG adsorption by hydrochar. The model's sensitivity analysis demonstrated that the adsorbent dosage exerted the most substantial influence on the adsorption process, with MG concentration, pH, and time following in decreasing order of impact.

Intelligent modeling and experimental study on methylene blue adsorption by sodium alginate-kaolin beads.

As tighter regulations on color in discharges to water bodies are more widely implemented worldwide, the demand for reliable inexpensive technologies for dye removal grows. In this study, the removal of the basic dye, methylene blue, by adsorption onto low-cost sodium alginate-kaolin beads was investigated to determine the effect of operating parameters (initial dye concentration, contact time, pH, adsorbent dosage, temperature, agitation speed) on dye removal efficiency. The composite beads and individual components were characterized by a number of analytical techniques. Three models were developed to describe the adsorption as a function of the operating parameters using regression analysis, and two powerful intelligent modeling techniques, genetic programming and artificial neural network (ANN). The ANN model is best in predicting dye removal efficiency with R2 = 0.97 and RMSE = 3.59. The developed model can be used as a useful tool to optimize treatment processes using the promising adsorbent, to eliminate basic dyes from aqueous solutions. Adsorption followed a pseudo-second order kinetics and was best described by the Freundlich isotherm. Encapsulating the kaolin powder in sodium alginate resulted in removal efficiency of 99.56% and a maximum adsorption capacity of 188.7 mg.g-1, a more than fourfold increase over kaolin alone.