Zone Preference in the Visual Water Maze
June 26, 2025
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To find out the inorganic phosphate in a protein-containing liquid biological sample an aqueous reagent comprises an acid. This acid is capable of reacting with the molybdate salt to form molybdic acid for complexation with phosphate to form phosphomolybdate complexes, a molybdate salt, a nonionic surfactant reagent in an amount sufficient to further inhibit turbidity in the sample, and a ferric salt in an amount sufficient to inhibit turbidity in the sample.
(Important! Do not use ammonium molybdate!)
Step 1:
Put 1.5 ml of Soln. B and 2.0 ml of Soln. C into phosphate-free test tubes. The sample, which should contain no more than 100 nmoles phosphates, is mixed with an equal volume of Soln. A.
Step 2:
To the above mixture of B and C give 0.5 ml of this mixture. For about 30 seconds, shake the obtained mixture vigorously and then spin in a centrifuge for a short period to separate the phases. The extraction should be done at 0 ◦C or below to avoid the decomposition of labile organic phosphates.
The molybdatophosphate complex is removed and read at 725 nm against a blank which remains in the organic phase. Standards are made in the range of 5–100 nmoles phosphate per 0.5 ml to calculate the data.
Wastewater is relatively rich in phosphorus compounds. Phosphorus is a nutrient used by organisms for growth. It occurs in wastewater and natural water bound to oxygen to form phosphates. Phosphates come from a variety of sources including agricultural fertilizers, domestic wastewater, detergents, industrial process wastes, and geological formations.
The discharge of wastewater containing phosphorus may lead to the growth of algae in quantities sufficient to cause odor problems in drinking water supplies, and affect its taste. Decaying and dead algae can cause oxygen depletion problems which in turn can kill aquatic organisms in streams along with the fishes. Phosphates are classified as organic phosphates, polyphosphates, and orthophosphates. By colorimetric analysis, orthophosphates can be determined directly in this procedure. Other types require a digestion step to convert the “combined” phosphate to the ortho form for analysis. A digestion step is required in other types to convert the “combined” phosphate to the ortho form for analysis. This gives the Total Phosphorus result.
Ashing (Step 1)
If phosphate is at least partially covalently bound, ashing must be done as, for example, in nucleotides, phosphoproteins or nucleic acids. To 1–2 ml of aqueous sample, add 0.2 ml of conc. sulfuric acid. At about 130 ◦C, concentrate the liquid carefully in a hood and then heat to 280 ◦C until white fog appears.
Add one to two drops of conc. nitric acid after cooling and heat again until nitrous gases are visible. Add 2 ml of Soln. D after cooling and boil the solution for a short period. After that, fill up to 5.0 ml with ddH2O.
Determination (Step 2)
Mix 2.0 ml of the sample solution after digestion or sample in Soln. A with Soln. E and F. Incubate in the dark at 37 ◦C for 1.5–2 h and close the test tubes.
After the incubation, read samples, blank, and standards at 750 nm. The standard curve should be made in the range of 50–350 nmoles phosphate.
Data can be calculated by:
100 nmoles phosphate = 9.497 µg PO4 = 3.097 µg P
In one-dimensional thin-layer chromatography, two efficient solvent systems are described for the separation of lysophospholipids and phospholipids. For separation of 10 phospholipids, suitable chloroform–methanol-acetic acid–acetone-water solvent system (35:25:4:14:2, v/v) was found using a silica gel G plate. Chloroform–methanol–28% aqueous ammonia solvent system (65:35:8, v/v) also provided a clear separation of major phospholipids and their lyso forms.
Step 1:
With 80 µl ddH2O wet the lyophilized sample. Add 300 µl of Soln. After that and homogenize the sample with a glass-Teflon homogenization. Centrifuge the mixture for phase separation after the addition of 100 µl chloroform. Repeat this extraction three to four times.
Step 2:
Vaporize the organic solvents in a nitrogen stream after combining the organic phases. The remaining solvent is removed in a vacuum.
Use the dry residue for phosphate determination and digestion as described in the determination of Total Phosphate.
Data can be calculated by:
1 nmoles phosphate ≈ 85 ng phospholipid; 1 µg phosphate ≈ 8.3 µg phospholipid with an average Mr of 800.
Monosaccharides are water-soluble crystalline compounds. They are aliphatic ketones or aldehydes which contain one or more hydroxyl groups and one carbonyl group. Most natural monosaccharides have either five (pentoses) or six (hexoses) carbon atoms. Commonly occurring hexoses in foods are galactose, glucose, and fructose, while commonly occurring pentoses are xylose and arabinose. The reactive centers of monosaccharides are the carbonyl and hydroxyl groups.
To find out the total carbohydrates in a sample, the phenol-sulfuric acid method is a simple and rapid colorimetric method. The method detects nearly all classes of carbohydrates, including mono-, di-, oligo-, and polysaccharides. The absorptivity of the different carbohydrates varies although the method detects almost all carbohydrates. Thus, the results must be expressed arbitrarily in terms of one carbohydrate unless a sample is known to contain only 1 carbohydrate. The concentrated sulfuric acid breaks down any disaccharides, oligosaccharides, and polysaccharides into monosaccharides in this method. Hexoses (6-carbon compounds) are then dehydrated to hydroxymethylfurfural and pentoses (5-carbon compounds) to furfural. These compounds reacting with phenol produce a yellow-gold color. The standard curve for the assay for products that are very high in xylose (a pentose) is created using Xylose, and the absorption can be measured at 480 nm. Glucose is mostly used to create the standard curve for products that are high in hexose sugars, and the absorption is measured at 490 nm. The method’s accuracy is within ±2% under proper conditions, and the color for this reaction is stable for several hours.
The quantitative determinations of the monosaccharides ribose and deoxyribose have been explained in the quantitative determination of Nucleic Acids. The following protocol is useful for all monosaccharides.
Step 1:
Into a centrifuge tube give 1.0 ml of the monosaccharide-containing solution (10–70 µg of saccharide) and mix with 20 µl of Soln. A.
Step 2:
Onto the surface of the liquid (caution, corrosive!) add 2.5 ml of conc. sulfuric acid, then cool for 10 m in and keep at 25–30 ◦C for 10–20 min.
After an additional 30 min at RT (mixture in step 2), read the absorption (hexoses) at 490 nm and pentoses at 480 nm. From the appropriate monosaccharide dissolved in water, the standard curve is made.
Most of the quantitative methods have only a relatively small linear correlation between measuring signal and amount. As far as the used standards cover this range, interpolation between standard and sample is possible. If extrapolations are used attention should be paid to them because especially in the higher range the standard curve becomes flat followed by unacceptable mistakes in calculated values.
Since interactions occur between chromophores and other molecules6, therefore, the Beer-Lambert law often is not valid at higher concentrations. Observance of this effect is visible in the reading of proteins in the UV. The solvent may influence the absorbance too, because, for example, some of the aromatic amino acid residues are buried within the hydrophobic core of the molecule and become exposed during the unfolding of the protein when the composition of the solvent is changed, or the protein is denatured by dilution.
If you are sure that the Beer-Lambert law is fulfilled and a linear correlation between signal and amount is approximately given, the amount of analyte may be calculated by a simple equation:
QU = MU − MB · QS
MS − MB
Where:
Q: quantity or concentration;
M: analytical signal;
U: unknown (sample);
S: standard;
B: blank.
It is recommended to run each test with appropriate controls since an unknown set of circumstances influences most of the analytical methods. Blank is the most important control, i.e., the assay containing all components with the exception of the analyte. To get a rational mean and to detect false values it is recommended to run each sample in triplicate.
In behavioral neuroscience, the Open Field Test (OFT) remains one of the most widely used assays to evaluate rodent models of affect, cognition, and motivation. It provides a non-invasive framework for examining how animals respond to novelty, stress, and pharmacological or environmental manipulations. Among the test’s core metrics, the percentage of time spent in the center zone offers a uniquely normalized and sensitive measure of an animal’s emotional reactivity and willingness to engage with a potentially risky environment.
This metric is calculated as the proportion of time spent in the central area of the arena—typically the inner 25%—relative to the entire session duration. By normalizing this value, researchers gain a behaviorally informative variable that is resilient to fluctuations in session length or overall movement levels. This makes it especially valuable in comparative analyses, longitudinal monitoring, and cross-model validation.
Unlike raw center duration, which can be affected by trial design inconsistencies, the percentage-based measure enables clearer comparisons across animals, treatments, and conditions. It plays a key role in identifying trait anxiety, avoidance behavior, risk-taking tendencies, and environmental adaptation, making it indispensable in both basic and translational research contexts.
Whereas simple center duration provides absolute time, the percentage-based metric introduces greater interpretability and reproducibility, especially when comparing different animal models, treatment conditions, or experimental setups. It is particularly effective for quantifying avoidance behaviors, risk assessment strategies, and trait anxiety profiles in both acute and longitudinal designs.
This metric reflects the relative amount of time an animal chooses to spend in the open, exposed portion of the arena—typically defined as the inner 25% of a square or circular enclosure. Because rodents innately prefer the periphery (thigmotaxis), time in the center is inversely associated with anxiety-like behavior. As such, this percentage is considered a sensitive, normalized index of:
Critically, because this metric is normalized by session duration, it accommodates variability in activity levels or testing conditions. This makes it especially suitable for comparing across individuals, treatment groups, or timepoints in longitudinal studies.
A high percentage of center time indicates reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance. reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance.
The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity, where animals exhibit persistent avoidance of the center due to heightened emotional reactivity. This metric can also distinguish between acute anxiety responses and enduring trait anxiety, especially in longitudinal or developmental studies. Its normalized nature makes it ideal for comparing across cohorts with variable locomotor profiles, helping researchers detect true affective changes rather than activity-based confounds.
Rodents that spend more time in the center zone typically exhibit broader and more flexible exploration strategies. This behavior reflects not only reduced anxiety but also cognitive engagement and environmental curiosity. High center percentage is associated with robust spatial learning, attentional scanning, and memory encoding functions, supported by coordinated activation in the prefrontal cortex, hippocampus, and basal forebrain. In contrast, reduced center engagement may signal spatial rigidity, attentional narrowing, or cognitive withdrawal, particularly in models of neurodegeneration or aging.
The open field test remains one of the most widely accepted platforms for testing anxiolytic and psychotropic drugs. The percentage of center time reliably increases following administration of anxiolytic agents such as benzodiazepines, SSRIs, and GABA-A receptor agonists. This metric serves as a sensitive and reproducible endpoint in preclinical dose-finding studies, mechanistic pharmacology, and compound screening pipelines. It also aids in differentiating true anxiolytic effects from sedation or motor suppression by integrating with other behavioral parameters like distance traveled and entry count (Prut & Belzung, 2003).
Sex-based differences in emotional regulation often manifest in open field behavior, with female rodents generally exhibiting higher variability in center zone metrics due to hormonal cycling. For example, estrogen has been shown to facilitate exploratory behavior and increase center occupancy, while progesterone and stress-induced corticosterone often reduce it. Studies involving gonadectomy, hormone replacement, or sex-specific genetic knockouts use this metric to quantify the impact of endocrine factors on anxiety and exploratory behavior. As such, it remains a vital tool for dissecting sex-dependent neurobehavioral dynamics.
The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity. Because it is normalized, this metric is especially helpful for distinguishing between genuine avoidance and low general activity.
Environmental Control: Uniformity in environmental conditions is essential. Lighting should be evenly diffused to avoid shadow bias, and noise should be minimized to prevent stress-induced variability. The arena must be cleaned between trials using odor-neutral solutions to eliminate scent trails or pheromone cues that may affect zone preference. Any variation in these conditions can introduce systematic bias in center zone behavior. Use consistent definitions of the center zone (commonly 25% of total area) to allow valid comparisons. Software-based segmentation enhances spatial precision.
Evaluating how center time evolves across the duration of a session—divided into early, middle, and late thirds—provides insight into behavioral transitions and adaptive responses. Animals may begin by avoiding the center, only to gradually increase center time as they habituate to the environment. Conversely, persistently low center time across the session can signal prolonged anxiety, fear generalization, or a trait-like avoidance phenotype.
To validate the significance of center time percentage, it should be examined alongside results from other anxiety-related tests such as the Elevated Plus Maze, Light-Dark Box, or Novelty Suppressed Feeding. Concordance across paradigms supports the reliability of center time as a trait marker, while discordance may indicate task-specific reactivity or behavioral dissociation.
When paired with high-resolution scoring of behavioral events such as rearing, grooming, defecation, or immobility, center time offers a richer view of the animal’s internal state. For example, an animal that spends substantial time in the center while grooming may be coping with mild stress, while another that remains immobile in the periphery may be experiencing more severe anxiety. Microstructure analysis aids in decoding the complexity behind spatial behavior.
Animals naturally vary in their exploratory style. By analyzing percentage of center time across subjects, researchers can identify behavioral subgroups—such as consistently bold individuals who frequently explore the center versus cautious animals that remain along the periphery. These classifications can be used to examine predictors of drug response, resilience to stress, or vulnerability to neuropsychiatric disorders.
In studies with large cohorts or multiple behavioral variables, machine learning techniques such as hierarchical clustering or principal component analysis can incorporate center time percentage to discover novel phenotypic groupings. These data-driven approaches help uncover latent dimensions of behavior that may not be visible through univariate analyses alone.
Total locomotion helps contextualize center time. Low percentage values in animals with minimal movement may reflect sedation or fatigue, while similar values in high-mobility subjects suggest deliberate avoidance. This metric helps distinguish emotional versus motor causes of low center engagement.
This measure indicates how often the animal initiates exploration of the center zone. When combined with percentage of time, it differentiates between frequent but brief visits (indicative of anxiety or impulsivity) versus fewer but sustained center engagements (suggesting comfort and behavioral confidence).
The delay before the first center entry reflects initial threat appraisal. Longer latencies may be associated with heightened fear or low motivation, while shorter latencies are typically linked to exploratory drive or low anxiety.
Time spent hugging the walls offers a spatial counterbalance to center metrics. High thigmotaxis and low center time jointly support an interpretation of strong avoidance behavior. This inverse relationship helps triangulate affective and motivational states.
By expressing center zone activity as a proportion of total trial time, researchers gain a metric that is resistant to session variability and more readily comparable across time, treatment, and model conditions. This normalized measure enhances reproducibility and statistical power, particularly in multi-cohort or cross-laboratory designs.
For experimental designs aimed at assessing anxiety, exploratory strategy, or affective state, the percentage of time spent in the center offers one of the most robust and interpretable measures available in the Open Field Test.
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