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Acid and bases

Understanding and Using Acids

An acid is a chemical, which when added to water, increases the amount or concentration of hydrogen ions, or H+, making it acidic.

A hydrogen ion (H+) is just a hydrogen atom without its electron. And, as a hydrogen atom is just one electron and one proton, the term hydrogen ion (H+) can be used interchangeably with proton.

 

How does a chemical increase the concentration of H+ when added to water?

Historically, three models were proposed to describe how a chemical increases H+. The first model was proposed by Svante Arrhenius. He defined an acid as a chemical that “releases” H+ into water.

The second model was developed independently by chemists Johannes Nicolaus Brønsted and Thomas Martin Lowry. They defined an acid as a chemical that “donates” H+ to water, like the Arrhenius model.

The third model was proposed by Gilbert Lewis. He defined an acid as a chemical that can “accept” a pair of electrons. According to this model, H+ is considered an acid because it can accept a pair of electrons from a hydroxide base (OH). This definition is the most comprehensive as it covers all chemical acids. But without a background in chemistry, it’s more difficult to grasp.

General Properties of Acids
  • They are liquids
  • They have a sour taste like vinegar
  • They have a sticky texture
  • They have a sharp smell that can burn your nose
  • They turn litmus paper red

 

Classes of Acids

Acids can be classified into different subgroups depending on their properties and atomic structure. There are strong acids, weak acids, concentrated and dilute acids, oxyacids and hydracids, and organic and inorganic acids.

Strong Acids

Strong acids are chemicals that donate all their H+ when dissolved in water. The most common example is hydrochloric acid or HCl. When dissolved, it completely splits into H+ and Cl.

Strong acids can be corrosive because they donate all the H+ to water. A high concentration of H+ in water can react with biological tissue. Protective clothing must be worn when handling strong acids. If handled properly, they can be useful. Here is a list of some common strong acids and uses.

  • Hydrochloric acid (HCl) is best known as the acid in our stomachs. It helps with digestion and protection from infectious microbes. But it’s also used as a cleaning agent, from toilet bowl cleaners to steel production.
  • Sulphuric (H2SO4) acid is another example of a strong acid. It completely splits into 2H+ and SO42- (sulphate). It has a variety of industrial processes such as making medicines, fertilizers, dyes and pigments, explosives, and it can also be used in the home as a drain cleaner.
  • Nitric acid (HNO3) is a common strong acid. It’s mostly used to produce fertilizer, but it’s also used as a cleaning agent, to make nylon, and even artificially aging wood to make it look antique.
  • One of the most powerful strong acids is perchloric acid (HClO4). It’s dangerously corrosive and readily forms explosive mixtures. It’s so powerful, it’s used to produce rocket fuel and to etch and polish metal.
Weak Acids

Weak acids, on the other hand, donate only a tiny fraction of H+ when dissolved in water.

  • The most well-known weak acid is acetic acid (CH3COOH), the acid in vinegar. In the home, we use acetic acid as a food condiment, to pickle vegetables, or to descale coffee machines. Industrially, acetic acid is used in photography, adhesives, paints, dyes, and perfumes.
  • Another well-known acid is lactic acid (C3H6O3). It’s found in sour milk products like yogurt of kefir, as well as sourdough bread and even sour beers. And when you exercise, the burning sensation you feel is caused by a buildup of lactic acid, which is an important metabolite.
  • Phosphoric acid (H3PO4) is also a weak acid. It’s mainly used to make fertilizers, however, its uses extend to foods, such as soft drinks and jam. Other industrial uses include making soaps, detergents, toothpaste, as well as water treatment.
  • Formic acid (HCOOH) is an acid primarily used as a preservative and antibacterial agent in livestock feed. It’s also used to descale household appliances, to tan leather, and to process latex.
  • Hydrofluoric acid (HF) is used as a starting material for cleaning, silicon chip manufacturing, industrial chemistry, mining, and glass finishing.

 

Concentrated and Dilute Acids

Concentrated and dilute acids shouldn’t be confused with strong and weak acids. Whether an acid is strong or weak depends on properties of the acid itself, as we already discussed. When you add an acid to water, we dilute it. If we dilute it a little, it’s concentrated. The more water we add, the more diluted it becomes.

Concentrated acids can be dangerous even if they’re strong or weak acids. Concentrated weak acids like acetic acid or phosphoric acid can cause burns. But diluted acids can be useful, like acetic acid in vinegar, which is only 5% acetic acid and 95% water.

When we use acids, we typically dilute them with water to a useful strength. Pure acids can be either solid, like a powder, or liquids. We can calculate how much water to add to achieve the strength we want. For example, an 80% HCl solution can be made by adding 4 parts 100% HCl to 1-part water. Likewise, a 5% acetic acid solution (vinegar) can be made by adding 1-part acetic acid to 19-parts water.

Oxyacids and Hydracids

Acids can have alternative classifications based on their chemical structure. An oxyacid is an acid that has at least one oxygen atom, such as sulphuric acid (H2SO4) or acetic acid (CH3COOH). A hydracid is an acid that doesn’t have any oxygen atoms, such as hydrochloric acid (HCl).

Organic and Inorganic Acids

By definition, any chemical with carbon atoms is an organic chemical. So, organic acids have carbon, such as acetic acid (CH3COOH) or lactic acid (C3H6O3), while inorganic acids don’t contain carbon, like hydrochloric acid (HCl) or sulphuric acid (H2SO4).

 

Quantifying Acids
pH – Measures Acid Strength

pH is a way to measure the acidity or the H+ concentration in a solution. pH is calculated using the equation: pH = -log[H+], where [H+] is the concentration of H+ in water. So, if [H+] is 0.001 Molar (M) or 10-3, then the pH is 3. If the [H+] is 0.1 M or 10-1, then the pH is 1. The higher the [H+], the lower the pH value.

We can measure the pH of a solution using a pH meter. A pH meter is like a voltmeter that measures voltage. H+ has a positive charge. The more H+, the higher the positive charge, and the higher potential to generate an electrical current.

To measure the pH of a solution, the pH meter must first be calibrated with a solution of a known pH. Typically, pH meters are calibrated with solutions at pH 4, 7, and 10. Once the pH meter has been calibrated, it’s dipped into the solution and the pH can be read.

pKa – Describes Acid Strength

The pKa of an acid is a numerical way of describing the strength of an acid. As we discussed, strong acids are strong because they release all H+ to water, while weak acids release only a small fraction of H+. The pKa value tells us how much H+ is released.

For example, when we mix acetic acid (CH3COOH) and water, some of the H+ is released (or dissociates). In Figure 1, the released H+ binds to water (H2O) to form H3O+ (hydronium ion) leaving a negatively charged acetate ion (CH3COO), called the conjugate base.

 

The Ka value, or dissociation constant, is the ratio of released H+ and CH3COO to acetic acid and is described by the equation: Ka = ([H3O+][CH3COO]) / [CH3COOH)]. Acids that release more H+ have a higher Ka value. And like pH, we use a log scale for convenience. So, the pKa = -log(Ka).

For example, acetic acid has a pKa = 4.76, meaning a ([H3O+][CH3COO]) / [CH3COOH)] ratio of 10-4.76 or 0.000017. Indeed, a tiny fraction. On the other hand, HCl has a pKa of -6.3. Likewise, the ratio of ([H+][Cl]) / [HCl] is 106.3 or almost 2,000,000!

 

It’s also possible to determine the pKa of an acid by adding small amounts of a strong base such as NaOH (titration). As NaOH is added to an acetic acid solution, it splits into Na+ and OH. The OH removes H+ from acetic acid to generate H2O and an acetate ion, CH3COO, lowering the amount of available H+, causing the pH to rise. With each addition of NaOH, the pH rises incrementally. Eventually, all the H+ is removed from the acid and the pH will reach a limit. If we plot on a graph the pH values versus the amount of NaOH (titration curve), the pKa value will be the midpoint in the rise (or inflection point).

 

Preparation of Acids

Acids can be made by mixing other chemicals together in specific ways. Here are some examples.

Strong Acid Preparation
  • HCl is made by the chlor-alkali method. This involves electrolyzing a NaCl solution which produces hydrogen gas (H2), NaOH, and chlorine gas (Cl2). Under UV light, these gases combine to form HCl. HCl can also be made by taking advantage of acid boiling points. Mixing H2SO4 (which has a higher boiling point than HCl) with NaCl (the salt of HCl) generates HCl.
  • Sulphuric acid (H2SO4) is made by first burning sulphur with oxygen to generate SO2 (sulphur dioxide). Then SO2 is converted to SO3 (sulphur trioxide) in the presence of a vanadium(V) oxide catalyst. Finally, SO3 is mixed with water to produce H2SO4. Other oxides like carbon dioxide (CO2) reacts with water to form carbonic acid (H2CO3).
Weak acid Preparation
  • Acetic acid is primarily made by carbonylation. In this process, methanol (CH3OH) and carbon monoxide (CO) react in the presence of a catalyst to produce acetic acid. A small amount of acetic acid is made by bacterial fermentation, which metabolically converts sugars like glucose to acetic acid.
  • Phosphoric acid can be synthesized by a wet process and a thermal process. The wet process involves mixing a phosphate-containing mineral, such as calcium hydroxyapatite with sulphuric acid to generate phosphoric acid and calcium sulphate (CaSO4 or gypsum). The thermal process takes phosphate ore and burns to make elemental phosphorus, which is distilled out of the furnace with air to make phosphorus pentoxide (P2O5). When dissolved in water, P2O5 produces phosphoric acid.
 
Biology
Deoxyribonucleic Acid (DNA)

Acids are critical for life. The most famous biological acid is deoxyribonucleic acid or DNA. DNA contains phosphoric acid. But as physiological pH is around 7, all the H+ has been removed leaving DNA with a negative charge.

Amino Acids

Amino acids are another important acid (Figure 4). The acid portion of an amino acid is like acetic acid that reacts with an amino group of another amino acid forming a chain. Chains with more than 40 amino acids are otherwise known as proteins.

Figure 4: The general structure of an amino acid. There are 20 different R groups making up 20 different amino acids.

Energy Production with H+

The release of H+ is used to generate chemical energy used by the plants and animals. Plants cells use light energy from the sun to pump H+ across a membrane to create an H+ gradient, with more H+ on one side of the membrane. The flow of H+ back through the membrane generates adenosine triphosphate (ATP). ATP is the chemical energy source plants use to make glucose from water and carbon dioxide (CO2). In a similar fashion, animal cells use the energy from the breakdown of glucose to pump H+ across a membrane to generate ATP for energy.

 

Conclusion

Acids are an important part of our world. They’re handy in the kitchen and around the home. By understanding their properties, they’ve played a key role in many scientific and industrial applications, and are part of many critical biochemical processes.

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Introduction

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.

What Does Percentage of Time in the Centre Measure?

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:

  • Exploratory drive vs. risk aversion: High center time reflects an animal’s willingness to engage with uncertain or exposed environments, often indicative of lower anxiety and a stronger intrinsic drive to explore. These animals are more likely to exhibit flexible, information-gathering behaviors. On the other hand, animals that spend little time in the center display a strong bias toward the safety of the perimeter, indicative of a defensive behavioral state or trait-level risk aversion. This dichotomy helps distinguish adaptive exploration from fear-driven avoidance.

  • Emotional reactivity: Fluctuations in center time percentage serve as a sensitive behavioral proxy for changes in emotional state. In stress-prone or trauma-exposed animals, decreased center engagement may reflect hypervigilance or fear generalization, while a sudden increase might indicate emotional blunting or impaired threat appraisal. The metric is also responsive to acute stressors, environmental perturbations, or pharmacological interventions that impact affective regulation.

  • Behavioral confidence and adaptation: Repeated exposure to the same environment typically leads to reduced novelty-induced anxiety and increased behavioral flexibility. A rising trend in center time percentage across trials suggests successful habituation, reduced threat perception, and greater confidence in navigating open spaces. Conversely, a stable or declining trend may indicate behavioral rigidity or chronic stress effects.

  • Pharmacological or genetic modulation: The percentage of time in the center is widely used to evaluate the effects of pharmacological treatments and genetic modifications that influence anxiety-related circuits. Anxiolytic agents—including benzodiazepines, SSRIs, and cannabinoid agonists—reliably increase center occupancy, providing a robust behavioral endpoint in preclinical drug trials. Similarly, genetic models targeting serotonin receptors, GABAergic tone, or HPA axis function often show distinct patterns of center preference, offering translational insights into psychiatric vulnerability and resilience.

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.

Behavioral Significance and Neuroscientific Context

1. Emotional State and Trait Anxiety

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.

2. Exploration Strategies and Cognitive Engagement

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.

3. Pharmacological Responsiveness

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).

4. Sex Differences and Hormonal Modulation

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.

Methodological Considerations

  • Zone Definition: Accurately defining the center zone is critical for reliable and reproducible data. In most open field arenas, the center zone constitutes approximately 25% of the total area, centrally located and evenly distanced from the walls. Software-based segmentation tools enhance precision and ensure consistency across trials and experiments. Deviations in zone parameters—whether due to arena geometry or tracking inconsistencies—can result in skewed data, especially when calculating percentages.

     

  • Trial Duration: Trials typically last between 5 to 10 minutes. The percentage of time in the center must be normalized to total trial duration to maintain comparability across animals and experimental groups. Longer trials may lead to fatigue, boredom, or habituation effects that artificially reduce exploratory behavior, while overly short trials may not capture full behavioral repertoires or response to novel stimuli.

     

  • Handling and Habituation: Variability in pre-test handling can introduce confounds, particularly through stress-induced hypoactivity or hyperactivity. Standardized handling routines—including gentle, consistent human interaction in the days leading up to testing—reduce variability. Habituation to the testing room and apparatus prior to data collection helps animals engage in more representative exploratory behavior, minimizing novelty-induced freezing or erratic movement.

     

  • Tracking Accuracy: High-resolution tracking systems should be validated for accurate, real-time detection of full-body center entries and sustained occupancy. The system should distinguish between full zone occupancy and transient overlaps or partial body entries that do not reflect true exploratory behavior. Poor tracking fidelity or lag can produce significant measurement error in percentage calculations.

     

  • 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.

Interpretation with Complementary Metrics

Temporal Dynamics of Center Occupancy

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.

Cross-Paradigm Correlation

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.

Behavioral Microstructure Analysis

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.

Inter-individual Variability and Subgroup Classification

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.

Machine Learning-Based Behavioral Clustering

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 Distance Traveled

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.

Number of Center Entries

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).

Latency to First Center Entry

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.

Thigmotaxis Time

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.

Applications in Translational Research

  • Drug Discovery: The percentage of center time is a key behavioral endpoint in the development and screening of anxiolytic, antidepressant, and antipsychotic medications. Its sensitivity to pharmacological modulation makes it particularly valuable in dose-response assessments and in distinguishing therapeutic effects from sedative or locomotor confounds. Repeated trials can also help assess drug tolerance and chronic efficacy over time.
  • Genetic and Neurodevelopmental Modeling: In transgenic and knockout models, altered center percentage provides a behavioral signature of neurodevelopmental abnormalities. This is particularly relevant in the study of autism spectrum disorders, ADHD, fragile X syndrome, and schizophrenia, where subjects often exhibit heightened anxiety, reduced flexibility, or altered environmental engagement.
  • Hormonal and Sex-Based Research: The metric is highly responsive to hormonal fluctuations, including estrous cycle phases, gonadectomy, and hormone replacement therapies. It supports investigations into sex differences in stress reactivity and the behavioral consequences of endocrine disorders or interventions.
  • Environmental Enrichment and Deprivation: Housing conditions significantly influence anxiety-like behavior and exploratory motivation. Animals raised in enriched environments typically show increased center time, indicative of reduced stress and greater behavioral plasticity. Conversely, socially isolated or stimulus-deprived animals often show strong center avoidance.
  • Behavioral Biomarker Development: As a robust and reproducible readout, center time percentage can serve as a behavioral biomarker in longitudinal and interventional studies. It is increasingly used to identify early signs of affective dysregulation or to track the efficacy of neuromodulatory treatments such as optogenetics, chemogenetics, or deep brain stimulation.
  • Personalized Preclinical Models: This measure supports behavioral stratification, allowing researchers to identify high-anxiety or low-anxiety phenotypes before treatment. This enables within-group comparisons and enhances statistical power by accounting for pre-existing behavioral variation. Used to screen anxiolytic agents and distinguish between compounds with sedative vs. anxiolytic profiles.

Enhancing Research Outcomes with Percentage-Based Analysis

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.

Explore high-resolution tracking solutions and open field platforms at

References

  • Prut, L., & Belzung, C. (2003). The open field as a paradigm to measure the effects of drugs on anxiety-like behaviors: a review. European Journal of Pharmacology, 463(1–3), 3–33.
  • Seibenhener, M. L., & Wooten, M. C. (2015). Use of the open field maze to measure locomotor and anxiety-like behavior in mice. Journal of Visualized Experiments, (96), e52434.
  • Crawley, J. N. (2007). What’s Wrong With My Mouse? Behavioral Phenotyping of Transgenic and Knockout Mice. Wiley-Liss.
  • Carola, V., D’Olimpio, F., Brunamonti, E., Mangia, F., & Renzi, P. (2002). Evaluation of the elevated plus-maze and open-field tests for the assessment of anxiety-related behavior in inbred mice. Behavioral Brain Research, 134(1–2), 49–57.

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