Highly divergent satellitomes of two barley species of agronomic importance, Hordeum chilense and H. vulgare.

In this paper, we have performed an in-depth study of the complete set of the satellite DNA (satDNA) families (i.e. the satellitomes) in the genome of two barley species of agronomic value in a breeding framework, H. chilense (H1 and H7 accessions) and H. vulgare (H106 accession), which can be useful tools for studying chromosome associations during meiosis. The study has led to the analysis of a total of 18 satDNA families in H. vulgare, 25 satDNA families in H. chilense (accession H1) and 27 satDNA families in H. chilense (accession H7) that constitute 46 different satDNA families forming 36 homology groups. Our study highlights different important contributions of evolutionary and applied interests. Thus, both barley species show very divergent satDNA profiles, which could be partly explained by the differential effects of domestication versus wildlife. Divergence derives from the differential amplification of different common ancestral satellites and the emergence of new satellites in H. chilense, usually from pre-existing ones but also random sequences. There are also differences between the two H. chilense accessions, which support genetically distinct groups. The fluorescence in situ hybridization (FISH) patterns of some satDNAs yield distinctive genetic markers for the identification of specific H. chilense or H. vulgare chromosomes. Some of the satellites have peculiar structures or are related to transposable elements which provide information about their origin and expansion. Among these, we discuss the existence of different (peri)centromeric satellites that supply this region with some plasticity important for centromere evolution. These peri(centromeric) satDNAs and the set of subtelomeric satDNAs (a total of 38 different families) are analyzed in the framework of breeding as the high diversity found in the subtelomeric regions might support their putative implication in chromosome recognition and pairing during meiosis, a key point in the production of addition/substitution lines and hybrids.

Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study.

BACKGROUND: Fabry disease (FD) is a rare X-linked lysosomal storage disorder marked by alpha-galactosidase-A (α-Gal A) deficiency, caused by pathogenic mutations in the GLA gene, resulting in the accumulation of glycosphingolipids within lysosomes. The current screening test relies on measuring α-Gal A activity. However, this approach is limited to males. Infrared (IR) spectroscopy is a technique that can generate fingerprint spectra of a biofluid's molecular composition and has been successfully applied to screen numerous diseases. Herein, we investigate the discriminating vibration profile of plasma chemical bonds in patients with FD using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. RESULTS: The Fabry disease group (n = 47) and the healthy control group (n = 52) recruited were age-matched (39.2 ± 16.9 and 36.7 ± 10.9 years, respectively), and females were predominant in both groups (59.6% and 65.4%, respectively). All patients had the classic phenotype (100%), and no late-onset phenotype was detected. A generated partial least squares discriminant analysis (PLS-DA) classification model, independent of gender, allowed differentiation of samples from FD vs. control groups, reaching 100% sensitivity, specificity and accuracy. CONCLUSION: ATR-FTIR spectroscopy harnessed to pattern recognition algorithms can distinguish between FD patients and healthy control participants, offering the potential of a fast and inexpensive screening test.

Mother-Offspring Bonding after Calving in Water Buffalo and Other Ruminants: Sensory Pathways and Neuroendocrine Aspects.

The cow-calf bonding is a process that must be developed within the first six hours after calving. Both the buffalo dam and the newborn calf receive a series of sensory cues during calving, including olfactory, tactile, auditory, and visual stimuli. These inputs are processed in the brain to develop an exclusive bond where the dam provides selective care to the filial newborn. The limbic system, sensory cortices, and maternal-related hormones such as oxytocin mediate this process. Due to the complex integration of the maternal response towards the newborn, this paper aims to review the development of the cow-calf bonding process in water buffalo (Bubalus bubalis) via the olfactory, tactile, auditory, and visual stimuli. It will also discuss the neuroendocrine factors motivating buffalo cows to care for the calf using examples in other ruminant species where dam-newborn bonding has been extensively studied.

Machine Learning-Based Tomato Fruit Shape Classification System.

Fruit shape significantly impacts the quality and commercial value of tomatoes (Solanum lycopersicum L.). Precise grading is essential to elucidate the genetic basis of fruit shape in breeding programs, cultivar descriptions, and variety registration. Despite this, fruit shape classification is still primarily based on subjective visual inspection, leading to time-consuming and labor-intensive processes prone to human error. This study presents a novel approach incorporating machine learning techniques to establish a robust fruit shape classification system. We trained and evaluated seven supervised machine learning algorithms by leveraging a public dataset derived from the Tomato Analyzer tool and considering the current four classification systems as label variables. Subsequently, based on class-specific metrics, we derived a novel classification framework comprising seven discernible shape classes. The results demonstrate the superiority of the Support Vector Machine model in terms of its accuracy, surpassing human classifiers across all classification systems. The new classification system achieved the highest accuracy, averaging 88%, and maintained a similar performance when validated with an independent dataset. Positioned as a common standard, this system contributes to standardizing tomato fruit shape classification, enhancing accuracy, and promoting consensus among researchers. Its implementation will serve as a valuable tool for overcoming bias in visual classification, thereby fostering a deeper understanding of consumer preferences and facilitating genetic studies on fruit shape morphometry.

Quantifying the Transition from Unconscious to Conscious Detection of Temporal Patterns in Vigilance Tasks: A Unique Adaptation of Mackworth's Clock Test.

This study investigates the cognitive mechanisms underlying vigilance and pattern recognition using a novel adaptation of Mackworth's Clock Test. We aimed to quantify the time it takes for temporal patterns detected unconsciously through implicit learning to surface in the conscious mind within a dynamic vigilance task environment. Forty-eight participants detected random and non-disclosed rhythmic anomalous clock hand movements in this setting. Our results indicate significant variability in detection accuracy, reaction times, and the ability to recognize the hidden pattern among participants. Notably, 23% of all participants and 56% of those who consciously reported the pattern exhibited statistically lower reaction times indicative of knowledge of the pattern 40 s before conscious identification. These findings provide valuable insights into the transition from unconscious to conscious detection, highlighting the complexity of sustained attention and pattern recognition. The study's implications extend to designing training programs and tasks for high-stakes professions requiring prolonged vigilance. Future research should further explore the cognitive and neural correlates of these processes and the impact of task complexity on performance.

Fusion of PCA and ICA in Statistical Subset Analysis for Speech Emotion Recognition.

Speech emotion recognition is key to many fields, including human-computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal-Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases-Berlin EmoDB, SAVEE, and RAVDES-were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%.

A Combined CNN Architecture for Speech Emotion Recognition.

Emotion recognition through speech is a technique employed in various scenarios of Human-Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%.

Emotion Comprehension and Socio-cognitive Skills in Children with High Functioning Autism Spectrum Disorders.

BACKGROUND AND OBJECTIVES: This study aimed to compare socio-cognitive skills and emotion comprehension between children with Autism Spectrum Disorder (ASD) and children with neurotypical development. METHODS: This quantitative, cross-sectional, controlled study involved 19 children in each group, matched by age (6-12 years) and sex. The assessments examined cognitive aspects (Intelligence Quotient was assessed using the Vocabulary and Matrix Reasoning subtests; working memory using the digit span and letter-number sequencing subtests; attention using the Continuous Performance Test - Identical Pairs; and executive functions using the Trail Making Test), social functions (Children's Social Skills, Behavior Problems, and Academic Competence Inventory), and emotion comprehension (language was assessed using the Strange Stories Test; emotional facial expressions using the digital emotion comprehension test; emotional/affective prosody using the Profiling Elements of Prosody in Speech-Communication - Brazilian Portuguese adapted version). RESULTS: The group with ASD exhibited better performance in executive functions (p=0.02). However, they lagged the control group in social skills (p=0.04), behavior problems (p=0.03), and emotion comprehension (language, facial expressions, and prosody) (all p<0.05). CONCLUSION: The findings indicate that children with ASD have diminished performance in social skills and emotion comprehension compared to children with neurotypical development. Therefore, the development of technologies and/or therapeutic interventions that address these deficits among children with ASD is recommended.

Neural Correlates of Telicity in Spanish-Speaking Children with and without Developmental Language Disorder.

BACKGROUND: It is broadly acknowledged that children with Developmental Language Disorder (DLD) show verb-related limitations. While most previous studies have focused on tense, the mastery of lexical aspect-particularly telicity-has not been the primary focus of much research. Lexical aspect refers to whether an action has a defined endpoint (telic verbs) or not (atelic verbs). OBJECTIVE: This study investigates the effect of telicity on verb recognition in Chilean children with DLD compared to their typically developing (TD) peers using the Event-Related Potential (ERP) technique. METHOD: The research design is a mixed factorial design with between-group factors of 2 (DLD/TD) and within-group factors of 2 (telic/atelic verbs) and 2 (coherent/incoherent sentences). The participants were 36 school-aged children (18 DLD, 18 TD) aged 7 to 7 years and 11 months. The task required subjects to listen to sentences that either matched or did not match an action in a video, with sentences including telic or atelic verbs. RESULTS: The study found notable differences between groups in how they processed verbs (N400 and post-N400 components) and direct objects (N400 and P600 components). CONCLUSIONS: Children with DLD struggled to differentiate telic and atelic verbs, potentially because they employed overgeneralization strategies consistent with the Event Structural Bootstrapping model.

Microglia and Systemic Immunity.

Microglia are specialized immune cells that reside in the central nervous system (CNS) and play a crucial role in maintaining the homeostasis of the brain microenvironment. While traditionally regarded as a part of the innate immune system, recent research has highlighted their role in adaptive immunity. The CNS is no longer considered an immune-privileged organ, and increasing evidence suggests bidirectional communication between the immune system and the CNS. Microglia are sensitive to systemic immune signals and can respond to systemic inflammation by producing various inflammatory cytokines and chemokines. This response is mediated by activating pattern recognition receptors (PRRs), which recognize pathogen- and danger-associated molecular patterns in the systemic circulation. The microglial response to systemic inflammation has been implicated in several neurological conditions, including depression, anxiety, and cognitive impairment. Understanding the complex interplay between microglia and systemic immunity is crucial for developing therapeutic interventions to modulate immune responses in the CNS.

Screening and Characterization of Immunobiotic Lactic Acid Bacteria with Porcine Immunoassay Systems.

Microorganisms with the ability to modulate the immune system (immunobiotics) have shown to interact with different pattern recognition receptors (PRRs) expressed in nonimmune and immune cells and exert beneficial effects on host's health maintenance and promotion. Suitable assay systems are necessary for an efficient and rapid screening of potential immunobiotic strains. More than a decade of research has allowed us to develop efficient in vitro models based on porcine receptors and cells (porcine immunoassay systems) to study the immunomodulatory effects of lactic acid bacteria (LAB). In addition, detailed studies of model immunobiotic LAB strains with proved abilities to improve immune health in humans (Lactobacillus rhamnosus CRL1505) or pigs (Lactobacillus jensenii TL2937) allowed us to select the most suitable biomarkers that have to be evaluated in those porcine immunoassay systems. Our in vitro models, utilizing transfectant cells expressing PRRs along with an established porcine intestinal epitheliocyte (PIE) cell line, have proven to be valuable tools for immunobiotic selection and for gaining insights into the molecular mechanisms responsible for their beneficial effects.

LSWH100: A handshape dataset for Brazilian sign language (Libras) using SignWriting.

This paper presents Libras SignWriting Handshape (LSWH100), a new handshape dataset focused on Sign Language Recognition. The dataset includes 144,000 synthetic images of a realistic human hand, covering 100 distinct handshape classes used in Brazilian Sign Language (Libras). Handshapes are named using the convention from SignWriting, a writing system for sign languages. The dataset contains annotations for classification, detection, segmentation, depth estimation, and 3D hand keypoints. Images include indoor and outdoor scenes during different times of day, centered on a single hand that can change size, 3D rotation, and skin tone. We generated these images using Blender, a free and open-source 3D creation software. This is a challenging dataset that can be further explored. With a focus on sign language, this dataset has the potential to advance sign language recognition systems, positively impacting those who rely on sign language for communication.

Analysis of Variance Combined with Optimized Gradient Boosting Machines for Enhanced Load Recognition in Home Energy Management Systems.

Load recognition remains not comprehensively explored in Home Energy Management Systems (HEMSs). There are gaps in current approaches to load recognition, such as enhancing appliance identification and increasing the overall performance of the load-recognition system through more robust models. To address this issue, we propose a novel approach based on the Analysis of Variance (ANOVA) F-test combined with SelectKBest and gradient-boosting machines (GBMs) for load recognition. The proposed approach improves the feature selection and consequently aids inter-class separability. Further, we optimized GBM models, such as the histogram-based gradient-boosting machine (HistGBM), light gradient-boosting machine (LightGBM), and XGBoost (extreme gradient boosting), to create a more reliable load-recognition system. Our findings reveal that the ANOVA-GBM approach achieves greater efficiency in training time, even when compared to Principal Component Analysis (PCA) and a higher number of features. ANOVA-XGBoost is approximately 4.31 times faster than PCA-XGBoost, ANOVA-LightGBM is about 5.15 times faster than PCA-LightGBM, and ANOVA-HistGBM is 2.27 times faster than PCA-HistGBM. The general performance results expose the impact on the overall performance of the load-recognition system. Some of the key results show that the ANOVA-LightGBM pair reached 96.42% accuracy, 96.27% F1, and a Kappa index of 0.9404; the ANOVA-HistGBM combination achieved 96.64% accuracy, 96.48% F1, and a Kappa index of 0.9434; and the ANOVA-XGBoost pair attained 96.75% accuracy, 96.64% F1, and a Kappa index of 0.9452; such findings overcome rival methods from the literature. In addition, the accuracy gain of the proposed approach is prominent when compared straight to its competitors. The higher accuracy gains were 13.09, 13.31, and 13.42 percentage points (pp) for the pairs ANOVA-LightGBM, ANOVA-HistGBM, and ANOVA-XGBoost, respectively. These significant improvements highlight the effectiveness and refinement of the proposed approach.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers.

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Emotion Detection from EEG Signals Using Machine Deep Learning Models.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.

Study of Speech Recognition in Noise and Working Memory in Adults and Elderly with Normal Hearing.

Introduction In clinical practice, patients with the same degree and configuration of hearing loss, or even with normal audiometric thresholds, present substantially different performances in terms of speech perception. This probably happens because other factors, in addition to auditory sensitivity, interfere with speech perception. Thus, studies are needed to investigate the performance of listeners in unfavorable listening conditions to identify the processes that interfere in the speech perception of these subjects. Objective To verify the influence of age, temporal processing, and working memory on speech recognition in noise. Methods Thirty-eight adult and elderly individuals with normal hearing thresholds participated in the study. Participants were divided into two groups: The adult group (G1), composed of 10 individuals aged 21 to 33 years, and the elderly group (G2), with 28 participants aged 60 to 81 years. They underwent audiological assessment with the Portuguese Sentence List Test, Gaps-in-Noise test, Digit Span Memory test, Running Span Task, Corsi Block-Tapping test, and Visual Pattern test. Results The Running Span Task score proved to be a statistically significant predictor of the listening-in-noise variable. This result showed that the difference in performance between groups G1 and G2 in relation to listening in noise is due not only to aging, but also to changes in working memory. Conclusion The study showed that working memory is a predictor of listening performance in noise in individuals with normal hearing, and that this task can provide important information for investigation in individuals who have difficulty hearing in unfavorable environments.

Vertebral Column Pathology Diagnosis Using Ensemble Strategies Based on Supervised Machine Learning Techniques.

One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal examination of their performance to identify the best conditions that enhance the ML method. This work proposes variants of the Voting and Stacking (VC and SC) ensemble strategies based on diverse auto-tuning supervised machine learning techniques to increase the efficacy of traditional baseline classifiers for the automatic diagnosis of vertebral column orthopedic illnesses. The ensemble strategies are created by first combining a complete set of auto-tuned baseline classifiers based on different processes, such as geometric, probabilistic, logic, and optimization. Next, the three most promising classifiers are selected among k-Nearest Neighbors (kNN), Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Tree (DT). The grid-search K-Fold cross-validation strategy is applied to auto-tune the baseline classifier hyperparameters. The performances of the proposed ensemble strategies are independently compared with the auto-tuned baseline classifiers. A concise analysis evaluates accuracy, precision, recall, F1-score, and ROC-ACU metrics. The analysis also examines the misclassified disease elements to find the most and least reliable classifiers for this specific medical problem. The results show that the VC ensemble strategy provides an improvement comparable to that of the best baseline classifier (the kNN). Meanwhile, when all baseline classifiers are included in the SC ensemble, this strategy surpasses 95% in all the evaluated metrics, standing out as the most suitable option for classifying vertebral column diseases.

Toxic Peptides from the Mexican Scorpion Centruroides villegasi: Chemical Structure and Evaluation of Recognition by Human Single-Chain Antibodies.

Alternative recombinant sources of antivenoms have been successfully generated. The application of such strategies requires the characterization of the venoms for the development of specific neutralizing molecules against the toxic components. Five toxic peptides to mammals from the Mexican scorpion Centruroides villegasi were isolated by chromatographic procedures by means of gel filtration on Sephadex G-50, followed by ion-exchange columns on carboxy-methyl-cellulose (CMC) resins and finally purified by high-performance chromatography (HPLC) columns. Their primary structures were determined by Edman degradation. They contain 66 amino acids and are maintained well packed by four disulfide bridges, with molecular mass from 7511.3 to 7750.1 Da. They are all relatively toxic and deadly to mice and show high sequence identity with known peptides that are specific modifiers of the gating mechanisms of Na+ ion channels of type beta-toxin (ß-ScTx). They were named Cv1 to Cv5 and used to test their recognition by single-chain variable fragments (scFv) of antibodies, using surface plasmon resonance. Three different scFvs generated in our laboratory (10FG2, HV, LR) were tested for recognizing the various new peptides described here, paving the way for the development of a novel type of scorpion antivenom.

A pseudonymized corpus of occupational health narratives for clinical entity recognition in Spanish.

Despite the high creation cost, annotated corpora are indispensable for robust natural language processing systems. In the clinical field, in addition to annotating medical entities, corpus creators must also remove personally identifiable information (PII). This has become increasingly important in the era of large language models where unwanted memorization can occur. This paper presents a corpus annotated to anonymize personally identifiable information in 1,787 anamneses of work-related accidents and diseases in Spanish. Additionally, we applied a previously released model for Named Entity Recognition (NER) trained on referrals from primary care physicians to identify diseases, body parts, and medications in this work-related text. We analyzed the differences between the models and the gold standard curated by a physician in detail. Moreover, we compared the performance of the NER model on the original narratives, in narratives where personal information has been masked, and in texts where the personal data is replaced by another similar surrogate value (pseudonymization). Within this publication, we share the annotation guidelines and the annotated corpus.

Calorie Restriction Attenuates Memory Impairment and Reduces Neuroinflammation in Obese Aged Rats.

Obesity and aging collectively potentiate inflammatory responses, particularly within the central nervous system. Managing obesity presents a significant challenge, even more so considering the context of aging. Caloric restriction (CR) has been extensively documented in the literature for its multiple health benefits. Motivated by these findings, we hypothesized that CR could serve as a valuable intervention to address the brain alterations and cognitive decline associated with obesity in aged rats. Our investigation revealed that cafeteria diet increased hippocampal and hypothalamic transcripts related to neuroinflammation, along with cognitive deficits determined in the object recognition test in 18-month-old male rats. Western blot data indicate that the obesogenic diet may disrupt the blood-brain barrier and lead to an increase in Toll-like receptor 4 in the hippocampus, events that could contribute to the cognitive deficits observed. Implementing CR after the onset of obesity mitigated neuroinflammatory changes and cognitive impairments. We found that CR increases GABA levels in the hippocampus of aged animals, as demonstrated by liquid chromatography coupled with mass spectrometry analysis. These findings underscore the potential of CR as a therapeutic opportunity to ameliorate the neuroinflammatory and cognitive alterations of obesity, especially in the context of aging.