Cloning, expression, and localization of a rat brain high-affinity glycine transporter

A cDNA clone encoding a glycine transporter has been isolated from rat brain by a combined PCR and plaque-hybridization strategy. mRNA synthesized from this clone (designated GLYT1) directs the expression of sodium-and chloride-dependent, high-affinity uptake of [3H]glycine by Xenopus oocytes. [3H]Glycine transport mediated by clone GLYT1 is blocked by sarcosine but is not blocked by methylaminoisobutyric acid or L-alanine, a substrate specificity similar to that described for a previously identified glycine-uptake system called system Gly. In situ hybridization reveals that GLYT1 is prominently expressed in the cervical spinal cord and brainstem, two regions of the central nervous system where glycine is a putative neurotransmitter. GLYT1 is also strongly expressed in the cerebellum and olfactory bulb and is expressed at lower levels in other brain regions. The open reading frame of the GLYT1 cDNA predicts a protein containing 633 amino acids with a molecular mass of ≈70 kDa. The primary structure and hydropathicity profile of GLYT1 protein reveal that this protein is a member of the sodium- and chloride-dependent superfamily of transporters that utilize neurotransmitters and related substances as substrates

Stochastic simulations for the time evolution of systems which obey generalized statistics: Fractional exclusion statistics and Gentile's statistics

We present a stochastic method for the simulation of the time evolution in systems which obey generalized statistics, namely fractional exclusion statistics and Gentile's statistics. The transition rates are derived in the framework of canonical ensembles. This approach introduces a tool for describing interacting fermionic and bosonic systems in non-equilibrium as ideal FES systems, in a computationally efficient manner. The two types of statistics are analyzed comparatively, indicating their intrinsic thermodynamic differences and revealing key aspects related to the species size.Comment: 14 pages, 5 figures, IOP forma

D-cycloserine augmentation of exposure-based cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders: a systematic review and meta-analysis of individual participant data

Importance: Whether and under which conditions D-cycloserine (DCS) augments the effects of exposure-based cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders is unclear. Objective: To clarify whether DCS is superior to placebo in augmenting the effects of cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders and to evaluate whether antidepressants interact with DCS and the effect of potential moderating variables. Data Sources: PubMed, EMBASE, and PsycINFO were searched from inception to February 10, 2016. Reference lists of previous reviews and meta-analyses and reports of randomized clinical trials were also checked. Study Selection: Studies were eligible for inclusion if they were (1) double-blind randomized clinical trials of DCS as an augmentation strategy for exposure-based cognitive behavior therapy and (2) conducted in humans diagnosed as having specific phobia, social anxiety disorder, panic disorder with or without agoraphobia, obsessive-compulsive disorder, or posttraumatic stress disorder. Data Extraction and Synthesis: Raw data were obtained from the authors and quality controlled. Data were ranked to ensure a consistent metric across studies (score range, 0-100). We used a 3-level multilevel model nesting repeated measures of outcomes within participants, who were nested within studies. Results: Individual participant data were obtained for 21 of 22 eligible trials, representing 1047 of 1073 eligible participants. When controlling for antidepressant use, participants receiving DCS showed greater improvement from pretreatment to posttreatment (mean difference, -3.62; 95% CI, -0.81 to -6.43; P = .01; d = -0.25) but not from pretreatment to midtreatment (mean difference, -1.66; 95% CI, -4.92 to 1.60; P = .32; d = -0.14) or from pretreatment to follow-up (mean difference, -2.98, 95% CI, -5.99 to 0.03; P = .05; d = -0.19). Additional analyses showed that participants assigned to DCS were associated with lower symptom severity than those assigned to placebo at posttreatment and at follow-up. Antidepressants did not moderate the effects of DCS. None of the prespecified patient-level or study-level moderators was associated with outcomes. Conclusions and Relevance: D-cycloserine is associated with a small augmentation effect on exposure-based therapy. This effect is not moderated by the concurrent use of antidepressants. Further research is needed to identify patient and/or therapy characteristics associated with DCS response.2018-05-0

GABA transporter function, oligomerization state, and anchoring: correlates with subcellularly resolved FRET

The mouse γ-aminobutyric acid (GABA) transporter mGAT1 was expressed in neuroblastoma 2a cells. 19 mGAT1 designs incorporating fluorescent proteins were functionally characterized by [^3H]GABA uptake in assays that responded to several experimental variables, including the mutations and pharmacological manipulation of the cytoskeleton. Oligomerization and subsequent trafficking of mGAT1 were studied in several subcellular regions of live cells using localized fluorescence, acceptor photobleach Förster resonance energy transfer (FRET), and pixel-by-pixel analysis of normalized FRET (NFRET) images. Nine constructs were functionally indistinguishable from wild-type mGAT1 and provided information about normal mGAT1 assembly and trafficking. The remainder had compromised [^3H]GABA uptake due to observable oligomerization and/or trafficking deficits; the data help to determine regions of mGAT1 sequence involved in these processes. Acceptor photobleach FRET detected mGAT1 oligomerization, but richer information was obtained from analyzing the distribution of all-pixel NFRET amplitudes. We also analyzed such distributions restricted to cellular subregions. Distributions were fit to either two or three Gaussian components. Two of the components, present for all mGAT1 constructs that oligomerized, may represent dimers and high-order oligomers (probably tetramers), respectively. Only wild-type functioning constructs displayed three components; the additional component apparently had the highest mean NFRET amplitude. Near the cell periphery, wild-type functioning constructs displayed the highest NFRET. In this subregion, the highest NFRET component represented ~30% of all pixels, similar to the percentage of mGAT1 from the acutely recycling pool resident in the plasma membrane in the basal state. Blocking the mGAT1 C terminus postsynaptic density 95/discs large/zona occludens 1 (PDZ)-interacting domain abolished the highest amplitude component from the NFRET distributions. Disrupting the actin cytoskeleton in cells expressing wild-type functioning transporters moved the highest amplitude component from the cell periphery to perinuclear regions. Thus, pixel-by-pixel NFRET analysis resolved three distinct forms of GAT1: dimers, high-order oligomers, and transporters associated via PDZ-mediated interactions with the actin cytoskeleton and/or with the exocyst

Automated Detection of Recent Mud Extrusions Using UAV Imagery and Deep Learning: A Comparative Analysis of Traditional and CNN-Based Approaches

Mud volcanoes are geological formations resulting from the expulsion of mud, gases, and fluids from deep underground. Monitoring these formations provides critical insights into subsurface processes and geological hazards. This study focuses on detecting recent mud extrusions in mud volcano environments using high-resolution aerial imagery acquired by unmanned aerial vehicles (UAVs). Using UAV-based surveys instead of satellite imagery, we obtain finer spatial detail suitable for identifying subtle textural and chromatic variations in relatively small sites. A binary image classification pipeline was developed to distinguish recent mud from non-mud areas. Traditional machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), were compared with deep learning architectures such as Convolutional Neural Networks (CNNs), both leveraging transfer learning and custom models. Traditional algorithms rely on handcrafted features, while CNNs learn hierarchical representations directly from raw data. Feature extraction methods were selected based on their ability to distinguish between the two designated classes effectively. To enhance model robustness and generalization, a designed augmentation pipeline was applied before each training epoch or cross-validation fold. This strategy introduced controlled and random variations to simulate real-world imaging conditions, such as varying viewpoints and lighting, ensuring the models generalization, moreover it also minimized data leakage by presenting distinct image variations throughout training. CNNs achieved the highest accuracy, outperforming traditional algorithms and demonstrating the advantages of combining deep learning with effective data augmentation. These findings underscore the potential of CNNs for accurate and efficient monitoring of dynamic geological environments

Oxytocin's neurochemical effects in the medial prefrontal cortex underlie recovery of task-specific brain activity in autism: a randomized controlled trial

The neuropeptide oxytocin may be an effective therapeutic strategy for the currently untreatable social and communication deficits associated with autism. Our recent paper reported that oxytocin mitigated autistic behavioral deficits through the restoration of activity in the ventromedial prefrontal cortex (vmPFC), as demonstrated with functional magnetic resonance imaging (fMRI) during a socio-communication task. However, it is unknown whether oxytocin exhibited effects at the neuronal level, which was outside of the specific task examined. In the same randomized, double-blind, placebo-controlled, within-subject cross-over clinical trial in which a single dose of intranasal oxytocin (24 IU) was administered to 40 men with high-functioning autism spectrum disorder (UMIN000002241/000004393), we measured N-acetylaspartate (NAA) levels, a marker for neuronal energy demand, in the vmPFC using (1)H-magnetic resonance spectroscopy ((1)H-MRS). The differences in the NAA levels between the oxytocin and placebo sessions were associated with oxytocin-induced fMRI signal changes in the vmPFC. The oxytocin-induced increases in the fMRI signal could be predicted by the NAA differences between the oxytocin and placebo sessions (P=0.002), an effect that remained after controlling for variability in the time between the fMRI and (1)H-MRS scans (P=0.006) and the order of administration of oxytocin and placebo (P=0.001). Furthermore, path analysis showed that the NAA differences in the vmPFC triggered increases in the task-dependent fMRI signals in the vmPFC, which consequently led to improvements in the socio-communication difficulties associated with autism. The present study suggests that the beneficial effects of oxytocin are not limited to the autistic behavior elicited by our psychological task, but may generalize to other autistic behavioral problems associated with the vmPFC

Automated Detection of Recent Mud Extrusions Using UAV Imagery and Deep Learning: A Comparative Analysis of Traditional and CNN-Based Approaches

Mud volcanoes are geological formations resulting from the expulsion of mud, gases, and fluids from deep underground. Monitoring these formations provides critical insights into subsurface processes and geological hazards. This study focuses on detecting recent mud extrusions in mud volcano environments using high-resolution aerial imagery acquired by unmanned aerial vehicles (UAVs). Using UAV-based surveys instead of satellite imagery, we obtain finer spatial detail suitable for identifying subtle textural and chromatic variations in relatively small sites. A binary image classification pipeline was developed to distinguish recent mud from non-mud areas. Traditional machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), were compared with deep learning architectures such as Convolutional Neural Networks (CNNs), both leveraging transfer learning and custom models. Traditional algorithms rely on handcrafted features, while CNNs learn hierarchical representations directly from raw data. Feature extraction methods were selected based on their ability to distinguish between the two designated classes effectively. To enhance model robustness and generalization, a designed augmentation pipeline was applied before each training epoch or cross-validation fold. This strategy introduced controlled and random variations to simulate real-world imaging conditions, such as varying viewpoints and lighting, ensuring the models generalization, moreover it also minimized data leakage by presenting distinct image variations throughout training. CNNs achieved the highest accuracy, outperforming traditional algorithms and demonstrating the advantages of combining deep learning with effective data augmentation. These findings underscore the potential of CNNs for accurate and efficient monitoring of dynamic geological environments

genetic diversity and relationship among the three autochthonous sicilian donkey populations assessed by microsatellite markers

AbstractIn the developed countries donkey has lost its main function as draft animal because of the mechanization in agri-culture; as a consequence donkey population was greatly reduced. According to SAVE monitoring institute, three of the eight Italian endangered donkey breeds are native of Sicily (Ragusano, Pantesco, Grigio Siciliano). Urgent safeguard plans are required. The aim of this work is to investigate the distribution of genetic diversity and the relationships among the three Sicilian autochthonous donkey breeds using a set of microsatellite markers. A total of 116 blood samples (61 Ragusano, 39 Pantesco, 16 Grigio Siciliano) were collected in 9 herds all over Sicily. Representative samples of Ragusano and Grigio donkey populations consist of unrelated individuals, whereas the sample of Pantesco represents nearly the entire studbook-registered population managed by "Ispettorato Ripartimentale delle Foreste di Erice (TP)" in the "Azienda S. Matteo". Genomic DNA was amplified at 11 microsatellite..

Effects of selective serotonin reuptake inhibitor treatment on plasma oxytocin and cortisol in major depressive disorder

Background: Oxytocin is known for its capacity to facilitate social bonding, reduce anxiety and for its actions on the stress hypothalamopituitary adrenal (HPA) axis. Since oxytocin can physiologically suppress activity of the HPA axis, clinical applications of this neuropeptide have been proposed in conditions where the function of the HPA axis is dysregulated. One such condition is major depressive disorder (MDD). Dysregulation of the HPA system is the most prominent endocrine change seen with MDD, and normalizing the HPA axis is one of the major targets of recent treatments. The potential clinical application of oxytocin in MDD requires improved understanding of its relationship to the symptoms and underlying pathophysiology of MDD. Previous research has investigated potential correlations between oxytocin and symptoms of MDD, including a link between oxytocin and treatment related symptom reduction. The outcomes of studies investigating whether antidepressive treatment (pharmacological and non-pharmacological) influences oxytocin concentrations in MDD, have produced conflicting outcomes. These outcomes suggest the need for an investigation of the influence of a single treatment class on oxytocin concentrations, to determine whether there is a relationship between oxytocin, the HPA axis (e.g., oxytocin and cortisol) and MDD. Our objective was to measure oxytocin and cortisol in patients with MDD before and following treatment with selective serotonin reuptake inhibitors, SSRI. Method: We sampled blood from arterial plasma. Patients with MDD were studied at the same time twice; pre- and post- 12 weeks treatment, in an unblinded sequential design (clinicaltrials.govNCT00168493). Results: Results did not reveal differences in oxytocin or cortisol concentrations before relative to following SSRI treatment, and there were no significant relationships between oxytocin and cortisol, or these two physiological variables and psychological symptom scores, before or after treatment. Conclusions: These outcomes demonstrate that symptoms of MDD were reduced following effective treatment with an SSRI, and further, stress physiology was unlikely to be a key factor in this outcome. Further research is required to discriminate potential differences in underlying stress physiology for individuals with MDD who respond to antidepressant treatment, relative to those who experience treatment resistance.Charlotte Keating, Tye Dawood, David A Barton, Gavin W Lambert and Alan J Tilbroo