Inspired by the neural mechanisms of conditioned fear, this paper designs a memristive neuromorphic circuit based on the Pavlovian associative memory experiment to simulate the functions of learning, forgetting, long-term memory, formation and regulation of conditioned fear, generalization and differentiation, as well as the nested generalization and differentiation. The circuit mainly consists of three modules: the Pavlovian associative memory module, the conditioned fear associative memory and fear regulation module, and the generalization and differentiation module. The Pavlovian associative memory module simulates the process of forming long-term memory through repeated associative learning between food and ring. It achieves a biological mechanism in which the natural forgetting rate approaches zero, while the forgetting rate decreased significantly under the ring stimulus. The conditioned fear associative memory and fear regulation module simulates the formation of conditioned fear, and the fear signal disconnects the synaptic between the ring and the salivation neuron, thereby inhibiting the secretion of salivary neuron. The last module simulates the generalization and differentiation processes for food-ring association and conditioned fear, and it implements the nesting of generalization and differentiation. The correctness and effectiveness of the circuit are verified through PSPICE simulations. Monte Carlo analysis and temperature analysis are operated to demonstrate the robustness of the circuit.
Monolithic three-dimensional (M3D) integration is a promising solution for next-generation integrated circuits, offering enhanced signal propagation, high integration density, and lower fabrication costs than planar architectures. Amorphous oxide semiconductors, with room-temperature deposition capability and large-scale uniformity, are well-suited for 3D applications, yet developing multitier high-performance oxide transistors compatible with traditional technologies remains challenging. Here, we present a threshold voltage modulation strategy for indium gallium zinc oxide (IGZO) transistors via channel thickness control and atomic-layer-deposited surface modification. A four-tier vertically stacked IGZO transistor array has been manufactured with sequential layer-by-layer integration; optimized transistors across the tiers exhibited a low subthreshold swing of 150 mV/dec and an on/off ratio exceeding 108. Combined with via-hole interconnects, we demonstrated functional computing-in-memory 3D circuits featuring inverter modules (tier 1-2) and dynamic random access memory (DRAM) components (tier 3-4). The work advances oxide semiconductors' applications in future advanced 3D circuits.
Beliefs about states of the world profoundly impact decision-making and learning, but little is known about how neural circuits represent and update beliefs. We performed projection-specific recordings and perturbations from neurons in the orbitofrontal cortex (OFC) projecting to the intermediate or rostral cau-date putamen (CPi/CPr) in rats performing a task with hidden reward states. Stimulating OFC→CPi neurons biased rats' beliefs towards high reward states. Recordings from optogenetically-tagged OFC→CPi neurons showed that they encoded categorical evidence for high reward states, shaped by a saturating non-linearity in neural responses. Downstream neurons could, in principle, decode the full belief distribution over reward states as rats deliberated about deci-sions. Finally, projection-specific perturbations disrupted encoding of hidden states within OFC. These findings reveal the circuit implementation of a core cognitive computation, updating subjective beliefs about abstract latent states of the environment.
Early-onset psychosis (EOP) is associated with marked cognitive impairment and poorer health-related and psychosocial outcomes compared to adult-onset illness, yet the neural mechanisms underlying these deficits during adolescence remain incompletely understood. Despite evidence implicating dorsolateral prefrontal cortex (dlPFC) dysfunction, the region's functional and structural correlates in youth with EOP are not well characterized. Adolescents with EOP (N=31) and healthy controls (HC; N=20) completed NIH Toolbox cognitive assessments and PROMIS self-reports. Prefrontal function was assessed using functional near-infrared spectroscopy (fNIRS) during a Stroop task and at rest. Multimodal MRI, including resting-state fMRI, diffusion tensor imaging, and structural imaging, was used to examine connectivity and morphology of prefrontal and fronto-cerebellar circuits. Adolescents with EOP exhibited poorer cognitive performance across executive functioning domains compared to HC. fNIRS revealed reduced right dlPFC activation on a cognitive control paradigm (p=0.007) and increased resting-state connectivity between right dlPFC and ventrolateral PFC (p=0.02). Furthermore, resting-state fMRI showed increased dlPFC-striatal connectivity and reduced connectivity with cerebellar Crus I/II (p-FDR<0.05). White matter integrity of the superior longitudinal fasciculus correlated with dlPFC activation during task performance. Structural analyses identified reduced frontal cortical thickness and decreased cerebellar Crus II volumes (p-FWE<0.05) in patients with EOP, with frontal morphology associating with cognitive measures. In summary, cognitive impairment in adolescents with EOP is associated with convergent abnormalities in dlPFC function, fronto-striatal connectivity, and fronto-cerebellar structure. These findings support a model of disrupted prefrontal circuit maturation in EOP and highlight multimodal imaging markers with potential relevance for early identification and targeted intervention.
Nociceptors detect damaging stimuli and evoke pain in healthy animals. We conducted an optogenetic activation screen to identify genetically defined nociceptor populations that elicit place aversion and nocifensive behaviors in response to stimulation. Smr2Cre- and Bmpr1bCre-labeled Aδ high-threshold mechanoreceptors (HTMRs) emerged as two of the few nociceptor populations, and we focused on investigating their physiological, morphological, functional, and synaptic properties. These neurons densely innervate skin and other organs, are activated only by intense, potentially damaging stimuli, and are necessary for protective responses to sharp mechanical stimuli. Centrally, Aδ-HTMR projections span multiple spinal segments and terminate across spinal cord laminae, forming strong, monosynaptic connections onto anterolateral tract projection neurons, including antenna cells of the deep dorsal horn. Aδ-HTMRs also engage a local spinal reflex circuit, enabling a remarkably rapid limb withdrawal. Thus, Aδ-HTMRs are myelinated nociceptors with unique properties that can be exploited for the development of new analgesics.
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Neurotransmitter co-transmission has become recognized as a fundamental organizing principle of neural communication, challenging the traditional view that individual neurons operate through a single transmitter system. Current evidence demonstrates that many neurons utilize multiple transmitters via distinct synaptic architectures, such as co-packaging within the same vesicle, release from separate vesicle pools within the same terminal, and segregation of transmitters across different boutons or neuronal processes. These organizational modes are not simply structural variants; they impose distinct rules for release, target engagement, and short-term dynamics, thereby shaping circuit function in specific ways. Across neural systems, several common principles have emerged: co-transmission expands signaling across multiple timescales, enhances target specificity, and allows transmitter balance to shift according to firing patterns and circuit state. A major conceptual and technical challenge in the field is that no single method can definitively establish the release mechanism. Consequently, recent advances have relied on integrating molecular profiling, electrophysiology, high-resolution anatomy, optogenetics, and genetically encoded neurotransmitter sensors. Collectively, these approaches are beginning to clarify how multi-transmitter neurons are organized and how their signaling is regulated. Future progress will likely depend on multimodal strategies that connect synaptic architecture to release dynamics, circuit computation, and behavior in vivo. In this context, co-transmission should be viewed not as an exception to canonical neurotransmission but as a versatile mechanism that enhances the flexibility, precision, and context-dependence of neural circuit output.
Cortical expansion in the human lineage was accompanied by alterations in cortical circuit architecture, including an elaboration of cortico-cortical connectivity. Yet how these changes reshape network computation to support behavior remains poorly understood. Here we leverage a mouse model expressing SRGAP2C , a human-specific gene duplication that modifies cortical circuit development and increases cortico-cortical connectivity, to ask how this remodeled architecture shapes network dynamics during learning. As mice acquire expertise on a sensory discrimination task, SRGAP2C drives broader interhemispheric correlation, distributed task-relevant encoding, and enhanced directed influence from frontal and associative regions. Texture representations in primary sensory cortex become more separable during motor preparation, supporting improved discrimination under demanding conditions. These findings demonstrate that human-specific changes in cortical circuit architecture do not simply scale up but rather reconfigure the cortical dynamics that organize sensory-to-motor transformation in a manner that tracks behavioral performance under demanding conditions, linking genomic innovations in the Homo lineage to large-scale cortical network function.
GABAergic synaptic inhibition is heterogenous across neuronal compartments, and plays a critical role in shaping local, cellular and circuit excitability. In pyramidal neurons, inhibition is mediated by GABA A receptors (GABA A Rs) clustered at the inhibitory postsynaptic domain (iPSD). Synaptic strength depends not only on the number of GABA A Rs within the iPSD, but also on their precise nanoscale organization into discrete sub-synaptic domains (SSDs). These SSDs often align with presynaptic GABA release sites to form nanocolumn structures that enhance synaptic efficacy. While nanocolumn organization is increasingly recognized as a key determinant of synaptic function, most studies of GABAergic synapses have focused on archetypal dendritic synapses, which control the plasticity and integration of excitatory inputs. Nonetheless, it remains unclear whether somatic synapses - which deliver and provide robust inhibition to suppress neuronal output - share a similar nanoscale organization. Here, we used complementary super-resolution imaging approaches to directly compare inhibitory synapses in somatic and dendritic compartments. We found that somatic synapses are larger and exhibit greater structural diversity and nanoscale complexity than dendritic synapses. Dendritic synapses display relatively compact architectures with GABA A R SSDs frequently arranged into nanocolumns. In contrast, somatic synapses show a broader range of organizations, including aligned nanocolumns as well as more disorganized configurations with additional misaligned release sites or receptor SSDs. Computational modeling revealed that these structural differences produce distinct functional outcomes, including increased IPSC amplitude and altered kinetics at somatic synapses. Together, our findings demonstrate that nanoscale organization differentially shapes inhibitory strength and signaling properties across neuronal compartments. Diverse GABAergic synaptic inhibition is crucial to control brain excitability and its efficacy is influenced by the nanoscale trans-synaptic alignment of GABA A Rs and GABA release sites. Although GABA A R nano-architecture is defined at dendritic synapses, the extent to which this organization is conserved across GABAergic synapses with distinct synaptic properties is unknown. Using super-resolution imaging methods, we report that inhibitory synapses in the soma are larger and more structurally diverse than dendritic synapses, exhibiting both aligned and more disorganized configurations. Combined with computational modeling indicating distinct nanoarchitectures can create heterogeneous inhibitory currents, these findings suggest a key role for nanoscale organization in the generation of diverse synaptic outputs across the neuron, which could serve distinct circuit functions.
Growth hormone secretagogue receptors (GHSRs) modulate reward and cost processing separately, but their role in conflict-based decision-making is unclear. We observed dose-dependent effects of the GHSR agonist ibutamoren (IBU) on rat decision-making performance. Moderate, but not low or high doses, increased cost sensitivity selectively in conflict paradigms. We observed dense GHSR expression in dorsomedial striosomes, a region critical for conflict decision-making. Moderate GHSR activation increased neuronal activity in dorsomedial striosomes that project to lateral habenula (LHb) and enhanced inactivation of dopaminergic neurons in the substantia nigra pars compacta (daSNc). Chemogenetic manipulation of dorsomedial striosomes activity confirmed their causal influence on both GHSR-mediated increases in cost-sensitivity and circuit activity in LHb and daSNc. Our results identify a novel circuit mechanism linking endocrine activity with conflict decision-making.
Bipolar disorder (BD) is a severe psychiatric disease characterized by recurrent mania, depression, and circadian rhythm disruption. Among circadian regulators implicated in mood-related dysfunction, Clock has emerged as a particularly strong mechanistic candidate. However, cell type-specific functions of Clock within mood-relevant circuits remain incompletely defined. Here, we developed and applied a Cre-dependent AAV-SaCas9 gene-editing strategy to disrupt Clock selectively in ventral tegmental area dopamine neurons. We first established a rapid in vitro screening pipeline for guide RNA selection that accurately predicted in vivo editing efficiency. We then targeted Clock in vivo using a single AAV-based editing strategy and observed robust titer-dependent reduction of Clock expression, by targeted sequencing, in situ hybridization, and immunohistochemistry. We assessed the functional consequences of Clock disruption across analysis levels, including a behavioral battery, circadian and sleep-wake measurements using EEG and EMG, and electrophysiological recordings. These results establish a practical framework for rapid, cell-type-specific disruption of candidate psychiatric risk genes and provide a mechanistically grounded model for investigating how loss of Clock function in mesolimbic dopaminergic circuits contributes to BD-relevant phenotypes.
The dielectric characterization of crude oils and their fractions, particularly asphaltenes and maltenes (saturated and aromatic hydrocarbons and resins) are essential for understanding critical phenomena in the petroleum industry, such as oil aggregation, colloidal stability, and electrical conductivity. This work advances the dielectric characterization of crude oils and their fractions using electrochemical impedance spectroscopy over a wide frequency range, combining equivalent-circuit analysis with rigorous model validation. Measurements were performed on two crude oil samples (A and M) from the same source rock but from different reservoir rocks and their corresponding maltene fractions at 25 ± 0.1 °C using an electrochemical cell with carbon-steel electrodes separated by a 5 mm gap. Impedance spectra were acquired over the frequency range of 10 kHz to 10 mHz with a logarithmic distribution of 10 points per decade using an AC perturbation amplitude of 200 mV. Nyquist diagrams exhibit single semicircular arcs for all samples, a characteristic feature of highly resistive dielectric systems. Quantitative analysis through equivalent circuits revealed a clear resistivity hierarchy: Rct (A Oil) < Rct (A Maltene) < Rct (M Oil) < Rct (M Maltene), indicating that the presence of resins in the M Maltene and asphaltenes + resins in the M Oil strongly influences dielectric behavior. Samples from M Oil exhibit significantly higher charge-transfer resistance, suggesting that M Oil is denser and more concentrated in polar organic compounds (resins and asphaltenes), making it structurally more complex. Asphaltene removal results in a substantial increase in the insulating character of the maltene matrix, demonstrating that asphaltenes act as charge-active centers, facilitating conduction through mechanisms such as charge hopping. The results reinforce the importance of asphaltenes in modulating charge-transport properties and dielectric stability in petroleum systems, with significant implications for industrial applications related to stability, aggregation, and transport of oils.
In this study, first-principles calculations were performed to explore structural, mechanical, electronic, carrier transport, and optical properties of strontium-based perovskites (Sr3MCl3 (M = Sb, P)) using density functional theory (DFT). These calculations reveal that both materials are thermodynamically and mechanically stable. Employing the GGA-PBE functional, they possess a direct-band-gap semiconductor behavior with an energy of 1.704 eV (Sr3SbCl3) and 1.677 eV (Sr3PCl3). The analysis of the density of states (DOS) further corroborates the semiconducting behavior. Carrier mobility calculations indicate that electron/hole mobilities of 89.15/110.52 cm2/V·s are achieved for Sr3SbCl3 and 137.16/100.60 cm2/V·s for Sr3PCl3. In the visible region, a light absorption coefficient above 105 cm-1 is reached for both materials, highlighting their suitability as an absorber layer (AL) in perovskite solar cells (PSCs). Considering band-to-band recombination (radiative and Auger), SCAPS-1D was used to conduct an inquiry into the photovoltaic performance of various devices, integrating different electron and hole transport layers (ETL/HTL): Ag/FTO/ETL/uniform-AL/HTL/Ni. Among all configurations examined in this study, the Ag/FTO/IGZO/uniform-AL/Cu2O/Ni architecture achieves the highest photovoltaic performance parameters, upon optimization of AL thickness, AL-doping concentration, AL-bulk and interface defect densities, radiative recombination coefficient, and series/shunt resistances. The Sr3SbCl3-based PSC (Device I) attains a power conversion efficiency (PCE) of ∼25.80%, with an open-circuit voltage (V OC) of 1.31 V, a short-circuit current density (J SC) of 21.82 mA/cm2, and a fill factor (FF) of 90.27%, whereas the Sr3PCl3-based PSC (Device II) achieves a PCE of approximatively 26.22%, with V OC = 1.28 V, J SC = 22.71 mA/cm2, and FF = 90.09%. Finally, replacing the single uniform-AL with a graded-Sr3Sb1-x P x Cl3 AL (Device III), adopting linear and parabolic graded physical parameters, does not demonstrate marked improvements in device performance. Consequently, this study positions Sr3MCl3 (M = Sb, P) perovskites as alternatives to lead-based ALs, which can constitute a suitable pathway for real-time experimentation of PSC.
The characterization of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and prognosis. But the analysis of brain tumors based on MRI is still difficult because of the significant intra-tumoral heterogeneity, ambiguous boundaries of lesions, high dimensionality of imaging features, and complicated nonlinear relationships between anatomical structures. While transformer-based deep learning (DL) models have greatly advanced automated tumor analysis, classical feature representations often fail to effectively capture complex feature interactions in pathological images, especially when distinguishing between subtle pathological variations and normal tissue needs to be done efficiently. Recent quantum machine learning (QML) developments indicate that quantum feature encoding can map complex data to exponentially larger Hilbert spaces, leading to a more expressive representation of nonlinear patterns and feature correlations that are challenging to model with conventional architectures. Encouraged by this potential, this study proposes a hybrid quantum-classical framework, named QFormer-Brain, which combines transformer-based segmentation with quantum representation learning for automated brain tumor segmentation and classification. To segment the tumor regions, a Shifted Window UNet Transformers (Swin-UNETR) architecture is first used to capture both local anatomical details and global contextual information through hierarchical self-attention. Then, the deep semantic features of the segmented lesions are mapped to the quantum feature mapping module with angle encoding and variational quantum circuits (VQCs), which can obtain richer nonlinear feature representation by quantum superposition and entanglement mechanisms. The obtained quantum embeddings are then combined with features obtained from the transformer and classified by a Quantum Transformer Classifier. The experiments were performed on the publicly available BraTS 2021 data set using an 8-qubit variational quantum circuit, with 1024 measurement shots and readout-error mitigation, via the IBM Qiskit Aer simulator. The proposed framework achieved a Dice Similarity Coefficient (DSC) of 97.1%, Intersection-over-Union (IoU) of 95.0%, classification accuracy of 98.5%, sensitivity of 98.1%, specificity of 99.0%, and F1-score of 98.3%, outperforming U-Net, nnU-Net, ResNet50, Vision Transformer (ViT), and conventional hybrid Convolutional Neural Network (CNN)-based models. These results show that quantum-inspired representation learning can be efficiently integrated into transformer-based models to boost the discriminative feature extraction from complex MRI data.
Direct uptake of an element from water by an aquatic organism and its retention, leading to a higher concentration than in its surrounding water, is an environmental concern known as bioconcentration. It occurs when an organism absorbs a substance faster than it is eliminated by various metabolic processes, such as catabolism and excretion. A bioassay experiment was performed to determine the 96-hour LC50 of cadmium chloride (CdCl2), a heavy-metal salt. For experimentation, pre-acclimated Limnodrilus hoffmeisteri worms were divided into eleven treatment groups (T1 through T11) and a control group (C), each of 50 number of individuals and were exposed to 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75 and 3.0 mg/L cadmium concentrations respectively for 96 h under semi-static condition in a closed-circuit flow-through system with complete renewal of water with the respective relevant cadmium concentration after 48 h. Post-exposure cadmium content per unit amount of tissue samples and of residual water samples was measured with atomic absorption spectrophotometry (AAS). Probit analysis revealed the 96-hour LC50 value of cadmium against L. hoffmeisteri as 1.002 mg/L. The outcome of this study established a low BCF for the experimental species for cadmium, indicating a positive correlation between BCF and exposure concentration and demonstrating the inherent bioconcentration nature of cadmium in L. hoffmeisteri. A gradual declining trend in cadmium content of the whole body was observed at exposure to a cadmium concentration higher than 2.0 mg/L. Concurrent autotomy of the caudal part of the organism has also been observed at this exposure level. Cadmium content of the autotomized tail has been estimated significantly (P < 0.05) higher. It is concluded that autotomization is a defensive mechanism evolved by L. hoffmeisteri to reduce the body's heavy metal load and, hence, toxicity, thereby serving as an indicator species.
In the current study, we investigated the structural, electrical, and optical characteristics of Mn-modified BNT ceramic systems with the nominal formula (Bi0.5Na0.5)1-x Mn x TiO3, (x = 0.01-0.05), obtained by the solid-state reaction method. The structural analysis using Rietveld refinement suggested a rhombohedral phase with the R3c space group in all samples. Relaxor behavior was observed in all the modified samples, and Mn doping also promoted this behavior. At the same time, lower dielectric loss was noticed in the x = 0.05 ceramics. Detailed electrical characterization was performed through impedance spectroscopy studies. The Nyquist plot was fitted with the proposed RQC-RC circuit and found to be of the non-Debye type. Correspondingly, the grain and grain-boundary resistance values were determined. The AC conductivity data at different temperatures were fitted by the Jonscher's power law equation, σ tot(ω) = A(T) × ω s + σ dc(T); 0 < s < 1, supporting the correlated barrier hopping (CBH) model. The reduction in optical band gap energy (E g) obtained from UV-visible spectroscopy and the decrease in the hopping barrier height (W M) suggest that Mn doping may induce defect states. The results are consistent with possible oxygen vacancy formation, which may contribute to localized hopping conduction in the modified ceramic system. Furthermore, the slim polarization loop confirmed the relaxor behavior observed in the modified samples. The combined results suggest that Mn doping is an appropriate approach for optimizing the functional properties of BNT ceramics, with x = 0.05 as the most favorable in terms of overall performance.
Background Basal forebrain (BF) degeneration is a key pathological feature of Alzheimer's disease (AD) and is closely associated with cognitive decline. Although parvalbumin (PV)-expressing neurons are abundant in the BF and are important regulators of cortical network activity, their contribution to AD pathogenesis remains poorly understood. Methods To investigate the role of BF-PV neurons in AD, we examined the effects of PV knockdown in the BF of APP/PS1 mice. Electrophysiological recordings, behavioral analyses, retrograde tracing, hippocampal transcriptome profiling, and integrative computational analyses, including meta-correlation analysis and artificial neural network (ANN) modeling, were performed to assess the impact of BF-PV loss on neural network activity, hippocampal pathology, and cognitive function. Results BF-PV knockdown disrupted theta oscillations and theta-gamma coupling in the parietal cortex and impaired hippocampal synaptic activity and memory-related behaviors in mice. Retrograde tracing demonstrated that BF neuronal circuits project directly to the hippocampus. Transcriptome analysis revealed that BF-PV knockdown increased amyloidosis- and microvessel-associated gene signatures while reducing synaptic plasticity-related gene expression in the hippocampus. Furthermore, meta-correlation analyses and ANN modeling indicated that BF-PV dysfunction strongly predicts hippocampal pathology, disrupted EEG coupling, and behavioral abnormalities in AD mice. Conclusions These findings identify BF-PV neuronal dysfunction as an important contributor to hippocampal pathology, neural network dysregulation, and cognitive impairment in AD, highlighting BF-PV neurons as a potential mechanistic link between BF degeneration and AD progression.
Neural activity unfolds across three-dimensional circuits on millisecond-to-microsecond timescales, yet most optical microscopes still acquire volumes sequentially, limiting their ability to capture fast, distributed dynamics. Light-field microscopy (LFM) addresses this unmet need by encoding spatial and angular information into a single camera exposure, enabling snapshot volumetric imaging with low latency and strong robustness to motion. Here we review emerging advances in light-field neuroimaging, from brain-wide calcium recordings in freely moving animals to recent progress that brings kilohertz-class volumetric voltage imaging within reach. We argue that LFM should be evaluated based on information throughput, latency, photon efficiency, and motion robustness at the speed frontier, but not as a direct resolution or contrast competitor to confocal, multiphoton, or light-sheet microscopy. We conclude by highlighting future directions that preserve the LFM's snapshot advantage, including speed-preserving improvements in image quality, extreme temporal-bandwidth architectures that prioritize quantitative inference over visual appearance, and multimodal light-field sensing that adds spectral, lifetime, and polarization contrast.
Hoxa5 encodes a transcription factor essential for embryonic patterning and organogenesis, with sustained expression in hindbrain precerebellar nuclei during postnatal development. Given prior evidence implicating HOXA5 in synaptogenesis and early postnatal circuit maturation, we investigated whether its inactivation during this critical developmental window contributes to neurodevelopmental disorder (NDD)-related phenotypes. Using previously generated transcriptomic data, we identified multiple deregulated genes classified as autism spectrum disorder (ASD) risk genes in the SFARI database, several of which are associated with a cerebellar phenotype in mice. We then performed a comprehensive behavioral assessment across motor, social, stereotypical, anxiety-related, and attentional domains in a postnatal inactivation mouse model (Hoxa5-cKO). Motor coordination, learning, gait, and sensorimotor functions were preserved. Social behavior assays yielded no consistent genotype-dependent effects, although results were sensitive to analytical methods and cohort variability. In contrast, Hoxa5-cKO mice exhibited increased stereotypical behaviors, including elevated scratching and marble burying, in the absence of anxiety- or locomotion-related confounds. Importantly, interpretation of social and cognitive phenotypes was impacted by well-known constraints of behavioral neuroscience. We discuss these downfalls and propose additional guidelines. Altogether, our findings indicate that postnatal Hoxa5 deficiency selectively enhances stereotyped behaviors without broadly affecting motor or social functions. The data support a model in which HOXA5 acts as a modulator of postnatal precerebellar circuit connectivity and/or function, with subtle behavioral consequences that require further research in specific genetic or environmental contexts.
Newly synthesized secretory proteins and lipids are transported from the endoplasmic reticulum (ER) to the Golgi prior to their ultimate destinations, which is tightly regulated during adaptation to environmental stress. However, regulatory pathways governing the formation of COPII vesicles budded from the ER remain insufficiently explored. Here, we present evidence indicating that COPII-mediated vesicle transport is transcriptionally controlled through the phosphatidic acid (PA)-dependent Opi1-Ino2/Ino4 regulatory circuit. Our analysis shows that YIP3, a target gene of Ino2/Ino4, exerts a negative regulatory impact on COPII-mediated vesicle transport. We demonstrate that Ino2/Ino4, but not Yip3 modulates Sar1 activation, the initial step in COPII vesicle formation, whereas Yip3 hinders Sec16 assembly on the ER membrane, thereby implying that Ino2/Ino4 governs COPII vesicle formation at multiple steps. Finally, we show that under ER stress conditions which are accompanied by elevated PA, vesicular transport is restricted in a PA and Yip3-dependent manner. Thus, this study provides the first evidence for an ER sensing system that transcriptionally fine-tunes vesicle formation in response to alterations in lipid composition of the ER membrane during ER stress.