Cellular anatomy of the mouse primary motor cortex.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.

Systematic analyses of GWAS summary statistics from UK Biobank identified novel susceptibility loci and genes for upper gastrointestinal diseases.

In recent decades, upper gastrointestinal (GI) diseases have been highly prevalent worldwide. Although genome-wide association studies (GWASs) have identified thousands of susceptibility loci, only a few of them were conducted for chronic upper GI disorders, and most of them were underpowered and with small sample sizes. Additionally, for the known loci, only a tiny fraction of heritability can be explained and the underlying mechanisms and related genes remain unclear. In this study, we conducted a multi-trait analysis by the MTAG software and a two-stage transcriptome-wide association study (TWAS) with UTMOST and FUSION for seven upper GI diseases (oesophagitis, gastro-oesophageal reflux disease, other diseases of oesophagus, gastric ulcer, duodenal ulcer, gastritis and duodenitis and other diseases of stomach and duodenum) based on summary GWAS statistics from UK Biobank. In the MTAG analysis, we identified 7 loci associated with these upper GI diseases, including 3 novel ones at 4p12 (rs10029980), 12q13.13 (rs4759317) and 18p11.32 (rs4797954). In the TWAS analysis, we revealed 5 susceptibility genes in known loci and identified 12 novel potential susceptibility genes, including HOXC9 at 12q13.13. Further functional annotations and colocalization analysis indicated that rs4759317 (A>G) driven the association for GWAS signals and expression quantitative trait loci (eQTL) simultaneously at 12q13.13. The identified variant acted by decreasing the expression of HOXC9 to affect the risk of gastro-oesophageal reflux disease. This study provided insights into the genetic nature of upper GI diseases.

Bridging the gap between collective motility and epithelial-mesenchymal transitions through the active finite voronoi model.

We introduce an active version of the recently proposed finite Voronoi model of epithelial tissue. The resultant Active Finite Voronoi (AFV) model enables the study of both confluent and non-confluent geometries and transitions between them, in the presence of active cells. Our study identifies six distinct phases, characterized by aggregation-segregation, dynamical jamming-unjamming, and epithelial-mesenchymal transitions (EMT), thereby extending the behavior beyond that observed in previously studied vertex-based models. The AFV model with rich phase diagram provides a cohesive framework that unifies the well-observed progression to collective motility via unjamming with the intricate dynamics enabled by EMT. This approach should prove useful for challenges in developmental biology systems as well as the complex context of cancer metastasis. The simulation code is also provided.

Shear-Driven Solidification and Nonlinear Elasticity in Epithelial Tissues.

Biological processes, from morphogenesis to tumor invasion, spontaneously generate shear stresses inside living tissue. The mechanisms that govern the transmission of mechanical forces in epithelia and the collective response of the tissue to bulk shear deformations remain, however, poorly understood. Using a minimal cell-based computational model, we investigate the constitutive relation of confluent tissues under simple shear deformation. We show that an initially undeformed fluidlike tissue acquires finite rigidity above a critical applied strain. This is akin to the shear-driven rigidity observed in other soft matter systems. Interestingly, shear-driven rigidity can be understood by a critical scaling analysis in the vicinity of the second order critical point that governs the liquid-solid transition of the undeformed system. We further show that a solidlike tissue responds linearly only to small strains and but then switches to a nonlinear response at larger stains, with substantial stiffening. Finally, we propose a mean-field formulation for cells under shear that offers a simple physical explanation of shear-driven rigidity and nonlinear response in a tissue.

Synaptic Specificity and Application of Anterograde Transsynaptic AAV for Probing Neural Circuitry.

Revealing the organization and function of neural circuits is greatly facilitated by viral tools that spread transsynaptically. Adeno-associated virus (AAV) exhibits anterograde transneuronal transport, however, the synaptic specificity of this spread and its broad application within a diverse set of circuits remains to be explored. Here, using anatomic, functional, and molecular approaches, we provide evidence for the preferential transport of AAV1 to postsynaptically connected neurons and reveal its spread is strongly dependent on synaptic transmitter release. In addition to glutamatergic pathways, AAV1 also spreads through GABAergic synapses to both excitatory and inhibitory cell types. We observed little or no transport, however, through neuromodulatory projections (e.g., serotonergic, cholinergic, and noradrenergic). In addition, we found that AAV1 can be transported through long-distance descending projections from various brain regions to effectively transduce spinal cord neurons. Combined with newly designed intersectional and sparse labeling strategies, AAV1 can be applied within a wide variety of pathways to categorize neurons according to their input sources, morphology, and molecular identities. These properties make AAV1 a promising anterograde transsynaptic tool for establishing a comprehensive cell-atlas of the brain, although its capacity for retrograde transport currently limits its use to unidirectional circuits.SIGNIFICANCE STATEMENT The discovery of anterograde transneuronal spread of AAV1 generates great promise for its application as a unique tool for manipulating input-defined cell populations and mapping their outputs. However, several outstanding questions remain for anterograde transsynaptic approaches in the field: (1) whether AAV1 spreads exclusively or specifically to synaptically connected neurons, and (2) how broad its application could be in various types of neural circuits in the brain. This study provides several lines of evidence in terms of anatomy, functional innervation, and underlying mechanisms, to strongly support that AAV1 anterograde transneuronal spread is highly synapse specific. In addition, several potentially important applications of transsynaptic AAV1 in probing neural circuits are described.

Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning.

Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.

Sensory- and Motor-Related Responses of Layer 1 Neurons in the Mouse Visual Cortex.

Cortical layer 1 (L1) contains a sparse and molecularly distinct population of inhibitory interneurons. Their location makes them ideally suited for affecting computations involving long-range corticocortical and subcortical inputs, yet their response properties remain largely unexplored. Here we attempt to characterize some of the functional properties of these neurons in the primary visual cortex of awake mice. We find that the strongest driver of L1 neuron activity is locomotion, with at least half of L1 neurons displaying locomotion-related activity. Visual responses are present in a similar fraction of neurons, but these responses are weaker and frequently suppressive. We also find that ∼43% of L1 neurons respond to noise stimuli and at least 14% respond to whisker touch, with these two populations being statistically independent. Finally, we find that 45% of L1 neurons have generally weak responses correlated with whisking activity. Overall, the spatial distributions of modality-specific responses were more or less random. Our work helps to establish the basic sensory- and motor-related responses of L1 interneurons, revealing several previously unreported characteristics.SIGNIFICANCE STATEMENT Cortical processing even in primary sensory areas is strongly influenced by nonlocal corticocortical and neuromodulatory inputs. Many of these inputs are known to converge onto layer 1 where they target not only distal dendrites of pyramidal neurons but also a sparse population of inhibitory neurons. Previous studies have suggested that layer 1 neurons may play a crucial role in mediating the effects of these long-range projections, but the different types of inputs have mostly been studied in isolation. Here, we take a closer look at the response properties of layer 1 neurons in mouse visual cortex, examining both their visual properties, likely caused by direct thalamocortical inputs, and other sensory and motor properties, likely reflecting corticocortical and neuromodulatory inputs.

Sparse Labeling and Neural Tracing in Brain Circuits by STARS Strategy: Revealing Morphological Development of Type II Spiral Ganglion Neurons.

Elucidating axonal and dendritic projection patterns of individual neurons is a key for understanding the cytoarchitecture of neural circuits in the brain. This requires genetic approaches to achieve Golgi-like sparse labeling of desired types of neurons. Here, we explored a novel strategy of stochastic gene activation with regulated sparseness (STARS), in which the stochastic choice between 2 competing Cre-lox recombination events is controlled by varying the lox efficiency and cassette length. In a created STARS transgenic mouse crossed with various Cre driver lines, sparse neuronal labeling with a relatively uniform level of sparseness was achieved across different brain regions and cell types in both central and peripheral nervous systems. Tracing of individual type II peripheral auditory fibers revealed for the first time that they undergo experience-dependent developmental refinement, which is impaired by attenuating external sound input. Our results suggest that STARS strategy can be applied for circuit mapping and sparse gene manipulation.

[Rhizosphere microorganism evidence on geo-authentic features of traditional Chinese medicine from Moutan(Paeonia suffruticosa)].

Paeonia suffruticosa also named Moutan that cultivated in five geographic regions during different growth stages were chosen in this study. Biolog and 454 pyrosequencing technology were used to detect the whole microbial activity and fungal diversity for exploring the relationship between the geo-authentic features of the medicinal plant and the rhizosphere microorganism. The results suggest that the value of average well color development(AWCD) from the rhizosphere soil of P. suffruticosa in the five regions at the four growth stage have an increasing tendency. 9 703 operational taxonomic unit(OTU) were obtained from 272 463 high quality sequences according to the similarity of 97% by the pyrosequencing. Fungi in five phyla, twenty-two classes, seventy orders, one hundred and thirty-nine families and two hundred and sixty-six genera were detected in the five regions excluding twelve percent to fifty-eight percent unidentified fungi. They were divided into four branches, i.e. Blastocladiales, Chytridiomycota, Dikarya and Glomeromycetes. Twenty-four genera such as Leptosphaeria were found in the five regions while six genera such as Curvularia were only detected in the geo-authentic regions. The dominant genera were Guehomyces, Exophiala and Fusarium in geo-authentic regions, whereas genus Leptosphaeria, Cryptococcus, Exophiala, Fusarium and Ceratobasidium in non-authentic regions. The results from principal co-ordinates analysis (PCoA) showed that the fungi formations were similar in Tongling and Nanling region at four different growth stages, and the same in Heze at the leaf bud and fruiting stage. According to heatmap analysis, Tongling and Nanling region showed a close similarity in fungal community structures on phylogenetic analysis, while Bozhou, Heze and Luoyang showed the same. In brief, the whole microbial activity was higher in geo-authentic regions than the non-authentic. Fungi in rhizosphere soil of the medicinal peony presented diversity and region specificity. We found not only the abundant new species in the five regions, but also the phylogenetic similarity in the geo-authentic regions.

Effect of a bolus dose of fentanyl on the ED50 and ED95 of sevoflurane in neonates.

BACKGROUND: The minimum alveolar concentration (MAC) of sevoflurane in neonates is 3.3%, but this value has not been verified in Chinese neonates and the effect of different doses of fentanyl on MAC in neonates has not been investigated. This study was designed to determine the ED50 and ED95 values of sevoflurane in Chinese neonates with and without fentanyl. MATERIAL AND METHODS: Ninety-three neonates were randomly assigned to receive sevoflurane alone (control group, n=30), 1 µg/kg sevoflurane (group fent1, n=29), or 2 µg/kg fentanyl (group fent2, n=32). Following inhalational induction and tracheal intubation, the end-tidal concentration of sevoflurane was adjusted to achieve the designated concentration, which was determined using the modified Dixon's up-and-down method starting with 3.0% in each group, with a 0.25% step size. Success was defined as no motor response within 60 s of skin incision. RESULTS: The MAC (standard deviation) values of sevoflurane were 2.91% (0.27) in the control group, 2.53% (0.31) in the fent1 group, and 2.34% (0.33) in the fent2 group according to Dixon's up-and-down method. Logistic probit regression analysis revealed that the ED50 and ED95 (95% CI) of sevoflurane in neonates were 2.82% (2.66-2.98) and 3.39% (2.89-3.89), respectively, in the control group; 2.44% (2.19-2.68) and 3.30% (2.51-4.09), respectively, in the fent1 group; and 2.21% (1.97-2.45) and 3.11% (2.35-3.88), respectively, in the fent2 group. CONCLUSIONS: The MAC value of sevoflurane in Chinese neonates was lower than previously reported and was reduced by the addition of fentanyl.

Cross-modal enhancement of defensive behavior via parabigemino-collicular projections.

Effective detection and avoidance from environmental threats are crucial for animals' survival. Integration of sensory cues associated with threats across different modalities can significantly enhance animals' detection and behavioral responses. However, the neural circuit-level mechanisms underlying the modulation of defensive behavior or fear response under simultaneous multimodal sensory inputs remain poorly understood. Here, we report in mice that bimodal looming stimuli combining coherent visual and auditory signals elicit more robust defensive/fear reactions than unimodal stimuli. These include intensified escape and prolonged hiding, suggesting a heightened defensive/fear state. These various responses depend on the activity of the superior colliculus (SC), while its downstream nucleus, the parabigeminal nucleus (PBG), predominantly influences the duration of hiding behavior. PBG temporally integrates visual and auditory signals and enhances the salience of threat signals by amplifying SC sensory responses through its feedback projection to the visual layer of the SC. Our results suggest an evolutionarily conserved pathway in defense circuits for multisensory integration and cross-modality enhancement.

Spatial confinement affects the heterogeneity and interactions between shoaling fish.

Living objects are able to consume chemical energy and process information independently from others. However, living objects can coordinate to form ordered groups such as schools of fish. This work considers these complex groups as living materials and presents imaging-based experiments of laboratory schools of fish to understand how activity, which is a non-equilibrium feature, affects the structure and dynamics of a group. We use spatial confinement to control the motion and structure of fish within quasi-2D shoals of fish and use image analysis techniques to make quantitative observations of the structures, their spatial heterogeneity, and their temporal fluctuations. Furthermore, we utilize Monte Carlo simulations to replicate the experimentally observed data which provides insight into the effective interactions between fish and confirms the presence of a confinement-based behavioral preference transition. In addition, unlike in short-range interacting systems, here structural heterogeneity and dynamic activities are positively correlated as a result of complex interplay between spatial arrangement and behavioral dynamics in fish collectives.

Adaptive patterning of vascular network during avian skin development: Mesenchymal plasticity and dermal vasculogenesis.

A vasculature network supplies blood to feather buds in the developing skin. Does the vasculature network during early skin development form by sequential sprouting from the central vasculature or does local vasculogenesis occur first that then connect with the central vascular tree? Using transgenic Japanese quail Tg(TIE1p.H2B-eYFP), we observe that vascular progenitor cells appear after feather primordia formation. The vasculature then radiates out from each bud and connects with primordial vessels from neighboring buds. Later they connect with the central vasculature. Epithelial-mesenchymal recombination shows local vasculature is patterned by the epithelium, which expresses FGF2 and VEGF. Perturbing noggin expression leads to abnormal vascularization. To study endothelial origin, we compare transcriptomes of TIE1p.H2B-eYFP+ cells collected from the skin and aorta. Endothelial cells from the skin more closely resemble skin dermal cells than those from the aorta. The results show developing chicken skin vasculature is assembled by (1) physiological vasculogenesis from the peripheral tissue, and (2) subsequently connects with the central vasculature. The work implies mesenchymal plasticity and convergent differentiation play significant roles in development, and such processes may be re-activated during adult regeneration. SUMMARY STATEMENT: We show the vasculature network in the chicken skin is assembled using existing feather buds as the template, and endothelia are derived from local bud dermis and central vasculature.

Research on automatic pilot repetition generation method based on deep reinforcement learning.

Using computers to replace pilot seats in air traffic control (ATC) simulators is an effective way to improve controller training efficiency and reduce training costs. To achieve this, we propose a deep reinforcement learning model, RoBERTa-RL (RoBERTa with Reinforcement Learning), for generating pilot repetitions. RoBERTa-RL is based on the pre-trained language model RoBERTa and is optimized through transfer learning and reinforcement learning. Transfer learning is used to address the issue of scarce data in the ATC domain, while reinforcement learning algorithms are employed to optimize the RoBERTa model and overcome the limitations in model generalization caused by transfer learning. We selected a real-world area control dataset as the target task training and testing dataset, and a tower control dataset generated based on civil aviation radio land-air communication rules as the test dataset for evaluating model generalization. In terms of the ROUGE evaluation metrics, RoBERTa-RL achieved significant results on the area control dataset with ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.9962, 0.992, and 0.996, respectively. On the tower control dataset, the scores were 0.982, 0.954, and 0.982, respectively. To overcome the limitations of ROUGE in this field, we conducted a detailed evaluation of the proposed model architecture using keyword-based evaluation criteria for the generated repetition instructions. This evaluation criterion calculates various keyword-based metrics based on the segmented results of the repetition instruction text. In the keyword-based evaluation criteria, the constructed model achieved an overall accuracy of 98.8% on the area control dataset and 81.8% on the tower control dataset. In terms of generalization, RoBERTa-RL improved accuracy by 56% compared to the model before improvement and achieved a 47.5% improvement compared to various comparative models. These results indicate that employing reinforcement learning strategies to enhance deep learning algorithms can effectively mitigate the issue of poor generalization in text generation tasks, and this approach holds promise for future application in other related domains.

The medial preoptic area mediates depressive-like behaviors induced by ovarian hormone withdrawal through distinct GABAergic projections.

Fluctuations in reproductive hormone levels are associated with mood disruptions in women, such as in postpartum and perimenopausal depression. However, the neural circuit mechanisms remain unclear. Here we report that medial preoptic area (MPOA) GABAergic neurons mediate multifaceted depressive-like behaviors in female mice after ovarian hormone withdrawal (HW), which can be attributed to downregulation of activity in Esr1 (estrogen receptor-1)-expressing GABAergic neurons. Enhancing activity of these neurons ameliorates depressive-like behaviors in HW-treated mice, whereas reducing their activity results in expression of these behaviors. Two separate subpopulations mediate different symptoms: a subpopulation projecting to the ventral tegmental area (VTA) mediates anhedonia and another projecting to the periaqueductal gray mediates immobility. These projections enhance activity of dopaminergic neurons in the VTA and serotonergic neurons in the dorsal raphe, respectively, with increased release of dopamine and serotonin, possibly through disinhibition mechanisms. Thus, the MPOA is a hub that mediates depressive-like behaviors resulting from transitions in reproductive hormone levels.

Constitutive model for the rheology of biological tissue.

The rheology of biological tissue is key to processes such as embryo development, wound healing, and cancer metastasis. Vertex models of confluent tissue monolayers have uncovered a spontaneous liquid-solid transition tuned by cell shape; and a shear-induced solidification transition of an initially liquidlike tissue. Alongside this jamming/unjamming behavior, biological tissue also displays an inherent viscoelasticity, with a slow time and rate-dependent mechanics. With this motivation, we combine simulations and continuum theory to examine the rheology of the vertex model in nonlinear shear across a full range of shear rates from quastistatic to fast, elucidating its nonlinear stress-strain curves after the inception of shear of finite rate, and its steady state flow curves of stress as a function of strain rate. We formulate a rheological constitutive model that couples cell shape to flow and captures both the tissue solid-liquid transition and its rich linear and nonlinear rheology.

Glutamatergic and GABAergic neurons in pontine central gray mediate opposing valence-specific behaviors through a global network.

Extracting the valence of environmental cues is critical for animals' survival. How valence in sensory signals is encoded and transformed to produce distinct behavioral responses remains not well understood. Here, we report that the mouse pontine central gray (PCG) contributes to encoding both negative and positive valences. PCG glutamatergic neurons were activated selectively by aversive, but not reward, stimuli, whereas its GABAergic neurons were preferentially activated by reward signals. The optogenetic activation of these two populations resulted in avoidance and preference behavior, respectively, and was sufficient to induce conditioned place aversion/preference. Suppression of them reduced sensory-induced aversive and appetitive behaviors, respectively. These two functionally opponent populations, receiving a broad range of inputs from overlapping yet distinct sources, broadcast valence-specific information to a distributed brain network with distinguishable downstream effectors. Thus, PCG serves as a critical hub to process positive and negative valences of incoming sensory signals and drive valence-specific behaviors with distinct circuits.

Enhancement and contextual modulation of visuospatial processing by thalamocollicular projections from ventral lateral geniculate nucleus.

In the mammalian visual system, the ventral lateral geniculate nucleus (vLGN) of the thalamus receives salient visual input from the retina and sends prominent GABAergic axons to the superior colliculus (SC). However, whether and how vLGN contributes to fundamental visual information processing remains largely unclear. Here, we report in mice that vLGN facilitates visually-guided approaching behavior mediated by the lateral SC and enhances the sensitivity of visual object detection. This can be attributed to the extremely broad spatial integration of vLGN neurons, as reflected in their much lower preferred spatial frequencies and broader spatial receptive fields than SC neurons. Through GABAergic thalamocollicular projections, vLGN specifically exerts prominent surround suppression of visuospatial processing in SC, leading to a fine tuning of SC preferences to higher spatial frequencies and smaller objects in a context-dependent manner. Thus, as an essential component of the central visual processing pathway, vLGN serves to refine and contextually modulate visuospatial processing in SC-mediated visuomotor behaviors via visually-driven long-range feedforward inhibition.

A bottom-up reward pathway mediated by somatostatin neurons in the medial septum complex underlying appetitive learning.

Valence detection and processing are essential for the survival of animals and their life quality in complex environments. Neural circuits underlying the transformation of external sensory signals into positive valence coding to generate appropriate behavioral responses remain not well-studied. Here, we report that somatostatin (SOM) subtype of GABAergic neurons in the mouse medial septum complex (MS), but not parvalbumin subtype or glutamatergic neurons, specifically encode reward signals and positive valence. Through an ascending pathway from the nucleus of solitary tract and then parabrachial nucleus, the MS SOM neurons receive rewarding taste signals and suppress the lateral habenula. They contribute essentially to appetitive associative learning via their projections to the lateral habenula: learning enhances their responses to reward-predictive sensory cues, and suppressing their responses to either conditioned or unconditioned stimulus impairs acquisition of reward learning. Thus, MS serves as a critical hub for transforming bottom-up sensory signals to mediate appetitive behaviors.