Emergence of Long-Range Angular Correlations in Low-Multiplicity Proton-Proton Collisions.

This Letter presents the measurement of near-side associated per-trigger yields, denoted ridge yields, from the analysis of angular correlations of charged hadrons in proton-proton collisions at sqrt[s]=13 TeV. Long-range ridge yields are extracted for pairs of charged particles with a pseudorapidity difference of 1.4<|Δη|<1.8 and a transverse momentum of 1

First Measurement of the |t| Dependence of Incoherent J/ψ Photonuclear Production.

The first measurement of the cross section for incoherent photonuclear production of J/ψ vector mesons as a function of the Mandelstam |t| variable is presented. The measurement was carried out with the ALICE detector at midrapidity, |y|<0.8, using ultraperipheral collisions of Pb nuclei at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02 TeV. This rapidity interval corresponds to a Bjorken-x range (0.3-1.4)×10^{-3}. Cross sections are given in five |t| intervals in the range 0.04<|t|<1 GeV^{2} and compared to the predictions by different models. Models that ignore quantum fluctuations of the gluon density in the colliding hadron predict a |t| dependence of the cross section much steeper than in data. The inclusion of such fluctuations in the same models provides a better description of the data.

Comparative connectomics of dauer reveals developmental plasticity.

A fundamental question in neurodevelopmental biology is how flexibly the nervous system changes during development. To address this, we reconstructed the chemical connectome of dauer, an alternative developmental stage of nematodes with distinct behavioral characteristics, by volumetric reconstruction and automated synapse detection using deep learning. With the basic architecture of the nervous system preserved, structural changes in neurons, large or small, were closely associated with connectivity changes, which in turn evoked dauer-specific behaviors such as nictation. Graph theoretical analyses revealed significant dauer-specific rewiring of sensory neuron connectivity and increased clustering within motor neurons in the dauer connectome. We suggest that the nervous system in the nematode has evolved to respond to harsh environments by developing a quantitatively and qualitatively differentiated connectome.

ψ(2S) Suppression in Pb-Pb Collisions at the LHC.

The production of the ψ(2S) charmonium state was measured with ALICE in Pb-Pb collisions at sqrt[s_{NN}]=5.02 TeV, in the dimuon decay channel. A significant signal was observed for the first time at LHC energies down to zero transverse momentum, at forward rapidity (2.5

Perioperative outcomes of laparoscopic low anterior resection using ArtiSential® versus robotic approach in patients with rectal cancer: a propensity score matching analysis.

BACKGROUND: Total mesorectal excision using conventional straight fixed devices may be technically difficult because of the narrow and concave pelvis. Several laparoscopic articulating tools have been introduced as an alternative to robotic systems. The aim of this study was to compare perioperative outcomes between laparoscopic low anterior resection using ArtiSential® and robot-assisted surgery for rectal cancer. METHODS: This retrospective study included 682 patients who underwent laparoscopic or robotic low anterior resection  for rectal cancer from September 2018 to December 2021. Among them, 82 underwent laparoscopic surgery using ArtiSential® (group A) and 201 underwent robotic surgery (group B). A total of 73 [group A; 66.37 ± 11.62; group B 65.79 ± 11.34] patients were selected for each group using a propensity score matching analysis. RESULTS: There was no significant difference in the baseline characteristics between group A and B. Mean operative time was longer in group B than A (163.5 ± 61.9 vs 250.1 ± 77.6 min, p < 0.001). Mean length of hospital stay was not significantly different between the two groups (6.2 ± 4.7 vs 6.7 ± 6.1 days, p = 0.617). Postoperative complications, reoperation, and readmission within 30 days after surgery were similar between the two groups. Pathological findings revealed that the circumferential resection margins were above 10 mm in both groups (11.00 ± 7.47 vs 10.17 ± 6.25 mm, p = 0.960). At least 12 lymph nodes were sufficiently harvested, with no significant difference in the number harvested between the groups (20.5 ± 9.9 vs 19.7 ± 7.3, p = 0.753). CONCLUSIONS: Laparoscopic low anterior resection using ArtiSential® can achieve acceptable clinical and oncologic outcomes. ArtiSential®, a multi-joint and articulating device, may serve a feasible alternative approach to robotic surgery in rectal cancer.

Petascale pipeline for precise alignment of images from serial section electron microscopy.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex.

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

Modeling Social Network Influences on Oral Health Outcomes among Women Living in Public Housing.

OBJECTIVE: Research into the influence and role of social networks on oral health outcomes has been limited. This study aims to demonstrate via explanatory modeling the influence of social networks on oral health outcomes among women in who live in public housing in Boston, Massachusetts. METHODS: Individual- and network-level data were obtained from a cross-sectional survey of adult female residents of 2 public housing developments in Boston, Massachusetts. Participants responded to close-ended questions about sociodemographic characteristics, oral and general health status, and health-related behaviors for themselves as well as their named social contacts. Based on this information, network-level variables were calculated for each participant, including the proportion of the social network with certain characteristics or attributes. To assess the salience of network measures in explaining the variability in self-reported oral health status, overall health status, use of dental services within the last year, and current dental treatment needs, logistic regression models with individual-level covariates were compared with corresponding models that additionally included network-level variables with McFadden R2 for comparison purposes. RESULTS: Model comparisons for each outcome of interest demonstrated that adding network-level covariates significantly improved model fit. Additionally, network-level covariates displayed strong independent associations with the outcomes of interest. The network proportion needing dental treatment was positively associated with participants' odds of reporting current dental treatment needs (odds ratio, 4.71; 95% CI, 1.47 to 15.05). An individual's odds of reporting no dental visit within the past year decreased with increasing exposure to social contacts with reported dental visits within the past year (odds ratio, 0.26; 95% CI, 0.07 to 0.89). CONCLUSION: The salience of specific network attributes depended on the outcome under study. Interventions aimed at reducing the poor oral health burden in this and similar population groups may benefit from integrating information on social networks, including tailoring intervention delivery and/or messaging to account for the potential influence of social networks. KNOWLEDGE TRANSFER STATEMENT: This work may be beneficial to those developing policy solutions and interventions aimed at reducing the burden of poor oral health outcomes in socioeconomically disadvantaged populations, such as women who reside in public housing developments. The potential for leveraging social networks to seed messaging about oral health, for example, may enhance efforts to reduce oral health disparities.

Enhanced Deuteron Coalescence Probability in Jets.

The transverse-momentum (p_{T}) spectra and coalescence parameters B_{2} of (anti)deuterons are measured in p-p collisions at sqrt[s]=13 TeV for the first time in and out of jets. In this measurement, the direction of the leading particle with the highest p_{T} in the event (p_{T}^{lead}>5 GeV/c) is used as an approximation for the jet axis. The event is consequently divided into three azimuthal regions, and the jet signal is obtained as the difference between the toward region, that contains jet fragmentation products in addition to the underlying event (UE), and the transverse region, which is dominated by the UE. The coalescence parameter in the jet is found to be approximately a factor of 10 larger than that in the underlying event. This experimental observation is consistent with the coalescence picture and can be attributed to the smaller average phase-space distance between nucleons in the jet cone as compared with the underlying event. The results presented in this Letter are compared to predictions from a simple nucleon coalescence model, where the phase-space distributions of nucleons are generated using pythia8 with the Monash 2013 tuning, and to predictions from a deuteron production model based on ordinary nuclear reactions with parametrized energy-dependent cross sections tuned on data. The latter model is implemented in pythia8.3. Both models reproduce the observed large difference between in-jet and out-of-jet coalescence parameters, although the almost flat trend of the B_{2}^{Jet} is not reproduced by the models, which instead give a decreasing trend.

CAVE: Connectome Annotation Versioning Engine.

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

Neuronal wiring diagram of an adult brain.

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

NEURD: automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Functional connectomics reveals general wiring rule in mouse visual cortex.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types.

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types 1 . Neural cell types have previously been defined by morphology 2, 3 , electrophysiology 4, 5 , transcriptomic expression 6-8 , connectivity 9-13 , or even a combination of such modalities 14-16 . More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20 . Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21 . It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25 , were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

Association of Left Vertebral Artery Hypoplasia with Posterior Circulation Stroke and the Functional Outcome of Patients with Atrial Fibrillation-Related Cardioembolic Stroke.

BACKGROUND AND PURPOSE: A cardiogenic embolus could reach the posterior circulation through the right vertebral artery because of a relatively larger diameter in cases of left vertebral artery hypoplasia. Hence, we investigated whether left vertebral artery hypoplasia is associated with cardiac embolisms with atrial fibrillation in the posterior circulation and its functional outcomes. MATERIALS AND METHODS: In this monocentric retrospective study, patients with acute cardioembolic stroke with atrial fibrillation were enrolled and underwent CT or neck MRA, which visualized the aortic arch and subclavian arteries. The laterality and size of vertebral artery hypoplasia were recorded. Posterior circulation stroke, basilar artery occlusion, and the functional outcomes after 3 months were investigated. RESULTS: This study included 407 patients; the patients with left vertebral artery hypoplasia experienced a higher rate of posterior circulation stroke (19 versus 73; 42.2% versus 20.2%; P = .001) and basilar artery occlusion (5 versus 10; 11.1% versus 2.8%; P = .005) than the patients without left vertebral artery hypoplasia. Multivariate analysis revealed that left vertebral artery hypoplasia showed an association with lower odds of achieving a good functional outcome 3 months after the stroke (OR = 0.4; 95% CI, 0.2-0.9; P = .027). CONCLUSIONS: Patients with cardioembolic stroke and left vertebral artery hypoplasia had posterior circulation stroke, basilar artery occlusion, and poor functional outcomes after 3 months.

Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling.

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

Developing and Validating a Korean Version of the Assessment of Children's Emotional Skills.

In this study, a Korean Assessment of Children's Emotional Skills (ACES) was developed by modifying the original ACES which was initially introduced in the United States. Specifically, the original ACES was translated into Korean and revised to better fit the Korean cultural context. The content validity of the revised Korean ACES was established via expert reviews. To test its reliability, the revised Korean ACES was conducted on 286 six-year-old children. A confirmatory factor analysis indicated that our newly developed Korean ACES can be used as an appropriate tool to measure Korean children's emotional skills. The Korean ACES can stimulate further studies on these emotional skills and contribute to various international collaborative studies that seek to compare the emotional skills of children from diverse cultural backgrounds.

Brain charts for the human lifespan.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Oral administration of Lactiplantibacillus plantarum NR16 isolated from Kimchi ameliorates murine allergic rhinitis.

Allergic rhinitis (AR) is a type I hypersensitivity mediated by dominant T helper 2 (Th2) response over the Th1 response after re-exposure to a specific allergen. Currently, socio-economic cost evoked by AR is quickly increasing since the prevalence of AR is gradually increasing in all ages worldwide. Several probiotic Lactobacillus strains have been described with potential immunomodulatory effects against type I hypersensitivity such as AR. Thus, the aim of the present work was to characterize basic probiotic property and immunomodulatory role of newly isolated Lactobacillus strains from Kimchi, a traditional fermented Korean food, in AR. Among the identified strains, Lactiplantibacillus plantarum NR16 revealed to be a powerful Th1 inducer since immune cells co-cultured with NR16 produced the highest quantity of interferon-γ (IFN-γ) and interleukin-12 (IL-12) but secreted a low amount of IL-4 in vitro. Therefore, NR16 was selected for the following assays conducted with mice with birch pollen-induced AR. Oral administration of NR16 reduced airway hyperresponsiveness and leukocyte infiltration in lesions of mice. In conclusion, oral administration of NR16 may mitigate symptoms of AR by inducing Th1 immune response, which might rebalance Th2/Th1 ratio by decreasing Th2 cytokine production in specific lesions of mucosa.