Montoyadougherty6568

Most social species self-organize into dominance hierarchies1,2, which decreases aggression and conserves energy3,4, but it is not clear how individuals know their social rank. We have only begun to learn how the brain represents social rank5-9 and guides behaviour on the basis of this representation. The medial prefrontal cortex (mPFC) is involved in social dominance in rodents7,8 and humans10,11. Yet, precisely how the mPFC encodes relative social rank and which circuits mediate this computation is not known. We developed a social competition assay in which mice compete for rewards, as well as a computer vision tool (AlphaTracker) to track multiple, unmarked animals. A hidden Markov model combined with generalized linear models was able to decode social competition behaviour from mPFC ensemble activity. Population dynamics in the mPFC predicted social rank and competitive success. Finally, we demonstrate that mPFC cells that project to the lateral hypothalamus promote dominance behaviour during reward competition. Thus, we reveal a cortico-hypothalamic circuit by which the mPFC exerts top-down modulation of social dominance.Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.Fabrics, by virtue of their composition and structure, have traditionally been used as acoustic absorbers1,2. Here, inspired by the auditory system3, we introduce a fabric that operates as a sensitive audible microphone while retaining the traditional qualities of fabrics, such as machine washability and draping. The fabric medium is composed of high-Young's modulus textile yarns in the weft of a cotton warp, converting tenuous 10-7-atmosphere pressure waves at audible frequencies into lower-order mechanical vibration modes. Woven into the fabric is a thermally drawn composite piezoelectric fibre that conforms to the fabric and converts the mechanical vibrations into electrical signals. Key to the fibre sensitivity is an elastomeric cladding that concentrates the mechanical stress in a piezocomposite layer with a high piezoelectric charge coefficient of approximately 46 picocoulombs per newton, a result of the thermal drawing process. Concurrent measurements of electric output and spatial vibration patterns in response to audible acoustic excitation reveal that fabric vibrational modes with nanometre amplitude displacement are the source of the electrical output of the fibre. With the fibre subsuming less than 0.1% of the fabric by volume, a single fibre draw enables tens of square metres of fabric microphone. Three different applications exemplify the usefulness of this study a woven shirt with dual acoustic fibres measures the precise direction of an acoustic impulse, bidirectional communications are established between two fabrics working as sound emitters and receivers, and a shirt auscultates cardiac sound signals.Interoception, the ability to timely and precisely sense changes inside the body, is critical for survival1-4. Vagal sensory neurons (VSNs) form an important body-to-brain connection, navigating visceral organs along the rostral-caudal axis of the body and crossing the surface-lumen axis of organs into appropriate tissue layers5,6. The brain can discriminate numerous body signals through VSNs, but the underlying coding strategy remains poorly understood. Here we show that VSNs code visceral organ, tissue layer and stimulus modality-three key features of an interoceptive signal-in different dimensions. Large-scale single-cell profiling of VSNs from seven major organs in mice using multiplexed projection barcodes reveals a 'visceral organ' dimension composed of differentially expressed gene modules that code organs along the body's rostral-caudal axis. We discover another 'tissue layer' dimension with gene modules that code the locations of VSN endings along the surface-lumen axis of organs. Using calcium-imaging-guided spatial transcriptomics, we show that VSNs are organized into functional units to sense similar stimuli across organs and tissue layers; this constitutes a third 'stimulus modality' dimension. The three independent feature-coding dimensions together specify many parallel VSN pathways in a combinatorial manner and facilitate the complex projection of VSNs in the brainstem. Our study highlights a multidimensional coding architecture of the mammalian vagal interoceptive system for effective signal communication.Infections of the central nervous system are among the most serious infections1,2, but the mechanisms by which pathogens access the brain remain poorly understood. The model microorganism Listeria monocytogenes (Lm) is a major foodborne pathogen that causes neurolisteriosis, one of the deadliest infections of the central nervous system3,4. Although immunosuppression is a well-established host risk factor for neurolisteriosis3,5, little is known about the bacterial factors that underlie the neuroinvasion of Lm. Here we develop a clinically relevant experimental model of neurolisteriosis, using hypervirulent neuroinvasive strains6 inoculated in a humanized mouse model of infection7, and we show that the bacterial surface protein InlB protects infected monocytes from Fas-mediated cell death by CD8+ T cells in a manner that depends on c-Met, PI3 kinase and FLIP. This blockade of specific anti-Lm cellular immune killing lengthens the lifespan of infected monocytes, and thereby favours the transfer of Lm from infected monocytes to the brain. The intracellular niche that is created by InlB-mediated cell-autonomous immune resistance also promotes Lm faecal shedding, which accounts for the selection of InlB as a core virulence gene of Lm. We have uncovered a specific mechanism by which a bacterial pathogen confers an increased lifespan to the cells it infects by rendering them resistant to cell-mediated immunity. This promotes the persistence of Lm within the host, its dissemination to the central nervous system and its transmission.The fungal microbiota (mycobiota) is an integral part of the complex multikingdom microbial community colonizing the mammalian gastrointestinal tract and has an important role in immune regulation1-6. read more Although aberrant changes in the mycobiota have been linked to several diseases, including inflammatory bowel disease3-9, it is currently unknown whether fungal species captured by deep sequencing represent living organisms and whether specific fungi have functional consequences for disease development in affected individuals. Here we developed a translational platform for the functional analysis of the mycobiome at the fungal-strain- and patient-specific level. Combining high-resolution mycobiota sequencing, fungal culturomics and genomics, a CRISPR-Cas9-based fungal strain editing system, in vitro functional immunoreactivity assays and in vivo models, this platform enables the examination of host-fungal crosstalk in the human gut. We discovered a rich genetic diversity of opportunistic Candida albicans strains that dominate the colonic mucosa of patients with inflammatory bowel disease. Among these human-gut-derived isolates, strains with high immune-cell-damaging capacity (HD strains) reflect the disease features of individual patients with ulcerative colitis and aggravated intestinal inflammation in vivo through IL-1β-dependent mechanisms. Niche-specific inflammatory immunity and interleukin-17A-producing T helper cell (TH17 cell) antifungal responses by HD strains in the gut were dependent on the C. albicans-secreted peptide toxin candidalysin during the transition from a benign commensal to a pathobiont state. These findings reveal the strain-specific nature of host-fungal interactions in the human gut and highlight new diagnostic and therapeutic targets for diseases of inflammatory origin.The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people2. Targeting is a central challenge in administering these programmes it remains a difficult task to rapidly identify those with the greatest need given available data3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes-including exclusion errors, total social welfare and measures of fairness-under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4-21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date.The engineering of autologous patient T cells for adoptive cell therapies has revolutionized the treatment of several types of cancer1. However, further improvements are needed to increase response and cure rates. CRISPR-based loss-of-function screens have been limited to negative regulators of T cell functions2-4 and raise safety concerns owing to the permanent modification of the genome. Here we identify positive regulators of T cell functions through overexpression of around 12,000 barcoded human open reading frames (ORFs). The top-ranked genes increased the proliferation and activation of primary human CD4+ and CD8+ T cells and their secretion of key cytokines such as interleukin-2 and interferon-γ. In addition, we developed the single-cell genomics method OverCITE-seq for high-throughput quantification of the transcriptome and surface antigens in ORF-engineered T cells. The top-ranked ORF-lymphotoxin-β receptor (LTBR)-is typically expressed in myeloid cells but absent in lymphocytes. When overexpressed in T cells, LTBR induced profound transcriptional and epigenomic remodelling, leading to increased T cell effector functions and resistance to exhaustion in chronic stimulation settings through constitutive activation of the canonical NF-κB pathway.