Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards<jats:sup>1</jats:sup>. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people<jats:sup>2</jats:sup>. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data<jats:sup>3,4</jats:sup>. 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methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Neuroanatomists have long speculated that expanded primate brains contain an increased morphological diversity of inhibitory neurons (INs)<jats:sup>1</jats:sup>, and recent studies have identified primate-specific neuronal populations at the molecular level<jats:sup>2</jats:sup>. However, we know little about the developmental mechanisms that specify evolutionarily novel cell types in the brain. Here, we reconstruct gene expression trajectories specifying INs generated throughout the neurogenic period in macaques and mice by analysing the transcriptomes of 250,181 cells. We find that the initial classes of INs generated prenatally are largely conserved among mammals. Nonetheless, we identify two contrasting developmental mechanisms for specifying evolutionarily novel cell types during prenatal development. First, we show that recently identified primate-specific <jats:italic>TAC3</jats:italic> striatal INs are specified by a unique transcriptional programme in progenitors followed by induction of a distinct suite of neuropeptides and neurotransmitter receptors in new-born neurons. Second, we find that multiple classes of transcriptionally conserved olfactory bulb (OB)-bound precursors are redirected to expanded primate white matter and striatum. These classes include a novel peristriatal class of striatum laureatum neurons that resemble dopaminergic periglomerular cells of the OB. We propose an evolutionary model in which conserved initial classes of neurons supplying the smaller primate OB are reused in the enlarged striatum and cortex. Together, our results provide a unified developmental taxonomy of initial classes of mammalian INs and reveal multiple developmental mechanisms for neural cell type evolution.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Interoception, the ability to timely and precisely sense changes inside the body, is critical for survival<jats:sup>1–4</jats:sup>. 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 layers<jats:sup>5,6</jats:sup>. 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.</jats:p>