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Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework. 

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications. 

Ancient biomolecules have become an increasingly important part of archaeological investigations interested in understanding population movements and health. Despite their ability to elucidate historically-attested contexts of human mobility and interaction between different cultural groups, biomolecular techniques are still underutilized in certain historical and archaeological contexts. One such context is the Roman Imperial limes, or border zone, along the lower reaches of the Danube, which saw more than five hundred years of migration, conflict, and accommodation among a wide range of populations, from Mediterranean settlers to steppe pastoralists. In this region, more than a century of archaeological investigation has unearthed the remains of tens of thousands of Roman-era individuals. However, only a limited number of contexts have undergone biomolecular analyses. While these deceased humans may offer an untapped reservoir of biomolecular information, many were collected during a period when the standard precautions and protocols for ancient biomolecular research were not yet established. Because contamination is a major barrier for successfully recovering ancient DNA and proteins, conducting a pilot study to assess bimolecular preservation of a small representative dataset of human remains before embarking on a more extensive research program may prevent unnecessary sampling. This study applies ancient DNA and paleoproteomic techniques to human remains from a Roman-period cemetery at Histria, a site located just south of the Danube at the edge of the Roman province of Moesia Inferior. The individuals from whom we sampled dentin and dental calculus were excavated between the 1940s and 1980s and were housed at the Francisc J. Rainer Institute since. Our results suggest that both microbial and human ancient DNA is preserved in the dental calculus and dentin samples. We also successfully recovered sex-specific amelogenin peptides in tooth enamel from three individuals, including a juvenile. In conclusion, our results are encouraging, signifying the feasibility of future aDNA and paleoproteomic research for this skeletal collection. Our analyses also showcase how sex estimation with genomic and proteomic methods may contradict traditional osteological approaches. These findings not only offer deeper insights into the lives of these individuals but also show promise for the investigation of broader anthropological questions, such as the impact of Roman annexation in this region. 

This study provides a normative theory for how Bayesian causal inference can be implemented in neural circuits. In both cognitive processes such as causal reasoning and perceptual inference such as cue integration, the nervous systems need to choose different models representing the underlying causal structures when making inferences on external stimuli. In multisensory processing, for example, the nervous system has to choose whether to integrate or segregate inputs from different sensory modalities to infer the sensory stimuli, based on whether the inputs are from the same or different sources. Making this choice is a model selection problem requiring the computation of Bayes factor, the ratio of likelihoods between the integration and the segregation models. In this paper, we consider the causal inference in multisensory processing and propose a novel generative model based on neural population code that takes into account both stimulus feature and stimulus reliability in the inference. In the case of circular variables such as heading direction, our normative theory yields an analytical solution for computing the Bayes factor, with a clear geometric interpretation, which can be implemented by simple additive mechanisms with neural population code. Numerical simulation shows that the tunings of the neurons computing Bayes factor are consistent with the "opposite neurons" discovered in dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas for visual-vestibular processing. This study illuminates a potential neural mechanism for causal inference in the brain.