Pan-African evolution of within- and between-country COVID-19 dynamics.

The coronavirus disease 2019 (COVID-19) pandemic is heterogeneous throughout Africa and threatening millions of lives. Surveillance and short-term modeling forecasts are critical to provide timely information for decisions on control strategies. We created a strategy that helps predict the country-level case occurrences based on cases within or external to a country throughout the entire African continent, parameterized by socioeconomic and geoeconomic variations and the lagged effects of social policy and meteorological history. We observed the effect of the Human Development Index, containment policies, testing capacity, specific humidity, temperature, and landlocked status of countries on the local within-country and external between-country transmission. One-week forecasts of case numbers from the model were driven by the quality of the reported data. Seeking equitable behavioral and social interventions, balanced with coordinated country-specific strategies in infection suppression, should be a continental priority to control the COVID-19 pandemic in Africa.

Genomic prediction with whole-genome sequence data in intensely selected pig lines.

BACKGROUND: Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. METHODS: We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. RESULTS: The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. CONCLUSIONS: Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

IDENTIFIABILITY OF INFECTION MODEL PARAMETERS EARLY IN AN EPIDEMIC.

It is known that the parameters in the deterministic and stochastic SEIR epidemic models are structurally identifiable. For example, from knowledge of the infected population time series I(t) during the entire epidemic, the parameters can be successfully estimated. In this article we observe that estimation will fail in practice if only infected case data during the early part of the epidemic (prepeak) is available. This fact can be explained using a well-known phenomenon called dynamical compensation. We use this concept to derive an unidentifiability manifold in the parameter space of SEIR that consists of parameters indistinguishable from I(t) early in the epidemic. Thus, identifiability depends on the extent of the system trajectory that is available for observation. Although the existence of the unidentifiability manifold obstructs the ability to exactly determine the parameters, we suggest that it may be useful for uncertainty quantification purposes. A variant of SEIR recently proposed for COVID-19 modeling is also analyzed, and an analogous unidentifiability surface is derived.

AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data.

SUMMARY: AlphaFamImpute is an imputation package for calling, phasing and imputing genome-wide genotypes in outbred full-sib families from single nucleotide polymorphism (SNP) array and genotype-by-sequencing (GBS) data. GBS data are increasingly being used to genotype individuals, especially when SNP arrays do not exist for a population of interest. Low-coverage GBS produces data with a large number of missing or incorrect naïve genotype calls, which can be improved by identifying shared haplotype segments between full-sib individuals. Here, we present AlphaFamImpute, an algorithm specifically designed to exploit the genetic structure of full-sib families. It performs imputation using a two-step approach. In the first step, it phases and imputes parental genotypes based on the segregation states of their offspring (i.e. which pair of parental haplotypes the offspring inherited). In the second step, it phases and imputes the offspring genotypes by detecting which haplotype segments the offspring inherited from their parents. With a series of simulations, we find that AlphaFamImpute obtains high-accuracy genotypes, even when the parents are not genotyped and individuals are sequenced at <1x coverage. AVAILABILITY AND IMPLEMENTATION: AlphaFamImpute is available as a Python package from the AlphaGenes website http://www.AlphaGenes.roslin.ed.ac.uk/AlphaFamImpute. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic variation in recombination rate in the pig.

BACKGROUND: Meiotic recombination results in the exchange of genetic material between homologous chromosomes. Recombination rate varies between different parts of the genome, between individuals, and is influenced by genetics. In this paper, we assessed the genetic variation in recombination rate along the genome and between individuals in the pig using multilocus iterative peeling on 150,000 individuals across nine genotyped pedigrees. We used these data to estimate the heritability of recombination and perform a genome-wide association study of recombination in the pig. RESULTS: Our results confirmed known features of the recombination landscape of the pig genome, including differences in genetic length of chromosomes and marked sex differences. The recombination landscape was repeatable between lines, but at the same time, there were differences in average autosome-wide recombination rate between lines. The heritability of autosome-wide recombination rate was low but not zero (on average 0.07 for females and 0.05 for males). We found six genomic regions that are associated with recombination rate, among which five harbour known candidate genes involved in recombination: RNF212, SHOC1, SYCP2, MSH4 and HFM1. CONCLUSIONS: Our results on the variation in recombination rate in the pig genome agree with those reported for other vertebrates, with a low but nonzero heritability, and the identification of a major quantitative trait locus for recombination rate that is homologous to that detected in several other species. This work also highlights the utility of using large-scale livestock data to understand biological processes.

A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens.

BACKGROUND: Body weight (BW) is an economically important trait in the broiler (meat-type chickens) industry. Under the assumption of polygenicity, a "large" number of genes with "small" effects is expected to control BW. To detect such effects, a large sample size is required in genome-wide association studies (GWAS). Our objective was to conduct a GWAS for BW measured at 35 days of age with a large sample size. METHODS: The GWAS included 137,343 broilers spanning 15 pedigree generations and 392,295 imputed single nucleotide polymorphisms (SNPs). A false discovery rate of 1% was adopted to account for multiple testing when declaring significant SNPs. A Bayesian ridge regression model was implemented, using AlphaBayes, to estimate the contribution to the total genetic variance of each region harbouring significant SNPs (1 Mb up/downstream) and the combined regions harbouring non-significant SNPs. RESULTS: GWAS revealed 25 genomic regions harbouring 96 significant SNPs on 13 Gallus gallus autosomes (GGA1 to 4, 8, 10 to 15, 19 and 27), with the strongest associations on GGA4 at 65.67-66.31 Mb (Galgal4 assembly). The association of these regions points to several strong candidate genes including: (i) growth factors (GGA1, 4, 8, 13 and 14); (ii) leptin receptor overlapping transcript (LEPROT)/leptin receptor (LEPR) locus (GGA8), and the STAT3/STAT5B locus (GGA27), in connection with the JAK/STAT signalling pathway; (iii) T-box gene (TBX3/TBX5) on GGA15 and CHST11 (GGA1), which are both related to heart/skeleton development); and (iv) PLAG1 (GGA2). Combined together, these 25 genomic regions explained ~ 30% of the total genetic variance. The region harbouring significant SNPs that explained the largest portion of the total genetic variance (4.37%) was on GGA4 (~ 65.67-66.31 Mb). CONCLUSIONS: To the best of our knowledge, this is the largest GWAS that has been conducted for BW in chicken to date. In spite of the identified regions, which showed a strong association with BW, the high proportion of genetic variance attributed to regions harbouring non-significant SNPs supports the hypothesis that the genetic architecture of BW35 is polygenic and complex. Our results also suggest that a large sample size will be required for future GWAS of BW35.

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling.

BACKGROUND: For assembling large whole-genome sequence datasets for routine use in research and breeding, the sequencing strategy should be adapted to the methods that will be used later for variant discovery and imputation. In this study, we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method that is well suited for large livestock populations. METHODS: We simulated marker array and whole-genome sequence data for 15 populations with simulated or real pedigrees that had different structures. In these populations, we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population, we considered four levels of investment in sequencing that were proportional to the size of the population. RESULTS: Imputation accuracy depended greatly on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence and it was critical for achieving high imputation accuracy in both early and late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of 2× rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2× provided high imputation accuracy. The gain in imputation accuracy from additional investment decreased with larger populations and higher levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones. CONCLUSIONS: Suitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing ~2% of the population at a uniform coverage 2×, distributed preferably across all generations of the pedigree, except for the few earliest generations that lack genotyped ancestors. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

Extending long-range phasing and haplotype library imputation algorithms to large and heterogeneous datasets.

BACKGROUND: We describe the latest improvements to the long-range phasing (LRP) and haplotype library imputation (HLI) algorithms for successful phasing of both datasets with one million individuals and datasets genotyped using different sets of single nucleotide polymorphisms (SNPs). Previous publicly available implementations of the LRP algorithm implemented in AlphaPhase could not phase large datasets due to the computational cost of defining surrogate parents by exhaustive all-against-all searches. Furthermore, the AlphaPhase implementations of LRP and HLI were not designed to deal with large amounts of missing data that are inherent when using multiple SNP arrays. METHODS: We developed methods that avoid the need for all-against-all searches by performing LRP on subsets of individuals and then concatenating the results. We also extended LRP and HLI algorithms to enable the use of different sets of markers, including missing values, when determining surrogate parents and identifying haplotypes. We implemented and tested these extensions in an updated version of AlphaPhase, and compared its performance to the software package Eagle2. RESULTS: A simulated dataset with one million individuals genotyped with the same 6711 SNPs for a single chromosome took less than a day to phase, compared to more than seven days for Eagle2. The percentage of correctly phased alleles at heterozygous loci was 90.2 and 99.9% for AlphaPhase and Eagle2, respectively. A larger dataset with one million individuals genotyped with 49,579 SNPs for a single chromosome took AlphaPhase 23 days to phase, with 89.9% of alleles at heterozygous loci phased correctly. The phasing accuracy was generally lower for datasets with different sets of markers than with one set of markers. For a simulated dataset with three sets of markers, 1.5% of alleles at heterozygous positions were phased incorrectly, compared to 0.4% with one set of markers. CONCLUSIONS: The improved LRP and HLI algorithms enable AlphaPhase to quickly and accurately phase very large and heterogeneous datasets. AlphaPhase is an order of magnitude faster than the other tested packages, although Eagle2 showed a higher level of phasing accuracy. The speed gain will make phasing achievable for very large genomic datasets in livestock, enabling more powerful breeding and genetics research and application.

Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations.

BACKGROUND: The coupling of appropriate sequencing strategies and imputation methods is critical for assembling large whole-genome sequence datasets from livestock populations for research and breeding. In this paper, we describe and validate the coupling of a sequencing strategy with the imputation method hybrid peeling in real animal breeding settings. METHODS: We used data from four pig populations of different size (18,349 to 107,815 individuals) that were widely genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most of them at 1× or 2× and 37-92 individuals per population, totalling 284, at 15-30×). We imputed whole-genome sequence data with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of the 284 individuals with high coverage, using a leave-one-out design. We simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees. RESULTS: Imputation accuracy was high for the majority of individuals in all four populations (median individual-wise dosage correlation: 0.97). Imputation accuracy was lower for individuals in the earliest generations of each population than for the rest, due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status, the availability of marker array data for immediate ancestors, and the degree of connectedness to the rest of the population, but sequencing coverage of the relatives had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. Results were validated with the empirical observations. CONCLUSIONS: We demonstrate that the coupling of an appropriate sequencing strategy and hybrid peeling is a powerful strategy for generating whole-genome sequence data with high accuracy in large pedigreed populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage. This is a critical step for the successful implementation of whole-genome sequence data for genomic prediction and fine-mapping of causal variants.

Family-specific genotype arrays increase the accuracy of pedigree-based imputation at very low marker densities.

BACKGROUND: In this paper, we evaluate the performance of using family-specific low-density genotype arrays to increase the accuracy of pedigree-based imputation. Genotype imputation is a widely used tool that decreases the costs of genotyping a population by genotyping the majority of individuals on a low-density array and using statistical regularities between the low-density and high-density individuals to fill in the missing genotypes. Previous work on population-based imputation has found that it is possible to increase the accuracy of imputation by maximizing the number of informative markers on an array. In the context of pedigree-based imputation, where the informativeness of a marker depends only on the genotypes of an individual's parents, it may be beneficial to select the markers on each low-density array on a family-by-family basis. RESULTS: In this paper, we examined four family-specific low-density marker selection strategies and evaluated their performance in the context of a real pig breeding dataset. We found that family-specific or sire-specific arrays could increase imputation accuracy by 0.11 at one marker per chromosome, by 0.027 at 25 markers per chromosome and by 0.007 at 100 markers per chromosome. CONCLUSIONS: These results suggest that there may be room to use family-specific genotyping for very-low-density arrays particularly if a given sire or sire-dam pairing have a large number of offspring.

Parentage assignment with genotyping-by-sequencing data.

In this paper, we evaluate using genotype-by-sequencing (GBS) data to perform parentage assignment in lieu of traditional array data. The use of GBS data raises two issues: First, for low-coverage (e.g., <2×) GBS data, it may not be possible to call the genotype at many loci, a critical first step for detecting opposing homozygous markers. Second, the amount of sequencing coverage may vary across individuals, making it challenging to directly compare the likelihood scores between putative parents. To address these issues, we extend the probabilistic framework of Huisman (Molecular Ecology Resources, 2017, 17, 1009) and evaluate putative parents by comparing their (potentially noisy) genotypes to a series of proposal distributions. These distributions describe the expected genotype probabilities for the relatives of an individual. We assign putative parents as a parent if they are classified as a parent (as opposed to e.g., an unrelated individual), and if the assignment score passes a threshold. We evaluated this method on simulated data and found that (a) high-coverage (>2×) GBS data performs similarly to array data and requires only a small number of markers to correctly assign parents and (b) low-coverage GBS data (as low as 0.1×) can also be used, provided that it is obtained across a large number of markers. When analysing the low-coverage GBS data, we also found a high number of false positives if the true parent is not contained within the list of candidate parents, but that this false positive rate can be greatly reduced by hand tuning the assignment threshold. We provide this parentage assignment method as a standalone program called AlphaAssign.

Assessment of the performance of hidden Markov models for imputation in animal breeding.

BACKGROUND: In this paper, we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, pedigree and heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. Recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations, particularly in populations where pedigree information is not readily available. METHODS: To test the performance of hidden Markov model-based imputation, we evaluated the accuracy and computational cost of several methods in a series of simulated populations and a real animal population without using a pedigree. First, we tested single-step (diploid) imputation, which performs both phasing and imputation. Second, we tested pre-phasing followed by haploid imputation. Overall, we used four available diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and Minimac3). RESULTS: We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, among all the methods tested, pre-phasing with Eagle2 or HAPI-UR and imputing with Minimac3 or IMPUTE2 gave the highest accuracies with both simulated and real data. CONCLUSIONS: The results of this study suggest that hidden Markov model-based imputation algorithms are an accurate and computationally feasible approach for performing imputation without a pedigree when pre-phasing and haploid imputation are used. Of the algorithms tested, the combination of Eagle2 and Minimac3 gave the highest accuracy across the simulated and real datasets.

Hybrid peeling for fast and accurate calling, phasing, and imputation with sequence data of any coverage in pedigrees.

BACKGROUND: In this paper, we extend multi-locus iterative peeling to provide a computationally efficient method for calling, phasing, and imputing sequence data of any coverage in small or large pedigrees. Our method, called hybrid peeling, uses multi-locus iterative peeling to estimate shared chromosome segments between parents and their offspring at a subset of loci, and then uses single-locus iterative peeling to aggregate genomic information across multiple generations at the remaining loci. RESULTS: Using a synthetic dataset, we first analysed the performance of hybrid peeling for calling and phasing genotypes in disconnected families, which contained only a focal individual and its parents and grandparents. Second, we analysed the performance of hybrid peeling for calling and phasing genotypes in the context of a full general pedigree. Third, we analysed the performance of hybrid peeling for imputing whole-genome sequence data to non-sequenced individuals in the population. We found that hybrid peeling substantially increased the number of called and phased genotypes by leveraging sequence information on related individuals. The calling rate and accuracy increased when the full pedigree was used compared to a reduced pedigree of just parents and grandparents. Finally, hybrid peeling imputed accurately whole-genome sequence to non-sequenced individuals. CONCLUSIONS: We believe that this algorithm will enable the generation of low cost and high accuracy whole-genome sequence data in many pedigreed populations. We make this algorithm available as a standalone program called AlphaPeel.

Conformity biased transmission in social networks.

In this paper we explore how the structure of a population can differentially influence the spread of novel behaviors, depending on the learning strategy of each individual. We use a series of simulations to analyze how frequency dependent learning rules might affect how easily novel behaviors can spread through a population on four artificial social networks, and three real social networks. We measured the likelihood that a novel behavior could spread through the population, and the likelihood that there were multiple behavioral variants in the population, a measure of cultural diversity. Surprisingly, we find few differences between networks on either measure. However, we do find that where a behavior originated on a network can have a substantial impact on the likelihood that it spreads, and that this location effect depends on the learning strategy of an individual. These results suggest that for first-order analysis of how behaviors spread through a population, social network structure can be ignored, but that the social network structure may be useful for more fine-tuned analyses and predictions.

The learning of action sequences through social transmission.

Previous empirical work on animal social learning has found that many species lack the ability to learn entire action sequences solely through reliance on social information. Conversely, acquiring action sequences through asocial learning can be difficult due to the large number of potential sequences arising from even a small number of base actions. In spite of this, several studies report that some primates use action sequences in the wild. We investigate how social information can be integrated with asocial learning to facilitate the learning of action sequences. We formalize this problem by examining how learners using temporal difference learning, a widely applicable model of reinforcement learning, can combine social cues with their own experiences to acquire action sequences. The learning problem is modeled as a Markov decision process. The learning of nettle processing by mountain gorillas serves as a focal example. Through simulations, we find that the social facilitation of component actions can combine with individual learning to facilitate the acquisition of action sequences. Our analysis illustrates that how even simple forms of social learning, combined with asocial learning, generate substantially faster learning of action sequences compared to asocial processes alone, and that the benefits of social information increase with the length of the action sequence and the number of base actions.

Thermal safety considerations for implantable micro-coil design.

Micro magnetic stimulation of the brain via implantable micro-coils is a promising novel technology for neuromodulation. Careful consideration of the thermodynamic profile of such devices is necessary for effective and safe designs.Objective.We seek to quantify the thermal profile of bent wire micro-coils in order to understand and mitigate thermal impacts of micro-coil stimulation.Approach. In this study, we use fine wire thermocouples and COMSOL finite element modeling to examine the profile of the thermal gradients generated near bent wire micro-coils submerged in a water bath during stimulation. We tested a range of stimulation parameters previously reported in the literature such as voltage amplitude, stimulus frequency, stimulus repetition rate and coil wire materials.Main results. We found temperature increases ranging from <1 °C to 8.4 °C depending upon the stimulation parameters tested and coil wire materials used. Numerical modeling of the thermodynamics identified hot spots of the highest temperatures along the micro-coil contributing to the thermal gradients and demonstrated that these thermal gradients can be mitigated by the choice of wire conductor material and construction geometry.Significance. ISO standard 14708-1 designates a thermal safety limit of 2 °C temperature increase for active implantable medical devices. By switching the coil wire material from platinum/iridium to gold, our study achieved a 5-6-fold decrease in the thermal impact of coil stimulation. The thermal gradients generated from the gold wire coil were measured below the 2 °C safety limit for all stimulation parameters tested.

Stabilizing the return to normal behavior in an epidemic.

Predicting the interplay between infectious disease and behavior has been an intractable problem because behavioral response is so varied. We introduce a general framework for feedback between incidence and behavior for an infectious disease. By identifying stable equilibria, we provide policy end-states that are self-managing and self-maintaining. We prove mathematically the existence of two new endemic equilibria depending on the vaccination rate: one in the presence of low vaccination but with reduced societal activity (the "new normal"), and one with return to normal activity but with vaccination rate below that required for disease elimination. This framework allows us to anticipate the long-term consequence of an emerging disease and design a vaccination response that optimizes public health and limits societal consequences.

MEMS micro-coils for magnetic neurostimulation.

Micro-coil magnetic stimulation of brain tissue presents new challenges for MEMS micro-coil probe fabrication. The main challenges are threefold; (i) low coil resistance for high power efficiency, (ii) low leak current from the probe into the in vitro experimental set-up, (iii) adaptive MEMS process technology because of the dynamic research area, which requires agile design changes. Taking on these challenges, we present a MEMS fabrication process that has three main features; (i) multilayer resist lift-off process to pattern up to 1800-nm-thick metal films, and special care is taken to obtain high conductivity thin-films by physical vapor deposition, and (ii) all micro-coil Al wires are encapsulated in at least 200 nm of ALD alumina and 6-µm-thick parylene C such the leak resistance is high (>210 GΩ), (iii) combining a multi-step DRIE process and maskless photolithography for adaptive design and device fabrication. The entire process requires four lithography steps. Because we avoided SOI wafers and lithography mask fabrication, the design-to-device time is shortened significantly. The resulting probes are 4-mm-long, 60-µm-thick, and down to 150 µm-wide. Selected MEMS coil devices were validated in vivo using mice and compared to previous work.

Paenibacillus spp infection among infants with postinfectious hydrocephalus in Uganda: an observational case-control study.

BACKGROUND: Paenibacillus thiaminolyticus is a cause of postinfectious hydrocephalus among Ugandan infants. To determine whether Paenibacillus spp is a pathogen in neonatal sepsis, meningitis, and postinfectious hydrocephalus, we aimed to complete three separate studies of Ugandan infants. The first study was on peripartum prevalence of Paenibacillus in mother-newborn pairs. The second study assessed Paenibacillus in blood and cerebrospinal fluid (CSF) from neonates with sepsis. The third study assessed Paenibacillus in CSF from infants with hydrocephalus. METHODS: In this observational study, we recruited mother-newborn pairs with and without maternal fever (mother-newborn cohort), neonates (aged ≤28 days) with sepsis (sepsis cohort), and infants (aged ≤90 days) with hydrocephalus with and without a history of neonatal sepsis and meningitis (hydrocephalus cohort) from three hospitals in Uganda between Jan 13, 2016 and Oct 2, 2019. We collected maternal blood, vaginal swabs, and placental samples and the cord from the mother-newborn pairs, and blood and CSF from neonates and infants. Bacterial content of infant CSF was characterised by 16S rDNA sequencing. We analysed all samples using quantitative PCR (qPCR) targeting either the Paenibacillus genus or Paenibacillus thiaminolyticus spp. We collected cranial ultrasound and computed tomography images in the subset of participants represented in more than one cohort. FINDINGS: No Paenibacillus spp were detected in vaginal, maternal blood, placental, or cord blood specimens from the mother-newborn cohort by qPCR. Paenibacillus spp was detected in 6% (37 of 631 neonates) in the sepsis cohort and, of these, 14% (5 of 37 neonates) developed postinfectious hydrocephalus. Paenibacillus was the most enriched bacterial genera in postinfectious hydrocephalus CSF (91 [44%] of 209 patients) from the hydrocephalus cohort, with 16S showing 94% accuracy when validated by qPCR. Imaging showed progression from Paenibacillus spp-related meningitis to postinfectious hydrocephalus over 1-3 months. Patients with postinfectious hydrocephalus with Paenibacillus spp infections were geographically clustered. INTERPRETATION: Paenibacillus spp causes neonatal sepsis and meningitis in Uganda and is the dominant cause of subsequent postinfectious hydrocephalus. There was no evidence of transplacental transmission, and geographical evidence was consistent with an environmental source of neonatal infection. Further work is needed to identify routes of infection and optimise treatment of neonatal Paenibacillus spp infection to lessen the burden of morbidity and mortality. FUNDING: National Institutes of Health and Boston Children's Hospital Office of Faculty Development.

Social integration predicts survival in female white-faced capuchin monkeys.

Across multiple species of social mammals, a growing number of studies have found that individual sociality is associated with survival. In long-lived species, like primates, lifespan is one of the main components of fitness. We used 18 years of data from the Lomas Barbudal Monkey Project to quantify social integration in 11 capuchin (Cebus capucinus) groups and tested whether female survivorship was associated with females' tendencies to interact with three types of partners: (1) all group members, (2) adult females, and (3) adult males. We found strong evidence that females who engaged more with other females in affiliative interactions and foraged in close proximity experienced increased survivorship. We found some weak evidence that females might also benefit from engaging in more support in agonistic contexts with other females. These benefits were evident in models that account for the females' rank and group size. Female interactions with all group members also increased survival, but the estimates of the effects were more uncertain. In interactions with adult males, only females who provided more grooming to males survived longer. The results presented here suggest that social integration may result in survival-related benefits. Females might enjoy these benefits through exchanging grooming for other currencies, such as coalitionary support or tolerance.