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We consider the treatment of fermionic dark matter interacting with photons via dimension-5 and -6 effective operators, arguing that one should always use hypercharge gauge field form factors, instead of those of the photon. Beyond the simple observation that the electromagnetic form factor description breaks down at the electroweak scale, we show how the additional couplings to the Z boson predicted by the hypercharge form factors modify the relic density calculation and indirect detection limits for dark matter masses of a few tens of GeV and above. Furthermore, constraints from the invisible Z decay width can be competitive for masses below 10 GeV. We review the phenomenology of hypercharge form factors at the LHC as well as for direct and indirect detection experiments. We highlight where the electromagnetic and hypercharge descriptions lead to wildly different conclusions about the viable parameter space and the relative sensitivity of various probes, namely vector boson fusion versus mono-jet constraints from the LHC, and indirect versus direct searches, for larger dark matter masses. We find that the dimension-5 operators are strongly constrained by direct detection bounds, while for dimension-6 operators LHC mono-jet searches are competitive or better than the other probes we consider.

Polygenic Scores (PSs) describe the genetic component of an individual’s quantitative phenotype or their susceptibility to diseases with a genetic basis. Currently, PSs rely on population-dependent contributions of many associated alleles, with limited applicability to understudied populations and recently admixed individuals. Here we introduce a combination of local ancestry deconvolution and partial PS computation to account for the population-specific nature of the association signals in individuals with admixed ancestry. We demonstrate partial PS to be a proxy for the total PS and that a portion of the genome is enough to improve susceptibility predictions for the traits we test. By combining partial PSs from different populations, we are able to improve trait predictability in admixed individuals with some European ancestry. These results may extend the applicability of PSs to subjects with a complex history of admixture, where current methods cannot be applied. Polygenic scores are believed to hold future promise for trait prediction and personalized medicine, but are sensitive to demographic history. Here, Marnetto et al . develop partial polygenic scores supplemented with local ancestry deconvolution which improves prediction accuracy into recently admixed European populations.

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.

In the growing Industry 4.0 market, there is strong need to implement automatic inspection methods to support manufacturing processes. Tool wear in turning is one of the biggest concerns that most expert operators are able to indirectly infer through the analysis of the removed chips. Automatising this operation would enable developing more efficient cutting processes that turns in easier process planning management toward the Zero Defect Manufacturing paradigm. This paper presents a deep learning approach, based on image processing applied to turning chips for indirectly identifying tool wear levels. The procedure extracts different indicators from the RGB and HSV image channels and instructs a neural network for classifying the chips, based on tool state conditions. Images were collected with a high-resolution digital camera during an experimental cutting campaign involving tool wear analysis with direct microscope imaging. The sensitivity analysis confirmed that the most sensible image channels are the hue value H that were used to teach the network, leading to performances in the range of 95 of proper classification. The feasibility of the deep learning approach for indirectly understanding the tool wear from the chip colour characterisation is confirmed. However, due to the big effects on chip colours of variables as the workpiece material and cutting process parameters, the applicability is limited to stable production flows. An industrial implementation can be foreseen by populating proper large databases and by implementing real-time chip segmentation analysis.

Ancient DNA is revealing new insights into the genetic relationship between Pleistocene hominins and modern humans. Nuclear DNA indicated Neanderthals as a sister group of Denisovans after diverging from modern humans. However, the closer affinity of the Neanderthal mitochondrial DNA (mtDNA) to modern humans than Denisovans has recently been suggested as the result of gene flow from an African source into Neanderthals before 100,000 years ago. Here we report the complete mtDNA of an archaic femur from the Hohlenstein–Stadel (HST) cave in southwestern Germany. HST carries the deepest divergent mtDNA lineage that splits from other Neanderthals ∼270,000 years ago, providing a lower boundary for the time of the putative mtDNA introgression event. We demonstrate that a complete Neanderthal mtDNA replacement is feasible over this time interval even with minimal hominin introgression. The highly divergent HST branch is indicative of greater mtDNA diversity during the Middle Pleistocene than in later periods. Ancient DNA keeps expanding our understanding of complex genetic relationships between Pleistocene hominins. Here, Posth and colleagues analyse the mitochondrial genome of an archaic human that diverged from other Neanderthals ∼270,000 years ago, providing the minimum age for an African introgression into Neanderthals.

The Italian Peninsula, a natural pier across the Mediterranean Sea, witnessed intricate population events since the very beginning of the human occupation in Europe. In the last few years, an increasing number of modern and ancient genomes from the area have been published by the international research community. This genomic perspective started unveiling the relevance of Italy to understand the post-Last Glacial Maximum (LGM) re-peopling of Europe, the earlier phase of the Neolithic westward migrations, and its linking role between Eastern and Western Mediterranean areas after the Iron Age. However, many open questions are still waiting for more data to be addressed in full. With this review, we summarize the current knowledge emerging from the available ancient Italian individuals and, by re-analysing them all at once, we try to shed light on the avenues future research in the area should cover. In particular, open questions concern (1) the fate of pre-Villabruna Europeans and to what extent their genomic components were absorbed by the post-LGM hunter-gatherers; (2) the role of Sicily and Sardinia before LGM; (3) to what degree the documented genetic structure within the Early Neolithic settlers can be described as two separate migrations; (4) what are the population events behind the marked presence of an Iranian Neolithic-like component in Bronze Age and Iron Age Italian and Southern European samples.

Given the importance of Africa to studies of human origins and disease susceptibility, detailed characterization of African genetic diversity is needed. The African Genome Variation Project provides a resource with which to design, implement and interpret genomic studies in sub-Saharan Africa and worldwide. The African Genome Variation Project represents dense genotypes from 1,481 individuals and whole-genome sequences from 320 individuals across sub-Saharan Africa. Using this resource, we find novel evidence of complex, regionally distinct hunter-gatherer and Eurasian admixture across sub-Saharan Africa. We identify new loci under selection, including loci related to malaria susceptibility and hypertension. We show that modern imputation panels (sets of reference genotypes from which unobserved or missing genotypes in study sets can be inferred) can identify association signals at highly differentiated loci across populations in sub-Saharan Africa. Using whole-genome sequencing, we demonstrate further improvements in imputation accuracy, strengthening the case for large-scale sequencing efforts of diverse African haplotypes. Finally, we present an efficient genotype array design capturing common genetic variation in Africa. The African Genome Variation Project contains the whole-genome sequences of 320 individuals and dense genotypes on 1,481 individuals from sub-Saharan Africa; it enables the design and interpretation of genomic studies, with implications for finding disease loci and clues to human origins. Genetic variation in sub-Saharan Africa The African Genome Variation Project (AGVP) is collecting data on the structure of African genomes to provide a central resource for genetic disease studies in Africa. It currently represents dense genotypes from 1,481 individuals and whole-genome sequences from 320 individuals across sub-Saharan Africa. Using these data, Manjinder Sandhu and colleagues identify new loci under selection, including those associated with malaria and hypertension. They show that modern imputation panels can identify association signals at highly differentiated loci across population groups. They demonstrate the utility of whole-genome sequences in further improving the imputation accuracy. In addition, they describe the first efficient genotype array design capturing common genetic variation in Africa.

Mountain gorillas are an endangered great ape subspecies and a prominent focus for conservation, yet we know little about their genomic diversity and evolutionary past. We sequenced whole genomes from multiple wild individuals and compared the genomes of all four Gorilla subspecies. We found that the two eastern subspecies have experienced a prolonged population decline over the past 100,000 years, resulting in very low genetic diversity and an increased overall burden of deleterious variation. A further recent decline in the mountain gorilla population has led to extensive inbreeding, such that individuals are typically homozygous at 34% of their sequence, leading to the purging of severely deleterious recessive mutations from the population. We discuss the causes of their decline and the consequences for their future survival.

Understanding the influence of process parameters and defect structure on the properties of parts produced via laser powder bed fusion (L-PBF) is a fundamental step towards the broader use of additive manufacturing technologies in critical applications. Furthermore, the ability to predict mechanical properties by simply considering information on the process parameters and defects observed via X-ray computer tomography (XCT) allows one to avoid expensive destructive testing, provide an in-depth understanding of the process quality and represents a viable solution towards process optimisation. Most of the previous works showed that energy density could be used as an excellent synthetic indicator to predict the mechanical properties of parts produced by L-PBF. This paper explores the effect of different energy density levels on the tensile properties of 316L stainless steel parts produced by L-PBF. Different from previous works in the literature, the same level of energy density is obtained considering various combinations of process parameters (speed, power and hatch distance). While energy density is shown to be a good synthetic indicator for predicting ultimate tensile strength (UTS) and yield strength (YS), a different behaviour is observed for elongation. Elongation shows a significant variability even when samples are produced at the same level of energy density, which contrasts with results obtained for UTS and YS. Synthetic indices representing the porosity structure are shown to be quite significant for predicting elongation even when the optimal energy density is considered. By combining process parameters with porosity structure, we show that almost a full prediction of the tensile properties can be achieved, paving the way for a significant reduction in expensive destructive tests.

A combination of evidence, based on genetic, fossil and archaeological findings, indicates that Homo sapiens spread out of Africa between ~70-60 thousand years ago (kya). However, it appears that once outside of Africa, human populations did not expand across all of Eurasia until ~45 kya. The geographic whereabouts of these early settlers in the timeframe between ~70-60 to 45 kya has been difficult to reconcile. Here we combine genetic evidence and palaeoecological models to infer the geographic location that acted as the Hub for our species during the early phases of colonisation of Eurasia. Leveraging on available genomic evidence we show that populations from the Persian Plateau carry an ancestry component that closely matches the population that settled the Hub outside Africa. With the paleoclimatic data available to date, we built ecological models showing that the Persian Plateau was suitable for human occupation and that it could sustain a larger population compared to other West Asian regions, strengthening this claim. The timing and chronology of the movement of Homo sapiens after migration out of Africa remains unclear. Here, the authors combine a genetic approach with a palaeoecological model to estimate that the Persian Plateau could have been a hub for migration out of Africa, suggesting the environment may have been suitable for population maintenance.