Out of Amazonia: late-Holocene climate change and the Tupi–Guarani trans-continental expansion

The late Holocene expansion of the Tupi-Guarani languages from southern Amazonia to SE South America constitutes one of the largest expansions of any linguistic family in the world, spanning ~ 4000 km between latitudes 0°S and 35°S at about 2500 yr B.P. However, the underlying reasons for this expansion are a matter of debate. Here, we compare continental-scale palaeoecological, palaeoclimate, and archaeological datasets, to examine the role of climate change in facilitating the expansion of this forestfarming culture. Because this expansion lies within the path of the South American Low-Level Jet, the key mechanism for moisture transport across lowland South America, we were able to explore the relationship between climate change, forest expansion, and the Tupi-Guarani. Our data synthesis shows broad synchrony between late Holocene increasing precipitation and southerly expansion of both tropical forest and Guarani archaeological sites – the southernmost branch of the Tupi-Guarani. We conclude that climate change likely facilitated the agricultural expansion of the Guarani forest-farming culture by increasing the area of forested landscape that they could exploit, showing a prime example of ecological opportunism

Prevalence, Distribution and Functional Significance of the −237C to T Polymorphism in the IL-12Rβ2 Promoter in Indian Tuberculosis Patients

Cytokine/cytokine receptor gene polymorphisms related to structure/expression could impact immune response. Hence, the −237 polymorphic site in the 5′ promoter region of the IL-12Rβ2 (SNP ID: rs11810249) gene associated with the AP-4 transcription motif GAGCTG, was examined. Amplicons encompassing the polymorphism were generated from 46 pulmonary tuberculosis patients, 35 family contacts and 28 miscellaneous volunteers and sequenced. The C allele predominated among patients, (93.4%, 43/46), and in all volunteers and contacts screened, but the T allele was exclusively limited to patients, (6.5%, 3/46). The functional impact of this polymorphism on transcriptional activity was assessed by Luciferase-reporter and electrophoretic mobility shift assays (EMSA). Luciferase-reporter assays showed a significant reduction in transcriptional efficiency with T compared to C allele. The reduction in transcriptional efficiency with the T allele construct (pGIL-12Rb2-T), in U-87MG, THP-1 and Jurkat cell lines, were 53, 37.6, and 49.8% respectively, compared to the C allele construct (pGIL-12Rb2-C). Similarly, densitometric analysis of the EMSA assay showed reduced binding of the AP-4 transcription factor, to T compared to the C nucleotide probe. Reduced mRNA expression in all patients (3/3) harboring the T allele was seen, whereas individuals with the C allele exhibited high mRNA expression (17/25; 68%, p = 0.05). These observations were in agreement with the in vitro assessment of the promoter activity by Luciferase-reporter and EMSA assays. The reduced expression of IL-12Rβ2 transcripts in 8 patients despite having the C allele was attributed to the predominant over expression of the suppressors (IL-4 and GATA-3) and reduced expression of enhancers (IFN-α) of IL-12Rβ2 transcripts. The 17 high IL-12Rβ2 mRNA expressers had significantly elevated IFN-α mRNA levels compared to low expressers and volunteers. Notwithstanding the presence of high levels of IL-12Rβ2 mRNA in these patients elevated IFN-α expression could modulate their immune responses to Mycobacterium tuberculosis

Genome Features of “Dark-Fly”, a Drosophila Line Reared Long-Term in a Dark Environment

Organisms are remarkably adapted to diverse environments by specialized metabolisms, morphology, or behaviors. To address the molecular mechanisms underlying environmental adaptation, we have utilized a Drosophila melanogaster line, termed “Dark-fly”, which has been maintained in constant dark conditions for 57 years (1400 generations). We found that Dark-fly exhibited higher fecundity in dark than in light conditions, indicating that Dark-fly possesses some traits advantageous in darkness. Using next-generation sequencing technology, we determined the whole genome sequence of Dark-fly and identified approximately 220,000 single nucleotide polymorphisms (SNPs) and 4,700 insertions or deletions (InDels) in the Dark-fly genome compared to the genome of the Oregon-R-S strain, a control strain. 1.8% of SNPs were classified as non-synonymous SNPs (nsSNPs: i.e., they alter the amino acid sequence of gene products). Among them, we detected 28 nonsense mutations (i.e., they produce a stop codon in the protein sequence) in the Dark-fly genome. These included genes encoding an olfactory receptor and a light receptor. We also searched runs of homozygosity (ROH) regions as putative regions selected during the population history, and found 21 ROH regions in the Dark-fly genome. We identified 241 genes carrying nsSNPs or InDels in the ROH regions. These include a cluster of alpha-esterase genes that are involved in detoxification processes. Furthermore, analysis of structural variants in the Dark-fly genome showed the deletion of a gene related to fatty acid metabolism. Our results revealed unique features of the Dark-fly genome and provided a list of potential candidate genes involved in environmental adaptation

Edwardsiella Comparative Phylogenomics Reveal the New Intra/Inter-Species Taxonomic Relationships, Virulence Evolution and Niche Adaptation Mechanisms

Edwardsiella bacteria are leading fish pathogens causing huge losses to aquaculture industries worldwide. E. tarda is a broad-host range pathogen that infects more than 20 species of fish and other animals including humans while E. ictaluri is host-adapted to channel catfish causing enteric septicemia of catfish (ESC). Thus, these two species consist of a useful comparative system for studying the intricacies of pathogen evolution. Here we present for the first time the phylogenomic comparisons of 8 genomes of E. tarda and E. ictaluri isolates. Genome-based phylogenetic analysis revealed that E. tarda could be separate into two kinds of genotypes (genotype I, EdwGI and genotype II, EdwGII) based on the sequence similarity. E. tarda strains of EdwGI were clustered together with the E. ictaluri lineage and showed low sequence conservation to E. tarda strains of EdwGII. Multilocus sequence analysis (MLSA) of 48 distinct Edwardsiella strains also supports the new taxonomic relationship of the lineages. We identified the type III and VI secretion systems (T3SS and T6SS) as well as iron scavenging related genes that fulfilled the criteria of a key evolutionary factor likely facilitating the virulence evolution and adaptation to a broad range of hosts in EdwGI E. tarda. The surface structure-related genes may underlie the adaptive evolution of E. ictaluri in the host specification processes. Virulence and competition assays of the null mutants of the representative genes experimentally confirmed their contributive roles in the evolution/niche adaptive processes. We also reconstructed the hypothetical evolutionary pathway to highlight the virulence evolution and niche adaptation mechanisms of Edwardsiella. This study may facilitate the development of diagnostics, vaccines, and therapeutics for this under-studied pathogen

Event log reconstruction using autoencoders

Poor quality of process event logs prevents high quality business process analysis and improvement. Process event logs quality decreases because of missing attribute values or after incorrect or irrelevant attribute values are identified and removed. Reconstructing a correct value for these missing attributes is likely to increase the quality of event log-based process analyses. Traditional statistical reconstruction methods work poorly with event logs, because of the complex interrelations among attributes, events and cases. Machine learning approaches appear more suitable in this context, since they can learn complex models of event logs through training. This paper proposes a method for reconstructing missing attribute values in event logs based on the use of autoencoders. Autoencoders are a class of feed-forward neural networks that reconstruct their own input after having learnt a model of its latent distribution. They suit problems of unsupervised learning, such as the one considered in this paper. When reconstructing missing attribute values in an event log, in fact, one cannot assume that a training set with true labels is available for model training. The proposed method is evaluated on two real event logs against baseline methods commonly used in the literature for imputing missing values in large datasets

Earth Movers’ Stochastic Conformance Checking

Process Mining aims to support Business Process Management (BPM) by extracting information about processes from real-life process executions recorded in event logs. In particular, conformance checking aims to measure the quality of a process model by quantifying differences between the model and an event log or another model. Even though event logs provide insights into the likelihood of observed behaviour, most state-of-the-art conformance checking techniques ignore this point of view. In this paper, we propose a conformance measure that considers the stochastic characteristics of both the event log and the process model. It is based on the “earth movers’ distance” and measures the effort to transform the distributions of traces of the event log into the distribution of traces of the model. We formalize this intuitive conformance metric and provide an approximation and a simplified variant. The latter two have been implemented in ProM and we evaluate them using several real-life examples.</p