VOSpace: a Prototype for Grid 2.0

As Grid 1.0 was characterized by distributed computation, so Grid 2.0 will be characterized by distributed data and the infrastructure needed to support and exploit it: the emerging success of Amazon S3 is already testimony to this. VOSpace is the IVOA interface standard for accessing distributed data. Although the base definition (VOSpace 1.0) only relates to flat, unconnected data stores, subsequent versions will add additional layers of functionality. In this paper, we consider how incorporating popular web concepts such as folksonomies (tagging), social networking, and data-spaces could lead to a much richer data environment than provided by a traditional collection of networked data stores

Ectopic Gene Conversions in the Genome of Ten Hemiascomycete Yeast Species

We characterized ectopic gene conversions in the genome of ten hemiascomycete yeast species. Of the ten species, three diverged prior to the whole genome duplication (WGD) event present in the yeast lineage and seven diverged after it. We analyzed gene conversions from three separate datasets: paralogs from the three pre-WGD species, paralogs from the seven post-WGD species, and common ohnologs from the seven post-WGD species. Gene conversions have similar lengths and frequency and occur between sequences having similar degrees of divergence, in paralogs from pre- and post-WGD species. However, the sequences of ohnologs are both more divergent and less frequently converted than those of paralogs. This likely reflects the fact that ohnologs are more often found on different chromosomes and are evolving under stronger selective pressures than paralogs. Our results also show that ectopic gene conversions tend to occur more frequently between closely linked genes. They also suggest that the mechanisms responsible for the loss of introns in S. cerevisiae are probably also involved in the gene 3′-end gene conversion bias observed between the paralogs of this species

Direct sequencing of Cryptosporidium in stool samples for public health

The protozoan parasite Cryptosporidium is an important cause of diarrheal disease (cryptosporidiosis) in humans and animals, with significant morbidity and mortality especially in severely immunocompromised people and in young children in low-resource settings. Due to the sexual life cycle of the parasite, transmission is complex. There are no restrictions on sexual recombination between sub-populations, meaning that large-scale genetic recombination may occur within a host, potentially confounding epidemiological analysis. To clarify the relationships between infections in different hosts, it is first necessary to correctly identify species and genotypes, but these differentiations are not made by standard diagnostic tests and more sophisticated molecular methods have been developed. For instance, multilocus genotyping has been utilized to differentiate isolates within the major human pathogens, Cryptosporidium parvum and Cryptosporidium hominis. This has allowed mixed populations with multiple alleles to be identified: recombination events are considered to be the driving force of increased variation and the emergence of new subtypes. As yet, whole genome sequencing (WGS) is having limited impact on public health investigations, due in part to insufficient numbers of oocysts and purity of DNA derived from clinical samples. Moreover, because public health agencies have not prioritized parasites, validation has not been performed on user-friendly data analysis pipelines suitable for public health practitioners. Nonetheless, since the first whole genome assembly in 2004 there are now numerous genomes of human and animal-derived cryptosporidia publically available, spanning nine species. It has also been demonstrated that WGS from very low numbers of oocysts is possible, through the use of amplification procedures. These data and approaches are providing new insights into host-adapted infectivity, the presence and frequency of multiple sub-populations of Cryptosporidium spp. within single clinical samples, and transmission of infection. Analyses show that although whole genome sequences do indeed contain many alleles, they are invariably dominated by a single highly abundant allele. These insights are helping to better understand population structures within hosts, which will be important to develop novel prevention strategies in the fight against cryptosporidiosis

N-Functionalised TsDPEN catalysts for asymmetric transfer hydrogenation; synthesis and applications

A series of Ru(II)/arene complexes containing N-alkylated derivatives of TsDPEN were prepared and tested in the asymmetric transfer hydrogenation (ATH) of ketones. The results demonstrated that a wide variety of functionality were tolerated on the basic amine of the TsDPEN ligand, without significantly disrupting the ability of the catalyst to catalyse hydrogen transfer reactions

Probabilistically Rewired Message-Passing Neural Networks

Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited expressive power in capturing relevant graph structures. Existing solutions to these challenges have primarily relied on heuristic methods, often disregarding the underlying data distribution. Hence, devising principled approaches for learning to infer graph structures relevant to the given prediction task remains an open challenge. In this work, leveraging recent progress in exact and differentiable $k$-subset sampling, we devise probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges while omitting less beneficial ones. For the first time, our theoretical analysis explores how PR-MPNNs enhance expressive power, and we identify precise conditions under which they outperform purely randomized approaches. Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures

Lagged association between climate variables and hospital admissions for pneumonia admissions in South Africa

Pneumonia is a leading cause of hospitalization in South Africa. Climate change could potentially affect its incidence via changes in meteorological conditions. We investigated the delayed effects of temperature and relative humidity on pneumonia hospital admissions at two large public hospitals in Limpopo province, South Africa. Using 4062 pneumonia hospital admission records from 2007 to 2015, a time-varying distributed lag non-linear model was used to estimate temperature-lag and relative humidity-lag pneumonia relationships. Mean temperature, relative humidity and diurnal temperature range were all significantly associated with pneumonia admissions. Cumulatively across the 21-day period, higher mean daily temperature (30 °C relative to 21 °C) was most strongly associated with a decreased rate of hospital admissions (relative rate ratios (RR): 0.34, 95% confidence interval (CI): 0.14–0.82), whereas results were suggestive of lower mean daily temperature (12 °C relative to 21 °C) being associated with an increased rate of admissions (RR: 1.27, 95%CI: 0.75–2.16). Higher relative humidity (>80%) was associated with fewer hospital admissions while low relative humidity (<30%) was associated with increased admissions. A proportion of pneumonia admissions were attributable to changes in meteorological variables, and our results indicate that even small shifts in their distributions (e.g., due to climate change) could lead to substantial changes in their burden. These findings can inform a better understanding of the health implications of climate change and the burden of hospital admissions for pneumonia now and in the future

Generation and characterisation of two D2A1 mammary cancer sublines to model spontaneous and experimental metastasis in a syngeneic BALB/c host

Studying the complex mechanisms underlying breast cancer metastasis and therapy response necessitates relevant in vivo models, particularly syngeneic models with an intact immune system. Two syngeneic spontaneously metastatic sublines, D2A1-m1 and D2A1-m2, were generated from the poorly metastasising BALB/c-derived D2A1 cell line by serial in vivo passaging. In vivo and in vitro analyses revealed distinct and shared characteristics of the metastatic D2A1-m1 and D2A1-m2 sublines. In particular, D2A1-m1 cells are more aggressive in experimental metastasis assays, while D2A1-m2 cells are more efficient at disseminating from the primary tumour in spontaneous metastasis assays. Surprisingly, classical metastasis-associated in vitro phenotypes such as enhanced proliferation, migration and invasion are reduced in the sublines compared to the parental cell line. Further, evasion of immune control cannot fully explain their enhanced metastatic properties. By contrast, both sublines show increased resistance to apoptosis when cultured in non-adherent conditions and, for the D2A1-m2 subline, increased 3D tumour spheroid growth. Moreover, the enhanced spontaneous metastatic phenotype of the D2A1-m2 subline is associated with an increased ability to recruit an activated tumour stroma. The metastatic D2A1-m1 and D2A1-m2 cell lines provide additional syngeneic models for investigating the different steps of the metastatic cascade and thereby represent valuable tools for breast cancer researchers. Finally, this study highlights that morphology and cell behaviour in 2D cell-based assays cannot be used as a reliable predictor of metastatic behaviour in vivo