Wellcome Centre for Anti-Infectives Research

Various methods to synthesize diverse nanoparticles with their different applications.

The packet pair technique estimates the capacity of a path (bottleneck bandwidth) from the dispersion (spacing) experienced by two back-to-back packets. We demonstrate that the dispersion of packet pairs in loaded paths follows a multimodal distribution, and discuss the queueing effects that cause the multiple modes. We show that the path capacity is often not the global mode, and so it cannot be estimated using standard statistical procedures. The effect of the size of the probing packets is also investigated, showing that the conventional wisdom of using maximum sized packet pairs is not optimal. We then study the dispersion of long packet trains. Increasing the length of the packet train reduces the measurement variance, but the estimates converge to a value, referred to as the asymptotic dispersion rate (ADR), that is lower than the capacity. We derive the effect of the cross traffic in the dispersion of long packet trains, showing that the ADR is not the available bandwidth in the path, as was assumed in previous work. Putting all the pieces together, we present a capacity estimation methodology that has been implemented in a tool called pathrate.

A genomics-based approach was used to identify the entire gene complement of putative two-component signal transduction systems (TCSTSs) in Streptococcus pneumoniae. A total of 14 open reading frames (ORFs) were identified as putative response regulators, 13 of which were adjacent to genes encoding probable histidine kinases. Both the histidine kinase and response regulator proteins were categorized into subfamilies on the basis of phylogeny. Through a systematic programme of mutagenesis, the importance of each novel TCSTS was determined with respect to viability and pathogenicity. One TCSTS was identified that was essential for the growth of S. pneumoniaeThis locus was highly homologous to the yycFG gene pair encoding the essential response regulator/histidine kinase proteins identified in Bacillus subtilis and Staphylococcus aureus. Separate deletions of eight other loci led in each case to a dramatic attenuation of growth in a mouse respiratory tract infection model, suggesting that these signal transduction systems are important for the in vivo adaptation and pathogenesis of S. pneumoniae. The identification of conserved TCSTSs important for both pathogenicity and viability in a Gram-positive pathogen highlights the potential of two-component signal transduction as a multicomponent target for antibacterial drug discovery.

We calculate an extensive set of characteristics for Internet AS topologies extracted from the three data sources most frequently used by the research community: traceroutes, BGP, and WHOIS. We discover that traceroute and BGP topologies are similar to one another but differ substantially from the WHOIS topology. Among the widely considered metrics, we find that the joint degree distribution appears to fundamentally characterize Internet AS topologies as well as narrowly define values for other important metrics. We discuss the interplay between the specifics of the three data collection mechanisms and the resulting topology views. In particular, we show how the data collection peculiarities explain differences in the resulting joint degree distributions of the respective topologies. Finally, we release to the community the input topology datasets, along with the scripts and output of our calculations. This supplement should enable researchers to validate their models against real data and to make more informed selection of topology data sources for their specific needs.

Diabetic kidney disease, or diabetic nephropathy (DN), is a major complication of diabetes and the leading cause of end-stage renal disease (ESRD) that requires dialysis treatment or kidney transplantation. In addition to the decrease in the quality of life, DN accounts for a large proportion of the excess mortality associated with type 1 diabetes (T1D). Whereas the degree of glycemia plays a pivotal role in DN, a subset of individuals with poorly controlled T1D do not develop DN. Furthermore, strong familial aggregation supports genetic susceptibility to DN. However, the genes and the molecular mechanisms behind the disease remain poorly understood, and current therapeutic strategies rarely result in reversal of DN. In the GEnetics of Nephropathy: an International Effort (GENIE) consortium, we have undertaken a meta-analysis of genome-wide association studies (GWAS) of T1D DN comprising ~2.4 million single nucleotide polymorphisms (SNPs) imputed in 6,691 individuals. After additional genotyping of 41 top ranked SNPs representing 24 independent signals in 5,873 individuals, combined meta-analysis revealed association of two SNPs with ESRD: rs7583877 in the AFF3 gene (P = 1.2 × 10(-8)) and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, rs12437854 (P = 2.0 × 10(-9)). Functional data suggest that AFF3 influences renal tubule fibrosis via the transforming growth factor-beta (TGF-β1) pathway. The strongest association with DN as a primary phenotype was seen for an intronic SNP in the ERBB4 gene (rs7588550, P = 2.1 × 10(-7)), a gene with type 2 diabetes DN differential expression and in the same intron as a variant with cis-eQTL expression of ERBB4. All these detected associations represent new signals in the pathogenesis of DN.

ABSTRACT The history of emotions is a burgeoning field—so much so, that some are invoking an “emotional turn.” As a way of charting this development, I have interviewed three of the leading practitioners of the history of emotions: William Reddy, Barbara Rosenwein, and Peter Stearns. The interviews retrace each historian's intellectual‐biographical path to the history of emotions, recapitulate key concepts, and critically discuss the limitations of the available analytical tools. In doing so, they touch on Reddy's concepts of “emotive,”“emotional regime,” and “emotional navigation,” as well as on Rosenwein's “emotional community” and on Stearns's “emotionology” and offer glimpses of each historian's ongoing research. The interviews address the challenges presented to historians by research in the neurosciences and the like, highlighting the distinctive contributions offered by a historical approach. In closing, the interviewees appear to reach a consensus, envisioning the history of emotions not as a specialized field but as a means of integrating the category of emotion into social, cultural, and political history, emulating the rise of gender as an analytical category since its early beginnings as “women's history” in the 1970s.

Research on performance, robustness, and evolution of the global Internet is fundamentally handicapped without accurate and thorough knowledge of the nature and structure of the contractual relationships between Autonomous Systems (ASs). In this work we introduce novel heuristics for inferring AS relationships. Our heuristics improve upon previous works in several technical aspects, which we outline in detail and demonstrate with several examples. Seeking to increase the value and reliability of our inference results, we then focus on validation of inferred AS relationships. We perform a survey with ASs' network administrators to collect information on the actual connectivity and policies of the surveyed ASs. Based on the survey results, we find that our new AS relationship inference techniques achieve high levels of accuracy: we correctly infer 96.5% customer to provider (c2p), 82.8% peer to peer (p2p), and 90.3% sibling to sibling (s2s) relationships. We then cross-compare the reported AS connectivity with the AS connectivity data contained in BGP tables. We find that BGP tables miss up to 86.2% of the true adjacencies of the surveyed ASs. The majority of the missing links are of the p2p type, which highlights the limitations of present measuring techniques to capture links of this type. Finally, to make our results easily accessible and practically useful for the community, we open an AS relationship repository where we archive, on a weekly basis, and make publicly available the complete Internet AS-level topology annotated with AS relationship information for every pair of AS neighbors.

We calculate an extensive set of characteristics for Internet AS topologies extracted from the three data sources most frequently used by the research community: traceroutes, BGP, and WHOIS. We discover that traceroute and BGP topologies are similar to one another but differ substantially from the WHOIS topology. Among the widely considered metrics, we find that the joint degree distribution appears to fundamentally characterize Internet AS topologies as well as narrowly define values for other important metrics. We discuss the interplay between the specifics of the three data collection mechanisms and the resulting topology views. In particular, we how how the data collection peculiarities explain differences in the resulting joint degree distributions of the respective topologies. Finally, we release to the community the input topology datasets, along with the scripts and output of our calculations. This supplement hould enable researchers to validate their models against real data and to make more informed election of topology data sources for their specific needs

The recent emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the underlying cause of Coronavirus Disease 2019 (COVID-19), has led to a worldwide pandemic causing substantial morbidity, mortality, and economic devastation. In response, many laboratories have redirected attention to SARS-CoV-2, meaning there is an urgent need for tools that can be used in laboratories unaccustomed to working with coronaviruses. Here we report a range of tools for SARS-CoV-2 research. First, we describe a facile single plasmid SARS-CoV-2 reverse genetics system that is simple to genetically manipulate and can be used to rescue infectious virus through transient transfection (without in vitro transcription or additional expression plasmids). The rescue system is accompanied by our panel of SARS-CoV-2 antibodies (against nearly every viral protein), SARS-CoV-2 clinical isolates, and SARS-CoV-2 permissive cell lines, which are all openly available to the scientific community. Using these tools, we demonstrate here that the controversial ORF10 protein is expressed in infected cells. Furthermore, we show that the promising repurposed antiviral activity of apilimod is dependent on TMPRSS2 expression. Altogether, our SARS-CoV-2 toolkit, which can be directly accessed via our website at https://mrcppu-covid.bio/, constitutes a resource with considerable potential to advance COVID-19 vaccine design, drug testing, and discovery science.

We analyzed 40 single nucleotide polymorphism and 19 short tandem repeat Y-chromosomal markers in a large sample of 1,525 indigenous individuals from 14 populations in the Caucasus and 254 additional individuals representing potential source populations. We also employed a lexicostatistical approach to reconstruct the history of the languages of the North Caucasian family spoken by the Caucasus populations. We found a different major haplogroup to be prevalent in each of four sets of populations that occupy distinct geographic regions and belong to different linguistic branches. The haplogroup frequencies correlated with geography and, even more strongly, with language. Within haplogroups, a number of haplotype clusters were shown to be specific to individual populations and languages. The data suggested a direct origin of Caucasus male lineages from the Near East, followed by high levels of isolation, differentiation, and genetic drift in situ. Comparison of genetic and linguistic reconstructions covering the last few millennia showed striking correspondences between the topology and dates of the respective gene and language trees and with documented historical events. Overall, in the Caucasus region, unmatched levels of gene-language coevolution occurred within geographically isolated populations, probably due to its mountainous terrain.

Research on performance, robustness, and evolution of the global Internet is fundamentally handicapped without accurate and thorough knowledge of the nature and structure of the contractual relationships between Autonomous Systems (ASs). In this work we introduce novel heuristics for inferring AS relationships. Our heuristics improve upon previous works in several technical aspects, which we outline in detail and demonstrate with several examples. Seeking to increase the value and reliability of our inference results, we then focus on validation of inferred AS relationships. We perform a survey with ASs' network administrators to collect information on the actual connectivity and policies of the surveyed ASs. Based on the survey results, we find that our new AS relationship inference techniques achieve high levels of accuracy: we correctly infer 96.5% customer to provider (c2p), 82.8% peer to peer (p2p), and 90.3% sibling to sibling (s2s) relationships. We then cross-compare the reported AS connectivity with the AS connectivity data contained in BGP tables. We find that BGP tables miss up to 86.2% of the true adjacencies of the surveyed ASs. The majority of the missing links are of the p2p type, which highlights the limitations of present measuring techniques to capture links of this type. Finally, to make our results easily accessible and practically useful for the community, we open an AS relationship repository where we archive, on a weekly basis, and make publicly available the complete Internet AS-level topology annotated with AS relationship information for every pair of AS neighbors.

We introduce Deformable Convolution v4 (DCNv4), a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor, DCNv3, with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2. optimizing memory access to minimize redundant operations for speedup. These improvements result in a significantly faster convergence compared to DCNv3 and a substantial increase in processing speed, with DCNv4 achieving more than three times the forward speed. DCNv4 demonstrates exceptional performance across various tasks, including image classification, instance and semantic segmentation, and notably, image generation. When integrated into generative models like U-Net in the latent diffusion model, DCNv4 outperforms its baseline, underscoring its possibility to enhance generative models. In practical applications, replacing DCNv3 with DCNv4 in the InternImage model to create FlashInternImage results in up to 80% speed increase and further performance improvement without further modifications. The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.

Flash worms follow a precomputed spread tree using prior knowledge of all systems vulnerable to the worm's exploit. In previous work we suggested that a flash worm could saturate one million vulnerable hosts on the Internet in under 30 seconds[18]. We grossly over-estimated.

The diffusion-based generative models have achieved remarkable success in text-based image generation. However, since it contains enormous randomness in generation progress, it is still challenging to apply such models for real-world visual content editing, especially in videos. In this paper, we propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. To edit videos consistently, we propose several techniques based on the pre-trained models. Firstly, in contrast to the straightforward DDIM inversion technique, our approach captures intermediate attention maps during inversion, which effectively retain both structural and motion information. These maps are directly fused in the editing process rather than generated during denoising. To further minimize semantic leakage of the source video, we then fuse self-attentions with a blending mask obtained by cross-attention features from the source prompt. Furthermore, we have implemented a reform of the self-attention mechanism in denoising UNet by introducing spatial-temporal attention to ensure frame consistency. Yet succinct, our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model. We also have a better zero-shot shape-aware editing ability based on the text-to-video model [52]. Extensive experiments demonstrate our superior temporal consistency and editing capability than previous works.

Visceral leishmaniasis (VL), caused by the protozoan parasites Leishmania donovani and Leishmania infantum , is one of the major parasitic diseases worldwide. There is an urgent need for new drugs to treat VL, because current therapies are unfit for purpose in a resource-poor setting. Here, we describe the development of a preclinical drug candidate, GSK3494245/DDD01305143/compound 8, with potential to treat this neglected tropical disease. The compound series was discovered by repurposing hits from a screen against the related parasite Trypanosoma cruzi . Subsequent optimization of the chemical series resulted in the development of a potent cidal compound with activity against a range of clinically relevant L. donovani and L. infantum isolates. Compound 8 demonstrates promising pharmacokinetic properties and impressive in vivo efficacy in our mouse model of infection comparable with those of the current oral antileishmanial miltefosine. Detailed mode of action studies confirm that this compound acts principally by inhibition of the chymotrypsin-like activity catalyzed by the β5 subunit of the L. donovani proteasome. High-resolution cryo-EM structures of apo and compound 8-bound Leishmania tarentolae 20S proteasome reveal a previously undiscovered inhibitor site that lies between the β4 and β5 proteasome subunits. This induced pocket exploits β4 residues that are divergent between humans and kinetoplastid parasites and is consistent with all of our experimental and mutagenesis data. As a result of these comprehensive studies and due to a favorable developability and safety profile, compound 8 is being advanced toward human clinical trials.

Leishmaniasis (visceral and cutaneous), Chagas disease and human African trypanosomiasis cause substantial death and morbidity, particularly in low- and middle-income countries. Although the situation has improved for human African trypanosomiasis, there remains an urgent need for new medicines to treat leishmaniasis and Chagas disease; the clinical development pipeline is particularly sparse for Chagas disease. In this Review, we describe recent advances in our understanding of the biology of the causative pathogens, particularly from the drug discovery perspective, and we explore the progress that has been made in the development of new drug candidates and the identification of promising molecular targets. We also explore the challenges in developing new clinical candidates and discuss potential solutions to overcome such hurdles. In this Review, Gilbert and colleagues discuss recent progress in drug discovery for kinetoplastid diseases and how an improved understanding of parasite biology affects the drug discovery process

Recent measurements and anecdotal evidence indicate that the Internet ecosystem is rapidly evolving from a multi-tier hierarchy built mostly with transit (customer-provider) links to a dense mesh formed with mostly peering links. This transition can have major impact on the global Internet economy as well as on the traffic flow and topological structure of the Internet. In this paper, we study this evolutionary transition with an agent-based network formation model that captures key aspects of the interdomain ecosystem, viz., interdomain traffic flow and routing, provider and peer selection strategies, geographical constraints, and the economics of transit and peering interconnections. The model predicts several substantial differences between the Hierarchical Internet and the Flat Internet in terms of topological structure, path lengths, interdomain traffic flow, and the profitability of transit providers. We also quantify the effect of the three factors driving this evolutionary transition. Finally, we examine a hypothetical scenario in which a large content provider produces more than half of the total Internet traffic.

The Internet's routing system is facing stresses due to its poor fundamental scaling properties. Compact routing is a research field that studies fundamental limits of routing scalability and designs algorithms that try to meet these limits. In particular, compact routing research shows that shortest-path routing, forming a core of traditional routing algorithms, cannot guarantee routing table (RT) sizes that on all network topologies grow slower than linearly as functions of the network size. However, there are plenty of compact routing schemes that relax the shortest-path requirement and allow for improved, sublinear RT size scaling that is mathematically provable for all static network topologies. In particular, there exist compact routing schemes designed for grids, trees, and Internet-like topologies that offer RT sizes that scale logarithmically with the network size. In this paper, we demonstrate that in view of recent results in compact routing research, such logarithmic scaling on Internet-like topologies is fundamentally impossible in the presence of topology dynamics or topology-independent (flat) addressing. We use analytic arguments to show that the number of routing control messages per topology change cannot scale better than linearly on Internet-like topologies. We also employ simulations to confirm that logarithmic RT size scaling gets broken by topology-independent addressing, a cornerstone of popular locator-identifier split proposals aiming at improving routing scaling in the presence of network topology dynamics or host mobility. These pessimistic findings lead us to the conclusion that a fundamental re-examination of assumptions behind routing models and abstractions is needed in order to find a routing architecture that would be able to scale "indefinitely.

et de quelque manire que ce soit, est interdite sauf accord pralable et crit de l'diteur, en dehors des cas prvus par la lgislation en vigueur en France. Il est prcis que son stockage dans une base de donnes est galement interdit.

Malaria and cryptosporidiosis, caused by apicomplexan parasites, remain major drivers of global child mortality. New drugs for the treatment of malaria and cryptosporidiosis, in particular, are of high priority; however, there are few chemically validated targets. The natural product cladosporin is active against blood- and liver-stage Plasmodium falciparum and Cryptosporidium parvum in cell-culture studies. Target deconvolution in P. falciparum has shown that cladosporin inhibits lysyl-tRNA synthetase ( Pf KRS1). Here, we report the identification of a series of selective inhibitors of apicomplexan KRSs. Following a biochemical screen, a small-molecule hit was identified and then optimized by using a structure-based approach, supported by structures of both Pf KRS1 and C. parvum KRS ( Cp KRS). In vivo proof of concept was established in an SCID mouse model of malaria, after oral administration (ED 90 = 1.5 mg/kg, once a day for 4 d). Furthermore, we successfully identified an opportunity for pathogen hopping based on the structural homology between Pf KRS1 and Cp KRS. This series of compounds inhibit Cp KRS and C. parvum and Cryptosporidium hominis in culture, and our lead compound shows oral efficacy in two cryptosporidiosis mouse models. X-ray crystallography and molecular dynamics simulations have provided a model to rationalize the selectivity of our compounds for Pf KRS1 and Cp KRS vs. (human) Hs KRS. Our work validates apicomplexan KRSs as promising targets for the development of drugs for malaria and cryptosporidiosis.