Detection of Vancomycin Resistant Enterococci with Van A genotype in Clinical Isolates from a Tertiary Care Centre

Enterococcal infections may of at least 12 species including Enterococcus faecalis, E. faecium,E. durans, E. avium, E. casseliflavus, E. gallinarum, E. hirae, E.malodoratus, E. mundtii, E. pseudoavium, E. raffinosus, and E. solitarius. Among enterococcal species, E.faecalis and E. faecium are the two major human pathogens accounting for 85-89% and 10-15% of all enterococcal infections, respectively. Common to all variants of Vancomycin resistance in enterococci is the ability to cause a change in the structure of the pentapeptide incorporated in the 3 dimensional web of peptidoglycans composing the bacterial cell wall: from the original D-Ala-D-Ala to either D-Ala-D-Lactate (D-Ala-DLac) or D-Ala-D-Serine (D-Ala-D-Ser). Enterococcus species have been recognised as a pathogen causing dieases like bacteremia, endocarditis, complicated urinary tract infections, intra abdominal infections, pelvic infections, wound and soft tissue infections etc. VRE has become an important nosocomial pathogen because of its rapid spread, high mortality rates associated with infections, limited option for treatment, and the possibility of transferring vancomycin resistance genes to other more virulent and more prevalent pathogens such as Staphylococcus aureus. The VRE isolates study could be harbouring other Van genes. PCR remains the gold standard for diagnosis of Vancomycin resistance. Emerging Vancomycin resistance among Enterococcus is a cause for concern as this leads to a great difficulty in treating serious infections caused by them

RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference

Motivation: Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets. // Results: We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric. // Availability and implementation: The code is available under GNU GPL at https://github.com/amkozlov/raxml-ng. RAxML-NG web service (maintained by Vital-IT) is available at https://raxml-ng.vital-it.ch/

Computational analysis of expressed sequence tags for understanding gene regulation.

High-throughput sequencing has provided a myriad of genetic data for thousands of organisms. Computational analysis of one data type, expressed sequence tags (ESTs) yields insight into gene expression, alternative splicing, tissue specificity gene functionality and the detection and differentiation of pseudogenes. Two computational methods have been developed to analyze alternative splicing events and to detect and characterize pseudogenes using ESTs. A case study of rat phosphodiesterase 4 (PDE4) genes yielded more than twenty-five previously unreported isoforms. These were experimentally verified through wet lab collaboration and found to be tissue specific. In addition, thirteen cytochrome-like gene and pseudogene sequences from the human genome were analyzed for pseudogene properties. Of the thirteen sequences, one was identified as the actual cytochrome gene, two were found to be non-cytochrome-related sequences, and eight were determined to be pseudogenes. The remaining two sequences were identified to be duplicates. As a precursor to applying the two new methods, the efficiency of three BLAST algorithms (NCBI BLAST, WU BLAST and mpiBLAST) were examined for comparing large numbers of short sequences (ESTs) to fewer large sequences (genomic regions). In general, WU BLAST was found to be the most efficient sequence comparison tool. These approaches illustrate the power of ESTs in understanding gene expression. Efficient computational analysis of ESTs (such as the two tools described) will be vital to understanding the complexity of gene expression as more high-throughput EST data is made available via advances in molecular sequencing technologies, such as the current next-generation approaches

An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System

Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly data movement between memory units and processing units, which consumes large amounts of energy and execution cycles. Memory-centric computing systems, i.e., with processing-in-memory (PIM) capabilities, can alleviate this data movement bottleneck. Our goal is to understand the potential of modern general-purpose PIM architectures to accelerate ML training. To do so, we (1) implement several representative classic ML algorithms (namely, linear regression, logistic regression, decision tree, K-Means clustering) on a real-world general-purpose PIM architecture, (2) rigorously evaluate and characterize them in terms of accuracy, performance and scaling, and (3) compare to their counterpart implementations on CPU and GPU. Our evaluation on a real memory-centric computing system with more than 2500 PIM cores shows that general-purpose PIM architectures can greatly accelerate memory-bound ML workloads, when the necessary operations and datatypes are natively supported by PIM hardware. For example, our PIM implementation of decision tree is $27\times$ faster than a state-of-the-art CPU version on an 8-core Intel Xeon, and $1.34\times$ faster than a state-of-the-art GPU version on an NVIDIA A100. Our K-Means clustering on PIM is $2.8\times$ and $3.2\times$ than state-of-the-art CPU and GPU versions, respectively. To our knowledge, our work is the first one to evaluate ML training on a real-world PIM architecture. We conclude with key observations, takeaways, and recommendations that can inspire users of ML workloads, programmers of PIM architectures, and hardware designers & architects of future memory-centric computing systems

High-Performance approaches for Phylogenetic Placement, and its application to species and diversity quantification

In den letzten Jahren haben Fortschritte in der Hochdurchsatz-Genesequenzierung, in Verbindung mit dem anhaltenden exponentiellen Wachstum und der Verfügbarkeit von Rechenressourcen, zu fundamental neuen analytischen Ansätzen in der Biologie geführt. Es ist nun möglich den genetischen Inhalt ganzer Organismengemeinschaften anhand einzelner Umweltproben umfassend zu sequenzieren. Solche Methoden sind besonders für die Mikrobiologie relevant. Die Mikrobiologie war zuvor weitgehend auf die Untersuchung jener Mikroben beschränkt, welche im Labor (d.h., in vitro) kultiviert werden konnten, was jedoch lediglich einen kleinen Teil der in der Natur vorkommenden Diversität abdeckt. Im Gegensatz dazu ermöglicht die Hochdurchsatzsequenzierung nun die direkte Erfassung der genetischen Sequenzen eines Mikrobioms, wie es in seiner natürlichen Umgebung vorkommt (d.h., in situ). Ein typisches Ziel von Mikrobiomstudien besteht in der taxonomischen Klassifizierung der in einer Probe enthaltenen Sequenzen (Querysequenzen). Üblicherweise werden phylogenetische Methoden eingesetzt, um detaillierte taxonomische Beziehungen zwischen Querysequenzen und vertrauenswürdigen Referenzsequenzen, die von bereits klassifizierten Organismen stammen, zu bestimmen. Aufgrund des hohen Volumens ($10 ^ 6$ bis $10 ^ 9$) von Querysequenzen, die aus einer Mikrobiom-Probe mittels Hochdurchsatzsequenzierung generiert werden können, ist eine akkurate phylogenetische Baumrekonstruktion rechnerisch nicht mehr möglich. Darüber hinaus erzeugen derzeit üblicherweise verwendete Sequenzierungstechnologien vergleichsweise kurze Sequenzen, die ein begrenztes phylogenetisches Signal aufweisen, was zu einer Instabilität bei der Inferenz der Phylogenien aus diesen Sequenzen führt. Ein weiteres typisches Ziel von Mikrobiomstudien besteht in der Quantifizierung der Diversität innerhalb einer Probe, bzw. zwischen mehreren Proben. Auch hierfür werden üblicherweise phylogenetische Methoden verwendet. Oftmals setzen diese Methoden die Inferenz eines phylogenetischen Baumes voraus, welcher entweder alle Sequenzen, oder eine geclusterte Teilmenge dieser Sequenzen, umfasst. Wie bei der taxonomischen Identifizierung können Analysen, die auf dieser Art von Bauminferenz basieren, zu ungenauen Ergebnissen führen und/oder rechnerisch nicht durchführbar sein. Im Gegensatz zu einer umfassenden phylogenetischen Inferenz ist die phylogenetische Platzierung eine Methode, die den phylogenetischen Kontext einer Querysequenz innerhalb eines etablierten Referenzbaumes bestimmt. Dieses Verfahren betrachtet den Referenzbaum typischerweise als unveränderlich, d.h. der Referenzbaum wird vor, während oder nach der Platzierung einer Sequenz nicht geändert. Dies erlaubt die phylogenetische Platzierung einer Sequenz in linearer Zeit in Bezug auf die Größe des Referenzbaums durchzuführen. In Kombination mit taxonomischen Informationen über die Referenzsequenzen ermöglicht die phylogenetische Platzierung somit die taxonomische Identifizierung einer Sequenz. Darüber hinaus erlaubt eine phylogenetische Platzierung die Anwendung einer Vielzahl zusätzlicher Analyseverfahren, die beispielsweise die Zuordnung der Zusammensetzungen humaner Mikrobiome zu klinisch-diagnostischen Eigenschaften ermöglicht. In dieser Dissertation präsentiere ich meine Arbeit bezüglich des Entwurfs, der Implementierung, und Verbesserung von EPA-ng, einer Hochleistungsimplementierung der phylogenetischen Platzierung anhand des Maximum-Likelihood Modells. EPA-ng wurde entwickelt um auf Milliarden von Querysequenzen zu skalieren und auf Tausenden von Kernen in Systemen mit gemeinsamem und verteiltem Speicher ausgeführt zu werden. EPA-ng beschleunigt auch die Verarbeitungsgeschwindigkeit auf einzelnen Kernen um das bis zu $30$-fache, im Vergleich zu dessen direkten Konkurrenzprogrammen. Vor kurzem haben wir eine zusätzliche Methode für EPA-ng eingeführt, welche die Platzierung in wesentlich größeren Referenzbäumen ermöglicht. Hierfür verwenden wir einen aktiven Speicherverwaltungsansatz, bei dem reduzierter Speicherverbrauch gegen größere Ausführungszeiten eingetauscht wird. Zusätzlich präsentiere ich einen massiv-parallelen Ansatz um die Diversität einer Probe zu quantifizieren, welcher auf den Ergebnissen phylogenetischer Platzierungen basiert. Diese Software, genannt \toolname{SCRAPP}, kombiniert aktuelle Methoden für die Maximum-Likelihood basierte phylogenetische Inferenz mit Methoden zur Abgrenzung molekularer Spezien. Daraus resultiert eine Verteilung der Artenanzahl auf den Kanten eines Referenzbaums für eine gegebene Probe. Darüber hinaus beschreibe ich einen neuartigen Ansatz zum Clustering von Platzierungsergebnissen, anhand dessen der Benutzer den Rechenaufwand reduzieren kann

High-Quality Hypergraph Partitioning

This dissertation focuses on computing high-quality solutions for the NP-hard balanced hypergraph partitioning problem: Given a hypergraph and an integer $k$, partition its vertex set into $k$ disjoint blocks of bounded size, while minimizing an objective function over the hyperedges. Here, we consider the two most commonly used objectives: the cut-net metric and the connectivity metric. Since the problem is computationally intractable, heuristics are used in practice - the most prominent being the three-phase multi-level paradigm: During coarsening, the hypergraph is successively contracted to obtain a hierarchy of smaller instances. After applying an initial partitioning algorithm to the smallest hypergraph, contraction is undone and, at each level, refinement algorithms try to improve the current solution. With this work, we give a brief overview of the field and present several algorithmic improvements to the multi-level paradigm. Instead of using a logarithmic number of levels like traditional algorithms, we present two coarsening algorithms that create a hierarchy of (nearly) $n$ levels, where $n$ is the number of vertices. This makes consecutive levels as similar as possible and provides many opportunities for refinement algorithms to improve the partition. This approach is made feasible in practice by tailoring all algorithms and data structures to the $n$-level paradigm, and developing lazy-evaluation techniques, caching mechanisms and early stopping criteria to speed up the partitioning process. Furthermore, we propose a sparsification algorithm based on locality-sensitive hashing that improves the running time for hypergraphs with large hyperedges, and show that incorporating global information about the community structure into the coarsening process improves quality. Moreover, we present a portfolio-based initial partitioning approach, and propose three refinement algorithms. Two are based on the Fiduccia-Mattheyses (FM) heuristic, but perform a highly localized search at each level. While one is designed for two-way partitioning, the other is the first FM-style algorithm that can be efficiently employed in the multi-level setting to directly improve $k$-way partitions. The third algorithm uses max-flow computations on pairs of blocks to refine $k$-way partitions. Finally, we present the first memetic multi-level hypergraph partitioning algorithm for an extensive exploration of the global solution space. All contributions are made available through our open-source framework KaHyPar. In a comprehensive experimental study, we compare KaHyPar with hMETIS, PaToH, Mondriaan, Zoltan-AlgD, and HYPE on a wide range of hypergraphs from several application areas. Our results indicate that KaHyPar, already without the memetic component, computes better solutions than all competing algorithms for both the cut-net and the connectivity metric, while being faster than Zoltan-AlgD and equally fast as hMETIS. Moreover, KaHyPar compares favorably with the current best graph partitioning system KaFFPa - both in terms of solution quality and running time

Advances in aquatic and subterranean beetles research: a tribute to Ignacio Ribera

It has been a bit longer than two years since our friend and colleague Ignacio (Nacho) Ribera passed away. The memory of him remains among those of us who were lucky enough to meet Nacho. This monograph is dedicated to him, bringing a set of scientific contributions from his colleagues whose topics are part of the main research lines (and passions) of his scientific work: taxonomy, systematics, biogeography and evolution of aquatic and subterranean beetles. In the last two years, several contributions have highlighted Nacho’ scientific and personal profile, including the complete list of his publications, and both the taxa described by and dedicated to him (BELLÉS, 2020; CEHRE, 2020; DELOCADO et al., 2020; FAILLE et al., 2020, 2021; JÄCH, 2020; MELIC, 2020; MILLÁN et al., 2020a, b; VALLADARES & MILLÁN, 2020). Nacho was a passionate biologist, interested in everything concerning beetles, especially their diversity, distributions, adaptations and evolution of aquatic and cave beetles. He published a total of 285 papers, in which he described 107 species new to science, highlighting the discovery of the Aspyditidae family. Besides, 7 species and subspecies, and one new genus have been dedicated to him in this monograph, extending to a total of 15 species and two genera. Concerning the aquatic beetles, we would like to emphasise his crucial contributions, such as the first complete checklist of aquatic and semi-aquatic beetles of the Iberian Peninsula. He was a pioneer in studying the adaptive morphology of the family Dytiscidae, and more importantly, he deepens in the phylogeny of the aquatic beetles, shedding light on the systematics and evolution of the families Dytiscidae and Hydraenidae. He postulated the “Habitat Constraint” hypothesis which, from an evolutionary point of view, highlights the importance of habitat stability as a determinant of species range sizes (via differences in their dispersal capabilities). He also dealt in-depth with evolutionary studies related to the habitat transition between lotic and lentic water bodies, but also the transition from freshwater to saline water, or vice versa. Certainly, Nacho, due to his work and publications on aquatic beetles, became one of the most prestigious specialists worldwide. Regarding the subterranean environment, Nacho revealed that Dalyat Mateu is a vicariant genus of carabid whose origin must be in the separation of the Iberian plate from the rest of Pangea in the Jurassic-Cretaceous boundary. He also addressed the position of Ildobates neboti Español, confirming it within the Zuphiini tribe. At the same time, he began to study the phylogeny of the hypogeal Trechini of the Pyrenees, and afterwards he addressed the diversification of Troglocharinus Reitter, opening the way to a new perspective on the evolution and dispersion of the subterranean fauna. He published the first morphological phylogeny through cladistic analysis of the Leptodirini tribe (Leiodidae), and the first molecular phylogenies for the two main groups that have colonised the subterranean environment, the tribes Leptodirini and Trechini. His contribution to the knowledge of underground and stygobic environments between 2005 and 2021, postulated him as one of the most important specialists worldwide also in this field. The complete list of scientific works signed by Nacho, the taxa described by him and those dedicated to his person can be found on the web: https://www.um.es/ecoaqua/index.php/external-collaborators. We do not want to extend further. We hope these pages serve as a tribute to his person, but also a tribute to a life and scientific style that would serve as an example for future generations of insect lovers. Our gratitude to the authors and reviewers of the articles that make up this monograph, as well as to the Asociación española de Entomología, which has greatly facilitated its preparation, edition and publication