Publications associées à Method of preparation of cdna library useful for efficient mrna sequencing and uses thereof

Comparison of Three Viral Nucleic Acid Preamplification Pipelines for Sewage Viral Metagenomics

Viral metagenomics is a useful tool for detecting multiple human viruses in urban sewage. However, more refined protocols are required for its effective use in disease surveillance. In this study, we investigated the performance of three different preampli ...

2024

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing

Bart Deplancke, Daniel Migliozzi, Riccardo Dainese, Daniel Alpern, Gilles Weder, Mustafa Demir, Dariia Gudkova, Hüseyin Baris Atakan

High-throughput transcriptomics is of increasing fundamental biological and clinical interest. The generation of molecular data from large collections of samples, such as biobanks and drug libraries, is boosting the development of new biomarkers and treatm ...

Springer2024

Microbial genome collection of aerobic granular sludge cultivated in sequencing batch reactors using different carbon source mixtures

Christof Holliger, Aline Sondra Adler, Laetitia Janine Andrée Cardona, Jaspreet Singh Saini, Pilar Natalia Rodilla Ramírez, Ruizhe Pei

Aerobic granular sludge (AGS) consists of a microbial consortium that has an important role in wastewater treatment. This study investigates AGS microorganisms cultivated in a laboratory-scale sequencing batch reactor. Metagenomic sequencing was conducted ...

Amer Soc Microbiology2024

Micropreparative Gel Electrophoresis for Purification of Nanoscale Bioconjugates

Ardemis Anoush Boghossian, Yahya Rabbani, Shang-Jung Wu, Vitalijs Zubkovs, Sayyed Hashem Sajjadi

Conventional techniques for purifying macromolecular conjugates often require complex and costly installments that are inaccessible to most laboratories. In this work, we develop a one-step micropreparative method based on a trilayered polyacrylamide gel e ...

2024

Benchmarking informatics approaches for virus discovery: caution is needed when combining in silico identification methods

Jaspreet Singh Saini

Understanding the ecological impacts of viruses on natural and engineered ecosystems relies on the accurate identification of viral sequences from community sequencing data. To maximize viral recovery from metagenomes, researchers frequently combine viral ...

Amer Soc Microbiology2024

SIMPLE HIERARCHICAL PLANNING WITH DIFFUSION

Caglar Gulcehre, Chang Chen

Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long- horizon tas ...

2024

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

A Reduced Order Model for Domain Decompositions with Non-conforming Interfaces

Alfio Quarteroni, Andrea Manzoni

In this paper, we propose a reduced-order modeling strategy for two-way Dirichlet-Neumann parametric coupled problems solved with domain-decomposition (DD) sub-structuring methods. We split the original coupled differential problem into two sub-problems wi ...

Springer/Plenum Publishers2024

High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

Roya Moghaddasi Fereidani

Molecular quantum dynamics simulations are essential for understanding many fundamental phenomena in physics and chemistry. They often require solving the time-dependent Schrödinger equation for molecular nuclei, which is challenging even for medium-sized ...

EPFL2024

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Elsevier Science Sa2024