The identification of cells and particles based on their transport properties in microfluidic devices is crucial for numerous applications in biology and medicine. Neutrally buoyant particles transported in microfluidic channels migrate laterally toward stable locations due to inertial effects. However, the effect of the particle and flow properties on these focusing positions remain largely unknown. We conduct large scale simulations with dissipative particle dynamics, demonstrating that freely moving particles exhibit significant differences in their focusing patterns from particles that are prevented from rotation. In circular pipes, we observe drastic changes in rotating versus nonrotating focusing positions. We demonstrate that rotation-induced lateral lift force is significant, unlike previously believed, and is linearly dependent on the rotation magnitude. A simple phenomenological explanation extending existing theories is presented, that agrees well with our numerical findings. In square ducts, we report four face-centered stable positions for rotating particles, in accordance with experimental studies on a range of Reynolds numbers 50 Re 200. However, nonrotating particles stay scattered on a concentric one-dimensional annulus, revealing qualitatively different behavior with respect to the free ones. Our findings suggest new designs for microparticle and cell sorting in inertia-based microfluidics devices.

Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Jülich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave–mean flow interaction.

Weather prediction centers are always looking for the best computational performance for their numerical weather prediction (NWP) model, given their financial budget. Over the last decades, most centers relied on computer systems with scalar x86 architectures. This, however, might not be the best choice for the mid-term future, as the development of CPUs with ever-increasing performance and memory bandwidth is slowing down. Nowadays, hardware manufacturers advertise massively multiprocessing GPUs as one future pathway. Unfortunately, GPUs have their own programming paradigms, as they are still requiring a CPU as their driver and bring their own memory. This necessitates significant adaptions of existing codes. Porting a large and continuously developed community code, such as ICON, to emerging hardware architectures poses its own special challenges. Many parts of the ICON framework have been made ready for GPU systems in a multi-institute effort over the past years. MeteoSwiss plans to use GPU ICON operationally for limited area forecasts in 2023. Current development activities also make ICON-GPU ready to support the enhanced feature set used operationally by the DWD (such as grid nesting and parametrizations for global simulations). It was decided to port ICON by introducing OpenACC compiler directives to the FORTRAN code. This iterative development model makes it possible to merge ported code back directly into the main code repository and to stay up-to-date with other developments like in model physics. A tolerance based testing suite was deployed to make sure that ported features remain functional also when non-GPU-related changes are introduced to already ported code sections. We present the general porting strategy and the current state of the port. We discuss specific optimizations and the lessons learned while porting an actively developed code. Finally, we present the performance on current GPU and CPU machines and compare them to the currently operational setup on the DWD vector supercomputer.

Hyperglycemia may contribute to the progression of carcinomas by triggering epithelial-to-mesenchymal transition (EMT). Some proteostasis systems are involved in metastasis; in this paper, we sought to explore the mechanism of Hsp70 chaperone in EMT. We showed that knockdown of Hsp70 reduced cell migration capacity concomitantly with levels of mRNA of the Slug, Snail, and Twist markers of EMT, in colon cancer cells incubated in high glucose medium. Conversely, treatment of cells with Hsp70 inducer U-133 were found to elevate cell motility, along with the other EMT markers. To prove that inhibiting Hsp70 may reduce EMT efficiency, we treated cells with a CL-43 inhibitor of the HSF1 transcription factor, which lowered Hsp70 and HSF1 content in the control and induced EMT in carcinoma cells. Importantly, CL-43 reduced migration capacity, EMT-linked transcription factors, and increased content of epithelial marker E-cadherin in colon cancer cells of three lines, including one derived from a clinical sample. To prove that Hsp70 chaperone should be targeted when inhibiting the EMT pathway, we treated cancer cells with 2-phenylethynesulfonamide (PES) and demonstrated that the compound inhibited substrate-binding capacity of Hsp70. Furthermore, PES suppressed EMT features, cell motility, and expression of specific transcription factors. In conclusion, the Hsp70 chaperone machine efficiently protects mechanisms of the EMT, and the safe inhibitors of the chaperone are needed to hamper metastasis at its initial stage.

Simulations of blood flows in microfluidic devices and physiological systems are gaining importance in complementing experimental and clinical studies. The predictive capabilities of these simulations hinge on the parameters of the red blood cell (RBC) model that are usually calibrated from experimental data. However, these parameter values may vary drastically when calibrated using different experimental quantities or experimental settings. In turn, the results of existing blood flow simulations largely depend on the utilized parameters that have been chosen to validate a particular experiment. We suggest a revision to this type of model calibration to properly integrate experimental data in the computational models and accordingly inform their predictions. In this context, we introduce the calibration of a popular RBC model using data-driven, hierarchical Bayesian inference. We employ data from classical experiments of RBC stretching by optical tweezers and tank treading in shear flows, and distinguish the calibration of the model parameters through single-level and hierarchical Bayesian uncertainty quantification. We find that the optimal model parameters depend not only on the data used for the inference but also on the way the data are used in the inference process. Single-level Bayesian models predict well the data used in their calibration, but are inferior to the hierarchical Bayesian model at predicting previously unseen data. This work demonstrates that the proper integration of experimental data is essential for the development of a robust and transferable RBC model. We believe that the present study can serve as a prototype across scientific fields, in revising the integration of computational models and heterogeneous experimental data.

Probiotics and vitamins can impact immune responses and modulate gut microbiome. We evaluated the effects of consuming a yogurt fortified with vitamins and probiotic Lacticaseibacillus casei and rhamnosus on upper respiratory tract infections frequency and gut microbiome during a 3-month intervention. The study included 2 case groups (consuming different flavours) and a placebo group of healthy adults (n = 158–160 in each group). The effects on URTI-related parameters in both case groups were gender-specific. The female subjects had lower URTI incidence and frequency, while for the males no significant differences were found. The URTI duration was shorter in one of the probiotic groups for females and in both such groups - for males. The observed changes in microbiome composition, blood and stool parameters were not different from those observed in the placebo group. Consumption of fortified fermented dairy foods is promising for improving immunity status within the general population.

Benchmark of cost-efficient and accurate methods for quantifying probiotics in dairy products represents great interest to the food industry. The advantages of cultivation-independent techniques over the traditionally used cultivation-based ones are to be investigated in this context. We evaluated the levels of Lacticaseibacillus casei and rhamnosus in multiple formulations of a fermented dairy product fortified with these probiotics during the shelf-life using cultivation, taxon-specific qPCR augmented with propidium monoazide (PMA) viability test and 16S rRNA gene sequencing. The analyzed products were the yogurts produced with traditional yogurts starter cultures including Bifidobacteria or without them. The effect of the starter culture on probiotics viability and abundance was assessed. The methods for probiotic profiling were compared. All methods confirmed high levels for the probiotics throughout the shelf-life. The PMA-qPCR showed that their non-viable proportion was low. The formulations with the starter cultures including Streptococcus and Lactobacillus were associated with a lower abundance of each probiotic compared to those that additionally had Bifidobacterium in the starter culture. The total microbial composition according to the sequencing was generally as expected, but the method was of limited use for profiling the probiotic levels due to the data compositionality and dominance of the starter culture taxa.

The ICON modelling framework is a unified numerical weather and climate model used for applications ranging from operational numerical weather prediction to low and high resolution climate projection. In view of further pushing the frontier of possible applications and to make use of the latest evolution in hardware technologies, parts of the model were recently adapted to run on heterogeneous GPU system. This initial GPU port focus on components required for high-resolution climate application, and allow considering multi-years simulations at 2.8 km on the Piz Daint heterogeneous supercomputer. These simulations are planned as part of the QUIBICC project “The Quasi-Biennial Oscillation (QBO) in a changing climate”, which propose to investigate effects of climate change on the dynamics of the QBO. Because of the low compute intensity of atmospheric model the cost of data transfer between CPU and GPU at every step of the time integration would be prohibitive if only some components would be ported to the accelerator. We therefore present a full port strategy where all components required for the simulations are running on the GPU. For the dynamics, most of the physical parameterizations and infrastructure code the OpenACC compiler directives are used. For the soil parameterization, a Fortran based domain specific language (DSL) the CLAW-DSL has been considered. We discuss the challenges associated to port a large community code, about 1 million lines of code, as well as to run simulations on large-scale system at 2.8 km horizontal resolution in terms of run time and I/O constraints. We show performance comparison of the full model on CPU and GPU, achieving a speed up factor of approximately 5x, as well as scaling results on up to 2000 GPU nodes. Finally we discuss challenges and planned development regarding performance portability and high level DSL which will be used with the ICON model in the near future.

Microbial ecosystems of the built environments have become key mediators of health as people worldwide tend to spend large amount of time indoors. Underexposure to microbes at an early age is linked to increased risks of allergic and autoimmune diseases. Transportation systems are of particular interest, as they are globally the largest space for interactions between city-dwellers. Here we performed the first pilot study of the Moscow subway microbiome by analyzing swabs collected from 5 types of surfaces at 4 stations using high-throughput 16S rRNA gene sequencing. The study was conducted as a part of The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) project. The most abundant microbial taxa comprising the subway microbiome originated from soil and human skin. Microbiome diversity was positively correlated with passenger traffic. No substantial evidence of major human pathogens presence was found. Co-occurrence analysis revealed clusters of microbial genera including combinations of microbes likely originating from different niches. The clusters as well as the most abundant microbes were similar to ones obtained for the published data on New-York City subway microbiome. Our results suggest that people are the main source and driving force of diversity in subway-associated microbiome. The data form a basis for a wider survey of Moscow subway microbiome to explore its longitudinal dynamics by analyzing an extended set of sample types and stations. Complementation of methods with viability testing, "shotgun" metagenomics, sequencing of bacterial isolates and culturomics will provide insights for public health, biosafety, microbial ecology and urban design.

Nowadays the advent of innovative high-throughput sequencing allows obtaining high-quality microbiome profiling. However, PCR-based tests are still considered the "golden standard" for many clinical applications. Here, we designed a qPCR-based platform with fluorescent-labeled oligonucleotide probes for assessing human gut microbiome composition. The system allows conducting qualitative and semiquantitative analysis for 12 prokaryotic taxa that are prevalent in the human gut and associated with diseases, diet, age and other factors. The platform was validated by comparing microbiome profile data obtained with two different methods - the platform and high-throughput 16S rRNA sequencing - across 42 stool samples. The test can form the basis for precise and cost-efficient microbiome assay for large-scale surveys including clinical trials with interventions related to diet and disease risks.

Genome-wide association studies have led to a significant progress in identification of genomic loci affecting coronary artery disease (CAD) risk. However, revealing the causal genes responsible for the observed associations is challenging. In the present study, we aimed to prioritize CAD-relevant genes based on cumulative evidence from the published studies and our own study of colocalization between eQTLs and loci associated with CAD using SMR/HEIDI approach. Prior knowledge of candidate genes was extracted from both experimental and in silico studies, employing different prioritization algorithms. Our review systematized information for a total of 51 CAD-associated loci. We pinpointed 37 genes in 36 loci. For 27 genes we infer they are causal for CAD, and for 10 further genes we judge them most likely causal. Colocalization analysis showed that for 18 out of these loci, association with CAD can be explained by changes in gene expression in one or more CAD-relevant tissues. Furthermore, for 8 out of 36 loci, existing evidence suggested additional CAD-associated genes. For the remaining 15 loci, we concluded that evidence for gene prioritization remains inconsistent, insufficient, or absent. Our results provide deeper insights into the genetic etiology of CAD and demonstrate knowledge gaps where further research is warranted.

The transport and manipulation of particles and cells in microfluidic devices has become a core methodology in domains ranging from molecular biology to manufacturing and drug design. The rational design and operation of such devices can benefit from simulations that resolve flow-structure interactions at sub-micron resolution. We present a computational tool for large scale, efficient and high throughput mesoscale simulations of fluids and deformable objects at complex microscale geometries. The code employs Dissipative Particle Dynamics for the description of the flow coupled with visco-elastic membrane model for red blood cells and can also handle rigid bodies and complex geometries. The software (MiRheo) is deployed on hybrid GPU/CPU architectures exhibiting unprecedented time-to-solution performance and excellent weak and strong scaling for a number of benchmark problems. MiRheo exploits the capabilities of GPU clusters, leading to speedup of up to 10 in terms of time to solution as compared to state-of-the-art software packages and reaches 90 - 99 percent weak scaling efficiency on 512 nodes of the Piz Daint supercomputer. The software MiRheo, relies on a Python interface to facilitate the solution of complex problems and it is open source. We believe that MiRheo constitutes a potent computational tool that can greatly assist studies of microfluidics.

BACKGROUND:Metagenomic surveys of human microbiota are becoming increasingly widespread in academic research as well as in food and pharmaceutical industries and clinical context. Intuitive tools for investigating experimental data are of high interest to researchers.RESULTS:Knomics-Biota is a web-based resource for exploratory analysis of human gut metagenomes. Users can generate and share analytical reports corresponding to common experimental schemes (like case-control study or paired comparison). Interactive visualizations and statistical analysis are provided in association with the external factors and in the context of thousands of publicly available datasets arranged into thematic collections. The web-service is available at https://biota.knomics.ru.CONCLUSIONS:Knomics-Biota web service is a comprehensive tool for interactive metagenomic data analysis.

Long Interspersed Nuclear Element-1 (LINE-1, L1) is a mobile genetic element active in human genomes. L1-encoded ORF1 and ORF2 proteins bind L1 RNAs, forming ribonucleoproteins (RNPs). These RNPs interact with diverse host proteins, some repressive and others required for the L1 lifecycle. Using differential affinity purifications, quantitative mass spectrometry, and next generation RNA sequencing, we have characterized the proteins and nucleic acids associated with distinctive, enzymatically active L1 macromolecular complexes. Among them, we describe a cytoplasmic intermediate that we hypothesize to be the canonical ORF1p/ORF2p/L1-RNA-containing RNP, and we describe a nuclear population containing ORF2p, but lacking ORF1p, which likely contains host factors participating in target-primed reverse transcription.

Background Crohn’s disease is associated with gut dysbiosis. Independent studies have shown an increase in the abundance of certain bacterial species, particularly Escherichia coli with the adherent-invasive pathotype , in the gut. The role of these species in this disease needs to be elucidated. Methods We performed a metagenomic study investigating the gut microbiota of patients with Crohn’s disease. A metagenomic reconstruction of the consensus genome content of the species was used to assess the genetic variability. Results The abnormal shifts in the microbial community structures in Crohn’s disease were heterogeneous among the patients. The metagenomic data suggested the existence of multiple E. coli strains within individual patients. We discovered that the genetic diversity of the species was high and that only a few samples manifested similarity to the adherent-invasive varieties. The other species demonstrated genetic diversity comparable to that observed in the healthy subjects. Our results were supported by a comparison of the sequenced genomes of isolates from the same microbiota samples and a meta-analysis of published gut metagenomes. Conclusions The genomic diversity of Crohn’s disease-associated E. coli within and among the patients paves the way towards an understanding of the microbial mechanisms underlying the onset and progression of the Crohn’s disease and the development of new strategies for the prevention and treatment of this disease.

We aimed to replicate, in a specific athletic event cohort (only track and field) and in two different ethnicities (Japanese and East European, i.e. Russian and Polish), original findings showing the association of the angiotensin-II receptor type-2 gene (AGTR2) rs11091046 A>C polymorphism with athlete status. We compared genotypic frequencies of the AGTR2 rs11091046 polymorphism among 282 track and field sprint/power athletes (200 men and 82 women), including several national record holders and Olympic medallists (214 Japanese, 68 Russian and Polish), and 2024 control subjects (842 men and 1182 women) (804 Japanese, 1220 Russian and Polish). In men, a meta-analysis from the two combined cohorts showed a significantly higher frequency of the C allele in athletes than in controls (odds ratio: 1.62, P=0.008, heterogeneity index I2=0%). With regard to respective cohorts, C allele frequency was higher in Japanese male athletes than in controls (67.7% vs. 55.9%, P=0.022), but not in Russian/Polish male athletes (61.9% vs. 51.0%, P=0.172). In women, no significant results were obtained by meta-analysis for the two cohorts combination (P=0.850). The AC genotype frequency was significantly higher in Russian/Polish women athletes than in controls (69.2% vs. 42.1%, P=0.022), but not in Japanese women athletes (P=0.226). Our results, in contrast to previous findings, suggested by meta-analysis that the C allele of the AGTR2 rs11091046 polymorphism is associated with sprint/power track and field athlete status in men, but not in women.