In vitro, batch-dissolution of biogenic silica in seawater – the application of recent modelling to real data

2005 ◽  
Vol 66 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Victor W. Truesdale ◽  
Jim E. Greenwood ◽  
Andrew R. Rendell
Keyword(s):  
Biogenic Silica ◽  
Real Data ◽  
Batch Dissolution

scholarly journals The nutritional evaluation of forage-based mixed rations in New Zealand using an in vitro gas production technique. 1: analytical survey

2021 ◽  
pp. 1-6
Author(s):  
N.D. Meads ◽  
R. Tahmasbi ◽  
N. Jantasila
Keyword(s):  
Dry Matter ◽  
Volatile Fatty Acids ◽  
Environmental Science ◽  
Ghg Emissions ◽  
Negative Relationship ◽  
Real Data ◽  
Gas Production ◽  
Production Technique ◽  
In Vitro Gas Production

Greenhouse gas (GHG) emissions from livestock are an important consideration in environmental science. Estimating GHG production can be problematic at a farm or animal level, and requires controlled conditions to produce real data. An in vitro gas production technique (IVGPT) was developed to evaluate forage-based total mixed rations in digestion kinetics and GHG production. Two hundred and sixty samples of complete mixed rations (MR), which included a pasture component used in commercial lactating dairy herds, were collected around NZ across three calendar years, 2017-2019. Twenty of the 260 samples were 100% total mixed rations (TMR) with no pasture content. The samples were submitted for proximate analysis as well as IVGPT to generate GHG production figures. The results showed an average total gas production (TGP) of 129.82 ml/g dry matter (DM), 78.6% true digestibility (TDMD), 125.06 mg/g DM microbial biomass (MB), 20.16 g CH4/kg DM, and 12.8 MJME/kg DM. The average nutrient composition was dry matter (DM) 31.55%, crude protein (CP) 21.85%, neutral detergent fibre (NDF) 44.35%, and starch 7.03%. The IVGPT CH4 production was negatively correlated to NDF (r=-0.312), ADF (r=-0.193), TGP (r=-0.216), and was positively correlated with TDMD (r=0.250), apparent digestibility (ADMD) (r=0.614), starch (r=0.117) and volatile fatty acids (r=0.538). The MR diet showed a strong positive relationship with ADMD digestibility (P=0.01) and a negative relationship with fibre content (NDF, P=0.01 and ADF, P=0.01). However, CH4 production reduced linearly with increasing TGP (P=0.01). The results indicated that a greater CH4 production may be related to higher digestibility of mixed ration.

scholarly journals A Spatio-Temporal Model and Inference Tools for Longitudinal Count Data on Multicolor Cell Growth

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
PuXue Qiao ◽  
Christina Mølck ◽  
Davide Ferrari ◽  
Frédéric Hollande
Keyword(s):  
Cell Growth ◽  
Image Data ◽  
Real Data ◽  
Cell Types ◽  
Spatial Autoregressive ◽  
Spatio Temporal ◽  
Different Cell Types ◽  
Temporal Interactions

AbstractMulticolor cell spatio-temporal image data have become important to investigate organ development and regeneration, malignant growth or immune responses by tracking different cell types both in vivo and in vitro. Statistical modeling of image data from common longitudinal cell experiments poses significant challenges due to the presence of complex spatio-temporal interactions between different cell types and difficulties related to measurement of single cell trajectories. Current analysis methods focus mainly on univariate cases, often not considering the spatio-temporal effects affecting cell growth between different cell populations. In this paper, we propose a conditional spatial autoregressive model to describe multivariate count cell data on the lattice, and develop inference tools. The proposed methodology is computationally tractable and enables researchers to estimate a complete statistical model of multicolor cell growth. Our methodology is applied on real experimental data where we investigate how interactions between cancer cells and fibroblasts affect their growth, which are normally present in the tumor microenvironment. We also compare the performance of our methodology to the multivariate conditional autoregressive (MCAR) model in both simulations and real data applications.

scholarly journals Recycler: an algorithm for detecting plasmids from de novo assembly graphs

2015 ◽  
Author(s):  
Roye Rozov ◽  
Aya Brown Kav ◽  
David Bogumil ◽  
Naama Shterzer ◽  
Eran Halperin ◽  
...  
Keyword(s):  
De Novo ◽  
Real Data ◽  
Microbial Evolution ◽  
Reference Sequence ◽  
Data Types ◽  
High Agreement ◽  
Assembly Algorithm ◽  
E Coli ◽  
Agricultural Applications

AbstractPlasmids are central contributors to microbial evolution and genome innovation. Recently, they have been found to have important roles in antibiotic resistance and in affecting production of metabolites used in industrial and agricultural applications. However, their characterization through deep sequencing remains challenging, in spite of rapid drops in cost and throughput increases for sequencing. Here, we attempt to ameliorate this situation by introducing a new plasmid-specific assembly algorithm, leveraging assembly graphs provided by a conventional de novo assembler and alignments of paired- end reads to assembled graph nodes. We introduce the first tool for this task, called Recycler, and demonstrate its merits in comparison with extant approaches. We show that Recycler greatly increases the number of true plasmids recovered while remaining highly accurate. On simulated plasmidomes, Recycler recovered 5-14% more true plasmids compared to the best extant method with overall precision of about 90%. We validated these results in silico on real data, as well as in vitro by PCR validation performed on a subset of Recycler’s predictions on different data types. All 12 of Recycler’s outputs on isolate samples matched known plasmids or phages, and had alignments having at least 97% identity over at least 99% of the reported reference sequence lengths. For the two E. Coli strains examined, most known plasmid sequences were recovered, while in both cases additional plasmids only known to be present in different hosts were found. Recycler also generated plasmids in high agreement with known annotation on real plasmidome data. Moreover, in PCR validations performed on 77 sequences, Recycler showed mean accuracy of 89% across all data types – isolate, microbiome, and plasmidome. Recycler is available at http://github.com/Shamir-Lab/Recycler

scholarly journals Robustness and applicability of functional genomics tools on scRNA-seq data

2019 ◽  
Author(s):  
Christian H. Holland ◽  
Jovan Tanevski ◽  
Jan Gleixner ◽  
Manu P. Kumar ◽  
Elisabetta Mereu ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Single Cell ◽  
In Silico ◽  
Single Cells ◽  
Real Data ◽  
Drop Out ◽  
Rna Seq ◽  
Knock Out ◽  
Genomics Tools

AbstractMany tools have been developed to extract functional and mechanistic insight from bulk transcriptome profiling data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events, low library sizes and a comparatively large number of samples/cells. It is thus not clear if functional genomics tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. To address this question, we performed benchmark studies on in silico and in vitro single-cell RNA-seq data. We included the bulk-RNA tools PROGENy, GO enrichment and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compared them against the tools AUCell and metaVIPER, designed for scRNA-seq. For the in silico study we simulated single cells from TF/pathway perturbation bulk RNA-seq experiments. Our simulation strategy guarantees that the information of the original perturbation is preserved while resembling the characteristics of scRNA-seq data. We complemented the in silico data with in vitro scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on both the simulated and real data revealed comparable performance to the original bulk data. Additionally, we showed that the TF and pathway activities preserve cell-type specific variability by analysing a mixture sample sequenced with 13 scRNA-seq different protocols. Our analyses suggest that bulk functional genomics tools can be applied to scRNA-seq data, outperforming dedicated single cell tools. Furthermore we provide a benchmark for further methods development by the community.

scholarly journals AptaTRACE: Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends in HT-SELEX Experiments

2016 ◽  
Author(s):  
Dao Phuong ◽  
Jan Hoinka ◽  
Yijie Wang ◽  
Mayumi Takahashi ◽  
Jiehua Zhou ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Real Data ◽  
Sequence Structure ◽  
Rna Sequences ◽  
Binding Motifs ◽  
Novel Approach ◽  
Signature Of Selection ◽  
Data Points ◽  
Exponential Enrichment

AbstractAptamers, short synthetic RNA/DNA molecules binding specific targets with high affinity and specificity, are utilized in an increasing spectrum of bio-medical applications. Aptamers are identified in vitro via the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) protocol. SELEX selects binders through an iterative process that, starting from a pool of random ssDNA/RNA sequences, amplifies target-affine species through a series of selection cycles. HT-SELEX, which combines SELEX with high throughput sequencing, has recently transformed aptamer development and has opened the field to even more applications. HT-SELEX is capable of generating over half a billion data points, challenging computational scientists with the task of identifying aptamer properties such as sequence structure motifs that determine binding. While currently available motif finding approaches suggest partial solutions to this question, none possess the generality or scalability required for HT-SELEX data, and they do not take advantage of important properties of the experimental procedure.We present AptaTRACE, a novel approach for the identification of sequence-structure binding motifs in HT-SELEX derived aptamers. Our approach leverages the experimental design of the SELEX protocol and identifies sequence-structure motifs that show a signature of selection. Because of its unique approach, AptaTRACE can uncover motifs even when these are present in only a minuscule fraction of the pool. Due to these features, our method can help to reduce the number of selection cycles required to produce aptamers with the desired properties, thus reducing cost and time of this rather expensive procedure. The performance of the method on simulated and real data indicates that AptaTRACE can detect sequence-structure motifs even in highly challenging data.

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