Sequence Counts Research Articles

Illuminating the Artefacts of NGS Seen in PGT-A & PGT-SR

Background: Advancements in next generation sequencing (NGS) based genomics over the past 10 years have allowed the routine adoption of preimplantation genetic testing for aneuploidy (PGT-A), and/or segmental rearrangements (PGT-SR) of [Formula: see text]10Mb 1 in the assisted reproductive technology (ART) clinic. However, one challenge remains within the assessment of aligned short read sequencing data, in the ability to discriminate between embryonic profiles with genuine pathological copy number variations (CNVs) from artefacts arising from erroneous DNA amplification or library preparation. Aim: To identify the common artefacts seen in next generation sequencing (NGS) based PGT to reduce inconclusive or false positive results. Method: NGS profiles generated from biopsied human embryonic trophectoderm cells were manually analysed for common genomic artefacts. Following trophectoderm biopsy within a clinical ART cycle, cell samples underwent DNA amplification (Illumina SureplexTM DNA amplification system), sequencing (VeriSeqTM PGS kit on the MiSeq[Formula: see text] System, and bioinformatic data analysis (Bluefuse Multi v4.4, Illumina). Results: Common artefacts were identified affecting chromosomes 7, 11, and 19. Artefacts imitated small CNVs typically displaying a [Formula: see text]50% increase in centromeric sequence counts on chromosomes 7 and 11, and a global increase of sequence counts of [Formula: see text]40% across the entire chromosome 19 (except at the centromere, presenting as diploid). Interestingly a technical repeat of the library preparation and sequencing of the original DNA template somewhat normalised these artefacts. Conclusion: The study found three commonly occurring artefacts involving chromosomes 7, 11, and 19 artificially introduced during suboptimal NGS library preparation. Importantly these artefacts may be normalised through technical repeat of library preparation and sequencing. The awareness of these artefacts may reduce false positive and/or inconclusive results, increasing clinical embryonic utilisation following PGT.

Soil depth and agricultural irrigation activities drive variation in microbial abundance and nitrogen cycling

Understanding the vertical diversity and abundance of soil microorganisms in shifting environments is crucial for the improvement of crop yield, tackling climate change and optimizing biogeochemical cycles in the Earth’s critical zone. In this study, a comprehensive survey of soil microbial communities was conducted in fields in an irrigated area and an abandoned area in western China. In total, 40 soil cores (600 cm deep) were taken from land under irrigation–fertilization management (IL; 34 cores) and abandoned land (AL; six cores) from July 2013 to April 2018. Spatiotemporal variation in the abundance of soil bacteria, fungi and nitrogen (N)-cycling microbes was evaluated. Soil depth was found to drive soil microbial abundance, but not diversity, throughout the vertical soil profile. The results suggest that in winter, the abundances were mainly driven by environmental variables (depth and temperature), but in summer, by depth, temperature and nutrient status. Depth had strong direct effects, and indirect impacts via temperature and TN, on microbial abundance in summer. The sequence counts for some prokaryotic taxa presented clear, mid-profile peaks in the vertical soil profiles. These distributions may be partially explained by soil texture. Consequently, the environmental filtering associated with depth controls local microbial abundance along resource gradients in the soil profile. Other factors, including temperature, moisture, pH and nutrients, were secondary regulators of microbial abundance. Further, irrigated agriculture land use could accelerate N cycling, increase nitrate leaching, and N2O and N2 production. Such findings, which enhance our understanding of how soil microbial communities respond to environmental changes and N-cycling pathways in the deeper soil profile, could improve field nitrogen management.

Telescoping bimodal latent Dirichlet allocation to identify expression QTLs across tissues.

Expression quantitative trait loci (eQTLs), or single-nucleotide polymorphisms that affect average gene expression levels, provide important insights into context-specific gene regulation. Classic eQTL analyses use one-to-one association tests, which test gene-variant pairs individually and ignore correlations induced by gene regulatory networks and linkage disequilibrium. Probabilistic topic models, such as latent Dirichlet allocation, estimate latent topics for a collection of count observations. Prior multimodal frameworks that bridge genotype and expression data assume matched sample numbers between modalities. However, many data sets have a nested structure where one individual has several associated gene expression samples and a single germline genotype vector. Here, we build a telescoping bimodal latent Dirichlet allocation (TBLDA) framework to learn shared topics across gene expression and genotype data that allows multiple RNA sequencing samples to correspond to a single individual's genotype. By using raw count data, our model avoids possible adulteration via normalization procedures. Ancestral structure is captured in a genotype-specific latent space, effectively removing it from shared components. Using GTEx v8 expression data across 10 tissues and genotype data, we show that the estimated topics capture meaningful and robust biological signal in both modalities and identify associations within and across tissue types. We identify 4,645 cis-eQTLs and 995 trans-eQTLs by conducting eQTL mapping between the most informative features in each topic. Our TBLDA model is able to identify associations using raw sequencing count data when the samples in two separate data modalities are matched one-to-many, as is often the case in biological data. Our code is freely available at https://github.com/gewirtz/TBLDA.

JACKIE: Fast Enumeration of Genome-Wide Single- and Multicopy CRISPR Target Sites and Their Off-Target Numbers.

Zinc finger protein-, transcription activator like effector-, and CRISPR-based methods for genome and epigenome editing and imaging have provided powerful tools to investigate functions of genomes. Targeting sequence design is vital to the success of these experiments. Although existing design software mainly focus on designing target sequence for specific elements, we report here the implementation of Jackie and Albert's Comprehensive K-mer Instances Enumerator (JACKIE), a suite of software for enumerating all single- and multicopy sites in the genome that can be incorporated for genome-scale designs as well as loaded onto genome browsers alongside other tracks for convenient web-based graphic-user-interface-enabled design. We also implement fast algorithms to identify sequence neighborhoods or off-target counts of targeting sequences so that designs with low probability of off-target can be identified among millions of design sequences in reasonable time. We demonstrate the application of JACKIE-designed CRISPR site clusters for genome imaging.

Dual bacterial strains TTI for monitoring fish quality in food cold chain.

Most microbial time-temperature indicators (TTIs) considered only one spoilage strain. This research compared single and dual spoilage strains-based microbial TTI for quality changes of chilled grouper fish (Epinephelus fuscoguttatus x E. lanceolatus) fillet products during distribution. The next-generation sequencing (NGS) and traditional plate count approach showed that Pseudomonas fragi and Vibrio parahaemolyticus were specific spoilage bacteria at 7 and 15°C. A dual-strain TTI response provides more accurate results than a single-strain TTI and provides an irreversible color change from yellow to reddish-brown, showing levels of fish freshness. The microbial TTI comprises fish spoilage bacteria strains with 3 log CFU/ml, a nutrient broth supplemented with 2% NaCl as a medium, and phenol red with 0.25 mg/ml as a pH indicator. Overall, this study points to the applicability of a dual-strain microbial TTI as a valuable tool for monitoring fish quality changes during cold chain break condition.

Environmental DNA metabarcoding of fish communities in a small hydropower dam reservoir: a comparison between the eDNA approach and established fishing methods

Environmental DNA methods have emerged as a promising tool in fish diversity studies and fishery management in various aquatic ecosystems. However, eDNA metabarcoding of fish communities in small hydropower dam reservoirs has received limited attention. In this study, we tested whether eDNA metabarcoding was an appropriate approach for the characterization of fish communities and fishery stock assessment by comparing its results with combined capture-based fishing methods and hydroacoustic surveys, which involved species detection and abundance/biomass evaluation. Our results indicated that the species detection performance of eDNA metabarcoding was basically similar to that of traditional capture-based fishing gear. However, it was also noteworthy that the eDNA method failed to cover all species detected by capture-based method, although some additional species were found. Besides, although we observed a significant correlation between site occurrence and sequence abundance for fishes, an effort to quantitatively establish a correlation between eDNA sequence counts and fish abundance/biomass was unsuccessful. Therefore, our study suggested that eDNA metabarcoding was an important supplementary tool to traditional capture-based fishing methods for the investigation and biomonitoring of fish diversity in small hydropower dam reservoirs. Further studies on the mechanisms of eDNA production, persistence, transportation and degradation in reservoirs might facilitate the interpretation of fish abundance and biomass from eDNA data.

Abstract 5043: Predicting melanoma staging using targeted RNA sequencing data using machine learning

Abstract Introduction: Cancer staging can take essential time and expenses away from patients, both of which should be patients’ management. In this current study, we aim to develop a machine learning-based early TNM staging model. Methods: Normalized ribonucleic acid sequencing (RNA-seq) counts data for melanoma patients was extracted from The Cancer Genome Atlas (TCGA). Six different experiments were run to produce machine learning algorithms for nodal metastasis, distant metastasis, combine (nodal or distant) metastasis, and higher vs lower tumor stage (T4/T3 vs T2/T1/Tis). All datasets were split using 80/20 for training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was used to address the imbalanced distribution of the outcome. The algorithm accuracies were determined by a percent of sensitivity, specificity, predictive values (positive and negative: PPV and NPV), and area under the receiver-operator curve (AUROC). Results: The best model for nodal metastasis was a random forest classifier (RF) with targeted gene expression (TE) showed higher sensitivity (98), specificity (100), PPV (100), NPV (94), and AUROC (1.00, 95%CI 0.91-0.99). TE for distant metastasis with RF, showed sensitivity (0), specificity (100), PPV (0), NPV (100), and AUROC (1.00, 95%CI 0.91-1.00). While TE for combined metastasis (nodal or distant) staging algorithm; Nodal or Distant Metastasis (TE) RF, showed sensitivity (98), specificity (100), PPV (100), NPV (99), and AUROC (1.00, 95%CI 0.88-0.98). The tumor staging (DEG and predicting higher stage i.e stage 3 or higher) algorithm; Tumor Staging (TE) RF, showed sensitivity (100), specificity (100), PPV (100), NPV (100), and AUROC (1.00, 95%CI 0.69-0.89) (Table 1). Conclusion: Our machine learning models can predict tumor staging including higher vs lower stage tumor, nodal metastasis, and combined metastasis with high accuracy. However, these results need to be further validated. Table 1. Machine learning models Experiment Sensitivity Specificity PPV NPV AUROC 95%CI Test Set Accuracy N Nodal Metastasis (Pan-Expression) DNN 66.22 16.98 52.69 26.47 0.495 0.25-0.47 54.55 479 Nodal Metastasis (Targeted Expression) DNN 81.13 100.0 100.0 88.10 0.982 0.62-0.83 90.21 479 Nodal Metastasis (Targeted Expression) RF 97.89 100.00 100.00 94 1.00 0.91-0.99 96.74 458 Distant Metastasis (Targeted Expression) RF 0.00 100.00 0.00 100 1.00 0.91-1.00 94.44 450* Nodal or Distant Metastasis (Targeted Expression) RF 97.56 100.00 100.00 98.56 1.00 0.88-0.98 98.91 458 Tumor Staging (Targeted Expression) RF 100.00 100.00 100.00 100.00 1.00 0.69-0.89 100.00 395 *27 cases with distant metastasis Citation Format: Fahad Shabbir Ahmed, Furqan Bin Irfan. Predicting melanoma staging using targeted RNA sequencing data using machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5043.

Successes and limitations of quantitative diet metabarcoding in a small, herbivorous mammal.

DNA metabarcoding is widely used to determine wild animal diets, but whether this technique provides accurate, quantitative measurements is still under debate. To test our ability to accurately estimate the abundance of dietary items using metabarcoding, we fed wild-caught desert woodrats (Neotoma lepida) diets consisting of constant amounts of juniper (Juniperus osteosperma, 15%) and varying amounts of creosote (Larrea tridentata, 1%-60%), cactus (Opuntia sp., 0%-100%) and commercial chow (0%-85%). Using metabarcoding, we compared the representation of items in the original diet samples to that in the faecal samples to test the sensitivity and accuracy of diet metabarcoding, the performance of different bioinformatic pipelines and our ability to correct sequence counts. Metabarcoding, using standard trnL primers, detected creosote, juniper and chow. Different pipelines for assigning taxonomy performed similarly. While creosote was detectable at dietary proportions as low as 1%, we failed to detect cactus in most samples, probably due to a primer mismatch. Creosote read counts increased as its proportion in the diet increased, and we could differentiate when creosote was a minor and major component of the diet. However, we found that estimates of juniper and creosote varied. Using previously suggested methods to correct these errors did not improve accuracy estimates of creosote, but did reduce error for juniper and chow. Our results indicate that metabarcoding can provide quantitative information on dietary composition, but may be limited. We suggest that researchers use caution when quantitatively interpreting diet metabarcoding results unless they first experimentally determine the extent of possible biases.

On Global-Local Shrinkage Priors for Count Data

Global-local shrinkage priors have been recognized as a useful class of priors that can strongly shrink small signals toward prior means while keeping large signals unshrunk. Although such priors have been extensively discussed under Gaussian responses, in practice, we often encounter count responses. Previous contributions on global-local shrinkage priors cannot be readily applied to count data. In this paper, we discuss global-local shrinkage priors for analyzing a sequence of counts. We provide sufficient conditions under which the posterior mean is unshrunk for very large signals, known as the tail robustness property. Then, we propose tractable priors to satisfy those conditions approximately or exactly and develop a custom posterior computation algorithm for Bayesian inference without tuning parameters. We demonstrate the proposed methods through simulation studies and an application to a real dataset.

Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function.

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules, using a custom negative-log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships. Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

The influence of glacial isostatic adjustment on continental shelf stratigraphic correlation

Abstract Glacial isostatic adjustment (GIA) imparts geographic variability in the amplitude and timing of local sea-level (LSL) change arising from glacial-interglacial oscillations relative to a global mean signal (eustasy). We modeled how GIA manifests in the stratigraphic record across four shelf-perpendicular transects moving progressively more distal to the Quaternary North American ice complex, subject to varying amounts of GIA during glacial-interglacial cycles. Along each transect, we obtained LSL histories for nine sites between 1 m and 250 m water depth from the output of a gravitationally self-consistent GIA model run from marine oxygen isotope stage (MIS) 11 to the present. We paired each site's unique LSL history with 50 identical annual sedimentation models to create a library of 400-k.y.-duration synthetic stratigraphic columns (each assuming no tectonics). Comparison of the suite of synthetic stratigraphic columns between transects for a given bathymetric depth reveals latitudinal differences in the stratigraphically determined number, magnitude, and age of glacial-interglacial cycles, as inferred from stratigraphic sequence count, apparent water-depth change, and age of preserved deglacial transgression. We conclude that, for many field locales, extraction of primary information about the number, scale, and duration of pre-Cenozoic glacial-interglacial cycles from continental shelf stratigraphic records near ice sheets demands a deconvolution of the GIA signal.

Negative binomial factor regression with application to microbiome data analysis.

The human microbiome provides essential physiological functions and helps maintain host homeostasis via the formation of intricate ecological host‐microbiome relationships. While it is well established that the lifestyle of the host, dietary preferences, demographic background, and health status can influence microbial community composition and dynamics, robust generalizable associations between specific host‐associated factors and specific microbial taxa have remained largely elusive. Here, we propose factor regression models that allow the estimation of structured parsimonious associations between host‐related features and amplicon‐derived microbial taxa. To account for the overdispersed nature of the amplicon sequencing count data, we propose negative binomial reduced rank regression (NB‐RRR) and negative binomial co‐sparse factor regression (NB‐FAR). While NB‐RRR encodes the underlying dependency among the microbial abundances as outcomes and the host‐associated features as predictors through a rank‐constrained coefficient matrix, NB‐FAR uses a sparse singular value decomposition of the coefficient matrix. The latter approach avoids the notoriously difficult joint parameter estimation by extracting sparse unit‐rank components of the coefficient matrix sequentially, effectively delivering interpretable bi‐clusters of taxa and host‐associated factors. To solve the nonconvex optimization problems associated with these factor regression models, we present a novel iterative block‐wise majorization procedure. Extensive simulation studies and an application to the microbial abundance data from the American Gut Project (AGP) demonstrate the efficacy of the proposed procedure. In the AGP data, we identify several factors that strongly link dietary habits and host life style to specific microbial families.

Sulfur Cycling as a Viable Metabolism under Simulated Noachian/Hesperian Chemistries.

Water present on the surface of early Mars (>3.0 Ga) may have been habitable. Characterising analogue environments and investigating the aspects of their microbiome best suited for growth under simulated martian chemical conditions is key to understanding potential habitability. Experiments were conducted to investigate the viability of microbes from a Mars analogue environment, Colour Peak Springs (Axel Heiberg Island, Canadian High Arctic), under simulated martian chemistries. The fluid was designed to emulate waters thought to be typical of the late Noachian, in combination with regolith simulant material based on two distinct martian geologies. These experiments were performed with a microbial community from Colour Peak Springs sediment. The impact on the microbes was assessed by cell counting and 16S rRNA gene amplicon sequencing. Changes in fluid chemistries were tested using ICP-OES. Both chemistries were shown to be habitable, with growth in both chemistries. Microbial communities exhibited distinct growth dynamics and taxonomic composition, comprised of sulfur-cycling bacteria, represented by either sulfate-reducing or sulfur-oxidising bacteria, and additional heterotrophic halophiles. Our data support the identification of Colour Peak Springs as an analogue for former martian environments, with a specific subsection of the biota able to survive under more accurate proxies for martian chemistries.

Internal oligo(dT) priming introduces systematic bias in bulk and single-cell RNA sequencing count data.

Significant advances in RNA sequencing have been recently made possible by using oligo(dT) primers for simultaneous mRNA enrichment and reverse transcription priming. The associated increase in efficiency has enabled more economical bulk RNA sequencing methods and the advent of high-throughput single-cell RNA sequencing, already one of the most widely adopted methods in transcriptomics. However, the effects of off-target oligo(dT) priming on gene expression quantification have not been appreciated. In the present study, we describe the extent, the possible causes, and the consequences of internal oligo(dT) priming across multiple public datasets obtained from various bulk and single-cell RNA sequencing platforms. To explore and address this issue, we developed a computational algorithm for RNA counting methods, which identifies the sequencing read alignments that likely resulted from internal oligo(dT) priming and removes them from the data. Directly comparing filtered datasets to those obtained by an alternative method reveals significant improvements in gene expression measurement. Finally, we infer a list of human genes whose expression quantification is most likely to be affected by internal oligo(dT) priming and predict that when measured using these methods, the expression of most genes may be inflated by at least 10% whereby some genes are affected more than others.

Using Metabarcoding to Investigate the Strength of Plant-Pollinator Interactions From Surveys of Visits to DNA Sequences

The ongoing decline in pollinators and increasing concerns about pollination services require a better understanding of complex pollination networks, particularly their response to global climate change. While metabarcoding is increasingly used for the identification of taxa in DNA mixtures, its reliability in providing quantitative information on plant-pollinator interactions is still the subject of debate. Combining metabarcoding and microscopy, we investigated the relationships between the number and composition of sequences and the abundance and composition of pollen in insect pollen loads (IPL) and how the two are linked to insect visits. Our findings confirm that metabarcoding is more effective than microscopy in identifying plant species in IPL. For a given species, we found a strong positive relationship between the amount of pollen in IPL and the number of sequences. The relationship was stable across species even if the abundance of co-occurring species in IPL (hereafter “co-occurring pollen”) tended to reduce the sequence yield (number of sequences obtained from one pollen grain) of a given species. We also found a positive relationship between the sequence count and the frequency of visits, and between the frequency and the amounts of pollen in IPL. Our results demonstrate the reliability of metabarcoding in assessing the strength of plant-pollinator interactions and in providing a broader perspective for the analyses of plant-pollinator interactions and pollination networks.

A modified fatigue damage model considering loading sequence effect

A modified fatigue damage model is proposed considering the loading sequence effect under variable amplitude loading conditions. The transition sequence is obtained from the counting sequence by an equivalent rainflow counting method and is used to provide the loading sequence information for fatigue damage accumulation prediction. Rainflow counting matrix and sequence transition matrix are formulated based on the rainflow counting sequence and the transition sequence. The relation between the two matrices and the original loading sequence is evaluated in detail. In the deterministic fatigue estimation, the rainflow counting matrix and the sequence transition matrix are integrated into the proposed modified damage model based on the linear damage rule. Mean stress transition is applied to the rainflow counting matrix for loading level effect. Existing experimental data are used to validate the fatigue damage estimation from the deterministic framework. In order to evaluate the fatigue under random loading conditions, the rainflow counting matrix and sequence transition matrix are mapped to one-dimensional exponential and Laplace distributions based on the stationary random process. Numerical simulation is conducted to validate the proposed mapping distributions. Meanwhile, the impact of the correlation length on the proposed distributions under stationary random loading sequence is investigated as well. Finally, with the given distributions of the rainflow counting matrix and sequence transition matrix, a reconstruction procedure is simulated and discussed for probabilistic fatigue estimation. The proposed model provides a sophisticated approximation to the original loading sequence with a sequence transition effect. The proposed modified fatigue damage model corrects the deficiencies of the linear damage rule with sufficient sequence information from the originally applied loading. Furthermore, the probabilistic fatigue estimation can evaluate the accumulated fatigue damages under a random loading process with the sequence transition effect.

The microbiome of common bedding materials before and after use on commercial dairy farms

BackgroundBovine mastitis is one of the most economically important diseases affecting dairy cows. The choice of bedding material has been identified as an important risk factor contributing to the development of mastitis. However, few reports examine both the culturable and nonculturable microbial composition of commonly used bedding materials, i.e., the microbiome. Given the prevalence of nonculturable microbes in most environments, this information could be an important step to understanding whether and how the bedding microbiome acts as a risk factor for mastitis. Therefore, our objective was to characterize the microbiome composition and diversity of bedding material microbiomes, before and after use.MethodsWe collected 88 bedding samples from 44 dairy farms in the U.S. Unused (from storage pile) and used (out of stalls) bedding materials were collected from four bedding types: new sand (NSA), recycled manure solids (RMS), organic non-manure (ON) and recycled sand (RSA). Samples were analyzed using 16S rRNA sequencing of the V3–V4 region.ResultsThe overall composition as well as the counts of several microbial taxa differed between bedding types, with Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes dominating across all types. Used bedding contained a significantly different microbial composition than unused bedding, but the magnitude of this difference varied by bedding type, with RMS bedding exhibiting the smallest difference. In addition, positive correlations were observed between 16S rRNA sequence counts of potential mastitis pathogens (bacterial genera) and corresponding bedding bacterial culture data.ConclusionOur results strengthen the role of bedding as a potential source of mastitis pathogens. The consistent shift in the microbiome of all bedding types that occurred during use by dairy cows deserves further investigation to understand whether this shift promotes pathogen colonization and/or persistence, or whether it can differentially impact udder health outcomes. Future studies of bedding and udder health may be strengthened by including a microbiome component to the study design.

Improving the Flavor of Fermented Sausage by Increasing Its Bacterial Quality via Inoculation with Lactobacillus plantarum MSZ2 and Staphylococcus xylosus YCC3.

This research aims to investigate the effects of Staphylococcus xylosus YCC3 (Sx YCC3) and Lactobacillus plantarum MSZ2 (Lp MSZ2) on lipid hydrolysis and oxidation, the bacterial community’s composition, and the volatile flavor compounds in fermented sausage. The bacterial community was examined by plate counting and high-throughput sequencing. Differential flavor compounds in non-inoculated and inoculated sausages were identified by principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that the free fatty acid (FFA) content was increased after inoculating with Sx YCC3 or Lp MSZ2. The pH, peroxide value (POV), thiobarbituric acid reactive substances (TBARS) value, lipoxygenase activity, and the counts of Enterobacteriaceae were lower in the inoculated sausage than in the non-inoculated sausage. The bacterial inoculation enhanced the competitiveness of Staphylococcus and Lactobacillus and restricted the growth of unwanted bacteria. The OPLS-DA revealed that (Z)-hept-2-enal, (E)-2-octenal, 1-nonanal, octanal, and 1-octen-3-ol were common differential flavor compounds that were found in the inoculated sausages but were not found in the non-inoculated sausages. A positive correlation was observed between the differential flavor compounds and the relative abundance of Staphylococcus or Lactobacillus, or the FFA content. Our results indicated that inoculation with Sx YCC3 or Lp MSZ2 can improve fermented sausages’ flavor by enhancing their bacterial quality and increasing their FFA content.

Tree-Based Analysis of Dietary Diversity Captures Associations Between Fiber Intake and Gut Microbiota Composition in a Healthy US Adult Cohort

BackgroundDiet patterns are a significant and modifiable contributing factor to the composition of the human gut microbiota. ObjectivesWe set out to identify reproducible relationships between diet and gut microbial community composition in a diverse, healthy US adult cohort. MethodsWe collected 2 to 3 automated self-administered 24-hour dietary recalls over 10–14 days, together with a single stool sample, from 343 healthy adults in a cross-sectional phenotyping study. This study examined a multi-ethnic cohort balanced for age (18–65 years), sex, and BMI (18.5–45 kg/m2). Dietary data were edited to a tree format according to published methods. The tree structure was annotated with the average total grams of dry weight, fat, protein, carbohydrate, or fiber from each food item reported. The alpha and beta diversity measurements, calculated using the tree structure, were analyzed relative to the microbial community diversity, determined by a Quantitative Insights Into Microbial Ecology (QIIME) 2 analysis of the bacterial 16S ribosomal RNA V4 region, sequenced from stool samples. K-means clustering was used to form groups of individuals consuming similar diets, and gut microbial communities were compared among groups using differential expression analysis for sequence count data. ResultsThe alpha diversity of diet dry weight was significantly correlated with the gut microbial community alpha diversity (r = 0.171). The correlation improved when diet was characterized using grams of carbohydrates (r = 0.186) or fiber (r = 0.213). Bifidobacterium was enriched with diets containing higher levels of total carbohydrate from cooked grains. Lachnospira, was enriched with diet patterns containing high consumption of fiber from fruits excluding berries. ConclusionsThe tree structure, annotated with grams of carbohydrate, is a robust analysis method for comparing self-reported diet to the gut microbial community composition. This method identified consumption of fiber from fruit robustly associated with an abundance of pectinolytic bacterial genus, Lachnospira, in the guts of healthy adults. This trial was registered at https://clinicaltrials.gov/ clinicaltrials.gov as NCT02367287.

Level-Crossing Sampling with Multiple Temporal Resolutions for Speech Signals

Level-crossing sampling (LCS) is an alternative to the uniform sampling method and is suitable for sparse and bursty signals. LCS, which is adaptive to amplitude variations of the signals, mainly focuses on reducing the total number of samples. Presently, speech signals are sampled with LCS methods that have a constant temporal resolution, and they require time-to-digital converters (TDC) with a wide dynamic range. A long sequence of overflow counts of the TDC counter may be generated during silence regions if the dynamic range is reduced. This paper proposes level-crossing samplers with multiple temporal resolutions for speech signals. Frequency scaling is applied to standard LCS, adaptive LCS and peak sampling. During the silence region, the frequency of the TDC clock is scaled down, which enables the representation of longer intervals with smaller words. The number of bits per sample and hence the total number of samples generated are decreased due to the reduction in the dynamic range of the TDC. Simulation results indicate that the proposed method outperforms the conventional level-crossing samplers, which do not employ frequency scaling. In addition to a significant reduction in data size, this method can also be used for real-time automatic detection of silence regions in a speech signal.