Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images

Background

Efficient computational recognition and segmentation of target organ from medical images are foundational in diagnosis and treatment, especially about pancreas cancer. In practice, the diversity in appearance of pancreas and organs in abdomen, makes detailed texture information of objects important in segmentation algorithm. According to our observations, however, the structures of previous networks, such as the Richer Feature Convolutional Network (RCF), are too coarse to segment the object (pancreas) accurately, especially the edge.

Method

In this paper, we extend the RCF, proposed to the field of edge detection, for the challenging pancreas segmentation, and put forward a novel pancreas segmentation network. By employing multi-layer up-sampling structure replacing the simple up-sampling operation in all stages, the proposed network fully considers the multi-scale detailed contexture information of object (pancreas) to perform per-pixel segmentation. Additionally, using the CT scans, we supply and train our network, thus get an effective pipeline.

Result

Working with our pipeline with multi-layer up-sampling model, we achieve better performance than RCF in the task of single object (pancreas) segmentation. Besides, combining with multi scale input, we achieve the 76.36% DSC (Dice Similarity Coefficient) value in testing data.

Conclusion

The results of our experiments show that our advanced model works better than previous networks in our dataset. On the other words, it has better ability in catching detailed contexture information. Therefore, our new single object segmentation model has practical meaning in computational automatic diagnosis.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Magnetic microbubble-mediated ultrasound-MRI registration based on robust optical flow model

Background

As a dual-modality contrast agent, magnetic microbubbles (MMBs) can not only improve contrast of ultrasound (US) image, but can also serve as a contrast agent of magnetic resonance image (MRI). With the help of MMBs, a new registration method between US image and MRI is presented.

Methods

In this method, MMBs were used in both ultrasound and magnetic resonance imaging process to enhance the most important information of interest. In order to reduce the influence of the speckle noise to registration, semi-automatic segmentations of US image and MRI were carried out by using active contour model. After that, a robust optical flow model between US image segmentation (floating image) and MRI segmentation (reference image) was built, and the vector flow field was estimated by using the Coarse-to-fine Gaussian pyramid and graduated non-convexity (GNC) schemes.

Results

Qualitative and quantitative analyses of multiple group comparison experiments showed that registration results using all methods tested in this paper without MMBs were unsatisfactory. On the contrary, the proposed method combined with MMBs led to the best registration results.

Conclusion

The proposed algorithm combined with MMBs contends with larger deformation and performs well not only for local deformation but also for global deformation. The comparison experiments also demonstrated that ultrasound-MRI registration using the above-mentioned method might be a promising method for obtaining more accurate image information.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Speckle reducing bilateral filter for cattle follicle segmentation

Background

Ultrasound imaging technology has wide applications in cattle reproduction and has been used to monitor individual follicles and determine the patterns of follicular development. However, the speckles in ultrasound images affect the post-processing, such as follicle segmentation and finally affect the measurement of the follicles. In order to reduce the effect of speckles, a bilateral filter is developed in this paper.

Results

We develop a new bilateral filter for speckle reduction in ultrasound images for follicle segmentation and measurement. Different from the previous bilateral filters, the proposed bilateral filter uses normalized difference in the computation of the Gaussian intensity difference. We also present the results of follicle segmentation after speckle reduction. Experimental results on both synthetic images and real ultrasound images demonstrate the effectiveness of the proposed filter.

Conclusions

Compared with the previous bilateral filters, the proposed bilateral filter can reduce speckles in both high-intensity regions and low intensity regions in ultrasound images. The segmentation of the follicles in the speckle reduced images by the proposed method has higher performance than the segmentation in the original ultrasound image, and the images filtered by Gaussian filter, the conventional bilateral filter respectively.

     

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm

Introduction

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease.

Objectives

In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses.

Methods

Urine ¹H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained.

Results

Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients.

Conclusion

These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

     

ImageParser: a tool for finite element generation from three-dimensional medical images

Background

The finite element method (FEM) is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures) of interest (ROIs) may be irregular and fuzzy.

Methods

A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements.

Results

The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues.

Conclusion

The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification

Background

Mixtures of beta distributions are a flexible tool for modeling data with values on the unit interval, such as methylation levels. However, maximum likelihood parameter estimation with beta distributions suffers from problems because of singularities in the log-likelihood function if some observations take the values 0 or 1.

Methods

While ad-hoc corrections have been proposed to mitigate this problem, we propose a different approach to parameter estimation for beta mixtures where such problems do not arise in the first place. Our algorithm combines latent variables with the method of moments instead of maximum likelihood, which has computational advantages over the popular EM algorithm.

Results

As an application, we demonstrate that methylation state classification is more accurate when using adaptive thresholds from beta mixtures than non-adaptive thresholds on observed methylation levels. We also demonstrate that we can accurately infer the number of mixture components.

Conclusions

The hybrid algorithm between likelihood-based component un-mixing and moment-based parameter estimation is a robust and efficient method for beta mixture estimation. We provide an implementation of the method (“betamix”) as open source software under the MIT license.

     

Digestive-resistant carbohydrates affect lipid metabolism in rats

Introduction

Digestion resistant carbohydrates (DRC) are complex carbohydrates that resist digestion and absorption in the small bowel. Diets high in DRC can have wide ranging impacts on the health of the host, which include changes to immunity and allergy, incidence of cardiovascular disease, and obesity.

Objectives

The aim of this study was to characterise the effects of DRC (inulin, konjac or resistant starch) on large intestinal short-chain fatty acid (SCFA) concentrations and serum metabolite and lipid profiles.

Methods

A rat model was used to compare the effects of feeding a basal diet or the basal diet containing 5 % inulin, konjac or resistant starch for 14 days.

Results

Of the three DRC, inulin had the greatest effect; ten serum phospholipids differed significantly in abundance between inulin-treated and control rats. In particular phosphatidylcholines and lysophosphatidylcholines containing fatty acyl chains 22:5, 22:4, 20:4, 18:0 and 16:0 were increased in the inulin-fed group, whereas phosphocholines containing fatty acyls 20:5 and 22:6 were decreased.

Conclusion

These results indicated an impact on both n-3 and n-6 fatty acid metabolism as a result of inulin dietary intake. Increased intestinal concentrations of SCFA were detected in rats fed DRC, but only inulin caused appreciable changes to serum lipid profiles.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Response to dietary carbohydrates in European seabass (<Emphasis Type="Italic">Dicentrarchus labrax</Emphasis>) muscle tissue as revealed by NMR-based metabolomics

Introduction

Feed optimization is a key step to the environmental and economic sustainability of aquaculture, especially for carnivorous species. Plant-derived ingredients can contribute to reduce costs and nitrogenous effluents while sparing wild fish stocks. However, the metabolic use of carbohydrates from vegetable sources by carnivorous fish is still not completely understood.

Objectives

We aimed to study the effects of diets with carbohydrates of different digestibilities, gelatinized starch (DS) and raw starch (RS), in the muscle metabolome of European seabass (Dicentrarchus labrax).

Methods

We followed an NMR-metabolomics approach, using two sample preparation procedures, the intact muscle (HRMAS) and the aqueous muscle extracts (¹H NMR), to compare the variations in muscle metabolome between the two diets.

Results

In muscle, multivariate analysis revealed similar metabolome shifts for DS and RS diets, when compared with the control diet. HRMAS of intact muscle, which included both hydrophobic and hydrophilic metabolites, showed increased lipid in DS-fed fish by univariate analysis. Regardless of the nature of the starch, increased glycine and phenylalanine, and decreased proline were observed when compared to the Ctr diet. Combined univariate analysis of intact muscle and aqueous extracts indicated specific diet related changes in lipid and amino acid metabolism, consistent with increased dietary carbohydrate supplementation.

Conclusions

Due to differential sample processing, outputs differ in detail but provide complementary information. After tracing nutritional alterations by profiling fillet components, DS seems to be the most promising alternative to fishmeal-based diets in aquaculture. This approach should be reproducible for other farmed fish species and provide valuable information on nutritional and organoleptic properties of the final product.

     

NS-kNN: a modified <Emphasis Type="Italic">k</Emphasis>-nearest neighbors approach for imputing metabolomics data

Introduction

A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation.

Objectives

Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations.

Methods

We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data.

Results

Our results show that NS-kNN typically outperforms kNN when at least 20–30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR.

Conclusion

Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.

     

GC–MS analysis of the ruminal metabolome response to thiamine supplementation during high grain feeding in dairy cows

Introduction

Thiamine is known to attenuate high-concentrate diet induced subacute ruminal acidosis (SARA) in dairy cows, however, the underlying mechanisms remain unclear.

Objectives

The major objective of this study was to investigate the metabolic mechanisms of thiamine supplementation on high-concentrate diet induced SARA.

Methods

Six multiparous, rumen-fistulated Holstein cows were used in a replicated 3?×?3 Latin square design. The treatments included a control diet (CON; 20% starch, dry matter basis), a SARA-inducing diet (SAID; 33.2% starch, dry matter basis) and SARA-inducing diet supplemented with 180 mg of thiamine/kg of dry matter intake (SAID?+?T). On d21 of each period, ruminal fluid samples were collected at 3 h post feeding, and GC/MS was used to analyze rumen fluid samples.

Results

PCA and OPLS-DA analysis demonstrated that the ruminal metabolite profile were different in three treatments. Compared with CON treatment, SAID feeding significantly decreased rumen pH, acetate, succinic acid, increased propionate, pyruvate, lactate, glycine and biogenic amines including spermidine and putrescine. Thiamine supplementation significantly decreased rumen content of propionate, pyruvate, lactate, glycine and spermidine; increase rumen pH, acetate and some medium-chain fatty acids. The enrichment analysis of different metabolites indicated that thiamine supplementation mainly affected carbohydrates, amino acids, pyruvate and thiamine metabolism compared with SAID treatment.

Conclusions

These findings revealed that thiamine supplementation could attenuate high-concentrate diet induced SARA by increasing pyruvate formate-lyase activity to promote pyruvate to generate acetyl-CoA and inhibit lactate generation. Besides, thiamine reduced biogenic amines to alleviate ruminal epithelial inflammatory response.

     

The fate of fertiliser P in soil under pasture and uptake by subterraneum clover – a field study using <Superscript>33</Superscript>P-labelled single superphosphate

Background and aims

Single superphosphate (SSP) is a major source of phosphorus (P) used in grazing systems to improve pasture production. The aim of this experiment was to determine the fate of fertiliser P in clover pastures under field conditions.

Methods

A procedure was developed to radiolabel SSP granules with a ³³P radiotracer, which was then applied to the soil surface (equivalent to ~12 kg P ha^?1) of a clover pasture. Recovery of fertiliser P was determined in clover shoots, fertiliser granules and soil fractions (surface layer: 0–4 cm and sub-surface layer: 4–8 cm).

Results

The P diffusion patterns of the ³³P-labelled SSP granules were not significantly different to those of commercial SSP granules (P?>?0.05). Recovery of fertiliser P in clover shoots was 30–35 %. A considerable proportion of the fertiliser P (~28 %) was recovered in the surface soil layer and was largely inorganic P.

Conclusions

Recovery of fertiliser P by clover plants was up to 35 % in the year of application. Much of the fertiliser P in soil fractions was inorganic P, which highlights the importance of inorganic P forms and dynamics in soils under clover pasture on a single season timeframe at these sites.

     

Extraction of natural products from bark of <Emphasis Type="Italic">Betula pendula</Emphasis> using ionic liquids

Introduction

Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.

Objectives

To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.

Methods

The extracted compounds were analyzed by LC/MS and GC/MS.

Results

The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.

Conclusion

From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.

     

Large-scale profiling of brown rice ionome in an ethyl methanesulphonate-mutagenized hitomebore population and identification of high- and low-cadmium lines

Background and aim

Recycled sources of phosphorus (P), such as struvite extracted from wastewater, have potential to substitute for more soluble manufactured fertilisers and help reduce the long-term threat to food security from dwindling finite reserves of phosphate rock (PR). This study aimed to determine whether struvite could be a component of a sustainable P fertiliser management strategy for arable crops.

Methods

A combination of laboratory experiments, pot trials and mathematical modelling of the root system examined the P release properties of commercial fertiliser-grade struvite and patterns of P uptake from a low-P sandy soil by two different crop types, in comparison to more soluble inorganic P fertilisers (di-ammonium phosphate (DAP) and triple super phosphate (TSP)).

Results

Struvite had greatly enhanced solubility in the presence of organic acid anions; buckwheat, which exudes a high level of organic acids, was more effective at mobilising struvite P than the low level exuder, spring wheat. Struvite granules placed with the seed did not provide the same rate of P supply as placed DAP granules for early growth of spring wheat, but gave equivalent rates of P uptake, yield and apparent fertiliser recovery at harvest, even though only 26 % of struvite granules completely dissolved. Fertiliser mixes containing struvite and DAP applied to spring wheat have potential to provide both optimal early and late season P uptake and improve overall P use efficiency.

Conclusions

We conclude that the potential resource savings and potential efficiency benefits of utilising a recycled slow release fertiliser like struvite offers a more sustainable alternative to only using conventional, high solubility, PR-based fertilisers.

     

NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

Introduction

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.

Objectives

The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.

Methods

NMRProcFlow was developed to be a graphical and interactive 1D NMR (¹H & ¹³C) spectra processing tool.

Results

NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.

Conclusion

Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

     

CellProfiler: Novel Automated Image Segmentation Procedure for Super-Resolution Microscopy

Background

Super resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution. This breakthrough in spatial resolution made image analysis a challenging procedure. Direct and automated segmentation of SR images remains largely unsolved, especially when it comes to providing meaningful biological interpretations.

Results

Here, we introduce a novel automated imaging analysis routine, based on Gaussian, followed by a segmentation procedure using CellProfiler software (www.cellprofiler.org). We tested this method and succeeded to segment individual nuclear pore complexes stained with gp210 and pan-FG proteins and captured by two-color STED microscopy. Test results confirmed accuracy and robustness of the method even in noisy STED images of gp210.

Conclusions

Our pipeline and novel segmentation procedure may benefit end-users of SR microscopy to analyze their images and extract biologically significant quantitative data about them in user-friendly and fully-automated settings.