CEU Mass Mediator 3.0: A Metabolite Annotation Tool.

CEU Mass Mediator (CMM, http://ceumass.eps.uspceu.es ) is an online tool that has evolved from a simple interface to query different metabolomic databases (CMM 1.0) to a tool that unifies the compounds from these databases and, using an expert system with knowledge about the experimental setup and the compounds properties, filters and scores the query results (CMM 2.0). Since this last major revision, CMM has continued to grow, expanding the knowledge base of its expert system and including new services to support researchers in the metabolite annotation and identification process. The information from external databases has been refreshed, and an in-house library with oxidized lipids not present in other sources has been added. This has increased the number of experimental metabolites up 332,665 and the number of predicted metabolites to 681,198. Furthermore, new taxonomy and ontology metadata have been included. CMM has expanded its functionalities with a service for the annotation of oxidized glycerophosphocholines, a service for spectral comparison from MS2 data, and a spectral quality-assessment service to determine the reliability of a spectrum for compound identification purposes. To facilitate the collaboration and integration of CMM with external tools and metabolomic platforms, a RESTful API has been created, and it has already been integrated into the HMDB (Human Metabolome Database). This paper will present the novel functionalities incorporated into version 3.0 of CMM.

Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review.

This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering.

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidence.

An Inexpensive and Easy to Use Cervical Range of Motion Measurement Solution Using Inertial Sensors.

Neck injuries and the related pain have a high prevalence and represent an important health problem. To properly diagnose and treat them, practitioners need an accurate system for measuring Cervical Range Of Motion (CROM). This article describes the development and validation of an inexpensive, small (4 cm × 4 cm × 8 cm), light (< 200 g) and easy to use solution for measuring CROM using wearable inertial sensors. The proposed solution has been designed with the clinical practice in mind, after consulting with practitioners. It is composed of: (a) two wearable wireless MEMS-based inertial devices, (b) a recording and report generation software application and (c) a measurement protocol for assessing CROM. The solution provides accurate (none of our results is outside the ROM ranges when compared with previously published results based on an optical tracking device) and reliable measurements (ICC = 0.93 for interrater reliability when compared with an optical tracking device and ICC > 0.90 for test-retest reliability), surpassing the popular CROM instrument's capabilities and precision. It also fulfills the needs for clinical practice attending to effectiveness, efficiency (4 min from setup to final report) and user's satisfaction (as reported by practitioners). The solution has been certified for mass-production and use in medical environments.

Differentiating signals to make biological sense - A guide through databases for MS-based non-targeted metabolomics.

Metabolite identification is one of the most challenging steps in metabolomics studies and reflects one of the greatest bottlenecks in the entire workflow. The success of this step determines the success of the entire research, therefore the quality at which annotations are given requires special attention. A variety of tools and resources are available to aid metabolite identification or annotation, offering different and often complementary functionalities. In preparation for this article, almost 50 databases were reviewed, from which 17 were selected for discussion, chosen for their online ESI-MS functionality. The general characteristics and functions of each database is discussed in turn, considering the advantages and limitations of each along with recommendations for optimal use of each tool, as derived from experiences encountered at the Centre for Metabolomics and Bioanalysis (CEMBIO) in Madrid. These databases were evaluated considering their utility in non-targeted metabolomics, including aspects such as identifier assignment, structural assignment and interpretation of results.

Evaluation of cervical posture improvement of children with cerebral palsy after physical therapy based on head movements and serious games.

BACKGROUND: This paper presents the preliminary results of a novel rehabilitation therapy for cervical and trunk control of children with cerebral palsy (CP) based on serious videogames and physical exercise. MATERIALS: The therapy is based on the use of the ENLAZA Interface, a head mouse based on inertial technology that will be used to control a set of serious videogames with movements of the head. METHODS: Ten users with CP participated in the study. Whereas the control group (n = 5) followed traditional therapies, the experimental group (n = 5) complemented these therapies with a series of ten sessions of gaming with ENLAZA to exercise cervical flexion-extensions, rotations and inclinations in a controlled, engaging environment. RESULTS: The ten work sessions yielded improvements in head and trunk control that were higher in the experimental group for Visual Analogue Scale, Goal Attainment Scaling and Trunk Control Measurement Scale (TCMS). Significant differences (27% vs. 2% of percentage improvement) were found between the experimental and control groups for TCMS (p < 0.05). The kinematic assessment shows that there were some improvements in the active and the passive range of motion. However, no significant differences were found pre- and post-intervention. CONCLUSIONS: Physical therapy that combines serious games with traditional rehabilitation could allow children with CP to achieve larger function improvements in the trunk and cervical regions. However, given the limited scope of this trial (n = 10) additional studies are needed to corroborate this hypothesis.

Correlations between physiological parameters related with kidney function and minute-by-minute urine output.

AIM: Recently, devices capable of measuring minute-by-minute urine output (UOm) have become available. It is not known how UOm correlates with physiological parameters in normal conditions and in disease states characterized by vascular dysfunction. This paper analyzes correlations between UOm and physiological parameters related to kidney perfusion to provide some insight about UOm pathophysiological interpretation and its relationship with renal blood flow. METHODS: We studied 14 male pigs were anesthetized, tracheostomized, and mechanically ventilated. Mean systemic blood pressure (PART ), mean pulmonary artery blood pressure (PPA ), carotid artery blood flow (QCA ), as well as total (QREN ), cortical (QCOR ) and medullary (QMED ) renal blood flows, and the renal resistive index (RRI) were measured or calculated. Animals received an intravenous dose of live E. coli for the induction of sepsis (septic group), or an equivalent amount of normal saline (nonseptic group). Three groups were studied: nonseptic (n = 6) and septic (n = 4), both receiving for resuscitation NaCl 0.9% at 4 mL/kg per h; and septic (n = 4), receiving for resuscitation NaCl 0.9% at 17 mL/kg per h. Animals were monitored for 5 h after the induction of sepsis. RESULTS: In septic animals, UOm was strongly positively correlated with QREN (Kendall's τ = 0.770, P < 0.05), QCOR (τ = -0.566, P < 0.05) and QMED (τ = 0.632, P < 0.05); and negatively correlated with PPA (τ = -0.524, P < 0.05) and RRI (τ = -0.672, P < 0.05). Control animals exhibited weaker correlations. CONCLUSION: UOm is a good physiological surrogate marker of total and regional renal blood flows and vascular resistance, particularly under septic conditions, probably reflecting glomerulo-tubular dysfunction in sepsis.

In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics.

The role of non-targeted metabolomics with its discovery power is constantly growing in many different fields of science. However, its biggest advantage of uncovering the unexpected is turning into one of its biggest bottlenecks, particularly in metabolite identification. Among different methods for metabolite identification or ID confirmation, tandem MS analysis plays a very important role. However, this method is limited to only certain types of MS analysers, making for example TOF-MS inaccessible for this type of metabolite identification. To overcome this, in-source fragmentation has been used to fragment molecules and obtain product ions. Since the molecule of interest is not isolated prior to its fragmentation, the acquired spectrum contains many different signals arising from the fragmentation of all compounds present in the sample. Therefore, to assign product ions to their precursors, a novel use of correlation analysis was tested with r ≥0.9 as an assignation of a product ion belonging to the precursor. This method and chosen cut-off was tested on three different sample complexity levels: conducting the analysis on a single standard, mix of co-eluting standards and on a plasma sample. Obtained results clearly proved the effectiveness of the proposed methodology for metabolite ID confirmation. Moreover, the proposed strategy can be successfully applied for semi-quantification of co-eluting molecules with the same monoisotopic mass but that differ in fragmentation pattern. The proposed methodology can greatly improve the robustness and throughput of identification in metabolomics studies by use of TOF-MS, which is crucial to obtain meaningful and trustful results.

Modelling the interaction of invasive-invaded species based on the general Bramson dynamics and with a density dependant diffusion and advection.

The main goal of the presented study is to introduce a model of a pairwise invasion interaction with a nonlinear diffusion and advection. The new equation is based on the further general works introduced by Bramson (1988) to describe the invasive-invaded dynamics. This type of model is made particular with a density dependent diffusion along with an advection term. The new resulting model is then analyzed to explore the regularity, existence and uniqueness of solutions. It is well known that density dependent diffusion operators induce a propagating front with finite speed for compactly supported functions. Based on this, we introduce an analytical approach to determine the evolution of such a propagating front in the invasion dynamics. Afterward, we study the problem with travelling wave profiles and a numerical assessment. As a main finding to remark: When both species propagate with significantly different travelling wave speeds, the interaction becomes unstable, while when the species propagate with similar low speeds, the interaction stabilizes.

An automatic critical care urine meter.

Nowadays patients admitted to critical care units have most of their physiological parameters measured automatically by sophisticated commercial monitoring devices. More often than not, these devices supervise whether the values of the parameters they measure lie within a pre-established range, and issue warning of deviations from this range by triggering alarms. The automation of measuring and supervising tasks not only discharges the healthcare staff of a considerable workload but also avoids human errors in these repetitive and monotonous tasks. Arguably, the most relevant physiological parameter that is still measured and supervised manually by critical care unit staff is urine output (UO). In this paper we present a patent-pending device that provides continuous and accurate measurements of patient's UO. The device uses capacitive sensors to take continuous measurements of the height of the column of liquid accumulated in two chambers that make up a plastic container. The first chamber, where the urine inputs, has a small volume. Once it has been filled it overflows into a second bigger chamber. The first chamber provides accurate UO measures of patients whose UO has to be closely supervised, while the second one avoids the need for frequent interventions by the nursing staff to empty the container.

Probabilistic metabolite annotation using retention time prediction and meta-learned projections.

Retention time information is used for metabolite annotation in metabolomic experiments. But its usefulness is hindered by the availability of experimental retention time data in metabolomic databases, and by the lack of reproducibility between different chromatographic methods. Accurate prediction of retention time for a given chromatographic method would be a valuable support for metabolite annotation. We have trained state-of-the-art machine learning regressors using the 80, 038 experimental retention times from the METLIN Small Molecule Retention Tim (SMRT) dataset. The models included deep neural networks, deep kernel learning, several gradient boosting models, and a blending approach. 5, 666 molecular descriptors and 2, 214 fingerprints (MACCS166, Extended Connectivity, and Path Fingerprints fingerprints) were generated with the alvaDesc software. The models were trained using only the descriptors, only the fingerprints, and both types of features simultaneously. Bayesian hyperparameter search was used for parameter tuning. To avoid data-leakage when reporting the performance metrics, nested cross-validation was employed. The best results were obtained by a heavily regularized deep neural network trained with cosine annealing warm restarts and stochastic weight averaging, achieving a mean and median absolute errors of [Formula: see text] and [Formula: see text], respectively. To the best of our knowledge, these are the most accurate predictions published up to date over the SMRT dataset. To project retention times between chromatographic methods, a novel Bayesian meta-learning approach that can learn from just a few molecules is proposed. By applying this projection between the deep neural network retention time predictions and a given chromatographic method, our approach can be integrated into a metabolite annotation workflow to obtain z-scores for the candidate annotations. To this end, it is enough that just as few as 10 molecules of a given experiment have been identified (probably by using pure metabolite standards). The use of z-scores permits considering the uncertainty in the projection when ranking candidates, and not only the accuracy. In this scenario, our results show that in 68% of the cases the correct molecule was among the top three candidates filtered by mass and ranked according to z-scores. This shows the usefulness of this information to support metabolite annotation. Python code is available on GitHub at https://github.com/constantino-garcia/cmmrt.

Development and Clinical Validation of a Rehabilitation Platform for Hip Fracture in Elderly Population.

Hip fracture is one of the most common traumatisms associated with falls in the elderly, severely affecting the patient's mobility and independence. In recent years, the use of robotic technology has proven to be effective in gait rehabilitation, especially for neurological disorders. However, there is a lack of research validating these devices for hip fracture in elderly patients. This paper presents the design and evaluation of a novel assistive platform for hip rehabilitation, SWalker, aimed at improving the rehabilitation of this condition. Functional validation of the SWalker platform was carried out with five healthy elderly subjects and two physiotherapists. Clinical validation was conducted with 34 patients with hip fracture. The control group ( [Formula: see text], age = 86.38±6.16 years, 75% female) followed conventional therapy, while the intervention group ( [Formula: see text], age = 86.80±6.32 years, 90% female) was rehabilitated using SWalker. The functional validation of the device reported good acceptability (System Usability Scale >85). In the clinical validation, the control group required 68.09±27.38 rehabilitation sessions compared to 22.60±16.75 in the intervention group ( [Formula: see text]). Patients in the control group needed 120.33±53.64 days to reach ambulation, while patients rehabilitated with SWalker achieved that stage in 67.11±51.07 days ( [Formula: see text]). FAC and Tinetti indexes presented a larger improvement in the intervention group when compared with the control group ( [Formula: see text] and [Formula: see text], respectively). The SWalker platform can be considered an effective tool to enhance autonomous gait and shorten rehabilitation therapy in elderly hip fracture patients. This result encourages further research on robotic rehabilitation platforms for hip fracture.

A novel virtual reality application for autonomous assessment of cervical range of motion: development and reliability study.

Background: Neck pain, one of the most common musculoskeletal diseases, affects 222 million people worldwide. The cervical range of motion (CROM) is a tool used to assess the neck's state across three movement axes: flexo-extension, rotation, and lateral flexion. People with neck pain often have a reduced CROM, and they feel pain at the end-range and/or accompany neck movements with compensatory trunk movements. Virtual reality (VR) setups can track the movement of the head and other body parts in order to create the sensation of immersion in the virtual environment. Using this tracking position information, a CROM assessment can be performed using a VR setup that may be carried out autonomously from the user's home. The objectives of this study were to develop a VR experience that could be used to perform a CROM assessment, and to evaluate the intra-rater and inter-rater reliability of the CROM measures guided by this VR experience. To the best of our knowledge, a study of this type has not been carried out before. Materials & Methods: A total of 30 asymptomatic adults were assessed using a VR device (HTC Vive Pro Eye™). Two raters provided support with the VR setup, and the participants were guided by the VR experience as they performed the movements. Each rater tested each subject twice, in random order. In addition to a head-mounted display (HMD), a tracker located on the subject's back was used to measure trunk compensatory movements. The CROM was estimated using only the HMD position and this measurement was corrected using the tracker data. The mean and standard deviation were calculated to characterize the CROM. To evaluate the reliability, the interclass correlation coefficients (ICC) were calculated for intra-rater and inter-rater analysis. The standard error of measurement and minimum detectable change were also calculated. The usability of the VR system was measured using the Spanish version of the System Usability Scale. Results: The mean CROM values in each axis of movement were compatible with those described in the literature. ICC values ranged between 0.86 and 0.96 in the intra-rater analysis and between 0.83 and 0.97 in the inter-rater analysis; these values were between good and excellent. When applying the correction of the trunk movements, both the intra-rater and inter-rater ICC values slightly worsened except in the case of the lateral flexion movement, where they slightly improved. The usability score of the CROM assessment/VR system was 86 points, which is an excellent usability score. Conclusion: The reliability of the measurements and the usability of the system indicate that a VR setup can be used to assess CROM. The reliability of the VR setup can be affected by slippage of the HMD or tracker. Both slippage errors are additive, i.e., only when the sum of these two errors is less than the compensatory movement do the measurements improve when considering the tracker data.

Enhancing confidence of metabolite annotation in Capillary Electrophoresis-Mass Spectrometry untargeted metabolomics with relative migration time and in-source fragmentation.

Capillary electrophoresis coupled to mass spectrometry is a power tool in untargeted metabolomics studies to analyze charged and polar compounds. However, identification is a challenge due to the variability of migration times and the lack of MS/MS spectra in CE-TOF-MS, the type of instruments most frequently employed. We present here a CE-MS search platform incorporated in CEU Mass Mediator to annotate metabolites with a confidence level L2. For its the development we analyzed 226 compounds using two fragmentor voltages: 100 and 200 V. The information obtained, such as relative migration times (RMT) and in-source fragments, were incorporated into the platform. In addition, we validated the CE-MS search functionality using different types of biological samples such as plasma samples (human, rat, and rabbit), mouse macrophages, and human urine. The RMT tolerance percentage for the search of metabolites has been determined, establishing 5% for all compounds, except for the compounds migrating in the electro-osmotic flow, for which the tolerance should be of 10%. It has also been demonstrated the robustness of the in-source fragmentation, which makes possible the annotation of compounds by means of their fragmentation pattern. As an example, 3-methylhistidine and 1-methilhistidine, whose RMT are very close, have been annotated. Studies of the fragmentation mechanisms of acyl-L-carnitines have shown that in-source fragmentation follows the general fragmentation rules and is a suitable alternative to MS/MS.

Aspergillus Metabolome Database for Mass Spectrometry Metabolomics.

The Aspergillus Metabolome Database is a free online resource to perform metabolite annotation in mass spectrometry studies devoted to the genus Aspergillus. The database was created by retrieving and curating information on 2811 compounds present in 601 different species and subspecies of the genus Aspergillus. A total of 1514 scientific journals where these metabolites are mentioned were added as meta-information linked to their respective compounds in the database. A web service to query the database based on m/z (mass/charge ratio) searches was added to CEU Mass Mediator; these queries can be performed over the Aspergillus database only, or they can also include a user-selectable set of other general metabolomic databases. This functionality is offered via web applications and via RESTful services. Furthermore, the complete content of the database has been made available in .csv files and as a MySQL database to facilitate its integration into third-party tools. To the best of our knowledge, this is the first database and the first service specifically devoted to Aspergillus metabolite annotation based on m/z searches.

A device for automatically measuring and supervising the critical care patient's urine output.

Critical care units are equipped with commercial monitoring devices capable of sensing patients' physiological parameters and supervising the achievement of the established therapeutic goals. This avoids human errors in this task and considerably decreases the workload of the healthcare staff. However, at present there still is a very relevant physiological parameter that is measured and supervised manually by the critical care units' healthcare staff: urine output. This paper presents a patent-pending device capable of automatically recording and supervising the urine output of a critical care patient. A high precision scale is used to measure the weight of a commercial urine meter. On the scale's pan there is a support frame made up of Bosch profiles that isolates the scale from force transmission from the patient's bed, and guarantees that the urine flows properly through the urine meter input tube. The scale's readings are sent to a PC via Bluetooth where an application supervises the achievement of the therapeutic goals. The device is currently undergoing tests at a research unit associated with the University Hospital of Getafe in Spain.

A low cost device for monitoring the urine output of critical care patients.

In critical care units most of the patients' physiological parameters are sensed by commercial monitoring devices. These devices can also supervise whether the values of the parameters lie within a pre-established range set by the clinician. The automation of the sensing and supervision tasks has discharged the healthcare staff of a considerable workload and avoids human errors, which are common in repetitive and monotonous tasks. Urine output is very likely the most relevant physiological parameter that has yet to be sensed or supervised automatically. This paper presents a low cost patent-pending device capable of sensing and supervising urine output. The device uses reed switches activated by a magnetic float in order to measure the amount of urine collected in two containers which are arranged in cascade. When either of the containers fills, it is emptied automatically using a siphon mechanism and urine begins to collect again. An electronic unit sends the state of the reed switches via Bluetooth to a PC that calculates the urine output from this information and supervises the achievement of therapeutic goals.

Validity and reliability of inertial sensors for elbow and wrist range of motion assessment.

BACKGROUND: Elbow and wrist chronic conditions are very common among musculoskeletal problems. These painful conditions affect muscle function, which ultimately leads to a decrease in the joint's Range Of Motion (ROM). Due to their portability and ease of use, goniometers are still the most widespread tool for measuring ROM. Inertial sensors are emerging as a digital, low-cost and accurate alternative. However, whereas inertial sensors are commonly used in research studies, due to the lack of information about their validity and reliability, they are not widely used in the clinical practice. The goal of this study is to assess the validity and intra-inter-rater reliability of inertial sensors for measuring active ROM of the elbow and wrist. MATERIALS AND METHODS: Measures were taken simultaneously with inertial sensors (Werium™ system) and a universal goniometer. The process involved two physiotherapists ("rater A" and "rater B") and an engineer responsible for the technical issues. Twenty-nine asymptomatic subjects were assessed individually in two sessions separated by 48 h. The procedure was repeated by rater A followed by rater B with random order. Three repetitions of each active movement (elbow flexion, pronation, and supination; and wrist flexion, extension, radial deviation and ulnar deviation) were executed starting from the neutral position until the ROM end-feel; that is, until ROM reached its maximum due to be stopped by the anatomy. The coefficient of determination (r 2) and the Intraclass Correlation Coefficient (ICC) were calculated to assess the intra-rater and inter-rater reliability. The Standard Error of the Measurement and the Minimum Detectable Change and a Bland-Altman plots were also calculated. RESULTS: Similar ROM values when measured with both instruments were obtained for the elbow (maximum difference of 3° for all the movements) and wrist (maximum difference of 1° for all the movements). These values were within the normal range when compared to literature studies. The concurrent validity analysis for all the movements yielded ICC values ≥0.78 for the elbow and ≥0.95 for the wrist. Concerning reliability, the ICC values denoted a high reliability of inertial sensors for all the different movements. In the case of the elbow, intra-rater and inter-rater reliability ICC values range from 0.83 to 0.96 and from 0.94 to 0.97, respectively. Intra-rater analysis of the wrist yielded ICC values between 0.81 and 0.93, while the ICC values for the inter-rater analysis range from 0.93 to 0.99. CONCLUSIONS: Inertial sensors are a valid and reliable tool for measuring elbow and wrist active ROM. Particularly noteworthy is their high inter-rater reliability, often questioned in measurement tools. The lowest reliability is observed in elbow prono-supination, probably due to skin artifacts. Based on these results and their advantages, inertial sensors can be considered a valid assessment tool for wrist and elbow ROM.

Error analysis in the determination of the electron microscopical contrast transfer function parameters from experimental power Spectra.

BACKGROUND: The transmission electron microscope is used to acquire structural information of macromolecular complexes. However, as any other imaging device, it introduces optical aberrations that must be corrected if high-resolution structural information is to be obtained. The set of all aberrations are usually modeled in Fourier space by the so-called Contrast Transfer Function (CTF). Before correcting for the CTF, we must first estimate it from the electron micrographs. This is usually done by estimating a number of parameters specifying a theoretical model of the CTF. This estimation is performed by minimizing some error measure between the theoretical Power Spectrum Density (PSD) and the experimentally observed PSD. The high noise present in the micrographs, the possible local minima of the error function for estimating the CTF parameters, and the cross-talking between CTF parameters may cause errors in the estimated CTF parameters. RESULTS: In this paper, we explore the effect of these estimation errors on the theoretical CTF. For the CTF model proposed in 1 we show which are the most sensitive CTF parameters as well as the most sensitive background parameters. Moreover, we provide a methodology to reveal the internal structure of the CTF model (which parameters influence in which parameters) and to estimate the accuracy of each model parameter. Finally, we explore the effect of the variability in the detection of the CTF for CTF phase and amplitude correction. CONCLUSION: We show that the estimation errors for the CTF detection methodology proposed in 1 does not show a significant deterioration of the CTF correction capabilities of subsequent algorithms. All together, the methodology described in this paper constitutes a powerful tool for the quantitative analysis of CTF models that can be applied to other models different from the one analyzed here.

LAS: A Lipid Annotation Service Capable of Explaining the Annotations It Generates.

The Lipid Annotation Service (LAS) is a representational state transfer (REST) application programming interface (API) service designed to aid researchers performing lipid annotation. It assigns certainty levels (very unlikely, unlikely, likely, and very likely) to the putative annotations received as input and explains the rationale of such assignments. Its rules, obtained from the Centre for Metabolomics and Bioanalysis (CEMBIO) and from a literature review, enable LAS to extract evidence to support or refute the annotations automatically by checking the inter-rule relationships. LAS is the first metabolite annotation tool capable of explaining in natural language (English) the evidence that supports or refutes the annotations. This facilitates the understanding of the results by the user and, thus, increases the user's confidence in the results. Concerning its performance, in an evaluation of blood plasma samples whose compounds had previously been identified using well-established standards, LAS yielded an F-measure higher than 80%.