Tracking auditory mismatch negativity responses during full conscious state and coma

The mismatch negativity (MMN) is considered the electrophysiological change-detection response of the brain, and therefore a valuable clinical tool for monitoring functional changes associated with return to consciousness after severe brain injury. Using an auditory multi-deviant oddball paradigm, we tracked auditory MMN responses in seventeen healthy controls over a 12-h period, and in three comatose patients assessed over 24 h at two time points. We investigated whether the MMN responses show fluctuations in detectability over time in full conscious awareness, or whether such fluctuations are rather a feature of coma. Three methods of analysis were utilized to determine whether the MMN and subsequent event-related potential (ERP) components could be identified: traditional visual analysis, permutation t-test, and Bayesian analysis. The results showed that the MMN responses elicited to the duration deviant-stimuli are elicited and reliably detected over the course of several hours in healthy controls, at both group and single-subject levels. Preliminary findings in three comatose patients provide further evidence that the MMN is often present in coma, varying within a single patient from easily detectable to undetectable at different times. This highlights the fact that regular and repeated assessments are extremely important when using MMN as a neurophysiological predictor of coma emergence.

Non-Specific Signal Peptidase Processing of Extracellular Proteins in Staphylococcus aureus N315

Staphylococcus aureus is one of the major community-acquired human pathogens, with growing multidrug-resistance, leading to a major threat of more prevalent infections to humans. A variety of virulence factors and toxic proteins are secreted during infection via the general secretory (Sec) pathway, which requires an N-terminal signal peptide to be cleaved from the N-terminus of the protein. This N-terminal signal peptide is recognized and processed by a type I signal peptidase (SPase). SPase-mediated signal peptide processing is the crucial step in the pathogenicity of S. aureus. In the present study, the SPase-mediated N-terminal protein processing and their cleavage specificity were evaluated using a combination of N-terminal amidination bottom-up and top-down proteomics-based mass spectrometry approaches. Secretory proteins were found to be cleaved by SPase, specifically and non-specifically, on both sides of the normal SPase cleavage site. The non-specific cleavages occur at the relatively smaller residues that are present next to the -1, +1, and +2 locations from the original SPase cleavage site to a lesser extent. Additional random cleavages at the middle and near the C-terminus of some protein sequences were also observed. This additional processing could be a part of some stress conditions and unknown signal peptidase mechanisms.

Gray matter volume drives the brain age gap in schizophrenia: a SHAP study

Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; p_corr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.

The sound of silence: Predictive error responses to unexpected sound omission in adults

The human auditory system excels at detecting patterns needed for processing speech and music. According to predictive coding, the brain predicts incoming sounds, compares predictions to sensory input, and generates a prediction error whenever a mismatch between the prediction and sensory input occurs. Predictive coding can be indexed in EEG with the mismatch negativity (MMN) and P3a components, two ERP components that are elicited by infrequent deviant sounds (e.g., differing in pitch, duration, loudness) in a stream of frequent sounds. If these components reflect prediction error, they should also be elicited by omitting an expected sound, but few studies have examined this. We compared ERPs elicited by infrequent randomly occurring omissions (unexpected silences) in tone sequences presented at 2 tones/sec to ERPs elicited by frequent, regularly occurring omissions (expected silences) within a sequence of tones and resting state EEG (a constant silence). We found that unexpected silences elicited significant MMN and P3a, although the magnitude of these components was quite small and variable. These results provide evidence for hierarchical predictive coding, indicating that the brain predicts silences as well as sounds.

A machine learning approach to identification of self-harm and suicidal ideation among military and police Veterans

LAY SUMMARY

Combat Veterans are vulnerable to suicidal thoughts and behaviour. Many who die by suicide deny having suicidal ideation (SI). Typically, researchers try to find variables indicating the presence of SI using traditional statistical approaches. These approaches do not possess the capacity to detect highly complex multivariable interactions. In contrast, machine learning (ML) is designed to detect such patterns and can consequently yield much higher predictive accuracy. In this study, the authors trained ML algorithms using 192 variables extracted from questionnaires administered to 738 Veterans and serving personnel to detect the presence of self-harm and SI (SHSI). Using the 10 most predictive non-suicide-related items, the ML algorithms could detect SHSI with 75.3% accuracy. Most of these items reflect psychological phenomena that can change quickly over time, allowing repeated risk reassessment from day to day. The study’s findings suggest that ML methods may play an important role in the discovery, within a large data set, of predictive patterns that might be useful in suicide risk assessment.

Longitudinal Changes in Sleep, Biological Rhythms, and Light Exposure From Late Pregnancy to Postpartum and Their Impact on Peripartum Mood and Anxiety

Objective: In one of the largest and most comprehensive studies investigating the link between objective parameters of sleep and biological rhythms with peripartum mood and anxiety to date, we prospectively investigated the trajectory of subjective and objective sleep and biological rhythms, levels of melatonin, and light exposure from late pregnancy to postpartum and their relationship with depressive and anxiety symptoms across the peripartum period. Methods: One hundred women were assessed during the third trimester of pregnancy, of whom 73 returned for follow-ups at 1-3 weeks and 6-12 weeks postpartum. Participants were recruited from an outpatient clinic and from the community from November 2015 to May 2018. Subjective and objective measures of sleep and biological rhythms were obtained, including 2 weeks of actigraphy at each visit. Questionnaires validated in the peripartum period were used to assess mood and anxiety. Results: Discrete patterns of longitudinal changes in sleep and biological rhythm variables were observed, such as fewer awakenings (F = 23.46, P < .001) and increased mean nighttime activity (F = 55.41, P < .001) during postpartum compared to late pregnancy. Specific longitudinal changes in biological rhythm parameters, most notably circadian quotient, activity during rest at night, and probability of transitioning from rest to activity at night, were most strongly linked to higher depressive and anxiety symptoms across the peripartum period. Conclusions: Biological rhythm variables beyond sleep were most closely associated with severity of depressive and anxiety symptoms across the peripartum period. Findings from this study emphasize the importance of biological rhythms and activity beyond sleep to peripartum mood and anxiety.

Interpretation of Anomalously Long Crosslinks in Ribosome Crosslinking reveals the ribosome interaction in stationary phase E. coli

Crosslinking mass spectrometry (XL-MS) of bacterial ribosomes revealed the dynamic intra and intermolecular interactions within the ribosome structure. It has been also extended to capture the interactions of ribosome binding...

Aqueous Solutions of Peptides and Trialkylamines Lead to Unexpected Peptide Modification

When trialkylamines are added to buffered solutions of peptides, unexpected adducts can be formed. These adducts correspond to Schiff base products. The source of the reaction is the unexpected presence of aldehydes in amines. The aldehydes can be detected in a few ways. Most importantly, they can lead to unanticipated results in proteomics experiments. Their undesirable effects can be minimized through the addition of other amines.

Subset of Fluorophores Is Responsible for Radiation Brightening in Viromimetic Particles

In certain conditions, dye-conjugated icosahedral virus shells exhibit suppression of concentration quenching. The recently observed radiation brightening at high fluorophore densities has been attributed to coherent emission, i.e., to a cooperative process occurring within a subset of the virus-supported fluorophores. Until now, the distribution of fluorophores among potential conjugation sites and the nature of the active subset remained unknown. With the help of mass spectrometry and molecular dynamics simulations, we found which conjugation sites in the brome mosaic virus capsid are accessible to fluorophores. Reactive external surface lysines but also those at the lumenal interface where the coat protein N-termini are located showed virtually unrestricted access to dyes. The third type of labeled lysines was situated at the intercapsomeric interfaces. Through limited proteolysis of flexible N-termini, it was determined that dyes bound to them are unlikely to be involved in the radiation brightening effect. At the same time, specific labeling of genetically inserted cysteines on the exterior capsid surface alone did not lead to radiation brightening. The results suggest that lysines situated within the more rigid structural part of the coat protein provide the chemical environments conducive to radiation brightening, and we discuss some of the characteristics of these environments.

Advanced Signal Processing Methods for Characterization of Schizophrenia

OBJECTIVE

Schizophrenia is a severe mental disorder associated with nerobiological deficits. Auditory oddball P300 have been found to be one of the most consistent markers of schizophrenia. The goal of this study is to find quantitative features that can objectively distinguish patients with schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory odd-ball P300 electroencephalogram (EEG) data.

METHODS

Using EEG dataset, we develop a machine learning (ML) algorithm to distinguish 57 SCZs from 66 HCs. The proposed ML algorithm has three steps. In the first step, a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on EEG signals to extract source waveforms from 30 specified brain regions. In the second step, a method for estimating effective connectivity, referred to as symbolic transfer entropy (STE), is applied to the source waveforms. In the third step the ML algorithm is applied to the STE connectivity matrix to determine whether a set of features can be found that successfully discriminate SCZ from HC.

RESULTS

The findings revealed that the SCZs have significantly higher effective connectivity compared to HCs and the selected STE features could achieve an accuracy of 92.68%, with a sensitivity of 92.98% and specificity of 92.42%.

CONCLUSION

The findings imply that the extracted features are from the regions that are mainly affected by SCZ and can be used to distinguish SCZs from HCs.

SIGNIFICANCE

The proposed ML algorithm may prove to be a promising tool for the clinical diagnosis of schizophrenia.

A Machine Learning Approach Using Effective Connectivity to Predict Response to Clozapine Treatment

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment. In this study, we develop a machine learning (ML) algorithm based on pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in 57 TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the positive and negative syndrome scale (PANSS). The ML algorithm has three steps: 1) a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on the EEG signals to extract source waveforms from 30 specified brain regions. 2) An effective connectivity measure named symbolic transfer entropy (STE) is applied to the source waveforms. 3) A ML algorithm is applied to the STE matrix to determine whether a set of features can be found to discriminate most-responder (MR) SCZ patients from least-responder (LR) ones. The findings of this study reveal that STE features can achieve an accuracy of 95.83%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the source STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.

Use of Multiple Ion Fragmentation Methods to Identify Protein Cross-Links and Facilitate Comparison of Data Interpretation Algorithms

Multiple ion fragmentation methods involving collision-induced dissociation (CID), higher-energy collisional dissociation (HCD) with regular and very high energy settings, and electron-transfer dissociation with supplementary HCD (EThcD) are implemented to improve the confidence of cross-link identifications. Three different S. cerevisiae proteasome samples cross-linked by diethyl suberthioimidate (DEST) or bis(sulfosuccinimidyl)suberate (BS³) are analyzed. Two approaches are introduced to combine interpretations from the above four methods. Working with cleavable cross-linkers such as DEST, the first approach searches for cross-link diagnostic ions and consistency among the best interpretations derived from all four MS² spectra associated with each precursor ion. Better agreement leads to a more definitive identification. Compatible with both cleavable and noncleavable cross-linkers such as BS³, the second approach multiplies scoring metrics from a number of fragmentation experiments to derive an overall best match. This significantly increases the scoring gap between the target and decoy matches. The validity of cross-links fragmented by HCD alone and identified by Kojak, MeroX, pLink, and Xi was evaluated using multiple fragmentation data. Possible ways to improve the identification credibility are discussed. Data are available via ProteomeXchange with identifier PXD018310.

On the time-course of functional connectivity: theory of a dynamic progression of concussion effects

The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.

Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment

Concussion has been shown to leave the afflicted with significant cognitive and neurobehavioural deficits. The persistence of these deficits and their link to neurophysiological indices of cognition, as measured by event-related potentials (ERP) using electroencephalography (EEG), remains restricted to population level analyses that limit their utility in the clinical setting. In the present paper, a convolutional neural network is extended to capitalize on characteristics specific to EEG/ERP data in order to assess for post-concussive effects. An aggregated measure of single-trial performance was able to classify accurately (85%) between 26 acutely to post-acutely concussed participants and 28 healthy controls in a stratified 10-fold cross-validation design. Additionally, the model was evaluated in a longitudinal subsample of the concussed group to indicate a dissociation between the progression of EEG/ERP and that of self-reported inventories. Concordant with a number of previous studies, symptomatology was found to be uncorrelated to EEG/ERP results as assessed with the proposed models. Our results form a first-step towards the clinical integration of neurophysiological results in concussion management and motivate a multi-site validation study for a concussion assessment tool in acute and post-acute cases.

Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

ETD-Cleavable Linker for Confident Cross-linked Peptide Identifications

Peptide cross-links formed using the homobifunctional-linker diethyl suberthioimidate (DEST) are shown to be ETD-cleavable. DEST has a spacer arm consisting of a 6-carbon alkyl chain and it cleaves at the amidino groups created upon reaction with primary amines. In ETD MS² spectra, DEST cross-links can be recognized based on mass pairs consisting of peptide-NH₂^• and peptide+linker+NH₃ ions, and backbone cleavages are more equally distributed over the two constituent peptides compared with collisional activation. Dead ends that are often challenging to distinguish from cross-links are diagnosed by intense reporter ions. ETD mass pairs can be used in MS³ experiments to confirm cross-link identifications. These features provide a simple but reliable approach to identify cross-links that should facilitate studies of protein complexes.

Development of a point of care system for automated coma prognosis: a prospective cohort study protocol

INTRODUCTION

Coma is a deep state of unconsciousness that can be caused by a variety of clinical conditions. Traditional tests for coma outcome prediction are based mainly on a set of clinical observations. Recently, certain event-related potentials (ERPs), which are transient electroencephalogram (EEG) responses to auditory, visual or tactile stimuli, have been introduced as useful predictors of a positive coma outcome (ie, emergence). However, such tests require the skills of clinical neurophysiologists, who are not commonly available in many clinical settings. Additionally, none of the current standard clinical approaches have sufficient predictive accuracies to provide definitive prognoses.

OBJECTIVE

The objective of this study is to develop improved machine learning procedures based on EEG/ERP for determining emergence from coma.

METHODS AND ANALYSIS

Data will be collected from 50 participants in coma. EEG/ERP data will be recorded for 24 consecutive hours at a maximum of five time points spanning 30 days from the date of recruitment to track participants' progression. The study employs paradigms designed to elicit brainstem potentials, middle-latency responses, N100, mismatch negativity, P300 and N400. In the case of patient emergence, data are recorded on that occasion to form an additional basis for comparison. A relevant data set will be developed from the testing of 20 healthy controls, each spanning a 15-hour recording period in order to formulate a baseline. Collected data will be used to develop an automated procedure for analysis and detection of various ERP components that are salient to prognosis. Salient features extracted from the ERP and resting-state EEG will be identified and combined to give an accurate indicator of prognosis.

ETHICS AND DISSEMINATION

This study is approved by the Hamilton Integrated Research Ethics Board (project number 4840). Results will be disseminated through peer-reviewed journal articles and presentations at scientific conferences.

TRIAL REGISTRATION NUMBER

NCT03826407.

Identification of N-terminal protein processing sites by chemical labeling mass spectrometry

RATIONALE

Proteins undergo post-translational modifications and proteolytic processing that can affect their biological function. Processing often involves the loss of single residues. Cleavage of signal peptides from the N-terminus is commonly associated with translocation. Recent reports have suggested that other processing sites also exist.

METHODS

The secreted proteins from S. aureus N315 were precipitated with trichloroacetic acid (TCA) and amidinated with S-methyl thioacetimidate (SMTA). Amidinated proteins were digested with trypsin and analyzed with a high-resolution orbitrap mass spectrometer.

RESULTS

Sixteen examples of Staphylococcus aureus secretory proteins that lose an N-terminal signal peptide during their export were identified using this amidination approach. The N-termini of proteins with and without methionine were identified. Unanticipated protein cleavages due to sortase and an unknown protease were also uncovered.

CONCLUSIONS

A simple N-terminal amidination based mass spectrometry approach is described that facilitates identification of the N-terminus of a mature protein and the discovery of unexpected processing sites.

Analysis of Methylation, Acetylation, and Other Modifications in Bacterial Ribosomal Proteins

A wide variety of posttranslational modifications of expressed proteins are known to occur in living organisms (Krishna R, Wold F. Post-translational modification of proteins. In: Meister A (ed) Advances in enzymology and related areas of molecular biology. Wiley, New York, 1993, pp 265-296). Although their presence in an organism cannot be predicted from the genome, these modifications can play critical roles in protein structure and function. The identification of posttranslational modifications is critical to our understanding of the functions of proteins involved in important biological pathways and mass spectrometry offers a fast, accurate method for observing them. A combined top-down/bottom-up approach can be used for identification and localization of posttranslational modifications of ribosomal proteins. This chapter describes procedures for analyzing Escherichia coli ribosomal proteins and their modifications by matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry. It also covers the analysis of gram-negative Caulobacter crescentus and gram-positive Bacillus subtilis ribosomal proteins by electrospray quadrupole time-of-flight (ESI-QTOF) mass spectrometry. Confirmation of the occurrence and localization of PTMs is obtained through mass spectrometric analysis of tryptic peptides.

A Machine Learning Framework for Automatic and Continuous MMN Detection With Preliminary Results for Coma Outcome Prediction

Mismatch negativity (MMN) is a component of the event-related potential (ERP) that is elicited through an odd-ball paradigm. The existence of the MMN in a coma patient has a good correlation with coma emergence; however, this component can be difficult to detect. Previously, MMN detection was based on visual inspection of the averaged ERPs by a skilled clinician, a process that is expensive and not always feasible in practice. In this paper, we propose a practical machine learning (ML) based approach for detection of MMN component, thus, improving the accuracy of prediction of emergence from coma. Furthermore, the method can operate on an automatic and continuous basis thus alleviating the need for clinician involvement. The proposed method is capable of the MMN detection over intervals as short as two minutes. This finer time resolution enables identification of waxing and waning cycles of a conscious state. An auditory odd-ball paradigm was applied to 22 healthy subjects and 2 coma patients. A coma patient is tested by measuring the similarity of the patient's ERP responses with the aggregate healthy responses. Because the training process for measuring similarity requires only healthy subjects, the complexity and practicality of training procedure of the proposed method are greatly improved relative to training on coma patients directly. Since there are only two coma patients involved with this study, the results are reported on a very preliminary basis. Preliminary results indicate we can detect the MMN component with an accuracy of 92.7% on healthy subjects. The method successfully predicted emergence in both coma patients when conventional methods failed. The proposed method for collecting training data using exclusively healthy subjects is a novel approach that may prove useful in future, unrelated studies where ML methods are used.

Unusual fragmentation of derivatized cysteine-containing peptides

RATIONALE

Modification of cysteines by aminoethylation results in side chains similar to those of lysine. Trypsin cleaves at this modified residue and this labeling method can facilitate the analysis of proteins, specifically antibodies. In this work, the ability to identify peptides containing aminoethylated cysteines is investigated through digestion, covalent labeling, and low-energy ion fragmentation.

METHODS

A prototype antibody was reduced, aminoethylated, and digested with either Lys-N or Glu-C. The resulting peptides were amidinated with SMTA and analyzed by PSD in a MALDI-TOF/TOF mass spectrometer or by CID in an ESI ion trap/orbitrap mass spectrometer.

RESULTS

PSD and CID fragmentation of peptides with an amidinated aminoethylated cysteine can produce an intense characteristic loss from this modified residue. A neutral loss of 118 Da or charged loss of 119 Da is observed when peptides have low charges. This fragment can form when the cysteine is located in any position in the peptide. The rationalization for this ion is that the amidino group can be initially neutral or protonated and initiates fragmentation.

CONCLUSIONS

The combination of a dual-labeling technique and low-energy fragmentation produces an abundant diagnostic ion for the analysis of cysteine-containing peptides. These 118 and 119 Da losses are observed when protons are sequestered.

Logistic Localized Modeling of the Sample Space for Feature Selection and Classification

Conventional feature selection algorithms assign a single common feature set to all regions of the sample space. In contrast, this paper proposes a novel algorithm for localized feature selection for which each region of the sample space is characterized by its individual distinct feature subset that may vary in size and membership. This approach can therefore select an optimal feature subset that adapts to local variations of the sample space, and hence offer the potential for improved performance. Feature subsets are computed by choosing an optimal coordinate space so that, within a localized region, within-class distances and between-class distances are, respectively, minimized and maximized. Distances are measured using a logistic function metric within the corresponding region. This enables the optimization process to focus on a localized region within the sample space. A local classification approach is utilized for measuring the similarity of a new input data point to each class. The proposed logistic localized feature selection (lLFS) algorithm is invariant to the underlying probability distribution of the data; hence, it is appropriate when the data are distributed on a nonlinear or disjoint manifold. lLFS is efficiently formulated as a joint convex/increasing quasi-convex optimization problem with a unique global optimum point. The method is most applicable when the number of available training samples is small. The performance of the proposed localized method is successfully demonstrated on a large variety of data sets. We demonstrate that the number of features selected by the lLFS method saturates at the number of available discriminative features. In addition, we have shown that the Vapnik-Chervonenkis dimension of the localized classifier is finite. Both these factors suggest that the lLFS method is insensitive to the overfitting issue, relative to other methods.

A Novel Triethylphosphonium Charge Tag on Peptides: Synthesis, Derivatization, and Fragmentation

Charge tagging is a peptide derivatization process that commonly localizes a positive charge on the N-terminus. Upon low energy activation (e.g., collision-induced dissociation or post-source decay) of charge tagged peptides, relatively few fragment ions are produced due to the absence of mobile protons. In contrast, high energy fragmentation, such as 157 nm photodissociation, typically leads to a series of a-type ions. Disadvantages of existing charge tags are that they can produce mobile protons or that they are undesirably large and bulky. Here, we investigate a small triethylphosphonium charge tag with two different linkages: amide (158 Da) and amidine bonds (157 Da). Activation of peptides labeled with a triethylphosphonium charge tag through an amide bond can lead to loss of the charge tag and the production of protonated peptides. This enables low intensity fragment ions from both the protonated and charge tagged peptides to be observed. Triethylphosphonium charge tagged peptides linked through an amidine bond are more stable. Post-source decay and photodissociation yield product ions that primarily contain the charge tag. Certain amidine induced fragments are also observed. The previously reported tris(trimethoxyphenyl) phosphonium acetic acid N-hydroxysuccinimidyl ester charge tag shows a similar fragment ion distribution, but the mass of the triethylphosphonium tag label is 415 Da smaller. Graphical Abstract ᅟ.

Automatic and continuous assessment of ERPs for mismatch negativity detection

Accurate and fast detection of event related potential (ERP) components is an unresolved issue in neuroscience and critical health care. Mismatch negativity (MMN) is a component of the ERP to an odd stimulus in a sequence of identical stimuli which has good correlation with coma awakening. All of the previous studies for MMN detection are based on visual inspection of the averaged ERPs (over a long recording time) by a skilled neurophysiologist. However, in practical situations, such an expert may not be available or familiar with all aspects of evoked potential methods. Further, we may miss important clinically essential events due to the implicit averaging process used to acquire the ERPs. In this paper we propose a practical machine learning approach for automatic and continuous assessment of the ERPs for detecting the presence of the MMN component. The proposed method is realized in a classification framework. Performance of the proposed method is demonstrated on 22 healthy subjects through a leave-one subject-out strategy where the MMN components are identified with about 93% accuracy.