Interpreting Mass Spectra Differing from Their Peptide Models by Several Modifications

Background: In proteomics, mass spectra representing peptides carrying multiple unknown modifications are particularly difficult to interpret. This issue results in a large number of unidentified spectra.Methods: We developed SpecGlob, a dynamic programming algorithm that aligns pairs of spectra – each pair given by a Peptide-Spectrum Match (PSM) – provided by any Open Modification Search (OMS) method. For each PSM, SpecGlob computes the best alignment according to a given score system, interpreting the mass delta within the PSM as one or several unspecified modification(s). All the alignments are provided in a file, using a specific syntax. These alignments are then post-processed by an additional algorithm, which aims at interpreting the detected modifications.Results: Using a large collection of theoretical spectra generated from the human proteome, we demonstrate that running SpecGlob as a post-analysis of an OMS method can significantly increase the number of correctly interpreted spectra, since SpecGlob is able to infer several, and possibly many, modifications. The post-processing algorithm is able to interpret unambiguously most of the modifications detected by SpecGlob in PSMs. In addition, we performed an extensive analysis to provide insight into the potential reasons for incomplete or erroneous interpretations that may remain after alignments of PSMs.Conclusion: SpecGlob is able to correctly align spectra that differ by one or more modification(s) without any a priori. Since SpecGlob explores all possible alignments that may explain the mass delta within a PSM, it reduces interpretation errors generated by incorrect assumptions about the modifications present in the sample or the number and the specificity of modifications carried by peptides. Our results demonstrate that SpecGlob should be relevant to align experimental spectra, even if this consists in a more challenging task.

Development of New Staining Procedures for Diagnosing Cryptosporidium spp. in Fecal Samples by Computerized Image Analysis

Interpretation errors may still represent a limiting factor for diagnosing Cryptosporidium spp. oocysts with the conventional staining techniques. Humans and machines can interact to solve this problem. We developed a new temporary staining protocol associated with a computer program for the diagnosis of Cryptosporidium spp. oocysts in fecal samples. We established 62 different temporary staining conditions by studying 20 experimental protocols. Cryptosporidium spp. oocysts were concentrated using the Three Fecal Test (TF-Test®) technique and confirmed by the Kinyoun method. Next, we built a bank with 299 images containing oocysts. We used segmentation in superpixels to cluster the patches in the images; then, we filtered the objects based on their typical size. Finally, we applied a convolutional neural network as a classifier. The trichrome modified by Melvin and Brooke, at a concentration use of 25%, was the most efficient dye for use in the computerized diagnosis. The algorithms of this new program showed a positive predictive value of 81.3 and 94.1% sensitivity for the detection of Cryptosporidium spp. oocysts. With the combination of the chosen staining protocol and the precision of the computational algorithm, we improved the Ova and Parasite exam (O&P) by contributing in advance toward the automated diagnosis.

Median-to-Ulnar Nerve Communication in Carpal Tunnel Syndrome: An Electrophysiological Study

The median-to-ulnar communicating branch (MUC) is an asymptomatic variant of the upper limb innervation that can lead to interpretation errors in routine nerve conduction studies. The diagnosis of carpal tunnel syndrome (CTS) or ulnar nerve lesions can be complicated by the presence of MUC. In this study, we describe electrophysiological features of MUC in CTS patients presenting to our clinic. We enrolled MUB cases from consecutive CTS patients referred to our laboratory between the years 2014 and 2019. MUC was present in 53 limbs (36 patients) from the studied population. MUC was bilateral in 53% of patients. MUC type II was the most common subtype (74%), followed by types III and I; more coexisting MUC types were found in the majority of tested limbs. A positive correlation was demonstrated between the severity of CTS and the presence of positive onset, faster CV, or a double component of the compound muscle action potentials. We emphasize the importance of suspecting the presence of MUC in CTS in the presence of a positive onset or a double component in routine motor conduction studies.

Denoising Method Based on Spectral Subtraction in Time-Frequency Domain

In view of the key problem that a large amount of noise in seismic data can easily induce false anomalies and interpretation errors in seismic exploration, the time-frequency spectrum subtraction (TF-SS) method is adopted into data processing to reduce random noise in seismic data. On this basis, the main frequency information of seismic data is calculated and used to optimize the filtering coefficients. According to the characteristics of effective signal duration between seismic data and voice data, the time-frequency spectrum selection method and filtering coefficient are modified. In addition, simulation tests were conducted by using different S/R, which indicates the effectiveness of the TF-SS in removing the random noise.

Reappraisal of Factors Disturbing the Relationship between Body Water Volumes and Total Body Electrical Resistance in Patients on Hemodialysis

Background: Measurements of Total Body Electrical Resistance (TBER) are used to improve fluid balance management in patients on Hemodialysis (HD). This approach is based on the inverse relation that exists between TBER and body water volumes. Interpretation errors may occur if TBER measurements are affected by factors that are not related to changes in body water. Aim of this paper was to provide an overview of the methodological artifacts commonly encountered in a clinical setting, and to strengthen current evidence of their disturbing effects by performing additional experiments. Methods: This study includes an analysis of available literature data, supplemented with additional experiments in healthy adults and patients. A cutoff of 2.7% was used to classify changes in TBER as significant within individual subjects. Results: Electrode position, electrode interference, differences of measurements performed at the right or left side of the body, presence of orthopedic prosthesis located in the limbs, fluid redistribution induced by longterm changes in body position, and electrolyte abnormalities were the main disturbing factors that can induce a significant change in TBER. Other factors either had no significant disturbing effect or could be easily avoided. Conclusion: TBER measurements require a high degree of standardization to minimize interpretation errors.

A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry

Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database—as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a “species-specific” peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.

Classify Epileptic EEG Signals Using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection

EEG signals aids in diagnosing various wave signals recorded by the activities of the brain. It also produces unavoidable artifacts, in the recording process. The purpose of this study therefore is to detect ictal and artefact signals, with the aim of reducing interpretation errors especially those related to the muscle which are quite difficult to distinguish. The data used are EEG signal recording results obtained from Rumah Sakit Universitas Airlangga. It consisted of two classes, namely ictal and muscle artefact. The signal decomposition method used is a wavelet transform, known as DWT. While the extraction feature utilized, consist of quartile, maximum, minimum, mean and standard deviation. This study also utilized the SVM with linear, polynomial, RBF and ELM (ESVM) kernels. Research results shows that the ESVM classification time is faster than the SVM and other kernels. However, the values of accuracy, sensitivity, specificity and AUC are not better.