Effect of the Recycling Process on Drinking Water Treatment: Evaluation Based on Fluorescence EEM Analysis Using the Peak-Picking Technique and Self-Organizing Map

The recycling process is applied in many water treatment plants (WTPs), although this process can lead to adverse effects. The effect of the recycling process on the characteristics of dissolved organic matter was evaluated based on a fluorescence excitation-emission matrix using the peak-picking technique and self-organizing map (SOM). In this study, an evaluation of two WTPs, one with and one without a recycling system, was carried out. Both WTPs show moderate efficiency during the coagulation–flocculation process in removing DOC, fulvic acid-like, humic acid-like, and tryptophan-like substances. The recycling process causes increased values of fulvic acid-like, humic acid-like, and tryptophan-like substances and specific ultraviolet absorbance (SUVA) after the filtration process of about 31.0%, 35.7%, 22.2%, and 6%, respectively. Meanwhile, the WTP without recycling showed a reduction in the level of fulvic acid-like, humic acid-like, and tryptophan-like substances and SUVA by 23.3%, 52.9%, 27.8%, and 21.1%, respectively. Moreover, SOM analysis based on the peak-picking technique can determine differences in sample clusters due to the recycling process.

An update on techniques to assess normal-mode behavior of rock arches by ambient vibrations

Natural rock arches are rare and beautiful geologic landforms with important cultural value. As such, their management requires periodic assessment of structural integrity to understand environmental and anthropogenic influences on arch stability. Measurements of passive seismic vibrations represent a rapid and non-invasive technique to describe the dynamic properties of natural arches, including resonant frequencies, modal damping ratios, and mode shapes, which can be monitored over time for structural health assessment. However, commonly applied spectral analysis tools are often limited in their ability to resolve characteristics of closely spaced or complex higher-order modes. Therefore, we investigate two techniques well-established in the field of civil engineering through application to a set of natural arches previously characterized using polarization analysis and spectral peak-picking techniques. Results from enhanced frequency domain decomposition and parametric covariance-driven stochastic subspace identification modal analyses showed generally good agreement with spectral peak-picking and frequency-dependent polarization analyses. However, we show that these advanced techniques offer the capability to resolve closely spaced modes including their corresponding modal damping ratios. In addition, due to preservation of phase information, enhanced frequency domain decomposition allows for direct and convenient three-dimensional visualization of mode shapes. These techniques provide detailed characterization of dynamic parameters, which can be monitored to detect structural changes indicating damage and failure, and in addition have the potential to improve numerical models used for arch stability assessment. Results of our study encourage broad adoption and application of these advanced modal analysis techniques for dynamic analysis of a wide range of geological features.

PeakBot: Machine learning based chromatographic peak picking

MotivationChromatographic peak picking is among the first steps in software pipelines for processing LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Unfortunately, random noise, non-baseline separated compounds and unspecific background signals complicate this task.ResultsA machine-learning framework entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention time vs. m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box.In independent training and validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (F1 score of 0.99). A comparison of different sets of reference features showed that at least 100 reference features (including isotopologs) should be provided to achieve high-quality results for detecting new chromatographic peaks.PeakBot is implemented in Python (3.8) and uses the TensorFlow (2.4.1) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.AvailabilityThe framework is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/[email protected] informationSupplementary data are available at Bioinformatics online.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

Motivation: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high dimensionality, and spectral non-linearity. Preprocessing, including peak picking, has been used to reduce raw data complexity, however peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. Results: We propose a deep learning model, massNet, that provides the desired qualities of scalability, non-linearity, and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a 174-fold gain in speed compared to the established classical machine learning method, support vector machine. Availability and Implementation: The code is publicly available on GitHub.

Peak-picking identification technique for modal expansion of input impedance of brass instruments

The paper presents a method to obtain the modal expansion of the measured input impedance of a brass instrument. The method operates as a peak-picking procedure, which makes it particularly intuitive for users who are not experts in modal analysis. To bypass the limitation of usual peak-picking approaches, which are valid only for well separated resonances, the present method is based on a semi-local optimization problem. It consists in adjusting the frequency and damping of one mode at a time while taking into account the presence of all other modes in the basis. The practical application of the method involves four elementary actions, which can be chained in different ways to progressively approximate a measured input impedance. This procedure is illustrated through the approximation of the input impedance of a bass trombone. The supervised nature of the method allows the user to favour modes that have a physical meaning, i.e. that can be associated with a resonance peak. A single spurious mode can however be deliberately introduced to approximate the input impedance curve beyond the last visible peak. The method applies directly to the frequency-domain data provided by an impedance sensor and does not require any preprocessing. Nevertheless, it is fairly robust to noisy data. Since the method allows a reconstruction of the input impedance using either complex modes or real modes, results obtained with each approximation are critically compared.

Transmission of Binaural Cues by Bilateral Cochlear Implants: Examining the Impacts of Bilaterally Independent Spectral Peak-Picking, Pulse Timing, and Compression

Acoustic hearing listeners use binaural cues—interaural time differences (ITDs) and interaural level differences (ILDs)—for localization and segregation of sound sources in the horizontal plane. Cochlear implant users now often receive two implants (bilateral cochlear implants [BiCIs]) rather than one, with the goal to provide access to these cues. However, BiCI listeners often experience difficulty with binaural tasks. Most BiCIs use independent sound processors at each ear; it has often been suggested that such independence may degrade the transmission of binaural cues, particularly ITDs. Here, we report empirical measurements of binaural cue transmission via BiCIs implementing a common “ n-of- m” spectral peak-picking stimulation strategy. Measurements were completed for speech and nonspeech stimuli presented to an acoustic manikin “fitted” with BiCI sound processors. Electric outputs from the BiCIs and acoustic outputs from the manikin’s in-ear microphones were recorded simultaneously, enabling comparison of electric and acoustic binaural cues. For source locations away from the midline, BiCI binaural cues, particularly envelope ITD cues, were found to be degraded by asymmetric spectral peak-picking. In addition, pulse amplitude saturation due to nonlinear level mapping yielded smaller ILDs at higher presentation levels. Finally, while individual pulses conveyed a spurious “drifting” ITD, consistent with independent left and right processor clocks, such variation was not evident in transmitted envelope ITDs. Results point to avenues for improvement of BiCI technology and may prove useful in the interpretation of BiCI spatial hearing outcomes reported in prior and future studies.