Spectrum Matching Research Articles

A Data Set Comparison Method Using Noise Statistics Applied to VUV Spectrum Match Determinations.

It has been demonstrated that a pair of spectra exhibiting a coefficient of determination (R2) as low as 0.976 can originate from the same chemical species in one example, while a different pair of spectra exhibiting an R2 up to 0.9997 can originate from different chemical species. The R2 between spectra overlays depends on the signal-to-noise ratio, while the residual between any two spectra should look like noise only when the two spectra originate from the same chemical species. Numerical characteristics of the residual between two high-resolution spectra are invaluable toward the definitive elimination of many plausible matches of reference spectra to the sample spectra of analytes eluted from two-dimensional gas chromatography. Additionally, numerical characteristics beyond R2 facilitate a logical ranking of all plausible matches, making positive identification of a single-component analyte possible provided a reference spectrum exists for all plausible matches. Specifically, the experimental background noise is shown to follow a Gaussian distribution at all wavelengths, and a method is described to normalize the data such that the numerically adjusted noise distributions are independent of wavelength. The differences between matching spectra are further shown to exhibit numerical characteristics consistent with the background noise's Gaussian distribution, common to all wavelengths. Seven criteria are described for judging the similarity between spectra: R2 between the two spectra, R2 of a Q-Q plot with one axis being ideal Gaussian quantiles and the other axis being the distribution of the numerically adjusted residual quantiles, the maximum count of consecutive (by wavelength) signs in the residual, and the first four moments of the residuals. One exemplar application of the methodology is a definitive match of n-undecane, n-dodecane, and n-tridecane sample spectra to their corresponding reference spectrum, which is among the most challenging set of species within the volatility range of jet fuel to differentiate by spectral methods. While this example is a significant stress test of the approach, the utility of the methodology generally is in the subtle math and transparent criteria that unambiguously identify mismatches because the distributions of residuals between mismatching spectra are very clearly not Gaussian and have a high consecutive sign count, even in cases where the R2 between the compared spectra is ambiguous.

GC-MS profiling of volatile metabolites produced by Klebsiella pneumoniae.

Currently used methods for diagnosing ventilator-associated pneumonia (VAP) are complex, time-consuming and require invasive procedures while empirical antibacterial therapy applies broad spectrum antibiotics that may promote antimicrobial resistance. Hence, novel and fast methods based on alternative markers are needed for VAP detection and differentiation of causative pathogens. Pathogenic bacteria produce a broad range of volatile organic compounds (VOCs), some of which may potentially serve as biomarkers for microorganism identification. Additionally, monitoring of dynamically changing VOCs concentration profiles may indicate emerging pneumonia and allow timely implementation of appropriate antimicrobial treatment. This study substantially extends the knowledge on bacterial metabolites providing the unambiguous identification of volatile metabolites produced by carbapenem-resistant and susceptible strains of Klebsiella pneumoniae (confirmed with pure standards in addition to mass spectra match) but also revealing their temporary concentration profiles (along the course of pathogen proliferation) and dependence on the addition of antibiotic (imipenem) to bacteria. Furthermore, the clinical strains of K. pneumoniae isolated from bronchoalveolar lavage specimens collected from mechanically ventilated patients were investigated to reveal, whether bacterial metabolites observed in model experiments with reference strains could be relevant for wild pathogens as well. In all experiments, the headspace samples from bacteria cultures were collected on multibed sorption tubes and analyzed by GC-MS. Sampling was done under strictly controlled conditions at seven time points (up to 24h after bacteria inoculation) to follow the dynamic changes in VOC concentrations, revealing three profiles: release proportional to bacteria load, temporary maximum and uptake. Altogether 32 VOCs were released by susceptible and 25 VOCs by resistant strain, amongst which 2-pentanone, 2-heptanone, and 2-nonanone were significantly higher for carbapenem-resistant KPN. Considerably more metabolites (n = 64) were produced by clinical isolates and in higher diversity compared to reference KPN strains.

Learning-Based Resource Allocation for Ultra-Reliable V2X Networks With Partial CSI

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

Ternary alloy incorporated Janus hydrogel photothermal film for desalination

Here, we developed a ternary alloy (Cu 0.4 W 0.6 /Ni 17 W 3 ) based Janus hydrogel photothermal film for solar-driven water evaporation. We first prepared the copper-doped nickel tungstate, then obtained the Cu 0.4 W 0.6 /Ni 17 W 3 using the hydrogen reduction method, and applied it as the photothermal conversion material for the upper layer of the Janus photothermal film. The Cu 0.4 W 0.6 /Ni 17 W 3 had low cost, perfect solar spectrum matching, and excellent thermal stability. A composite hydrogel composed of gelatin and agar was used to prepare the Janus photothermal film. The Janus photothermal film showed an excellent water evaporation rate of 1.72 kg m -2 h −1 and a high light-to-heat conversion efficiency (η) of 98% under one-sun irradiation. Besides, Janus-1 possessed excellent desalination performance.

Coupling High-Field Asymmetric Ion Mobility Spectrometry with Capillary Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry Improves Protein Identifications in Bottom-Up Proteomic Analysis of Low Nanogram Samples.

In this work, we pioneered the assessment of coupling high-field asymmetric waveform ion mobility spectrometry (FAIMS) with ultrasensitive capillary electrophoresis hyphenated with tandem mass spectrometry (CE-MS/MS) to achieve deeper proteome coverage of low nanogram amounts of digested cell lysates. An internal stepping strategy using three or four compensation voltages per analytical run with varied cycle times was tested to determine optimal FAIMS settings and MS parameters for the CE-FAIMS-MS/MS method. The optimized method applied to bottom-up proteomic analysis of 1 ng of HeLa protein digest standard identified 1314 ± 30 proteins, 4829 ± 200 peptide groups, and 7577 ± 163 peptide spectrum matches (PSMs) corresponding to a 16, 25, and 22% increase, respectively, over CE-MS/MS alone, without FAIMS. Furthermore, the percentage of acquired MS/MS spectra that resulted in PSMs increased nearly 2-fold with CE-FAIMS-MS/MS. Label-free quantitation of proteins and peptides was also assessed to determine the precision of replicate analyses from FAIMS methods with increased cycle times. Our results also identified from 1 ng of HeLa protein digest without any prior enrichment 76 ± 9 phosphopeptides, 18% of which were multiphosphorylated. These results represent a 46% increase in phosphopeptide identifications over the control experiments without FAIMS yielding 2.5-fold more multiphosphorylated peptides.

Nondestructive Forensic Comparison of Nylon Trace Evidence Using Room-Temperature Fluorescence Spectroscopy.

The increasing accessibility of 3D printers makes their use for criminal activity more likely. Current forensic analysis of trace evidence left by 3D-printed materials focuses on identifying the general type of plastic, which includes acrylonitrile butadiene styrene, polylactic acid, nylon, polycarbonate, polyethylene terephthalate, and chlorinated polyethylene. Herein, we present a nondestructive approach capable of differentiating among different types of nylons. The new approach is based on room-temperature fluorescence spectroscopy. Excitation-emission matrices, excitation and emission spectra, and synchronous fluorescence spectra are directly recorded from single microplastics with the aid of a fiber-optic probe coupled to a commercial spectrofluorometer. The comparison of spectral features demonstrates the capability to differentiate microparticles originating from Nylon 11, Nylon 12, Nylon 6/6, and Nylon 6/12. The observed differences are attributed to the presence of fluorescent impurities embedded in the polymer during its fabrication. The outstanding matching of excitation-emission matrices, excitation and emission spectra, and synchronous fluorescence spectra demonstrates the potential of this approach to link trace evidence to a specific source beyond its general plastic type.

Usefulness of toxicology screening by high resolution mass spectrometry in methamphetamine and amphetamine users attended in emergency departments

Objective The purpose of this preliminary study was to assess the utility of applying a non-targeted comprehensive drug screening using High Resolution Mass Spectrometry to a cohort of patients attending the Emergency Department (ED) due to drug intoxication; and to evaluate the differences in toxicological profile between methamphetamine (MAMP) and amphetamine (AMP) intoxicated patients. Retrospective observational study of thirty-nine patients, 24 in the group MAMP and 15 in AMP group, which were attended in the ED of public hospitals in the Balearic Islands due to recreational drug poisoning between March 2019 and December 2020. Urine samples (500 μL) were extracted by solid phase extraction using reversed-phase cartridges. The extracts were analyzed using an Agilent 1290 Series UPLC system coupled to an Agilent 6550 QTOF-MS system working in ESI positive mode. For chromatographic separation an Acquity UPLC® HSS T3 (2.1 × 75 mm, 1.8 μm) column with formic acid 0.1% in water and acetonitrile as mobile phases was used. Data mining was performing employing data independent acquisition mode using MassHunter Qualitative software (version B.08.00) in combination with a PCDL library (ForTox PCDL database, Agilent Technologies). [M + H]+ ions were recorded from 50–1000 m/z values using 3 different collision energies (10, 20, 40 eV). MS/MS spectra matching was manually checked and for tentative identification at least two product ions had to match those in the library with a score ≥ 70%. The most frequent classical drugs of abuse detected were cocaine (94.9%), MDMA (51.3%) and ketamine (41.0%), without significant differences between MAMP and AMP patients. MAMP related compounds such as 4-hydroxymethamphetamine (29.2%), 1,2-dimethyl-3-phenylaziridine (37.5%) and cyclopentamine (4.2%) were detected in MAMP users, while 3-hydroxyamphetamine (20%) was the only AMP related compound detected in AMP users. The most frequent new psychoactive substances (NPS) detected were cathinones, including mephedrone (23.1%), methylone (17.9%), metamfepramone (10.3%), 4’-methyl-α-pyrrolidinopropiophenone (2.6%) and 3′4′-methylendioxi-alfa-pyrrolidinopropiophenone (2.6%). Mephedrone were detected only in MAMP patients. Piperazines were detected in MAMP patients (58.3%) but not in AMP patients, specifically benzylpiperazine (33.3%) and N-(2-hydroxyphenyl)piperazine (2.6%). Moreover, 12.5% and 6.7% of the MAMP and AMP patients, respectively, were positive to tryptamines (5.1% to 5 or 6-metoxytryptamine, 5.1% to N,N-diethyltryptamine and 2.6% to etryptramine). Methyl lysergate was detected in 5.1% AMP patients. Regarding medicinal drugs, benzodiazepines were found in 50.0% and 46.7% of the MAMP and AMP patients, respectively, mainly oxazepam and midazolam. In addition, opiates were found in 29.2% and 33.3% of MAMP and AMP users, respectively, antidepressants in 25.0% and 33.3% (mainly clomipramine), and neuroleptics in 16.7% and 26.7% (mainly risperidone). A statistically significant higher prevalence of risperidone was found in AMP patients ( P = 0.014). Codeine and dextromethorphan were mainly found in AMP patients, although not statistically significant. Other less common medical drugs (frequency between 5–15%) included paracetamol, anticonvulsants, NSAIDs, antihistamines, anesthetics, antiretroviral drugs, beta-blockers, and ranitidine. Candidates from many classes of drugs of abuse and medicinal drugs were tentatively identified in both MAMP and AMP patients using non-targeted drug screening. MAMP patients showed a higher prevalence of NPS (mephedrone and piperazines) while AMP patients showed a higher prevalence of medicinal drugs (opiates and opioids, antidepressants and neuroleptics). Conclusion: MAMP and AMP overdosed users represent a high-risk population due to their elevated polyconsumption of drugs. MAMP users drug profile seems to be different with respect AMP users. We were able to identify a combination of classical, medicinal and NPS drugs in both populations using QTOF-MS methodology.

Evaluating Peptide Fragment Ion Detection Using Traveling Wave Ion Mobility Spectrometry with Signal-Enhanced MSE (SEMSE).

The inherent poor sampling of fragment ions in time-of-flight mass analyzers was recently improved for data-dependent acquisition (DDA) by considering their drift times in traveling wave ion mobility spectrometry (TWIMS). Here, we extend this TWIMS-DDA approach to the data-independent acquisition (DIA) mode MSE to improve the signal intensities of fragment ions by providing improved ion beam sampling efficiency, which we termed therefore signal-enhanced MSE (SEMSE). The theoretical expectation that SEMSE improves the number of identified peptides, the number of quantifiable peptides, and the lower limit of quantitation in wideband DIA was evaluated on an electrospray ionisation-ion mobility spectrometry-quadrupole-time-of-flight-MS (ESI-IMS-Q-TOF-MS) (Synapt G2-Si) in comparison to five established TWIMS-DDA and TWIMS-MSE methods with respect to the number of peptide identifications, the spectral quality of supporting peptide spectra matches, and (most importantly) fragment ion signal sensitivity. A comparison of the fragment signals clearly indicated that SEMSE provides 6.8- to 11.5-fold larger peak areas than established MSE techniques. While this clearly shows the advantages of SEMSE, the inherent limitations of the current software tools do not allow using all benefits in routine analyses. As the simultaneous fragmentation of co-eluting peptides limited peptide identification, DDA and MSE data sets were integrated using Skyline.

First-principles study of the electronic and optical properties of Ho_{text{W}} impurities in single-layer tungsten disulfide

The electronic and optical properties of single-layer (SL) tungsten disulfide (WS_2) in the presence of substitutional Holmium impurities (Ho_{text{W}}) are studied. Although Ho is much larger than W, density functional theory (DFT) including spin-orbit coupling is used to show that Ho:SL WS_2 is stable. The magnetic moment of the Ho impurity is found to be 4.75mu _B using spin-dependent DFT. The optical selection rules identified in the optical spectrum match exactly the optical selection rules derived by means of group theory. The presence of neutral Ho_W impurities gives rise to localized impurity states (LIS) with f-orbital character in the band structure. Using the Kubo-Greenwood formula and Kohn-Sham orbitals we obtain atom-like sharp transitions in the in-plane and out-of-plane components of the susceptibility tensor, Imchi _{parallel } and Imchi _{perp }. The optical resonances are in good agreement with experimental data.

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide–spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Abstract 3916: Cyanobacterial natural product Sec61α inhibitors induce broad suppression of the glioma cell secretome

Abstract Coibamide A is a cyclopeptide natural product with potent antiproliferative activity against cultured human cancer cells and subcutaneous U87-MG glioblastoma tumors in mice. We have recently determined that coibamide A binds to the Sec61 translocon channel at a site that is close, but not identical, to the binding site of the peptide/polyketide natural product apratoxin A. As the majority of secreted proteins in mammalian cells are trafficked through the conventional secretory pathway via the Sec61 translocon, this raised the possibility that coibamide A and apratoxin A block import and secretion of different Sec61 client proteins. In the present study, we analyzed and compared the secretome of human U87-MG glioblastoma cells in the presence and absence of both marine natural products. U87-MG glioblastoma cells were treated with coibamide A (5 nM), apratoxin A (5 nM), or vehicle for 24 h. The growth media was then replaced with serum-free media for a further 24 h. Tryptic proteomic samples were generated from cell-free conditioned media and analyzed on a LC-coupled Orbitrap Fusion mass spectrometer. The high-resolution Orbitrap mass analyzer was used to both identify precursor ions for tandem mass (MS2) analysis in the ion trap mass analyzer and to quantitate those ions (MS1). Secreted proteins were identified from peptide–spectrum matches to the human protein database using a decoy database to assign an FDR and quantified from their MS1 ion intensities. Comparison of mass spectrometric data against the human secretome database revealed 175 proteins in the U87-MG cell secretome that were identified with high confidence. Differences between coibamide A and apratoxin A treatment were not significant, however both Sec61 inhibitors induced significant suppression of the U87-MG secretome relative to vehicle-treated cells. Treatment with coibamide A or apratoxin A resulted in the reduction of 75% of quantifiable secretome (131 proteins). Of the remaining 44 proteins, 34 are directed to intracellular organelles or contain a motif associated with retention in the endoplasmic reticulum and 10 were not suppressed with treatment but were hypothesized to be Sec61 clients. Malignant glioblastoma cells secrete proteins into the extracellular space and use complex intercellular signaling mechanisms for a survival advantage. Our results indicate that broad-acting inhibitors of Sec61-dependent co-translational translocation block the progression of a significant percentage of the U87-MG glioblastoma cell secretome including several proteins that have previously been detected as biomarkers in the cerebrospinal fluid, blood, or urine of brain tumor patients. Taken together, our work illustrates the feasibility of inducing a reversible suppression of numerous (undrugged) extracellular signals by targeting Sec61-dependent protein biogenesis in the secretory pathway with drug-like molecules. Citation Format: Daphne R. Mattos, Jeffrey D. Serrill, Philip R. Gafken, Kerry L. McPhail, Walter K. Vogel, Jane E. Ishmael. Cyanobacterial natural product Sec61α inhibitors induce broad suppression of the glioma cell secretome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3916.

A Rapid and Universal Workflow for Label-Free-Quantitation-Based Proteomic and Phosphoproteomic Studies in Cereals.

Proteomics and phosphoproteomics are robust tools to analyze dynamics of post-transcriptional processes during growth and development. A variety of experimental methods and workflows have been published, but most of them were developed for model plants and have not been adapted to high-throughput platforms. Here, we describe an experimental workflow for proteome and phosphoproteome studies tailored to cereal crop tissues. The workflow consists of two parallel parts that are suitable for analyzing protein/phosphoprotein from total proteins and the microsomal membrane fraction. We present phosphoproteomic data regarding quantification coverage and analytical reproducibility for example preparations from maize root and shoot, wheat leaf, and a microsomal protein preparation from maize leaf. To enable users to adjust for tissue specific requirements, we provide two different methods of protein clean-up: traditional ethanol precipitation (PC) and a recently developed technology termed single-pot, solid-phase-enhanced sample preparation (SP3). Both the PC and SP3 methods are effective in the removal of unwanted substances in total protein crude extracts. In addition, two different methods of phosphopeptide enrichment are presented: a TiO2 -based method and Fe(III)-NTA cartridges on a robotized platform. Although the overall number of phosphopeptides is stable across protein clean-up and phosphopeptide enrichment methods, there are differences in the preferred phosphopeptides in each enrichment method. The preferred protocol depends on laboratory capabilities and research objective. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Total protein crude extraction Basic Protocol 2: Total protein clean-up with ethanol precipitation Alternate Protocol 1: Total protein clean-up with SP3 method Basic Protocol 3: Microsomal fraction protein extraction Basic Protocol 4: Protein concentration determination by Bradford assay Basic Protocol 5: In-solution digestion with trypsin Basic Protocol 6: Phosphopeptide enrichment with TiO2 Alternate Protocol 2: Phosphopeptide enrichment with Fe(III)-NTA cartridges Basic Protocol 7: Peptide desalting with C18 material Basic Protocol 8: LC-MS/MS analysis of (phospho)peptides and spectrum matching.

Induction of A549 Nonsmall-Cell Lung Cancer Cells Proliferation by Photoreleased Nicotine.

Caged compounds comprise the group of artificially synthesized, light-sensitive molecules that enable insitu derivation of biologically active constituents capable of affecting cells, tissues and/or biological processes upon exposure to light. Ruthenium-bispyridine (RuBi) complexes are photolyzed by biologically harmless visible light. In the present study, we show that RuBi-caged nicotine can be used as a source of free nicotine to induce proliferation of A549 nonsmall-cell lung cancer (NSCLC) cells by acting on nicotinic acetylcholine receptors expressed in these cells. RuBi-nicotine was photolyzed using LED light source with the spectrum matching RuBi-absorption. Photorelease of free nicotine ([Nic]p/r ) was quantified by high-performance liquid chromatography (HPLC). 5-s-long light exposure of 10 μm of RuBi-nicotine generated 2 μm [Nic]p/r which enhanced A549 cell proliferation similarly to the 2 μm of plain nicotine during 72 h of cell culturing. Both RuBi-nicotine per se and its photolysis byproduct exerted no effect on A549 cells. We conclude that RuBi-nicotine can be a good source of free nicotine for inducing short- and long-term biological effects. Photolysis of RuBi-nicotine is quite effective, and can produce biologically relevant concentrations of nicotine at acceptable concentrations of the source material with the use of simple, inexpensive, and easily accessible light sources.

Identification of alveolar proteins associated with claudin‐18 using BioID

Tight junctions are protein complexes that form at sites of cell contact that function to regulate paracellular flow of small molecules, water, and ions. Distinct protein components contribute to the barrier function of tight junctions including the claudin family of transmembrane proteins and scaffolding proteins such as Zonula Occludens‐1(ZO‐1). Changes in tight junction morphology from the established smooth cobblestone patterning to a spiked junction are associated with an increase in barrier permeability (leak). Tight junction spike formation is associated with changes in claudin‐18 interacting partners, including ZO‐1. This suggests that protein‐claudin‐18 interactions are essential for tight junction morphology and therefore function. Using rat alveolar epithelial cells (rAECs), we used BioID to investigate the proximal claudin‐18 proteome. BioID uses a promiscuous biotinylating enzyme, BirA, which we fused to claudin‐18 to biotinylate proximal and interacting proteins. To determine that BirA‐claudin‐18 localized to tight junctions, rAECs were transduced with adenovirus to express BirA‐claudin‐18 and stained with claudin‐18 and streptavidin‐Cy3. This showed that the assay was functional and our chimera trafficked to tight junctions and colocalized to proximal proteins. To identify biotinylated proteins, we used tandem mass spectrometry (MS/MS) and identified 3771 protein matches within the BirA‐claudin‐18 sample. Proteins of interest were vetted, and a list of protein candidates were compiled based on peptide spectrum matches (PSMs) and known protein localization. Proteins of interest include retinoic acid and palladin, which are of specific interest because they are associated with a spike like structure found in the testis, the tubulobulbar complex, which we hypothesize behaves as a signaling platform in the testis and is comparable to tight junction spikes found in the lung. These findings show that BioID is a valid method for identification of proteins to claudin‐18, providing a discovery‐based approach to identify novel proteins that play a role in barrier function and tight junction regulation.

Bandgap matching strategy for organic photovoltaic cells in oceanic applications

With the development of aquatic monitoring in oceanic science and environmental protection via tens of thousands of electronic detectors and sensors, the exploitation of energy supply technology for such off-grid electronic devices in ocean informatics is an urgent issue to be addressed. In particular, researchers are seeking alternative candidates to supply power to devices connected to the internet of things in oceanic conditions with diverse light intensities. Herein, we report the potential of organic photovoltaic materials in oceanic applications. The wide-bandgap PM6:IO-4Cl cell achieves a champion efficiency of 23.11% at a sea depth of 5 m because of film absorption spectrum matching with photons passing through the body of water. This work confirms the potential of wide-bandgap organic materials in oceanic photovoltaic applications. • The potential of OPV devices in oceanic applications is evaluated • PM6:IO-4Cl device achieves 23.11% PCE at an ocean depth of 5 m • We confirm the superiority of wide-bandgap organic materials in oceanic OPV devices Efficient energy supply for electronic devices for ocean informatics is becoming increasingly important. In this work, Yang and co-authors find that wide-bandgap organic solar cells based on the PM6:IO-4Cl cell achieve a champion efficiency of 23.11% at a sea depth of 5 m because of an effective bandgap-matched absorption.

A systematic evaluation of yeast sample preparation protocols for spectral identifications, proteome coverage and post-isolation modifications

The importance of obtaining comprehensive and accurate information from cellular proteomics experiments asks for a systematic investigation of sample preparation protocols. In particular when working with unicellular organisms with strong cell walls, such as found in the model organism and cell factory Saccharomyces cerevisiae. Here, we performed a systematic comparison of sample preparation protocols using a matrix of different conditions commonly applied in whole cell lysate, bottom-up proteomics experiments. The different protocols were evaluated for their overall fraction of identified spectra, proteome and amino acid sequence coverage, GO-term distribution and number of peptide modifications, by employing a combination of database and unrestricted modification search approaches. Ultimately, the best protocols enabled the identification of approximately 65–70% of all acquired fragmentation spectra, where additional de novo sequencing suggests that unidentified spectra were largely of too low spectral quality to provide confident spectrum matches. Generally, a range of peptide modifications could be linked to solvents, additives as well as filter materials. Most importantly, the use of moderate incubation temperatures and times circumvented excessive formation of modification artefacts. The collected protocols and large sets of mass spectrometric raw data provide a resource to evaluate and design new protocols and guide the analysis of (native) peptide modifications. SignificanceThe single-celled eukaryote yeast is a widely used model organism for higher eukaryotes in which, for example, the regulation of glycolysis is studied in the context of health and disease. Moreover, yeast is a widely employed cell factory because it is one of the few eukaryotic organisms that can efficiently grow under both aerobic and anaerobic conditions.Large-scale proteomics studies have become increasingly important for single-celled model organisms, such as yeast, in order to provide fundamental understanding of their metabolic processes and proteome dynamics under changing environmental conditions.However, comprehensive and accurate cellular proteomics experiments require optimised sample preparation procedures, in particular when working with unicellular organisms with rigid cell walls, such as found in yeast. Protocols may substantially bias towards specific protein fractions, modify native protein modifications or introduce artificial modifications. That lowers the overall number of spectral identifications and challenges the study of native protein modifications.Therefore, we performed a systematic study of a large array of protocols on yeast grown under highly controlled conditions. The obtained outcomes, the collected protocols and the mass spectrometric raw data enable the selection of suitable sample preparation elements and furthermore support the evaluation of (native) peptide modifications in yeast, and beyond.

Hierarchical classification and matching of mid-infrared spectra of paint samples for forensic applications

A novel fast and automatic methodology for the hierarchical classification and similarity matching of mid-infrared spectra of paint samples based on the principles of Soft Independent Modelling of Class Analogy (SIMCA) and on the definition and properties of the Mahalanobis distance is here proposed. This approach was tested in a so-called market study (i.e., targeting products largely accessible to the general public and conceived for a considerably wide range of usages) conducted across the surroundings of the city of Lille, in France, and has permitted not only to successfully achieve the chemical characterisation of most of the analysed samples but also to discover specific commonality patterns among specimens sharing the same chemical features.

Suspect and non-target screening workflow for studying the occurrence, fate, and environmental risk of contaminants in wastewater using data-independent acquisition

A comprehensive suspect and non-target screening workflow based on liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer was developed for the detection and identification of contaminants in wastewater using data-independent acquisition. The suspect screening workflow could identify 74 compounds from different classes (mainly pharmaceuticals and pesticides), of which37 compounds were confirmed by reference standards.The remaining 37 compounds were tentatively identified based on MS/MS spectra match. The occurrence and elimination of the identified compounds were studied and discussed in detail. Furthermore, the confirmed compounds were quantified where pharmaceuticals had the greatest overall concentrations in all samples, followed by flame retardants. The non-steroidal antiandrogen, bicalutamide, was detected at the highest concentration (843.9 to 3838 ng/L) at the wastewater effluents, where the flame retardant, tris(2-butoxyethyl) phosphate, exhibited a concentration in the range of 337.2 to 1304.6 ng/L. Consequently, the environmental toxicity and risk of the confirmed compounds were investigated. The pharmaceutical, telmisartan, with the insecticide, fipronil exhibited high-risk quotients ( 600∼1400 and 102∼290, respectively), demonstrating their potential toxicity at ecologically relevant amounts. Finally, multivariate analysis was applied to evaluate the efficiency of wastewater treatment . Principal component analysis was able to clearly discriminate between influent and effluent samples, demonstrating an effective treatment process..

Quality control of pesticide using infrared spectroscopic coupled with fingerprint analysis

The public concern and increasing awareness of pesticide calls for methods available to assure quality and authenticity. Spectra matching is widely used in various applications including the search for a spectrum of an unknown compound in an existing spectral database and quality control by means of comparing the spectra of products with standards. Although the infrared spectroscopy (IR) fingerprint has many applications in a lot of areas, there are several limitations in pesticide. The pesticide formulation is a significant impediment against analyzing IR fingerprint of pesticide with similarity estimation.In the present study, a novel strategy combining Fourier-transform infrared spectroscopy (FT-IR) analysis was developed and validated to allow comprehensive analysis and rapid authentication of pesticide. A pesticide IR spectral database which included 604 pesticide standards was established. A simple, fast, and environmentally friendly IR-based pre-treatment method was proposed that can eliminate the negative effect of pesticide formulation on similarity estimation. In order to verify the effectiveness of proposed method, 30 pesticide samples were randomly collected from market, including liquid and solid, with the concentration of active components from 1% to 80%.For 30 samples, the proposed strategy resulted in a 90% accuracy rate and a 3.33% error rate based on the liquid chromatography (LC) data. It is therefore clear that IR spectral database combining with the proposed pre-treatment appear to be a promising tool for monitoring and analysis in the pesticide industry.

Elucidation of the protein composition of mouse seminal vesicle fluid.

The seminal vesicles are male accessory sex glands that contribute the major portion of the seminal plasma in which mammalian spermatozoa are bathed during ejaculation. In addition to conveying sperm through the ejaculatory duct, seminal vesicle secretions support sperm survival after ejaculation, and influence the female reproductive tract to promote receptivity to pregnancy. Analysis of seminal vesicle fluid (SVF) composition by proteomics has proven challenging, due to its highly biased protein signature with a small subset of dominant proteins and the difficulty of solubilizing this viscous fluid. As such, publicly available proteomic datasets identify only 85 SVF proteins in total. To address this limitation, we report a new preparative methodology involving sequential solubilization of mouse SVF in guanidine hydrochloride, acetone precipitation, and analysis by label-free mass spectrometry. Using this strategy, we identified 126 SVF proteins, including 83 previously undetected in SVF. Members of the seminal vesicle secretory protein family were the most abundant, accounting for 79% of all peptide spectrum matches. Functional analysis identified inflammation and formation of the vaginal plug as the two most prominent biological processes. Other notable processes included modulation of sperm function and regulation of the female reproductive tract immune environment. Together, these findings provide a robust methodological framework for future SVF studies and identify novel proteins with potential to influence both male and female reproductive physiology.