Mass Error Research Articles

A split-step finite element method for the space-fractional Schrödinger equation in two dimensions

In this article, we propose a split-step finite element method (FEM) for the two-dimensional nonlinear Schrödinger equation (NLS) with Riesz fractional derivatives in space. The space-fractional NLS is first spatially discretized by finite element scheme and the semi-discrete variational scheme is obtained. We prove that it maintains the mass and energy conservation laws. Then, we establish a fully discrete split-step finite element scheme for the considered problem, which avoids the iteration at each time layer, thereby significantly reducing computational cost. The discrete mass conservation property and error estimate of this split-step finite element scheme is derived. Finally, illustrative tests and the numerical simulation of dynamic of wave solutions are included to confirm its effectiveness and capability.

Annotation of DOM metabolomes with an ultrahigh resolution mass spectrometry molecular formula library

Increased accessibility of liquid chromatography mass spectrometry (LC-MS) metabolomics instrumentation and software have expanded their use in studies of dissolved organic matter (DOM) and exometabolites released by microbes. Current strategies to annotate metabolomes generally rely on matching tandem MS/MS spectra to databases of authentic standards. However, spectral matching approaches typically have low annotation rates for DOM. An alternative approach is to annotate molecular formula based on accurate mass and isotopic fine structure measurements that can be obtained from state-of-the-art ultrahigh resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FT-ICR MS), but instrument accessibility for large metabolomic studies is generally limited. Here, we describe a strategy to annotate exometabolomes obtained from lower resolution LC-MS systems by matching metabolomic features to a molecular formula library generated for a representative sample analyzed by LC 21T- FT-ICR MS. The molecular formula library approach successfully annotated 53% of exometabolome features of the marine diatom Phaeodactylum tricornutum – a nearly ten-fold increase over the 6% annotation rate achieved using a conventional MS/MS approach. There was 94% agreement between assigned formula that were annotated with both approaches, and mass error analysis of the discrepancies suggested that the FT-ICR MS formula assignments were more reliable. Differences in the exometabolome of P. tricornutum grown under iron replete and iron limited conditions revealed 668 significant metabolites, including a suite of peptide-like molecules released by P. tricornutum in response to iron deficiency. These findings demonstrate the utility of FT-ICR MS formula libraries for extending the accuracy and comprehensiveness of metabolome annotations.

Suspect screening-data independent analysis workflow for the identification of arsenolipids in marine standard reference materials.

There has been limited research into arsenolipid toxicological risks and health-related outcomes due to challenges with their separation, identification, and quantification within complex biological matrices (e.g., fish, seaweed). Analytical approaches for arsenolipid identification such as suspect screening have not been well documented and there are no certified standard reference materials, leading to issues with reproducibility and uncertainty regarding the accuracy of results. In this study, a detailed workflow for the identification of arsenolipids utilizing suspect screening coupled with data independent analysis is presented and applied to three commercially available standard reference materials (Hijiki seaweed, dogfish liver, and tuna). Hexane and dichloromethane/methanol extraction, followed by reversed-phase high-performance liquid chromatography-inductively coupled plasma mass spectrometry and liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry. Using the workflow developed, mass fragmentation matching, mass error calculations, and retention time matching were performed to identify suspect arsenolipids. Arseno-fatty acids (AsFAs), arsenohydrocarbons (AsHCs), and arsenosugar phospholipids (AsSugPLs) were identified with high confidence; AsHC332, AsHC360, and AsSugPL720 in seaweed, AsHC332 in tuna, and AsFA474 and AsFA502 in the dogfish liver. AsHC332, AsHC360, and AsFA502 were identified as promising candidates for further work on synthesis, quantification using MS/MS, and toxicity testing.

Circumnuclear Dust in Luminous Early-type Galaxies. I. Sample Properties and Stellar Luminosity Models

Dusty circumnuclear disks (CNDs) in luminous early-type galaxies (ETGs) show regular, dynamically cold molecular gas kinematics. For a growing number of ETGs, Atacama Large Millimeter/sub-millimeter Array (ALMA) CO imaging and detailed gas-dynamical modeling facilitate moderate-to-high precision black hole (BH) mass (M BH) determinations. From the ALMA archive, we identified a subset of 26 ETGs with estimated M BH/M ⊙ ≳ 108 to a few × 109 and clean CO kinematics but that previously did not have sufficiently high-angular-resolution near-IR observations to mitigate dust obscuration when constructing stellar luminosity models. We present new optical and near-IR Hubble Space Telescope (HST) images of this sample to supplement the archival HST data, detailing the sample properties and data-analysis techniques. After masking the most apparent dust features, we measure stellar surface-brightness profiles and model the luminosities using the multi-Gaussian expansion (MGE) formalism. Some of these MGEs have already been used in CO dynamical modeling efforts to secure quality M BH determinations, and the remaining ETG targets here are expected to significantly improve the high-mass end of the current BH census, facilitating new scrutiny of local BH mass–host galaxy scaling relationships. We also explore stellar isophotal behavior and general dust properties, finding these CNDs generally become optically thick in the near-IR (A H ≳ 1 mag). These CNDs are typically well aligned with the larger-scale stellar photometric axes, with a few notable exceptions. Uncertain dust impact on the MGE often dominates the BH mass error budget, so extensions of this work will focus on constraining CND dust attenuation.

Dear-PSM: A deep learning-based peptide search engine enables full database search for proteomics.

Peptide spectrum matching is the process of linking mass spectrometry data with peptide sequences. An experimental spectrum can match thousands of candidate peptides with variable modifications leading to an exponential increase in candidates. Completing the search within a limited time is a key challenge. Traditional searches expedite the process by restricting peptide mass errors and variable modifications, but this limits interpretive capability. To address this challenge, we propose Dear-PSM, a peptide search engine that supports full database searching. Dear-PSM does not restrict peptide mass errors, matching each spectrum to all peptides in the database and increasing the number of variable modifications per peptide from the conventional 3-20. Leveraging inverted index technology, Dear-PSM creates a high-performance index table of experimental spectra and utilizes deep learning algorithms for peptide validation. Through these techniques, Dear-PSM achieves a speed breakthrough 7 times faster than mainstream search engines on a regular desktop computer, with a remarkable 240-fold reduction in memory consumption. Benchmark test results demonstrate that Dear-PSM, in full database search mode, can reproduce over 90% of the results obtained by mainstream search engines when handling complex mass spectrometry data collected from different species using various instruments. Furthermore, it uncovers a substantial number of new peptides and proteins. Dear-PSM has been publicly released on the GitHub repository https://github.com/jianweishuai/Dear-PSM.

Plasma-Enhanced Copper Sulfide/Oxide Electrode for Zinc-Air/Copper Dual Mode Battery

As search for novel electrochemical energy storage systems continues, metal-air batteries are one of the possible solution. Consisting of a metal anode and an air-breathing cathode, by design, they achieve much higher gravimetric energy density in theory. However, s-orbital metals suffer passivation issues and poses safety threats due to their reactivity with air and humidity. As a solution to this issue, safer d-block metals are being studied as an alternative. Zinc air batteries (ZABs) utilizes earth-abundant and cost-effective zinc as its anode, and has high theoretical capacity. At its cathode, oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) occur during charging and discharge processes. Sluggish kinetics of ORR degrades the discharge performance, and is the bottleneck in commercial application of the zinc air battery. Additionally, irreversibility of the cathode reaction results in dissatisfactory retention of battery capacity. Current state-of-the-art noble metal catalysts achieves nearly 80% of the theoretical capacity. However, its cycling stability is severely limited to less than 200 cycles despite low discharge rate applied.Herein, we present a nitrogen plasma-enhanced copper sulfide/oxide catalyst (referred to as N-CuS) that exhibits good catalytic activity for its application as the air-breathing cathode for ZABs. The material is grown directly on metal foam surface using anodization, which is then treated using nitrogen plasma. Anodization creates defect-rich flakes of copper sulfide on the oxide surface, highly increasing surface area and creating the active sites. The material has good electrical conductivity and highly open porous structure that alleviates mass transport issues. Additionally, nitrogen plasma treatment further increases the surface area about 80%, and is expected to introduce nitrogen dopants to the surface, enhancing conductivity of the material. ECSA estimated by measuring Cdl shows that each process increases the ECSA by over 40 times and about 1.8 times, respectively.LSV of CuS and N-CuS half-cells have been performed to measure and compare catalytic performance of the material. N-CuS shows far higher current density and better catalytic activity (35mV less overpotential) than CuS, and higher output voltage is observed with N-CuS cells with the same current density. By assessing the GCPL (potential limited galvanostatic cycling) results, it was concluded that N-CuS requires lower charging voltage and exhibits higher discharge voltages than CuS.The overall performance was evaluated in a stack cell configuration, utilizing thin zinc foil as its anode to minimize mass error. The measured specific capacity of the cell was 789.3 mA·h·g-1, which approximately is 85% of the theoretical specific capacity of zinc-air cells (820 mA·h·g-1). This cell yields gravimetric energy density of about 666 Wh·kg-1. It is notable that these ZAB characteristics are comparable to Pt/C, the state-of-the-art catalyst as of now. N-CuS may be a viable replacement for the Pt/C electrode, resulting in the cost-saving effect. The cycling stability of both N-CuS and Pt/C-based ZAB has been assessed to show that N-CuS-based ZAB has much prolonged (more than twice the cycles) cyclability compared to Pt/C-based ZAB (Fig. S11).Additionally, ZAB constructed using N-CuS can operate in oxygen-deficient environments. As the counter reaction it utilizes redox reaction of copper oxides (Zn-Cu mode) in the substrate when operating in anaerobic conditions. The reduction of divalent and monovalent copper oxides appears as plateaus at 1.1 V and 0.8 V versus the zinc anode during GCPL. Due to this alternate reaction, the cell can operate even at low concentrations of oxygen or even in its absence.DFT calculations have been performed to elucidate the reaction mechanism and the intrinsic catalytic activity of the material, and shows nitrogen dopants improve reactivity of the active sites, thus reducing the overpotential.In summary, we have synthesized N-CuS as the OER/ORR catalyst for ZAB. Through anodization the catalyst was directly grown on the substrate and nitrogen plasma treatment further enhances the catalyst properties to render it higher surface area, conductivity, and better stability. This allows better reversibility in the OER/ORR and results in enhanced cyclability of the overall cell. The constructed RZAB exhibited high specific capacity of 789.3 mA·h·g-1, which, has higher capacity than the analogue using Pt/C catalyst. In addition, N-CuS based RZAB is capable of operating under oxygen-deficient conditions utilizing the metal oxide substrate. This allows the dual mode operation of zinc-air and zinc-copper modes, and results in a more consistent output from the RZAB, as short-term air supply issues could be buffered by this effect. Figure 1

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics.

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

Misinterpretations about CT numbers, material decomposition, and elemental quantification.

Quantitative CT imaging, particularly iodine and calcium quantification, is an important CT-based biomarker. This study quantifies sources of errors in quantitative CT imaging in both single-energy and spectral CT. This work examines the theoretical relationship between CT numbers, linear attenuation coefficient, and material quantification. We derive four understandings: (1) CT numbers are not proportional with element mass in vivo, (2) CT numbers are proportional with element mass only when contained in a voxel of pure water, (3) iodine-water material decomposition is never accurate in vivo, and (4) for error-free material decomposition a voxel must only consist of the basis decomposition vectors. Misinterpretation-based errors are calculated using the National Institute of Standards and Technology (NIST) XCOM database for: tissue chemical compositions, clinical concentrations of hydroxyapatite (HAP), and iodine. Quantification errors are also demonstrated experimentally using phantoms. In single-energy CT, misinterpretation-induced errors for HAP density in adipose, muscle, lung, soft tissue, and blood ranged from 0-132%, i.e., a mass error of 0-749 mg/cm3. In spectral CT, errors with iodine in the same tissues resulted in a range of < 0.1-33% error, resulting in a mass error of < 0.1-1.2 mg/mL. Our work demonstrates material quantification is fundamentally limited when measured in vivo due to measurement conditions differing from assumed and the errors are at or above detection limits for bone mineral density (BMD) and spectral iodine quantification. To define CT-derived biomarkers, the errors we demonstrate should either be avoided or built into uncertainty bounds. Improving error bounds in quantitative CT biomarkers, specifically in iodine and BMD quantification, could lead to improvements in clinical care aspects based on quantitative CT. CT numbers are only proportional with element mass only when contained in a voxel of pure water, therefore iodine-water material decomposition is never accurate in vivo. Misinterpretation-induced errors ranged from 0-132% for HAP density and < 0.1-33% in spectral CT with iodine. For error-free material decomposition, a voxel must only consist of the basis decomposition vectors.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

Ingredient-guided multi-modal interaction and refinement network for RGB-D food nutrition assessment

RGB-D food nutrition assessment entails the direct prediction of nutritional content in pairs of RGB and depth food images using signal processing technology. However, existing methods present numerous challenges related to accuracy and computational complexity. In this paper, we introduce the Ingredient-guided Multi-modal Interaction and Refinement Network (IMIR-Net), a novel framework for RGB-D food nutrition assessment. We first utilize a visual grounding model to accurately identify the dish region containing food in the input image with complex semantics. The identified RGB-D dish regions are processed through a multi-modal interaction and refinement module to compute the fused embedding. Concurrently, we introduce an ingredient guidance module to effectively capture ingredient textual information, thereby facilitating the acquisition of the enhanced fused embedding. Finally, we design a decoupled nutrition predictor to predict nutrition from the fused RGB-D embedding. The IMIR-Net can capture the rich multi-modal semantics for RGB-D food nutrition assessment and achieves state-of-the-art performances on the Nutrition5k dataset. Specifically, the percentage of mean absolute error (PMAE) of Calories, Mass, Fat, Carb and Protein reached 14.5%, 10.4%, 21.8%, 20.4% and 20.0%, respectively. Compared with the existing best method, our IMIR-Net demonstrated superior prediction accuracy, with a mean PMAE value of 17.4%, an improvement of 1.1%. In terms of computational complexity, the floating-point operations (FLOPs) and parameters reached 63.87 and 105.11 via our method, which is improved by 5.2% and 7.4%, respectively. The codes, data, and model of our method are publicly available at https://github.com/nianfd/IMIR-Net-DSP.

Improved analysis of the decay width of t→Wb up to N3LO QCD corrections

In this paper, we analyze the top-quark decay t→Wb up to next-to-next-to-next-to-leading order (N3LO) QCD corrections. For the purpose, we first adopt the principle of maximum conformality (PMC) to deal with the initial perturbative QCD (pQCD) series. Then we adopt the Bayesian analysis approach, which quantifies the unknown higher-order terms’ contributions in terms of a probability distribution, to estimate the possible magnitude of the uncalculated N4LO terms. In our calculation, an effective strong coupling constant αs(Q*) is determined by using all nonconformal {βi} terms associated with the renormalization group equation. This leads to a next-to-leading-log PMC scale Q*(NLL)=10.3048 GeV, which can be regarded as the correct momentum flow of the process. Consequently, we obtain an improved scale-invariant pQCD prediction for the top-quark decay width, e.g., Γttot=1.3120±0.0038 GeV, whose error is the squared average of the uncertainties from the decay width of W-boson ΔΓW=±0.042 GeV, the coupling constant Δαs(mZ)=±0.0009, and the predicted N4LO-terms. The magnitude of the top-quark pole mass greatly affects the total decay width. By further taking the PDG top-quark pole mass error from cross-section measurements into consideration, e.g., Δmt=±0.7 GeV, we obtain Γttot=1.3120−0.0192+0.0194 GeV. Published by the American Physical Society 2024

Detection and Exclusion of False-Positive Molecular Formula Assignments via Mass Error Distributions in UHR Mass Spectra of Natural Organic Matter.

Ultrahigh resolution mass spectrometry (UHRMS) routinely detects and identifies thousands of mass peaks in complex mixtures, such as natural organic matter (NOM) and petroleum. The assignment of several chemically plausible molecular formulas (MFs) for a single accurate mass still poses a major problem for the reliable interpretation of NOM composition in a biogeochemical context. Applying sensible chemical rules for MF validation is often insufficient to eliminate multiple assignments (MultiAs)─especially for mass peaks with low abundance or if ample heteroatoms or isotopes are included - and requires manual inspection or expert judgment. Here, we present a new approach based on mass error distributions for the identification of true and false assignments among MultiAs. To this end, we used the mass error in millidalton (mDa), which was superior to the commonly used relative mass error in ppm. We developed an automatic workflow to group MultiAs based on their shared formula units and Kendrick mass defect values and to evaluate the mass error distribution. In this way, the number of valid assignments of chlorinated disinfection byproducts was increased by 8-fold as compared to only applying 37Cl/35Cl isotope ratio filters. Likewise, phosphorus-containing MFs can be differentiated against chlorine-containing MFs with high confidence. Further, false assignments of highly aromatic sulfur-containing MFs ("black sulfur") to sodium adducts in negative ionization mode can be excluded by applying our approach. Overall, MFs for mass peaks that are close to the detection limit or where naturally occurring isotopes are rare (e.g., 15N) or absent (e.g., P and F) can now be validated, substantially increasing the reliability of MF assignments and broadening the applicability of UHRMS analysis to even more complex samples and processes.

Data Acquisition and Intraoperative Tissue Analysis on a Mobile, Battery-Operated, Orbitrap Mass Spectrometer.

Mass spectrometry has been increasingly explored in intraoperative studies as a potential technology to help guide surgical decision making. Yet, intraoperative experiments using high-performance mass spectrometry instrumentation present a unique set of operational challenges. For example, standard operating rooms are often not equipped with the electrical requirements to power a commercial mass spectrometer and are not designed to accommodate their permanent installation. These obstacles can impact progress and patient enrollment in intraoperative clinical studies because implementation of MS instrumentation becomes limited to specific operating rooms that have the required electrical connections and space. To expand our intraoperative clinical studies using the MasSpec Pen technology, we explored the feasibility of transporting and acquiring data on Orbitrap mass spectrometers operating on battery power in hospital buildings. We evaluated the effect of instrument movement including acceleration and rotational speeds on signal stability and mass accuracy by acquiring data using direct infusion electrospray ionization. Data were acquired while rolling the systems in/out of operating rooms and while descending/ascending a freight elevator. Despite these movements and operating the instrument on battery power, the relative standard deviation of the total ion current was <5% and the magnitude of the mass error relative to the internal calibrant never exceeded 5.06 ppm. We further evaluated the feasibility of performing intraoperative MasSpec Pen analysis while operating the Orbitrap mass spectrometer on battery power during an ovarian cancer surgery. We observed that the rich and tissue-specific molecular profile commonly detected from ovarian tissues was conserved when running on battery power. Together, these results demonstrate that Orbitrap mass spectrometers can be operated and acquire data on battery power while in motion and in rotation without losses in signal stability or mass accuracy. Furthermore, Orbitrap mass spectrometers can be used in conjunction to the MasSpec Pen while on battery power for intraoperative tissue analysis.

The use of sequential window acquisition of all theoretical fragment ion spectra (SWATH), a data-independent acquisition high-resolution mass spectrometry approach, in forensic toxicological regimes: A review.

Sequential window acquisition of all theoretical fragment ion spectra (SWATH) is a type of high-resolution mass spectrometry that uses data-independent acquisition. Compared with more targeted acquisition schemes, the power behind this data-independent acquisition technique comes from its ability to mitigate interferences via the use of SWATH acquisition windows (Q1 quadrupole isolation windows) while still obtaining all accurate mass information. However, consistent with high-resolution mass spectrometry techniques, its routine and high throughput implementation in forensic toxicology is limited due to the complex processing power required to effectively manage the large amount of acquired data. It is therefore pivotal to create an efficient and validated identification criterion that confidently reports suspected positive detections as a confirmational technique for final reporting. This review examines all publications that implemented SWATH in a forensic toxicological framework with suggestive best practices and commonly used criteria. Seventeen publications were reviewed for extraction, liquid chromatography and mass spectrometry parameters, and more specifically for all SWATH applicable characteristics including spray voltages, collision energies and spreads, mass error, isotopic ratio difference, retention time error, and library score thresholds. Notwithstanding the challenges SWATH implementation faces for a laboratory, the technique demonstrates its potential to be utilized in routine forensic toxicology testing regimes and aids in the detection of both common and emerging novel drugs simultaneously.

Improved Mass Calibration in MALDI MSI Using Neural Network-Based Recalibration.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful imaging method for generating molecular maps of biological samples and has numerous applications in biomedical research. A key challenge in MALDI MSI is to reliably map observed mass peaks to theoretical masses, which can be difficult due to mass shifts that occur during the measurement process. In this paper, we propose MassShiftNet, a novel self-supervised learning framework for mass recalibration. We train a neural network on a data dependent and specifically augmented training data set to directly estimate and correct the mass shift in the observed spectra. In our evaluation, we show that this method is both able to reduce the absolute mass error and to increase the relative mass alignment between peptide MSI spectra acquired from FFPE-fixated tissue using a MALDI time-of-flight (TOF) instrument.

Investigating the Relationship between Maximum Aerobic Capacity and Cognitive Ability and Employees’ Workability in a Dairy Factory

Abstract Aim: Considering the importance and necessity of physical and mental fitness of the employees with their job demands, this study aimed to estimate the levels of aerobic capacity among the workers and determine the factors that might affect these levels. The relationship between the aerobic capacity levels and the values of workers’ cognitive function and workability was also investigated. Materials and Methods: Sixty healthy male employees from a dairy factory were recruited using a random sampling method. The study design consisted of the completion of checklists containing personal data, anthropometric measurements, and evaluation of job ability, maximum aerobic capacity along with the cognitive ability of the participants using the Workability Index (WAI) standard questionnaire, Queen’s College step test, and psychomotor vigilance test (PVT), respectively. Results: The mean values of VO2 max, reaction time (RT), and WAI of workers were 45.54 ± 6.13 mL/kg/min, 337.82 ± 29.93 ms, and 42.76 ± 5.02, respectively. Based on the Pearson correlation test, a weak statically significant correlation was observed between PVT and VO2 max (r = 0.3, P = 0.02) as well as PVT and physical work capacity (r = 0.263, P = 0.04). The findings of this study showed that there was a significant relationship between height, weight, body mass index, RT, and error percentage with maximal aerobic capacity (VO2 max). Conclusion: According to the physiological criteria, the employees’ capabilities and job requirements are not in balance; particularly for those working in laboratory, office, or service departments. Therefore, using standardized recruitment tests, providing sports facilities and developing integrated indicators are a principal element of establishing an ergonomic balance in this workplace.

A weighted shifted boundary method for immersed moving boundary simulations of Stokes' flow

The Shifted Boundary Method (SBM) belongs to the class of unfitted (or immersed, or embedded) finite element methods, and relies on reformulating the original boundary value problem over a surrogate (approximate) computational domain. The surrogate domain is constructed so as to avoid cut cells and the associated problematic implementation and numerical integration issues. Accuracy is maintained by modifying the original boundary conditions using Taylor expansions: hence the name of the method, that shifts the location and values of the boundary conditions. In this article, we extend the SBM to the simulation of incompressible Stokes flow, by appropriately weighting its variational form with the elemental volume fraction of active fluid. This approach allows to drastically reduce spurious pressure oscillations in time, which are produced if the total volume of active fluid were to change abruptly over a time step. The proposed Weighted SBM (W-SBM) exactly preserves states of hydrostatic equilibrium, and induces small mass and momentum conservation errors, which converge as the grid is refined. This is in analogy to cutFEMs and related unfitted approaches, which rely on an affine representation of cut boundaries. We demonstrate the robustness and accuracy of the proposed method with an extensive suite of two-dimensional tests.

Comparative Lipidomics of Oral Commensal and Opportunistic Bacteria.

The oral cavity contains a vast array of microbes that contribute to the balance between oral health and disease. In addition, oral bacteria can gain access to the circulation and contribute to other diseases and chronic conditions. There are a limited number of publications available regarding the comparative lipidomics of oral bacteria and fungi involved in the construction of oral biofilms, hence our decision to study the lipidomics of representative oral bacteria and a fungus. We performed high-resolution mass spectrometric analyses (<2.0 ppm mass error) of the lipidomes from five Gram-positive commensal bacteria: Streptococcus oralis, Streptococcus intermedius, Streptococcus mitis, Streptococcus sanguinis, and Streptococcus gordonii; five Gram-positive opportunistic bacteria: Streptococcus mutans, Staphylococcus epidermis, Streptococcus acidominimus, Actinomyces viscosus, and Nanosynbacter lyticus; seven Gram-negative opportunistic bacteria: Porphyromonas gingivalis. Prevotella brevis, Proteus vulgaris, Fusobacterium nucleatum, Veillonella parvula, Treponema denticola, and Alkermansia muciniphila; and one fungus: Candida albicans. Our mass spectrometric analytical platform allowed for a detailed evaluation of the many structural modifications made by microbes for the three major lipid scaffolds: glycerol, sphingosine and fatty acyls of hydroxy fatty acids (FAHFAs).

On development of lattice-Boltzmann method based multi phase flow solver

A numerical methodology based on lattice Boltzmann and level set function is presented to simulate the two-phase flows with stringent density and viscosity ratio. Two different strategies - single grid (SGLBLS) and dual grid (DGLBLS) are used. For accurate computation of interfacial forces in DGLBLS, the level set equations are solved on a uniform grid which is twice that for lattice-Boltzmann equations. The code validation and performance of SGLBLS and DGLBLS is carried on three different problems: Young’s Laplace law, 2D rising bubble and Rayleigh–Taylor instability. The results show that the representation of the interface is more accurate in DGLBLS with significantly less mass error - establishing it as an accurate method to handle stringent density and viscosity ratio. The comparison between DGLBLS and SGLBLS shows that the DGLBLS leads to a large improvement in the accuracy of the results with only slight increase in the computational cost.

An XIC-Centric Strategy for Improved Identification and Quantification in Proteomic Data Analyses.

Reproducibility is a "proteomic dream" yet to be fully realized. A typical data analysis workflow utilizing extracted ion chromatograms (XICs) often treats the information path from identification to quantification as a one-way street. Here, we propose an XIC-centric approach in which the data flow is bidirectional: identifications are used to derive XICs whose information is in turn applied to validate the identifications. In this study, we employed liquid chromatography-mass spectrometry data from glycoprotein and human hair samples to illustrate the XIC-centric concept. At the core of this approach was XIC-based monoisotope repicking. Taking advantage of the intensity information for all detected isotopes across the whole range of an XIC peak significantly improved the accuracy and uncovered misidentifications originating from monoisotope assignment mistakes. It could also rescue non-top-ranked glycopeptide hits. Identification of glycopeptides is particularly susceptible to precursor mass errors for their low abundances, large masses, and glycans differing by 1 or 2 Da easily confused as isotopes. In addition, the XIC-centric strategy significantly reduced the problem of one XIC peak associated with multiple unique identifications, a source of quantitative irreproducibility. Taken together, the proposed approach can lead to improved identification and quantification accuracy and, ultimately, enhanced reproducibility in proteomic data analyses.