Finite-size and finite-time scaling for kinetic rough interfaces.

We consider discrete models of kinetic rough interfaces that exhibit space-time scale invariance in height-height correlation. We use the generic scaling theory of Ramasco et al. [Phys. Rev. Lett. 84, 2199 (2000)0031-900710.1103/PhysRevLett.84.2199] to confirm that the dynamical structure factor of the height profile can uniquely characterize the underlying dynamics. We apply both finite-size and finite-time scaling methods that systematically allow an estimation of the critical exponents and the scaling functions, eventually establishing the universality class accurately. The finite-size scaling analysis offers an alternative way to characterize the anomalous rough interfaces. As an illustration, we investigate a class of self-organized interface models in random media with extremal dynamics. The isotropic version shows a faceted pattern and belongs to the same universality class (as shown numerically) as the Sneppen model (version A). We also examine an anisotropic version of the Sneppen model and suggest that the model belongs to the universality class of the tensionless Kardar-Parisi-Zhang (tKPZ) equation in one dimension.

Fitness fluctuations in the Bak-Sneppen model.

We study the one-dimensional Bak-Sneppen model for the evolution of species in an ecosystem. Of particular interest are the temporal fluctuations in fitness variables. We numerically compute the power spectral density and apply the finite-size scaling method to get data collapse. A clear signature of 1/f^{α} noise with α≈1.2 (long-time correlations) emerges for both local and global (or average) fitness noises. The limiting value of the spectral exponent, 0 or 2, corresponds to no interaction or a random neighbor version of the model, respectively. The local power spectra are spatially uncorrelated and also show an additional scaling, ∼1/L, in the frequency regime L^{-λ}≪f≪1/2, where L is the linear extent of the system.

Serum proteomics profiling identifies a preliminary signature for the diagnosis of early-stage lung cancer.

PURPOSE: Lung cancer is the most common cause of death from cancer worldwide, largely due to late diagnosis. Thus, there is an urgent need to develop new approaches to improve the detection of early-stage lung cancer, which would greatly improve patient survival. EXPERIMENTAL DESIGN: The quantitative protein expression profiles of microvesicles isolated from the sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on a peptide library matching approach. RESULTS: We identified 33 differentially expressed proteins that allow discriminating the two groups. We also built a machine learning model based on serum protein expression profiles that can correctly classify the majority of lung cancer cases and that highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors. CONCLUSIONS AND CLINICAL RELEVANCE: Our study identified a preliminary, non-invasive protein signature able to discriminate with high specificity and selectivity early-stage lung cancer patients from high-risk healthy subjects. These results provide the basis for future validation studies for the development of a non-invasive diagnostic tool for lung cancer.

Spatio-temporal analysis of air quality and its relationship with COVID-19 lockdown over Dublin.

Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO 2 ), Sulphur Dioxide (SO 2 ), Ozone (O 3 ), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28% reduction in average NO 2 level and a 27.7% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March-05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.

Finite-size scaling of critical avalanches.

We examine probability distribution for avalanche sizes observed in self-organized critical systems. While a power-law distribution with a cutoff because of finite system size is typical behavior, a systematic investigation reveals that it may also decrease with increasing the system size at a fixed avalanche size. We implement the scaling method and identify scaling functions. The data collapse ensures a correct estimation of the critical exponents and distinguishes two exponents related to avalanche size and system size. Our simple analysis provides striking implications. While the exact value for avalanches size exponent remains elusive for the prototype sandpile on a square lattice, we suggest the exponent should be 1. The simulation results represent that the distribution shows a logarithmic system size dependence, consistent with the normalization condition. We also argue that for the train or Oslo sandpile model with bulk drive, the avalanche size exponent is slightly less than 1, which differs significantly from the previous estimate of 1.11.

ProMetheusDB: An In-Depth Analysis of the High-Quality Human Methyl-proteome.

Protein arginine (R) methylation is a post-translational modification involved in various biological processes, such as RNA splicing, DNA repair, immune response, signal transduction, and tumor development. Although several advancements were made in the study of this modification by mass spectrometry, researchers still face the problem of a high false discovery rate. We present a dataset of high-quality methylations obtained from several different heavy methyl stable isotope labeling with amino acids in cell culture experiments analyzed with a machine learning-based tool and show that this model allows for improved high-confidence identification of real methyl-peptides. Overall, our results are consistent with the notion that protein R methylation modulates protein-RNA interactions and suggest a role in rewiring protein-protein interactions, for which we provide experimental evidence for a representative case (i.e., NONO [non-POU domain-containing octamer-binding protein]-paraspeckle component 1 [PSPC1]). Upon intersecting our R-methyl-sites dataset with the PhosphoSitePlus phosphorylation dataset, we observed that R methylation correlates differently with S/T-Y phosphorylation in response to various stimuli. Finally, we explored the application of heavy methyl stable isotope labeling with amino acids in cell culture to identify unconventional methylated residues and successfully identified novel histone methylation marks on serine 28 and threonine 32 of H3. The database generated, named ProMetheusDB, is freely accessible at https://bioserver.ieo.it/shiny/app/prometheusdb.

The phytochemical plumbagin reciprocally modulates osteoblasts and osteoclasts.

Bone metabolism is essential for maintaining bone mineral density and bone strength through a balance between bone formation and bone resorption. Bone formation is associated with osteoblast activity whereas bone resorption is linked to osteoclast differentiation. Osteoblast progenitors give rise to the formation of mature osteoblasts whereas monocytes are the precursors for multi-nucleated osteoclasts. Chronic inflammation, auto-inflammation, hormonal changes or adiposity have the potential to disturb the balance between bone formation and bone loss. Several plant-derived components are described to modulate bone metabolism and alleviate osteoporosis by enhancing bone formation and inhibiting bone resorption. The plant-derived naphthoquinone plumbagin is a bioactive compound that can be isolated from the roots of the Plumbago genus. It has been used as traditional medicine for treating infectious diseases, rheumatoid arthritis and dermatological diseases. Reportedly, plumbagin exerts its biological activities primarily through induction of reactive oxygen species and triggers osteoblast-mediated bone formation. It is plausible that plumbagin's reciprocal actions - inhibiting or inducing death in osteoclasts but promoting survival or growth of osteoblasts - are a function of the synergy with bone-metabolizing hormones calcitonin, Parathormone and vitamin D. Herein, we develop a framework for plausible molecular modus operandi of plumbagin in bone metabolism.

Software Options for the Analysis of MS-Proteomic Data.

Mass spectrometry (MS)-based proteomics is currently the most successful approach to measure and compare peptides and proteins in a large variety of biological samples. Modern mass spectrometers, equipped with high-resolution analyzers, provide large amounts of data output. This is the case of shotgun/bottom-up proteomics, which consists in the enzymatic digestion of protein into peptides that are then measured by MS-instruments through a data dependent acquisition (DDA) mode. Dedicated bioinformatic tools and platforms have been developed to face the increasing size and complexity of raw MS data that need to be processed and interpreted for large-scale protein identification and quantification. This chapter illustrates the most popular bioinformatics solution for the analysis of shotgun MS-proteomics data. A general description will be provided on the data preprocessing options and the different search engines available, including practical suggestions on how to optimize the parameters for peptide search, based on hands-on experience.

Linking space-time correlations for a class of self-organized critical systems.

The hypothesis of self-organized criticality explains the existence of long-range "space-time" correlations, observed inseparably in many natural dynamical systems. A simple link between these correlations is yet unclear, particularly in fluctuations at an "external drive" timescale. As an example, we consider a class of sandpile models displaying nontrivial correlations. We apply the scaling method and determine spatial cross-correlation by establishing a relationship between local and global temporal correlations. We find that the spatial cross-correlation decays in a power-law manner with an exponent γ=1-δ, where δ characterizes a scaling of the total power of the global temporal process with the system size.

Biochemical and Computational Approaches for the Large-Scale Analysis of Protein Arginine Methylation by Mass Spectrometry.

The absence of efficient mass spectrometry-based approaches for the large-scale analysis of protein arginine methylation has hindered the understanding of its biological role, beyond the transcriptional regulation occurring through histone modification. In the last decade, however, several technological advances of both the biochemical methods for methylated polypeptide enrichment and the computational pipelines for MS data analysis have considerably boosted this research field, generating novel insights about the extent and role of this post-translational modification. Here, we offer an overview of state-of-the-art approaches for the high-confidence identification and accurate quantification of protein arginine methylation by high-resolution mass spectrometry methods, which comprise the development of both biochemical and bioinformatics methods. The further optimization and systematic application of these analytical solutions will lead to ground-breaking discoveries on the role of protein methylation in biological processes.

Ultraviolet Photodissociation of ESI- and MALDI-Generated Protein Ions on a Q-Exactive Mass Spectrometer.

The identification of molecular ions produced by MALDI or ESI strongly relies on their fragmentation to structurally informative fragments. The widely diffused fragmentation techniques for ESI multiply charged ions are either incompatible (ECD and ETD) or show lower efficiency (CID, HCD), with the predominantly singly charged peptide and protein ions formed by MALDI. In-source decay has been successfully adopted to sequence MALDI-generated ions, but it further increases spectral complexity, and it is not compatible with mass-spectrometry imaging. Excellent UVPD performances, in terms of number of fragment ions and sequence coverage, has been demonstrated for electrospray ionization for multiple proteomics applications. UVPD showed a much lower charge-state dependence, and so protein ions produced by MALDI may exhibit equal propensity to fragment. Here we report UVPD implementation on an Orbitrap Q-Exactive Plus mass spectrometer equipped with an ESI/EP-MALDI. UVPD of MALDI-generated ions was benchmarked against MALDI-ISD, MALDI-HCD, and ESI-UVPD. MALDI-UVPD outperformed MALDI-HCD and ISD, efficiently sequencing small proteins ions. Moreover, the singly charged nature of MALDI-UVPD avoids the bioinformatics challenges associated with highly congested ESI-UVPD mass spectra. Our results demonstrate the ability of UVPD to further improve tandem mass spectrometry capabilities for MALDI-generated protein ions. Data are available via ProteomeXchange with identifier PXD011526.

Critical Pólya urn.

We propose a variant model of the Pólya urn process, where the dynamics consist of two competing elements: suppression of growth and enhancement of dormant character. Here the level of such features is controlled by an internal parameter in a random manner, so that the system self-organizes and characteristic observables exhibit scale invariance, suggesting the existence of criticality. Varying the internal control parameter, one can explain interesting universality classes for avalanche-type events. We also discuss different versions of the model. It is pointed out that such an underlying mechanism has earlier been noted to operate in a neural network.

Characterization of Degraded Proteins in Paintings Using Bottom-Up Proteomic Approaches: New Strategies for Protein Digestion and Analysis of Data.

Chemical hydrolysis assisted by microwave irradiation has been proposed as an alternative method for the analysis of proteins in highly insoluble matrices. In this work, chemical hydrolysis was applied for the first time to detect degraded proteins from paintings and polychromies. To evaluate the performance of this approach, the number of identified peptides, protein sequence coverage (%), and PSMs were compared with those obtained using two trypsin-based proteomics procedures used for the analysis of samples from cultural heritage objects. It was found that chemical hydrolysis allowed the successful identification of all proteinaceous materials in all paint samples analyzed except for egg proteins in one extremely degraded sample. Moreover, in one sample, casein was only identified by chemical digestion. In general, chemical hydrolysis identified more peptides, more PSM's, and greater sequence coverage in the samples containing caseins, and often also in animal glue, highlighting the great potential of this approach for the rapid digestion and identification of insoluble and degraded proteins from the field of the cultural heritage.

General mechanism for the 1/f noise.

We consider the response of a memoryless nonlinear device that acts instantaneously, converting an input signal ξ(t) into an output η(t) at the same time t. For input Gaussian noise with power-spectrum 1/f^{α}, the nonlinearity can modify the spectral index of the output to give a spectrum that varies as 1/f^{α^{'}} with α^{'}≠α. We show that the value of α^{'} depends on the nonlinear transformation and can be tuned continuously. This provides a general mechanism for the ubiquitous 1/f noise found in nature.

Correspondence between a noisy sample-space-reducing process and records in correlated random events.

We study survival time statistics in a noisy sample-space-reducing (SSR) process. Our simulations suggest that both the mean and standard deviation scale as ∼N/N^{λ}, where N is the system size and λ is a tunable parameter that characterizes the process. The survival time distribution has the form P_{N}(τ)∼N^{-θ}J(τ/N^{θ}), where J is a universal scaling function and θ=1-λ. Analytical insight is provided by a conjecture for the equivalence between the survival time statistics in the noisy SSR process and the record statistics in a correlated time series modeled as a drifted random walk with Cauchy distributed jumps.

In-Source Decay and Pseudo-MS3 of Peptide and Protein Ions Using Liquid AP-MALDI.

Atmospheric pressure MALDI on a Q-Exactive instrument was optimized for in-source decay and pseudo-MS3. The dependence of AP-MALDI ISD on the MALDI liquid matrix was investigated for peptides and proteins. The liquid matrices enabled long-life ISD signal, and exhibited high fragment ion yield and signal stability. Extensive a-, b-, c-, y-, and z-type fragment series were observed depending on the matrix used but were most extensive with 2,5-DHB. Complete sequence coverage of small peptide and intact protein-terminus sequence tags were obtained and confirmed using HCD as a pseudo-MS3 method. Graphical Abstract ᅟ.

Survival-time statistics for sample space reducing stochastic processes.

Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers {x(t)},0≤t≤τ, with boundary conditions x(0)=N and x(τ) = 1. This model is shown to be exactly solvable: P_{N}(τ), the probability that the process survives for time τ is analytically evaluated. In the limit of large N, the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: 〈τ〉∼lnN and σ_{τ}^{2}∼lnN. Correspondence can be made between survival-time statistics in the SSR process and record statistics of independent and identically distributed random variables.

Quantitative Site-Specific Phosphoproteomics of Trichoderma reesei Signaling Pathways upon Induction of Hydrolytic Enzyme Production.

The filamentous fungus Trichoderma reesei is used for industrial production of secreted enzymes including carbohydrate active enzymes, such as cellulases and hemicellulases. The production of many of these enzymes by T. reesei is influenced by the carbon source it grows on, where the regulation system controlling hydrolase genes involves various signaling pathways. T. reesei was cultivated in the presence of sorbitol, a carbon source that does not induce the production of cellulases and hemicellulases, and then exposed to either sophorose or spent-grain extract, which are efficient inducers of the enzyme production. Specific changes at phosphorylation sites were investigated in relation to the production of cellulases and hemicellulases using an MS-based framework. Proteome-wide phosphorylation following carbon source exchange was investigated in the early stages of induction: 0, 2, 5, and 10 min. The workflow involved sequential trypsin digestion, TiO2 enrichment, and MS analysis using a Q Exactive mass spectrometer. We report on the identification and quantitation of 1721 phosphorylation sites. Investigation of the data revealed a complex signaling network activated upon induction involving components related to light-mediated cellulase induction, osmoregulation, and carbon sensing. Changes in protein phosphorylation were detected in the glycolytic pathway, suggesting an inhibition of glucose catabolism at 10 min after the addition of sophorose and as early as 2 min after the addition of spent-grain extract. Differential phosphorylation of factors related to carbon storage, intracellular trafficking, cytoskeleton, and cellulase gene regulation were also observed.

Spore immobilization and its analytical performance for monitoring of aflatoxin M1 in milk.

Immobilization of Bacillus megaterium spores on Eppendorf tubes through physical adsorption has been used in the detection of aflatoxin M1 (AFM1) in milk within real time of 45 ± 5 min using visual observation of changes in a chromogenic substrate. The appearance of a sky-blue colour indicates the absence of AFM1 in milk, whereas no colour change indicates the presence of AFM1 in milk at a 0.5 ppb Codex maximum residue limit. The working performance of the immobilized spores was shown to persist for up to 6 months. Further, spores immobilized on 96-well black microtitre plates by physical adsorption and by entrapment on sensor disk showed a reduction in detection sensitivity to 0.25 ppb within a time period of 20 ± 5 min by measuring fluorescence using a microbiological plate reader through the addition of milk and fluorogenic substrate. A high fluorescence ratio indicated more substrate hydrolysis due to spore-germination-mediated release of marker enzymes of spores in the absence of AFM1 in milk; however, low fluorescence ratios indicated the presence of AFM1 at 0.25 ppb. Immobilized spores on 96-well microtitre plates and sensor disks have shown better reproducibility after storage at 4 °C for 6 months. Chromogenic assay showed 1.38% false-negative and 2.77% false-positive results while fluorogenic assay showed 4.16% false-positive and 2.77% false-negative results when analysed for AFM1 using 72 milk samples containing raw, pasteurized, and dried milk. Immobilization of spores makes these chromogenic and fluorogenic assays portable, selective, cost-effective for real-time detection of AFM1 in milk at the dairy farm, reception dock, and manufacturing units of the dairy industry.

Scaling behavior in probabilistic neuronal cellular automata.

We study a neural network model of interacting stochastic discrete two-state cellular automata on a regular lattice. The system is externally tuned to a critical point which varies with the degree of stochasticity (or the effective temperature). There are avalanches of neuronal activity, namely, spatially and temporally contiguous sites of activity; a detailed numerical study of these activity avalanches is presented, and single, joint, and marginal probability distributions are computed. At the critical point, we find that the scaling exponents for the variables are in good agreement with a mean-field theory.