Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study.

BACKGROUND: Current risk stratification tools for acute myocardial infarction (AMI) have limitations, particularly in predicting mortality. This study utilizes cardiac ultrasound radiomics (i.e., ultrasomics) to risk stratify AMI patients when predicting all-cause mortality. RESULTS: The study included 197 patients: (a) retrospective internal cohort (n = 155) of non-ST-elevation myocardial infarction (n = 63) and ST-elevation myocardial infarction (n = 92) patients, and (b) external cohort from the multicenter Door-To-Unload in ST-segment-elevation myocardial infarction [DTU-STEMI] Pilot Trial (n = 42). Echocardiography images of apical 2, 3, and 4-chamber were processed through an automated deep-learning pipeline to extract ultrasomic features. Unsupervised machine learning (topological data analysis) generated AMI clusters followed by a supervised classifier to generate individual predicted probabilities. Validation included assessing the incremental value of predicted probabilities over the Global Registry of Acute Coronary Events (GRACE) risk score 2.0 to predict 1-year all-cause mortality in the internal cohort and infarct size in the external cohort. Three phenogroups were identified: Cluster A (high-risk), Cluster B (intermediate-risk), and Cluster C (low-risk). Cluster A patients had decreased LV ejection fraction (P < 0.01) and global longitudinal strain (P = 0.03) and increased mortality at 1-year (log rank P = 0.05). Ultrasomics features alone (C-Index: 0.74 vs. 0.70, P = 0.04) and combined with global longitudinal strain (C-Index: 0.81 vs. 0.70, P < 0.01) increased prediction of mortality beyond the GRACE 2.0 score. In the DTU-STEMI clinical trial, Cluster A was associated with larger infarct size (> 10% LV mass, P < 0.01), compared to remaining clusters. CONCLUSIONS: Ultrasomics-based phenogroup clustering, augmented by TDA and supervised machine learning, provides a novel approach for AMI risk stratification.

Topological Structures in the Space of Treatment-Naïve Patients with Chronic Lymphocytic Leukemia.

Patients are complex and heterogeneous; clinical data sets are complicated by noise, missing data, and the presence of mixed-type data. Using such data sets requires understanding the high-dimensional "space of patients", composed of all measurements that define all relevant phenotypes. The current state-of-the-art merely defines spatial groupings of patients using cluster analyses. Our goal is to apply topological data analysis (TDA), a new unsupervised technique, to obtain a more complete understanding of patient space. We applied TDA to a space of 266 previously untreated patients with Chronic Lymphocytic Leukemia (CLL), using the "daisy" metric to compute distances between clinical records. We found clear evidence for both loops and voids in the CLL data. To interpret these structures, we developed novel computational and graphical methods. The most persistent loop and the most persistent void can be explained using three dichotomized, prognostically important factors in CLL: IGHV somatic mutation status, beta-2 microglobulin, and Rai stage. In conclusion, patient space turns out to be richer and more complex than current models suggest. TDA could become a powerful tool in a researcher's arsenal for interpreting high-dimensional data by providing novel insights into biological processes and improving our understanding of clinical and biological data sets.

Triadic percolation induces dynamical topological patterns in higher-order networks.

Triadic interactions are higher-order interactions which occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axo-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully fledged dynamical process in which the size of the giant component undergoes a route to chaos. However, in many real cases, triadic interactions are local and occur on spatially embedded networks. Here, we show that triadic interactions in spatial networks induce a very complex spatio-temporal modulation of the giant component which gives rise to triadic percolation patterns with significantly different topology. We classify the observed patterns (stripes, octopus, and small clusters) with topological data analysis and we assess their information content (entropy and complexity). Moreover, we illustrate the multistability of the dynamics of the triadic percolation patterns, and we provide a comprehensive phase diagram of the model. These results open new perspectives in percolation as they demonstrate that in presence of spatial triadic interactions, the giant component can acquire a time-varying topology. Hence, this work provides a theoretical framework that can be applied to model realistic scenarios in which the giant component is time dependent as in neuroscience.

Emerging perfluoroalkyl substances retard skeletal growth by accelerating osteoblasts senescence via ferroptosis.

Due to the persistent nature and significant negative impacts of perfluorooctanoic acid (PFOA) on human health and other organisms, the emergence of new PFOA alternatives, such as perfluoro (2-methyl-3-oxhexanoic) acid (GenX) and perfluoro-3,6,9-trioxyundecanoic acid (PFO3TDA), have drawn significant attention. However, the toxic effects of PFOA and its substitutes on bones remain limited. In this study, we administered different concentrations of PFOA, GenX, and PFO3TDA via gavage to 3-week-old male BALB/C mice for four weeks. X-ray and micro-CT scans revealed shortening of the femur and tibia and significant reduction in bone density. Additionally, PFOA, GenX, and PFO3TDA promoted osteoblast senescence and impaired osteogenic capabilities. This was characterized by a decrease in the expression of osteogenesis-related genes (OCN, ALP, Runx2, etc.) and an increase in the expression of aging and inflammation-related factors (p16INK4a, P21, MMP3, etc). Furthermore, RNA sequencing revealed activation of the ferroptosis pathway in PFOA-treated osteoblasts, characterized by notable lipid peroxidation and excessive iron accumulation. Finally, by inhibiting the ferroptosis pathway with ferrostatin-1 (Fer-1), we effectively alleviated the senescence of MC3T3-E1 cells treated with PFOA, GenX, and PFO3TDA, and improved their osteogenic capabilities. Therefore, our study provides a new therapeutic insight into the impact of PFOA and its substitutes on bone growth and development.

Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal-Organic Frameworks: Application to the Adsorptive Separation of C3 Components.

The shape and topology of pores have significant impacts on the gas storage properties of nanoporous materials. Metal-organic frameworks (MOFs) are ideal materials with which to tailor to the needs of specific applications, due to properties such as their tunable structure and high specific surface area. It is, therefore, particularly important to develop descriptors that accurately identify the topological features of MOF pores. In this work, a topological data analysis method was used to develop a topological descriptor, based on the pore topology, which was combined with the Extreme Gradient Boosting (XGBoost) algorithm to predict the adsorption performance of MOFs for methane/ethane/propane. The final results show that this descriptor can accurately predict the performance of MOFs, and the introduction of the topological descriptor also significantly improves the accuracy of the model, resulting in an increase of up to 17.55% in the R2 value of the model and a decrease of up to 46.1% in the RMSE, compared to commonly used models that are based on the structural descriptor. The results of this study contribute to a deeper understanding of the relationship between the performance and structure of MOFs and provide useful guidelines and strategies for the design of high-performance separation materials.

Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.

A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.

Risk assessment-based verification of the CertiPURTM limit values for toluene diamine and methylene dianiline in flexible polyurethane foam.

Flexible polyurethane foams (PUF) are used in many consumer products. PUF may contain trace levels of aromatic diamine impurities that could represent a potential health risk. The risk associated with sleeping on a PUF mattress was evaluated. Toxicity benchmarks for sensitization and non-cancer endpoints were derived from the respective points-of-departure using standard assessment factors. For the cancer endpoints, toxicity benchmarks were derived from the 25th-percentile values of animal studies. Recently published emission and migration data allowed to link exposure with the CertiPURTM voluntary quality limits of ≤5 mg.kg-1 for 2,4-toluene diamine and 4,4'-methylene dianiline in PUF. Using conservative exposure scenarios, lifetime-average daily internal doses from the combined inhalation and dermal exposures were calculated. Margins of safety for non-cancer and sensitization endpoints were >104. The theoretical excess cancer risk was ≤1.5 × 10-7. It is concluded that sleeping on a mattress that satisfies the CertiPUR limit value does not pose undue risk to consumers.

Innovative biochemisurgical treatment for stabilisation of an end-stage chronic wound in a complex vascular compromized patient.

INTRODUCTION: Treating advanced peripheral arterial occlusive disease (e.g. PAOD IV) poses a significant challenge, as conventional treatments quite often fall short at this stage. However, a range of interventions can be considered to postpone amputation. This study presents an example of advanced stage of Peripheral Artery Occlusive Disease (PAOD) stage IV, encompassing a history of a high thigh amputation on the left side, coupled with pronounced wound healing disorders. PRESENTATION OF CASE: Our patient, 55 years old, smoker and ASA Class III is in a left sided above-the knee-amputation situation. He presented to our outpatient clinic with blistering in the stump area, caused by non-proportinate pressure from the prosthesis. With an emerging septic course and advanced peripheral arterial occlusive disease (PAOD) at Fontaine class IV, revascularization was unfeasible in the left iliac artery axis and groin arteries. Additionally, a stage PAOD IV presents itself with poorly healing wounds on the right side which our patient still uses to support his transfers in and out bed and his wheelchair. Multiple surgical stump revisions and femur shortenings and diverse wound treatments were performed all were unsatisfying for patient and practitioners. We introduced a novel biochemisurgical treatment in our teaching hospital. DISCUSSION: Desiccating-agent-A is an innovative dehydrating agent with potent desiccating characteristics upon application to organic substances. Its formulation involves blending 83% methane sulfonic acid with proton acceptors and dimethyl sulfoxide, as outlined in patent application. The case description results in an illustrated follow up period of 16 months and is presented in line with the recommendations of the consensus-based surgical case reporting guideline development. CONCLUSION: The goal of achieving a secondary healing trend is to establish stability within the wound area or achieve complete healing. This endeavor becomes particularly intricate when severe blood circulation compromise exists. Nonetheless, progress in wound treatment measures has made it feasible to achieve this aim by fostering the formation of dry and clean necrotic tissue. This dry and clean wound is now manageable in a patient's home situation, allowing for effective care and a better chance at preventing further severe complications.

Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.

Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from Topological Data Analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of Topological Data Analysis, Mapper results are highly impacted by parameter selection. Given that non-invasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding "true" state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter-exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper on any dataset.

Self-emulsifying drug delivery systems (SEDDS): How organic solvent release governs the fate of their cargo.

Organic solvents are commonly used in self-emulsifying drug delivery systems (SEDDS) to increase payloads of orally administered poorly soluble drugs. Since such solvents are released to a varying extent after emulsification, depending on their hydrophilic nature, they have a substantial impact on the cargo. To investigate this impact in detail, quercetin and curcumin as model drugs were incorporated in SEDDS comprising organic solvents (SEDDS-solvent) of logP < 2 and > 2. SEDDS were characterized regarding size, payload, emulsification time and solvent release. The effect of solvent release on the solubility of these drugs was determined. Preconcentrates of SEDDS-solventlogP < 2 emulsified more rapidly (< 1.5 min) forming smaller droplets than SEDDS-solventlogP > 2. Although, SEDDS-solventlogP < 2 preconcentrates provided higher quercetin solubility than the latter, a more pronounced solvent release caused a more rapid quercetin precipitation after emulsification (1.5 versus 4 h). In contrast, the more lipophilic curcumin was not affected by solvent release at all. Particularly, SEDDS-solventlogP < 2 preconcentrates provided high drug payloads without showing precipitation after emulsification. According to these results, the fate of moderate lipophilic drugs such as quercetin is governed by the release of solvent, whereas more lipophilic drugs such as curcumin remain inside the oily phase of SEDDS even when the solvent is released.

Gourmet Table Salts: The Mineral Composition Showdown.

Table salts with their specialty flake size, textures, flavors, and colors can be considered a gastronomy niche food already increasing in demand worldwide. Being unrefined, they can contain trace elements potentially both healthy and toxic. In this study, 12 mineral elements (Al, Ca, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, and Zn) in 10 different salts commercially available in southern Italy namely, Atlantic grey, Baule volante, Guerande, Hawaiian pink, Hawaiian black, Himalayan pink, Maldon, Mozia, Persian blue, and smoked salts were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) and thermal decomposition amalgamation-atomic absorption spectrophotometry (TDA-AAS). The concentration of mineral elements was variable according to the type of salt and its geographical origin. Co, Cr, Cu, Hg, Pb, and Se levels were tolerable and Al, Ca, Fe, Mn, Ni, and Zn ranged significantly among the samples. Persian Blue and Atlantic Grey salts showed elevated levels of Fe and Zn; their intake can be helpful in some specific conditions. Nevertheless, Ni levels were high in Persian Blue and Smoked salts. Pb exceeded the maximum level in all samples. Additional monitoring analyses of mineral contents in table salts are recommended for human health.

A topological data analysis study on murine pulmonary arterial trees with pulmonary hypertension.

Pulmonary hypertension (PH), defined by a mean pulmonary arterial blood pressure above 20 mmHg in the main pulmonary artery, is a cardiovascular disease impacting the pulmonary vasculature. PH is accompanied by chronic vascular remodeling, wherein vessels become stiffer, large vessels dilate, and smaller vessels constrict. Some types of PH, including hypoxia-induced PH (HPH), also lead to microvascular rarefaction. This study analyzes the change in pulmonary arterial morphometry in the presence of HPH using novel methods from topological data analysis (TDA). We employ persistent homology to quantify arterial morphometry for control and HPH mice characterizing normalized arterial trees extracted from micro-computed tomography (micro-CT) images. We normalize generated trees using three pruning algorithms before comparing the topology of control and HPH trees. This proof-of-concept study shows that the pruning method affects the spatial tree statistics and complexity. We find that HPH trees are stiffer than control trees but have more branches and a higher depth. Relative directional complexities are lower in HPH animals in the right, ventral, and posterior directions. For the radius pruned trees, this difference is more significant at lower perfusion pressures enabling analysis of remodeling of larger vessels. At higher pressures, the arterial networks include more distal vessels. Results show that the right, ventral, and posterior relative directional complexities increase in HPH trees, indicating the remodeling of distal vessels in these directions. Strahler order pruning enables us to generate trees of comparable size, and results, at all pressure, show that HPH trees have lower complexity than the control trees. Our analysis is based on data from 6 animals (3 control and 3 HPH mice), and even though our analysis is performed in a small dataset, this study provides a framework and proof-of-concept for analyzing properties of biological trees using tools from Topological Data Analysis (TDA). Findings derived from this study bring us a step closer to extracting relevant information for quantifying remodeling in HPH.

The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI.

There is increasing interest in investigating brain function based on functional connectivity networks (FCN) obtained from resting-state functional magnetic resonance imaging (fMRI). FCNs, typically obtained using measures of time series association such as Pearson's correlation, are sensitive to data acquisition parameters such as sampling period. This introduces non-neural variability in data pooled from different acquisition protocols and MRI scanners, negating the advantages of larger sample sizes in pooled data. To address this, we hypothesize that the topology or shape of brain networks must be preserved irrespective of how densely it is sampled, and metrics which capture this topology may be statistically similar across sampling periods, thereby alleviating this source of non-neural variability. Accordingly, we present an end-to-end pipeline that uses persistent homology (PH), a branch of topological data analysis, to demonstrate similarity across FCNs acquired at different temporal sampling periods. PH, as a technique, extracts topological features by capturing the network organization across all continuous threshold values, as opposed to graph theoretic methods, which fix a discrete network topology by thresholding the connectivity matrix. The extracted topological features are encoded in the form of persistent diagrams that can be compared against one another using the earth-moving metric, also popularly known as the Wasserstein distance. We extract topological features from three data cohorts, each acquired at different temporal sampling periods and demonstrate that these features are statistically the same, hence, empirically showing that PH may be robust to changes in data acquisition parameters such as sampling period.

Temporal Mapper: Transition networks in simulated and real neural dynamics.

Characterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method-Temporal Mapper-built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects' behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics.

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort.

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics. Using Topological Data Analysis based Mapper approach, we construct the precise dynamic mapping (PDM) for each participant. To reveal the relationship between PDM and canonical functional representation of the resting brain, we annotated the graph using relative activation proportion of a set of large-scale resting-state networks (RSNs) and assigned the single brain volume to corresponding RSN-dominant or a hub state (not any RSN was dominant). Our results show that (i) low-frequency rTMS could induce changed temporal evolution of brain states; (ii) rTMS didn't alter the hub-periphery configurations underlined resting-state brain dynamics; and (iii) the rTMS effects on brain dynamics differ across the left frontal and occipital lobe. In conclusion, low-frequency rTMS significantly alters the individual temporo-spatial dynamics, and our finding further suggested a potential target-dependent alteration of brain dynamics. This work provides a new perspective to comprehend the heterogeneous effect of rTMS.

A lights-out forensic DNA analysis workflow for no-suspect crime.

An automated system of DNA profile processing (termed a 'lights-out' workflow) was trialled for no-suspect cases over a three-month period at Forensic Science SA (FSSA). The lights-out workflow utilised automated DNA profile reading using the neural network reading feature in FaSTR™ DNA with no analytical threshold. The profile information from FaSTR™ DNA was then processed in STRmix™ using a top-down analysis and automatically compared to a de-identified South Australian searchable DNA database. Computer scripts were used to generate link reports and upload reports and these were compared to the links and uploads that were obtained for the cases during their standard processing within the laboratory. The results of the lights-out workflow was an increase in both uploads and links compared to the standard workflow, with minimal adventitious links or erroneous uploads. Overall, the proof-of-concept study shows the potential for using automated DNA profile reading and top-down analysis to improve workflow efficiency in a no-suspect workflow.

Molecular Design of Luminescent Complexes of Eu(III): What Can We Learn from the Ligands.

The luminescent metal-organic complexes of rare earth metals are advanced materials with wide application potential in chemistry, biology, and medicine. The luminescence of these materials is due to a rare photophysical phenomenon called antenna effect, in which the excited ligand transmits its energy to the emitting levels of the metal. However, despite the attractive photophysical properties and the intriguing from a fundamental point of view antenna effect, the theoretical molecular design of new luminescent metal-organic complexes of rare earth metals is relatively limited. Our computational study aims to contribute in this direction, and we model the excited state properties of four new phenanthroline-based complexes of Eu(III) using the TD-DFT/TDA approach. The general formula of the complexes is EuL2A3, where L is a phenanthroline with -2-CH3O-C6H4, -2-HO-C6H4, -C6H5 or -O-C6H5 substituent at position 2 and A is Cl- or NO3-. The antenna effect in all newly proposed complexes is estimated as viable and is expected to possess luminescent properties. The relationship between the electronic properties of the isolated ligands and the luminescent properties of the complexes is explored in detail. Qualitative and quantitative models are derived to interpret the ligand-to-complex relation, and the results are benchmarked with respect to available experimental data. Based on the derived model and common molecular design criteria for efficient antenna ligands, we choose phenanthroline with -O-C6H5 substituent to perform complexation with Eu(III) in the presence of NO3¯. Experimental results for the newly synthesized Eu(III) complex are reported with a luminescent quantum yield of about 24% in acetonitrile. The study demonstrates the potential of low-cost computational models for discovering metal-organic luminescent materials.

Describing and Modeling Rough Composites Surfaces by Using Topological Data Analysis and Fractional Brownian Motion.

Many composite manufacturing processes employ the consolidation of pre-impregnated preforms. However, in order to obtain adequate performance of the formed part, intimate contact and molecular diffusion across the different composites' preform layers must be ensured. The latter takes place as soon as the intimate contact occurs and the temperature remains high enough during the molecular reptation characteristic time. The former, in turn, depends on the applied compression force, the temperature and the composite rheology, which, during the processing, induce the flow of asperities, promoting the intimate contact. Thus, the initial roughness and its evolution during the process, become critical factors in the composite consolidation. Processing optimization and control are needed for an adequate model, enabling it to infer the consolidation degree from the material and process features. The parameters associated with the process are easily identifiable and measurable (e.g., temperature, compression force, process time, ⋯). The ones concerning the materials are also accessible; however, describing the surface roughness remains an issue. Usual statistical descriptors are too poor and, moreover, they are too far from the involved physics. The present paper focuses on the use of advanced descriptors out-performing usual statistical descriptors, in particular those based on the use of homology persistence (at the heart of the so-called topological data analysis-TDA), and their connection with fractional Brownian surfaces. The latter constitutes a performance surface generator able to represent the surface evolution all along the consolidation process, as the present paper emphasizes.

Quality Control for the Target Decoy Approach for Peptide Identification.

Reliable peptide identification is key in mass spectrometry (MS) based proteomics. To this end, the target decoy approach (TDA) has become the cornerstone for extracting a set of reliable peptide-to-spectrum matches (PSMs) that will be used in downstream analysis. Indeed, TDA is now the default method to estimate the false discovery rate (FDR) for a given set of PSMs, and users typically view it as a universal solution for assessing the FDR in the peptide identification step. However, the TDA also relies on a minimal set of assumptions, which are typically never verified in practice. We argue that a violation of these assumptions can lead to poor FDR control, which can be detrimental to any downstream data analysis. We here therefore first clearly spell out these TDA assumptions, and introduce TargetDecoy, a Bioconductor package with all the necessary functionality to control the TDA quality and its underlying assumptions for a given set of PSMs.