Novel Method For Identification Research Articles

A novel method for identification of disturbance from surface coal mining using all available Landsat data in the GEE platform

Coal mining has contributed to a solid and stable global economy. Surface mining and underground mining are the principal methods for extraction of coal worldwide and given the developments in mining technology and equipment, these methods are increasingly generating more and more coal. Surface mining is also a subject that cannot be overlooked in the context of sustainable development because amongst human activities it causes comparatively strong land disturbance. Much of the damage and assessment of land and ecosystems from surface mining occurs at the site, and at the regional scale; hence access to the mining data is limited. Moreover, it is difficult for remote sensing to extract information on larger-scale areas with high precision. As a consequence, a frequency index of open coal mining can be created by merging the vast high-resolution images acquired by Landsat sensors with the spectral properties of open coal scraped from the overburden layer. Such indices and morphological principles can be used to develop strategies for quickly identifying the open coal boundaries in a vast area without mine-related information. In addition, LandTrendr may be used to reconstruct the spatial and temporal processes of surface disturbance and remediation caused by regional surface coal mining to identify surface mining disturbance and reclamation. Seven typical mining locations worldwide were selected to test and confirm the proposed method's practicality and robustness, including delivering accurate findings based on randomly testing sample points and comparing the mature data outputs. The results showed that: (1) the overall accuracy of the open coal areas identified by the OCFI (Open Coal Frequency Index) was over 85 % (range 0.85–0.96). In all study areas, the accuracy of recognizing disturbance and reclamation events was greater than 76 %. (2) The surface mining disturbed 3046.42 km2 of land and 1487.39 km2 was reclaimed in the seven mining areas investigated between 1986 and 2018, resulting in a reclamation rate of 48.82 %. The proposed method is more accurate and faster in identifying open coal locations than the current commonly used land cover datasets based on comparisons with other data products. The approach can extract surface coal mining in the absence of mining data and offer precise boundary data for mining research, which can be extremely useful for extending mining operations.

Normalization methods for RT‐qPCR assessment of circulating microRNAs in Alzheimer’s and other aging‐related diseases

AbstractBackgroundCirculating miRNAs in blood plasma present significant potential for use as minimally‐invasive biomarkers for diagnosis of aging‐related diseases, such as cancer or Alzheimer’s disease (AD). The most sensitive and inexpensive analysis method of circulating miRNA is RT‐qPCR. However, lack of RT‐qPCR standardisation is one of the major causes of data inconsistency across different studies and it hinders progress towards widespread implementation of miRNA biomarkers in clinical use. Normalisation of RT‐qPCR data to stably expressed miRNAs is a key step in data analysis. Current methods of identifying optimal normalisers are lacking in their evaluation of the stability of normalisers and their combinations in aging populations. To address these needs, we created a novel, transparent, method for selecting optimal normalisers in aging populations, using the Python programming language (v. 3.7.3).MethodOur method uses the Python packages: pandas (v. 1.2.3), numpy (v. 1.17.1), scipy (v. 1.3.1). The method uses three measures to evaluate the miRNA Cq values for potential normalisers: Kolmogorov‐Smirnov score (KS‐score), average displacement from a mean of zero and mean standard deviation. In each case, the comparators are two distinct biological groups of interest (such as AD and cognitively normal controls). Before comparison, the group of potential normalisers were normalised according to the log2(2−ΔΔCq) method. The miRNA data were collected from Polish subjects including 44 AD patients and 70 similar‐aged, cognitively normal, controls). Circulating miRNA molecules were isolated from plasma, followed by RT‐qPCR with a selection of 7 miRNAs tested as potential normalisers, along with suitable controls for mRNA isolation and reverse transcription.ResultWe provide a novel method for identification of optimal normalisers with the advantages of: assessments of a greater number of combined potential normalisers (7 in this study), transparency of decision making and the ability for end users to weight that decision making according to their judgment of relative importance.ConclusionWe recommend assessment of plasma miRNA levels in an aging population employing a novel set of normalisers.Work supported by the Polish National Science Centre grant OPUS 2018/29/B/NZ7/02757and by the EU Horizon2020 FETOPEN grant no 737390 (ArrestAD).

Genome instability-related LINC02577, LINC01133 and AC107464.2 are lncRNA prognostic markers correlated with immune microenvironment in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is a leading cause of malignancy-related deaths worldwide, and the efficacy of immunotherapy on PAAD is limited. Studies report that long non-coding RNAs (lncRNAs) play an important role in modulating genomic instability and immunotherapy. However, the identification of genome instability-related lncRNAs and their clinical significance has not been investigated in PAAD.MethodsThe current study developed a computational framework for mutation hypothesis based on lncRNA expression profile and somatic mutation spectrum in pancreatic adenocarcinoma genome. We explored the potential of GInLncRNAs(genome instability-related lncRNAs) through co-expression analysis and function enrichment analysis. We further analyzed GInLncRNAs by Cox regression and used the results to construct a prognostic lncRNA signature. Finally, we analyzed the relationship between GILncSig (genomic instability derived 3-lncRNA signature) and immunotherapy.ResultsA GILncSig was developed using bioinformatics analyses. It could divide patients into high-risk and low-risk groups, and there was a significant difference in OS between the two groups. In addition, GILncSig was associated with genome mutation rate in pancreatic adenocarcinoma, indicating its potential value as a marker for genomic instability. The GILncSig accurately grouped wild type patients of KRAS into two risk groups. The prognosis of the low-risk group was significantly improved. GILncSig was significantly correlated with the level of immune cell infiltration and immune checkpoint.ConclusionsIn summary, the current study provides a basis for further studies on the role of lncRNA in genomic instability and immunotherapy. The study provides a novel method for identification of cancer biomarkers related to genomic instability and immunotherapy.

A Transcriptomic Analysis to Identify Prevalent lncRNAs in Gingivobuccal Oral Cancer

Objective: Gingivobuccal cancer is a subtype of oral cancer, prevalent in developing countries where the use of tobacco and Areca (betel) nut is rampant. This study aims to search through the publically available NCBI data to find comparisons between the normal and cancer-affected transcripts of RNA, which are molecules that copied the genetic information from DNA and that will be “read” as instructions to produce other molecules in the body. Using bioinformatic tools, we analyzed the variations in the transcripts and examined how they were correlated with other molecular processes in the body. We specifically investigated the role of “long noncoding RNAs (lncRNAs)” because it is speculated that they play an active role in the causality of cancer. Our analysis found a correlation of CTA-384D8.35 lncRNA with gingivobuccal cancer after analyzing the Hazard Ratio using the Cox Proportional-Hazard Model and is expected to pave the way for future studies. Methods: In this study, transcriptomic analysis was performed on a publicly available gingivobuccal cancer dataset obtained from NCBI. We developed a novel method for identification of annotated lncRNAs, which helped us select and correlate the lncRNA transcripts with the gingivobuccal cancer disease. Furthermore, the results were validated using the GEPIA2 tool for integrated analysis of co-lncRNA and GEPIA databases. Findings: From 24 RNA-Seq samples (12 normal samples and 12 tumor samples), a total of 16,065 potential lncRNA transcripts were recognized. From these 16,065 lncRNA transcripts, 1201 lncRNA transcripts were further filtered out based on a threshold adjusted p value of <0.01. We were able to for the first time identify these 1201 lncRNA transcripts, which were not annotated for gingivobuccal squamous cell carcinoma previously. Integrated analysis of co-lncRNA and GEPIA databases revealed that a total of 11 lncRNAs were upregulated, whereas 29 lncRNAs were downregulated. Furthermore, CTA-384D8.35 showed a significant correlation with disease-free survival time in HNSC. CTA-384D8.35 which is downregulated in gingivobuccal cancer has been correlated with tumor stage, metastasis, cancer status, clinical stage, and grade. Novelty: In this report we are for the first time correlating the CTA-384D8.35 lncRNA with the gingivobuccal cancer disease with the help of statistical and survival analysis. Furthermore,1201 lncRNA transcripts, which have previously not been annotated for gingivobuccal squamous cell carcinoma, were identified in this study based on a threshold adjusted p value of <0.01. Finally we have also introduced a novel method of identifying annotated lncRNAs in a disease as well as improved upon the NGS data analysis pipeline by determining the strandness of the raw RNASeq data. Differential expression of lncRNAs could possibly play a significant role in carcinogenesis. Therefore the identification of lncRNAs gains priority, which are speculated to play a role in the mechanism and formation of various forms of cancer. For identification and analysis of lncRNAs, a comparison of their expression in normal and affected tissues is likely to confirm their involvement in the disease or the lack thereof. The introduction of a novel methodology along with the identification of CTA-384D8.35 lncRNA paves the way for further analysis pertaining to lncRNAs as prognostic or diagnostic biomarkers. Since CTA-384D8.35 lncRNA has previously been correlated with a number of cancer hallmarks, it gains significant traction to be considered as an important aspect in the biomarker identification pertaining to gingivobuccal cancer. This study has reported for the first time the lncRNAs that are prevalent in gingivobuccal squamous cell carcinoma. Keywords: Gingivobuccal cancer; lncRNA; oral cancer; epigenetics; NGS data analysis

A Novel Method for Identification and Classification of Indian Vegetables Using Random Forest Algorithm

It is only the farmer who faithfully plants seeds in the spring, who reaps a harvest in the autumn. The goal of this study is to create a useful classification method using the Random Forest (RF) algorithm. Different crops, namely brinjal, carrot, and onion, were examined, and many features have been derived dependent on the design, color, and texture. A preparation stage is described that uses image analysis to enhance the vegetables images dataset in order to minimize their color index. The features of the vegetable images are then retrieved. Finally, Random Forests (RF), a newly generated pattern recognition method, used in the vegetable’s classification process. The proposed method achieved higher accuracy in terms of identification and classification of the vegetables

Sustainable development: Is an impartial equilibrium between human needs and natural resources existent?

Social-economic plans are originally illusive under growing repetition/activity/duration of unexpected atypical weather phenomena recently occurred. The paper discusses conceptually this aspect of the sustainable development taking into account the limits of good-for-human-activities reproduction of a local seasonal weather pattern depending on directions of the dynamics evolution in the corresponding climate system. The discussion is developed from the fundamentals of the theory of nonlinear dynamics. This view clarifies indicators to existence/absence of sustainable development grounded on the insuperable physical laws. The corresponding novel method of identification of these indicators (denoted by the method of transients) is formulated and potentiality of its realization is investigated by processing the meteorological data of temperature observation. It is illustrated that a way to consider soundly non-stationary nonlinear dynamics opens once the perverted partition onto “weather” and “climate” is eliminated and nonlinear, regulatory and inertial properties of dynamical processes in local climate systems become analyzed integrally. Finally the conclusions are summarized.

A Novel Method for Identification of Glutarylation Sites Combining Borderline-SMOTE With Tomek Links Technique in Imbalanced Data.

Glutarylation is a type of post-translational modification that occurs on lysine residues. It plays an irreplaceable role in various cellular functions. Therefore, identification of glutarylation sites is significant for understanding the molecular mechanism of glutarylation. In this study, we proposed a method named DEXGB_Glu to identify lysine glutarylation sites using XGBoost as classifier which was optimized by differential evolution algorithm. Aiming at the imbalance between positive samples and negative samples, Borderline-SMOTE method was employed to synthesize positive samples, increasing their amount equal to negative samples. Then, Tomek links technique was applied to filter out noise data. Analysis of this method and its results showed that differential evolution algorithm obviously improved the performance and the combination of Borderline-SMOTE and Tomek links effectively solved the imbalance between positive samples and negative samples. Finally, the performance of this method was much better than other methods in prediction of glutarylation sites. The data and code are available on https://github.com/ningq669/DEXGB_Glu.

A novel method for identification of synchronous resonance frequency of blades based on random arrangement of tip-timing probes

Blade tip-timing (BTT) signal suffers from the undersampling feature, which causes the high-frequency information to be aliased to the low-frequency band, especially the aliasing problem of the synchronous resonance frequency more serious. In order to realize the frequency identification of undersampled signals in synchronous resonance, a novel multiscale cyclic-reverse optimized arrangement (MC-ROA) method based on the random arrangement of tip-timing probes is proposed in this paper. Firstly, a sparse model for frequency recovery is built based on the link between the known undersampled signals and the unknown original signals in the frequency domain, and the subspace pursuit (SP) algorithm of compressive sensing (CS) is used for frequency recovery. Then, under the random arrangement of probes, the number of ideal probes is adjusted at multiple scales and the datum probes are selected cyclically to obtain multiple sets of solutions. Finally, the target frequency weights are used to overcome the limitations of the restricted isometry property (RIP) of the parameterized compression matrix to determine the optimal recovery frequency. The BTT experiments are given to verify the effectiveness of the proposed method.

Raman Spectroscopy-A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings.

Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful—quick, non-invasive, and non-destructive —tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.

Aortic Root Downward Movement as a Novel Method for Identification of an Adequately Performed Valsalva Maneuver to Detect Patent Foramen Ovale during Transesophageal Echocardiograph.

The Valsalva maneuver (VM) is the most sensitive auxiliary method for the detection of patent foramen ovale (PFO), but it is difficult to assess whether the maneuver is adequately performed during transesophageal echocardiography (TEE). In this study, we tried to use aortic root downward movement as a novel method for judging whether VM was adequate or not, and to evaluate whether this novel method can increase the sensitivity of detecting PFO. A total of 224 patients with clinically suspected PFO were enrolled in this study. These patients were injected with activated normal saline to detect the right-to-left shunt (RLS), in the following three conditions: contrast-enhanced transthoracic echocardiography under adequate VM (AVM cTTE), contrast-enhanced TEE under non-adequate VM (non-AVM cTEE), and cTEE under adequate VM (AVM cTEE). A novel method in which the aorta root moves downward (movement range ≥16 mm) in the cTEE judged whether AVM was performed. The PFO detection rate and sensitivity of AVM cTEE were better than that of non-AVM cTEE (detection rate: 108 PFOs [48.2%] vs. 86 PFOs [38.4%], p = 0.036; sensitivity: 100% vs. 79.6%). Among AVM cTTE, non-AVM cTEE, and AVM cTEE, the RLS grade evaluation results were inconsistent, with significant differences (p < 0.05). Non-AVM cTEE had RLS underestimation or false negatives. Compared with non-AVM cTEE, AVM cTEE and AVM cTTE had better consistency in evaluating PFO RLS (kappa value = 0.675). Aortic root downward movement could be used as a novel method for judging the effectiveness of VM, which is critical for the detection of PFO in cTEE. Concerning effectiveness and convenience, this method should be promoted during the clinical detection of PFO.

Novel method for identification of individualized resonant frequencies for treatment of Major Depressive Disorder (MDD) using repetitive Transcranial Magnetic Stimulation (rTMS): A proof-of-concept study

BackgroundRepetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD), but therapeutic benefit is highly variable. Clinical improvement is related to changes in brain circuits, which have preferred resonant frequencies (RFs) and vary across individuals. ObjectiveWe developed a novel rTMS-electroencephalography (rTMS-EEG) interrogation paradigm to identify RFs using the association of power/connectivity measures with symptom severity and treatment outcome. Methods35 subjects underwent rTMS interrogation at 71 frequencies ranging from 3 to 17 Hz administered to left dorsolateral prefrontal cortex (DLPFC). rTMS-EEG was used to assess resonance in oscillatory power/connectivity changes (phase coherence [PC], envelope correlation [EC], and spectral correlation coefficient [SCC]) after each frequency. Multiple regression was used to detect relationships between 10 Hz resonance and baseline symptoms as well as clinical improvement after 10 sessions of 10 Hz rTMS treatment. ResultsBaseline symptom severity was significantly associated with SCC resonance in left sensorimotor (SM; p < 0.0004), PC resonance in fronto-parietal (p = 0.001), and EC resonance in centro-posterior channels (p = 0.002). Subjects significantly improved with 10 sessions of rTMS treatment. Only decreased SCC SM resonance was significantly associated with clinical improvement (r = 0.35, p = 0.04). Subjects for whom 10 Hz SM SCC was highly ranked as an RF among all stimulation frequencies had better outcomes from 10 Hz treatment. ConclusionsResonance of 10 Hz stimulation measured using SCC correlated with both symptom severity and improvement with 10 Hz rTMS treatment. Research should determine whether this interrogation paradigm can identify individualized rTMS treatment frequencies.

Development of a Novel Method for Identification of Alaria alata Mesocercariae by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry.

Alaria (A.) alata mesocercariae (AM) have increasingly appeared as incidental findings during the mandatory inspection of wild boars for Trichinella in many European countries. An Alaria spp.-specific PCR is available for the identification of AM; however, it is time- and cost-intensive. Therefore, we propose a rapid and cost-efficient MALDI-TOF assay for the identification of AM in wild boar meat that can be applied in routine diagnostics. In this study, a fast and methodologically simple protocol for the protein extraction of AM from different host species in different countries was established, and an AM-specific reference spectra database was created as part of the ongoing development of an existing Trichinella spp. database. A formic acid protein extraction was performed after pooling 10 AM from the same host individual. In total, 61 main spectra profiles (MSPs) from different host individuals were stored in an AM-specific MSP library. The cluster analysis of these 61 MSPs indicated a possible variation within the A. alata species with a tentative association with the geographical origin of the host, but not the host species. This MALDI-TOF assay allows for a fast verification of the AM isolates, which is the next step in the development of a universal database for the identification of several parasites isolated from meat.

Development of Latent Fingerprints on Non-Porous Surface with Fluorescent Dye Based Small Particle Reagent

Fingerprints are a useful source of evidence in establishing the identity of the person and hold large importance in crime scene investigations. A diverse range of physical and chemical methods are available for the development of latent fingerprints, but limited work is done on detection of prints from wet non-porous surfaces. The study aimed at developing a novel method for identification of latent fingerprints from such a type of surface. Small Particle Reagent was prepared using non-toxic chemicals and was proven to be effective in detecting latent fingerprints on Aluminum Foil. Fluorescent component, Basic Yellow-40 dye was used in the formulation. The Shelf life of the SPR solution was also considered an important factor in the study and was found to be 25 days.

Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy.

The identification and preservation of parathyroid glands (PGs) is a major issue in thyroidectomy. The PG is particularly difficult to distinguish from the surrounding tissues. Accidental damage or removal of the PG may result in temporary or permanent postoperative hypoparathyroidism and hypocalcemia. In this study, a novel method for identification of the PG was proposed based on laser-induced breakdown spectroscopy (LIBS) for the first time. LIBS spectra were collected from the smear samples of PG and non-parathyroid gland (NPG) tissues (thyroid and neck lymph node) of rabbits. The emission lines (related to K, Na, Ca, N, O, CN, C2, etc.) observed in LIBS spectra were ranked and selected based on the important weight calculated by random forest (RF). Three machine learning algorithms were used as classifiers to distinguish PGs from NPGs. The artificial neural network classifier provided the best classification performance. The results demonstrated that LIBS can be adopted to discriminate between smear samples of PG and NPG, and it has a potential in intra-operative identification of PGs.

Identification of the insertion site of transgenic DNA based on cyclization of the target gene with the flanking sequence and nested inverse PCR

We demonstrate a novel method for identification of the insertion site of transgenic DNA by construction a circular DNA containing part of the transgenic DNA (tDNA) and its flanking sequence, which would be different from those in the native genomic DNA (gDNA). Using follistatin (FST) as a model of tDNA, we cleaved the sequence of FST gene and the adjacent sequence with a proper combination of restriction endonuclease and nicking endonuclease and isolated the resultant fragments containing the target FST gene from the gDNA background. Two types of previously unknown ligation property of T7 DNA ligase were disclosed in our experiments. One is non-templated single-stranded overhang ligation capability; the other one is the successful ligation of 3’-overhang and 5’-recessed ends in the presence of two mismatched base pairs. By virtue of these efficient ligation reactions, we successfully construct the circular DNA with high cyclization yield. Further coupling with nested inverse PCR, the method enabled identification of transgenic follistatin a (fsta) cDNA in zebrafish embryonic DNA. The established method provides a practical tool to determine the insertion site of tDNA with a sensitivity of 103 copies in 1.0 μg of gDNA and high specificity. It may be further extended for gene doping detection and assessment of the safety of gene therapy and genetically modified organisms.

A new method to estimate the lifetime of long‐life product categories

AbstractIncreased recycling and reuse rates are a central part of the objectives laid out by the COP21. Nonetheless, the practical implementation of what has been called the circular economy, as well as its true potential, are not easily established. This is because the impact and implementation time scales of any intervention depend on knowing the lifetime of products, which is frequently unknown. This is particularly true in construction, responsible for 39% of worldwide emissions, 11% of which are embodied. Most material flow analysis (MFA) models will simply assume a range of plausible life expectancies when bottom‐up data are lacking. In this work, we propose a novel method of identification using the high quality but highly aggregated trade data available and use it to establish a “mortality curve” for buildings and other long‐lasting products. This identification method is intended to provide more reliable inputs to existing MFA models. It is widely applicable because of the general availability of the underlying data. Using it on United Kingdom trade data, we identify product classes at 1 year for packaging/home scrap, 1 to around 10 years for vehicles/equipment, and around 50 years for construction. The identification approach was then validated by using classical approaches using bottom‐up data for vehicles.

DRACP: a novel method for identification of anticancer peptides

BackgroundMillions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs.ResultsFirstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets.ConclusionWe developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.

Partial Discharge Detection Framework Employing Spectral Analysis of Horizontal Visibility Graph

In this article, we propose a novel method for identification of partial discharge (PD) signals employing horizontal visibility graph spectral analysis (HVGSA). Horizontal visibility graph (HVG) converts a time series into an undirected graphical network while preserving its temporal characteristics. In the present contribution, PD signals of single and multiple void discharges were measured using a high frequency current transformer (HFCT) sensor and subsequently transformed to undirected graphical networks using HVG. From the HVG of the PD signals, several spectral graph features were extracted for recognition of PD signals. The extracted features were further subjected to analysis of variance (ANOVA) test to examine their statistical significance and followed by FDR correction to select the most discriminative features. The PD signals were classified using three machine learning classifiers. We also investigated the performance of our method by varying penetrable distance- an important parameter of HVG for robust detection of PD signals in presence of noise. Investigations revealed that very high recognition accuracy has been obtained in discriminating different PD signals using HVGSA. Interestingly, we also observed that in the presence of noise, detection accuracy can be improved significantly by increasing the penetrable distance within a permissible range. Finally, an optimum value of penetrable distance parameter has been determined for accurate recognition of PD signals at different noise levels. The proposed technique can be applied for real-life PD signal detection for insulation condition monitoring.

A novel approach for increasing the traceability of 3D printed medical products

Abstract According to the European Medical Device Directive (MDR (EU) 2017/745), medical products of all risk classes are obliged to be labelled with Unique Device Identification Codes (UDI). In order to meet the requirements of the MDR, a novel method of identification for additively manufactured metallic products was developed at the Fraunhofer Institute for Machine Tools and Forming Technology (IWU). The method provides a product traceability within its production process and during the whole product lifespan, and also functions as a counterfeit protection. This paper shows the integration of a unique identification during an additive manufacturing process for a titanium part. The readout of the integrated identification was executed using ultrasonic and eddy current methods, as well as micro computed tomography. Obtained results are discussed in the paper.

An inverse Eikonal method for identifying ventricular activation sequences from epicardial activation maps

A key mechanism controlling cardiac function is the electrical activation sequence of the heart's main pumping chambers termed the ventricles. As such, personalization of the ventricular activation sequences is of pivotal importance for the clinical utility of computational models of cardiac electrophysiology. However, a direct observation of the activation sequence throughout the ventricular volume is virtually impossible.In this study, we report on a novel method for identification of activation sequences from activation maps measured at the outer surface of the heart termed the epicardium. Conceptually, the method attempts to identify the key factors governing the ventricular activation sequence – the timing of earliest activation sites (EAS) and the velocity tensor field within the ventricular walls – from sparse and noisy activation maps sampled from the epicardial surface and fits an Eikonal model to the observations.Regularization methods are first investigated to overcome the severe ill-posedness of the inverse problem in a simplified 2D example. These methods are then employed in an anatomically accurate biventricular model with two realistic activation models of varying complexity – a simplified trifascicular model (3F) and a topologically realistic model of the His-Purkinje system (HPS). Using epicardial activation maps at full resolution, we first demonstrate that reconstructing the volumetric activation sequence is, in principle, feasible under the assumption of known location of EAS and later evaluate robustness of the method against noise and reduced spatial resolution of observations.Our results suggest that the FIMIN algorithm is able to robustly recover the full 3D activation sequence using epicardial activation maps at a spatial resolution achievable with current mapping systems and in the presence of noise. Comparing the accuracy achieved in the reconstructed activation maps with clinical data uncertainties suggests that the FIMIN method may be suitable for the patient-specific parameterization of activation models.