Development of a soft sensor for detecting overpitched anodes : detailed investigation of an anode sticking event

Adjusting pitch ratio in green anode formulation is becoming difficult due to the increasing raw material variability. The optimal quantity of pitch yielding the best anode properties for a given aggregate, known as the optimal pitch demand (ODP), changes more frequently and is unknown a priori. Exceeding the OPD increases the risk of generating post-baking anode-sticking events. Previously, the potential of a principal component analysis (PCA)-based monitoring scheme for detecting the onset of these undesirable events was assessed by using a set of five green anode resistivity measurements collected from over 120,000 anodes produced over a two-year period. The squared prediction error (SPE) was shown to be sensitive to abnormal events such as anode sticking. The objective of this paper is to further validate the soft sensor by studying the SPE dynamic behavior during a post-baking sticking event when changes in the anode paste formulation were introduced as part of normal operation. Descriptive and statistical analyses demonstrate that the SPE metric reacts significantly to changes in the recipe. The provided example illustrates how the SPE metric used together with pitch ratio data could help advised the operators of manufacturing conditions posing a higher risk of generating post-baking sticking problems

Visible and near-infrared light transmission : a hybrid imaging method for non-destructive meat quality evaluation

Visual inspection of the amount of external marbling (intramuscular fat) on the meat surface is the official method used to assign the quality grading level of meat. However, this method is based exclusively on the analysis of the meat surface without any information about the internal content of the meat sample. In this paper, a new method using visible (VIS) and near-infrared (NIR) light transmission is used to evaluate the quality of beef meat based on the marbling detection. It is demonstrated that using NIR light in transmission mode, it is possible to detect the fat not only on the surface, as in traditional methods, but also under the surface. Moreover, in combining the analysis of the two sides of the meat simple, it is possible to estimate the volumetric marbling which is not accessible by visual methods commonly proposed in computer vision. To the best of our knowledge, no similar work or method has been published or developed. The experimental results confirm the expected properties of the proposed method and illustrate the quality of the results obtained

Correlation between the plasma characteristics and the surface ‎chemistry of plasma-treated polymers through partial least ‎squares analysis

We investigated the effect of various plasma parameters (relative density of atomic N and H, plasma temperature, and vibrational temperature) and process conditions (pressure and H2/(N2 + H2) ratio) on the chemical composition of modified poly(tetrafluoroethylene) (PTFE). The plasma parameters were measured by means of near-infrared (NIR) and UV-visible emission spectroscopy with and without actinometry. The process conditions of the N2-H2 microwave discharges were set at various pressures ranging from 100 to 2000 mTorr and H2/(N2+H2) gas mixture ratios between 0 and 0.4. The surface chemical composition of the modified polymers was determined by X-ray photoelectron spectroscopy (XPS). A mathematical model was constructed using the partial least-squares regression algorithm to correlate the plasma information (process condition and plasma parameters as determined by emission spectroscopy) with the modified surface characteristics. To construct the model, a set of data input variables containing process conditions and plasma parameters were generated, as well as a response matrix containing the surface composition of the polymer. This model was used to predict the composition of PTFE surfaces subjected to N2-H2 plasma treatment. Contrary to what is generally accepted in the literature, the present data demonstrate that hydrogen is not directly involved in the defluorination of the surface but rather produces atomic nitrogen and/or NH radicals that are shown to be at the origin of fluorine atom removal from the polymer surface. The results show that process conditions alone do not suffice in predicting the surface chemical composition and that the plasma characteristics, which cannot be easily correlated with these conditions, should be considered. Process optimization and control would benefit from plasma diagnostics, particularly infrared emission spectroscopy

A fluorophore-tagged RGD peptide to control endothelial cell adhesion to micropatterned surfaces

The long-term patency rates of vascular grafts and stents are limited by the lack of surface endothelialisation of the implanted materials. We have previously reported that GRGDS and WQPPRARI peptide micropatterns increase the endothelialisation of prosthetic materials in vitro. To investigate the mechanisms by which the peptide micropatterns affect endothelial cell adhesion and proliferation, a TAMRA fluorophore-tagged RGD peptide was designed. Live cell imaging revealed that the micropatterned surfaces led to directional cell spreading dependent on the location of the RGD-TAMRA spots. Focal adhesions formed within 3 h on the micropatterned surfaces near RGD-TAMRA spot edges, as expected for cell regions experiencing high tension. Similar levels of focal adhesion kinase phosphorylation were observed after 3 h on the micropatterned surfaces and on surfaces treated with RGD-TAMRA alone, suggesting that partial RGD surface coverage is sufficient to elicit integrin signaling. Lastly, endothelial cell expansion was achieved in serum-free conditions on gelatin-coated, RGD-TAMRA treated or micropatterned surfaces. These results show that these peptide micropatterns mainly impacted cell adhesion kinetics rather than cell proliferation. This insight will be useful for the optimization of micropatterning strategies to improve vascular biomaterials

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions

Acoustic emission techniques to measure the properties of coke particles: a first foray

The performance of the Hall-Héroult aluminium reduction process is strongly influenced by fluctuations of the baked carbon anode properties. The currently decreasing quality and increasing variability of the anode raw materials, coke and pitch, combined with the frequent supplier changes by anode manufacturers to meet their specifications and reduce purchasing costs make it very challenging to produce anodes with consistent properties. Furthermore, the coke quality control scheme traditionally used by aluminium smelters involving infrequent coke sampling and characterization in the laboratory is inadequate for tracking coke variability when formulating the anode paste, and applying timely corrective actions when necessary. Developing new rapid and non-destructive sensors for measuring key coke properties such as density and porosity directly from the production line is highly desirable. This work investigates the possibility of using acoustic emission techniques for measuring physical and/or mechanical properties of coke particles. A set-up was developed for recording the sound made by coke particles dropped on a metal sheet. The potential of the approach was tested on coke samples having different physical properties (several sizes and suppliers). The acoustic signature of each type of coke particle was correlated with their physical properties using regression analysis

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Interpolation of Pathway Based Non-Destructive Testing (NDT) Data for Defect Detection and Localization in Pre-Baked Carbon Anodes

Producing consistent quality pre-baked carbon anodes for the Hall–Héroult aluminum reduction process is challenging due to the decreasing quality and increasing variability of anode raw materials. Non-destructive testing techniques (NDT) have been developed and recently implemented in manufacturing plants to establish better suited and more efficient quality control schemes than core sampling and characterization. These technologies collect measurements representing effective properties of the materials located along a pathway between two transducers (emitter and receiver), and not spatially-resolved distribution of properties within the anode volume. A method to interpolate pathway-based measurements and provide spatially-resolved distribution of properties is proposed in this work to help NDT technologies achieve their full potential. The interpolation method is tested by simulating acousto-ultrasonic data collected from a large number of 2D and 3D toy examples representing simplified anode internal structures involving randomly generated defects. Experimental validation was performed by characterizing core samples extracted from a set of industrial anodes and correlating their properties with interpolated speed of sound by the algorithm. The method is shown to be successful in determining the defect positions, and the interpolated results are shown to correlate significantly with mechanical properties