Cross-compensation of FET sensor drift and matrix effects in the industrial continuous monitoring of ion concentrations

Field-effect transistor (FET) sensors are attractive potentiometric (bio)chemical measurement devices because of their fast response, low output impedance, and potential for miniaturization in standard integrated circuit manufacturing technologies. Yet the wide adoption of these sensors for real-world applications is still limited, mainly due to temporal drift and cross-sensitivities that introduce considerable error in the measurements. In this paper, we demonstrate that such non-idealities can be corrected by joint use of an array of FET sensors – selective to target and major interfering ions – with machine learning (ML) methods in order to accurately predict ion concentrations continuously and in the field. We studied the predictive performance of linear regression (LR), support vector regression (SVR), and state-of-art deep neural networks (DNNs) when monitoring pH from combinatorial H+, Na+, and K+ ion-sensitive FET (ISFET) sequences of readings collected over a period of 90 consecutive days in real water quality assessment conditions. The proposed ML algorithms were trained against reference online measurements obtained from a commercial pH sensor. Results show a greater capability of DNNs to provide precise pH monitoring for longer than a week, achieving a relative root-mean-square error reduction of 73% over standard two-point sensor calibration methods

Aberrant epigenome in iPSC-derived dopaminergic neurons from Parkinson's disease patients

The epigenomic landscape of Parkinson's disease (PD) remains unknown. We performed a genomewide DNA methylation and a transcriptome studies in induced pluripotent stem cell (iPSC)-derived dopaminergic neurons (DAn) generated by cell reprogramming of somatic skin cells from patients with monogenic LRRK2-associated PD (L2PD) or sporadic PD (sPD), and healthy subjects. We observed extensive DNA methylation changes in PD DAn, and of RNA expression, which were common in L2PD and sPD. No significant methylation differences were present in parental skin cells, undifferentiated iPSCs nor iPSC-derived neural cultures not-enriched-in-DAn. These findings suggest the presence of molecular defects in PD somatic cells which manifest only upon differentiation into the DAn cells targeted in PD. The methylation profile from PD DAn, but not from controls, resembled that of neural cultures not-enriched-in-DAn indicating a failure to fully acquire the epigenetic identity own to healthy DAn in PD. The PD-associated hypermethylation was prominent in gene regulatory regions such as enhancers and was related to the RNA and/or protein downregulation of a network of transcription factors relevant to PD (FOXA1, NR3C1, HNF4A, and FOSL2). Using a patient-specific iPSC-based DAn model, our study provides the first evidence that epigenetic deregulation is associated with monogenic and sporadic PD

Cross-compensation of FET sensor drift and matrix effects in the industrial continuous monitoring of ion concentrations

Under reviewField-Effect Transistor (FET) sensors are attractive potentiometric (bio)chemical measurement devices because of their fast response, low output impedance, and potential for miniaturization in standard CMOS VLSI technologies. Yet the wide adoption of these sensors for industrial applications is still limited mainly due to temporal drift and cross-sensitivities to confounding environmental variables and chemical substances. In this paper, we demonstrate that we can apply various Machine Learning (ML) methods - including current state-of-art Artificial Neural Networks (ANNs) - to correct FET sensor non-idealities and to predict ion concentrations using multimodal raw measurements collected continuously from an array of FETs selective to target and interfering ions. We studied pH accuracy from combinatorial H+, Na+ and K+ ion-sensitive FET (ISFET) readings collected over a period of 90 consecutive days in real water quality monitoring conditions, and we estimated the predicted accuracy against reference online measurements from a commercial pH sensor. Results show that the ANN-mediated fusion of H+ with Na+ or K+ ISFET measurements gives the best accuracy with prediction RMSEs 0.048 and R2 scores > 0.91 on a test set consisting of measurements performed over the latest nine days. On average, the ANN architectures achieve a relative RMSE reduction of 73.05%, 68.22% and 9.93% over the RMSEs using a two-point, standard pH FET sensor calibration, multivariate Linear Regression (LR), and multivariate Support Vector Regression (SVR) respectively.N

Aberrant epigenome in iPSC-derived dopaminergic neurons from Parkinson's disease patients

The epigenomic landscape of Parkinson's disease () remains unknown. We performed a genomewide methylation and a transcriptome studies in induced pluripotent stem cell ()-derived dopaminergic neurons (n) generated by cell reprogramming of somatic skin cells from patients with monogenic 2-associated (L2) or sporadic (), and healthy subjects. We observed extensive methylation changes in n, and of expression, which were common in L2 and . No significant methylation differences were present in parental skin cells, undifferentiated s nor -derived neural cultures not-enriched-in-n. These findings suggest the presence of molecular defects in somatic cells which manifest only upon differentiation into the n cells targeted in . The methylation profile from n, but not from controls, resembled that of neural cultures not-enriched-in-n indicating a failure to fully acquire the epigenetic identity own to healthy n in . The -associated hypermethylation was prominent in gene regulatory regions such as enhancers and was related to the and/or protein downregulation of a network of transcription factors relevant to (1, 3C1, 4A, and 2). Using a patient-specific -based n model, our study provides the first evidence that epigenetic deregulation is associated with monogenic and sporadic PD

Aberrant epigenome in -derived dopaminergic neurons from Parkinson's disease patients

The epigenomic landscape of Parkinson's disease () remains unknown. We performed a genomewide methylation and a transcriptome studies in induced pluripotent stem cell ()-derived dopaminergic neurons (n) generated by cell reprogramming of somatic skin cells from patients with monogenic 2-associated (L2) or sporadic (), and healthy subjects. We observed extensive methylation changes in n, and of expression, which were common in L2 and . No significant methylation differences were present in parental skin cells, undifferentiated s nor -derived neural cultures not-enriched-in-n. These findings suggest the presence of molecular defects in somatic cells which manifest only upon differentiation into the n cells targeted in . The methylation profile from n, but not from controls, resembled that of neural cultures not-enriched-in-n indicating a failure to fully acquire the epigenetic identity own to healthy n in . The -associated hypermethylation was prominent in gene regulatory regions such as enhancers and was related to the and/or protein downregulation of a network of transcription factors relevant to (1, 3C1, 4A, and 2). Using a patient-specific -based n model, our study provides the first evidence that epigenetic deregulation is associated with monogenic and sporadic PD