ObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of... Show moreObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).ResultsDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.ConclusionsAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance. Show less
Improving resource efficiency (RE) is an important objective of the Sustainable Development Goals. In this study we find a strong exponential relationship between economic complexity index (ECI)... Show moreImproving resource efficiency (RE) is an important objective of the Sustainable Development Goals. In this study we find a strong exponential relationship between economic complexity index (ECI) and RE of countries. ECI measures the level of accumulated knowledge of a society enabling the products it makes. The relationship between ECI and RE is stronger for primary material importers and countries with stable institutions. Assessing a country's level of ECI also allows the outlook of future RE trends. We explain how ECI influences RE at the product level by establishing the product space for each country and by defining core products that contribute to a high product complexity index, high RE (i.e., unit price) and promising expansibility (i.e., core number), which indicates the potential to produce more advanced products in the future. Policies that improve economic complexity and invest in core products seem to be a priority to achieve sustainable development. Show less
Background and Aims In patients with acute liver failure (ALF) who suffer from massive hepatocyte loss, liver progenitor cells (LPCs) take over key hepatocyte functions, which ultimately determines... Show moreBackground and Aims In patients with acute liver failure (ALF) who suffer from massive hepatocyte loss, liver progenitor cells (LPCs) take over key hepatocyte functions, which ultimately determines survival. This study investigated how the expression of hepatocyte nuclear factor 4 alpha (HNF4 alpha), its regulators, and targets in LPCs determines clinical outcome of patients with ALF. Approach and Results Clinicopathological associations were scrutinized in 19 patients with ALF (9 recovered and 10 receiving liver transplantation). Regulatory mechanisms between follistatin, activin, HNF4 alpha, and coagulation factor expression in LPC were investigated in vitro and in metronidazole-treated zebrafish. A prospective clinical study followed up 186 patients with cirrhosis for 80 months to observe the relevance of follistatin levels in prevalence and mortality of acute-on-chronic liver failure. Recovered patients with ALF robustly express HNF4 alpha in either LPCs or remaining hepatocytes. As in hepatocytes, HNF4 alpha controls the expression of coagulation factors by binding to their promoters in LPC. HNF4 alpha expression in LPCs requires the forkhead box protein H1-Sma and Mad homolog 2/3/4 transcription factor complex, which is promoted by the TGF-beta superfamily member activin. Activin signaling in LPCs is negatively regulated by follistatin, a hepatocyte-derived hormone controlled by insulin and glucagon. In contrast to patients requiring liver transplantation, recovered patients demonstrate a normal activin/follistatin ratio, robust abundance of the activin effectors phosphorylated Sma and Mad homolog 2 and HNF4 alpha in LPCs, leading to significantly improved coagulation function. A follow-up study indicated that serum follistatin levels could predict the incidence and mortality of acute-on-chronic liver failure. Conclusions These results highlight a crucial role of the follistatin-controlled activin-HNF4 alpha-coagulation axis in determining the clinical outcome of massive hepatocyte loss-induced ALF. The effects of insulin and glucagon on follistatin suggest a key role of the systemic metabolic state in ALF. Show less
Geraedts, V.J.; Koch, M.; Kuiper, R.; Kefalas, M.; Back, T.H.W.; Hilten, J.J. van; ... ; Tannemaat, M.R. 2021
Background Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine... Show moreBackground Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.Objective To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.Methods Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation.Results Tweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited.Conclusions Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. (c) 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society Show less
Objective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker... Show moreObjective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.Methods: A fully automated ML pipeline was applied to 112 PD patients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance.Results: Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (b =-0.23 (95% confidence interval (-0.29,-0.18))).Conclusions: Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PD patients according to cognition, rather than a single spectral EEG feature.Significance: Automated EEG assessment may have utility for cognitive profiling of PD patients during the DBS screening. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Wang, H.; Li, Z.; Tong, H.; Kolfschoten, M. van 2021
Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural... Show moreStem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different develop- mental stages with 97.5% accuracy. The present study demon-strates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified bio- marker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy. Show less
ImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk... Show moreImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk stratification and treatment decisions, clinical trial recruitment, and biomarker validation. ObjectiveTo derive and externally validate a prediction model for disease progression in IgAN that can be applied at the time of kidney biopsy in multiple ethnic groups worldwide. Design, Setting, and ParticipantsWe derived and externally validated a prediction model using clinical and histologic risk factors that are readily available in clinical practice. Large, multi-ethnic cohorts of adults with biopsy-proven IgAN were included from Europe, North America, China, and Japan. Main Outcomes and MeasuresCox proportional hazards models were used to analyze the risk of a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease, and were evaluated using the R-D(2) measure, Akaike information criterion (AIC), C statistic, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and calibration plots. ResultsThe study included 3927 patients; mean age, 35.4 (interquartile range, 28.0-45.4) years; and 2173 (55.3%) were men. The following prediction models were created in a derivation cohort of 2781 patients: a clinical model that included eGFR, blood pressure, and proteinuria at biopsy; and 2 full models that also contained the MEST histologic score, age, medication use, and either racial/ethnic characteristics (white, Japanese, or Chinese) or no racial/ethnic characteristics, to allow application in other ethnic groups. Compared with the clinical model, the full models with and without race/ethnicity had better R-D(2) (26.3% and 25.3%, respectively, vs 20.3%) and AIC (6338 and 6379, respectively, vs 6485), significant increases in C statistic from 0.78 to 0.82 and 0.81, respectively (Delta C, 0.04; 95% CI, 0.03-0.04 and Delta C, 0.03; 95% CI, 0.02-0.03, respectively), and significant improvement in reclassification as assessed by the NRI (0.18; 95% CI, 0.07-0.29 and 0.51; 95% CI, 0.39-0.62, respectively) and IDI (0.07; 95% CI, 0.06-0.08 and 0.06; 95% CI, 0.05-0.06, respectively). External validation was performed in a cohort of 1146 patients. For both full models, the C statistics (0.82; 95% CI, 0.81-0.83 with race/ethnicity; 0.81; 95% CI, 0.80-0.82 without race/ethnicity) and R-D(2) (both 35.3%) were similar or better than in the validation cohort, with excellent calibration. Conclusions and RelevanceIn this study, the 2 full prediction models were shown to be accurate and validated methods for predicting disease progression and patient risk stratification in IgAN in multi-ethnic cohorts, with additional applications to clinical trial design and biomarker research. Show less
Identifying speakers by their spoken output is a specialist task for forensic investigators. In the present study we focused on cross-linguistic speaker (Chinese, English, Dutch) identification... Show moreIdentifying speakers by their spoken output is a specialist task for forensic investigators. In the present study we focused on cross-linguistic speaker (Chinese, English, Dutch) identification based on (components of) English stops and fricatives, /p, b, t, d, k, g/ and the fricatives /f, v, θ, ð, s, z, ʃ, ʒ/. English noise bursts’ contribution to native language identification is presented and the special tokens which contribute the most were analyzed. Show less
Noordam, R.; Bos, M.M.; Wang, H.; Mook-Kanamori, D.; Heemst, D. van; Redline, S. 2018
Automatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background... Show moreAutomatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background of a foreign speaker of English, using phonetically interpretable measurements. The production of the ten monophthongs of (American) English by Dutch, Mandarin Chinese and American speakers was used as a test case. Vowel formants F1 (corresponding to articulatory vowel height), F2 (capturing vowel backness and lip rounding) and vowel duration were extracted. Clearly different duration and patterning of the vowels in the vowel space were seen. Automatic classification of the speaker’s native language was 90 percent correct when all acoustic parameters were used as predictors. Language identification was slightly poorer when only formant data were used (85% correct) and substantially poorer – but much better than chance – when only vowel duration was used (60% correct). We conclude that vowel duration provides a weaker cue to foreign-accent identification in English than the spectral properties but that the combination of both information sources yields the best results. Show less
Automatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background... Show moreAutomatic identification of a speaker’s native language background may have forensic applications. This paper explores the feasibility of automatic identification of the native language background of a foreign speaker of English, using phonetically interpretable measurements. The production of the ten monophthongs of (American) English by Dutch, Mandarin Chinese and American speakers was used as a test case. Vowel formants F1 (corresponding to articulatory vowel height), F2 (capturing vowel backness and lip rounding) and vowel duration were extracted. Clearly different duration and patterning of the vowels in the vowel space were seen. Automatic classification of the speaker’s native language was 90 percent correct when all acoustic parameters were used as predictors. Language identification was slightly poorer when only formant data were used (85% correct) and substantially poorer – but much better than chance – when only vowel duration was used (60% correct). We conclude that vowel duration provides a weaker cue to foreign-accent identification in English than the spectral properties but that the combination of both information sources yields the best results. Show less
Two hypotheses have been advanced in the recent literature with respect to the so-called Interlanguage Speech Intelligibility Benefit (ISIB): a nonnative speaker will be better understood by a... Show moreTwo hypotheses have been advanced in the recent literature with respect to the so-called Interlanguage Speech Intelligibility Benefit (ISIB): a nonnative speaker will be better understood by a another nonnative listener than a native speaker of the target language will be (a) only when the nonnatives share the same native language (matched interlanguage) or (b) even when the nonnatives have different mother tongues (non-matched interlanguage). Based on a survey of published experimental materials, the present article will demonstrate that both the restricted (a) and the generalized (b) hypotheses are false when the ISIB effect is evaluated in terms of absolute intelligibility scores.We will then propose a simple way to compute a relative measure for the ISIB (R-ISIB), which we claim is a more insightful way of evaluating the interlanguage benefit, and test the hypotheses in relative (R-ISIB) terms on the same literature data. We then find that our R-ISIB measure only supports the more restricted hypothesis (a) while rejecting the more general hypothesis (b). This finding shows that the native language shared by the interactants biases the listener toward interpreting sounds in terms of the phonology of the shared mother tongue. Show less
We determined the mutual intelligibility Mandarin Chinese, Dutch and American speakers of English in all nine logically possible combinations of speaker and listener native language backgrounds.... Show moreWe determined the mutual intelligibility Mandarin Chinese, Dutch and American speakers of English in all nine logically possible combinations of speaker and listener native language backgrounds. Designated speakers (one male, one female per language group) were selected from larger sets of 20 speakers so as to be optimally representative of their peer groups. All non-native speakers and listeners were university students who did not specialize in English and had never lived in an English speaking community. Intelligibility was tested in separate tests targeting vowels, onset consonants, onset consonant clusters, words in syntactically correct but semantically empty sentences (SUS test), and words in meaningful sentences in which they appeared in either low or high predictability contexts. We test the hypotheses that mutual intelligibility between speaker and listener is better as (i) their native languages resemble each other more, and (ii) if speaker and listener share the same native language. In order to test the second hypothesis we propose a new method for quantifying the so-called interlanguage speech intelligibility benefit (ISIB). Show less
