Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice

Modern neurophysiology research requires the interrogation of high-dimensionality datasets. ML/AI workflows have permeated into nearly all aspects of daily life in the developed world, but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community.

Diurnal variation in autonomic regulation among patients with genotyped Rett syndrome

BackgroundRett syndrome is a severe neurological disorder with a range of disabling autonomic and respiratory symptoms and resulting predominantly from variants in the methyl-CpG binding protein 2 gene on the long arm of the X-chromosome. As basic research begins to suggest potential treatments, sensitive measures of the dynamic phenotype are needed to evaluate the results of these research efforts. Here we test the hypothesis that the physiological fingerprint of Rett syndrome in a naturalistic environment differs from that of controls, and differs among genotypes within Rett syndrome.MethodsA comprehensive array of heart rate variability, cardiorespiratory coupling and cardiac repolarisation measures were evaluated from an existing database of overnight and daytime inhome ambulatory recordings in 47 cases and matched controls.ResultsDifferences between girls with Rett syndrome and matched controls were apparent in a range of autonomic measures, and suggest a shift towards sympathetic activation and/or parasympathetic inactivation. Daily temporal trends analysed in the context of circadian rhythms reveal alterations in amplitude and phase of diurnal patterns of autonomic balance. Further analysis by genotype class confirms a graded presentation of the Rett syndrome phenotype such that patients with early truncating mutations were most different from controls, while late truncating and missense mutations were least different from controls.ConclusionsComprehensive autonomic measures from extensive inhome physiological measurements can detect subtle variations in the phenotype of girls with Rett syndrome, suggesting these techniques are suitable for guiding novel therapies.

The effects of nitrogen on protein, oil and trypsin inhibitor content of soybean

Nitrogen fertilization have influence on protein, oil and trypsin inhibitor content of different soybean genotypes. Seed protein content was increased over control by 60 kg ha-1 nitrogen while trypsin inhibitor was reduced by all treatmens (30, 60,90 N kg ha-1) as compared to controls. Significant genetic variation in TI was found both within the genotype class with the Kunitz inhibitor present as well as within the class lacking this inhibitor. Genotypes containing the Kunitz trypsin inhibitor protein (KTI) exhibit a higher TI than genotypes lacking this protein, however, in both groups of genotypes TI was similary affected by nitrogen application. Oil content was reduced following nitrogen fertilisation.

4G/5G Promoter PAI-1 Gene Polymorphism Is Associated with Plasmatic PAI-1 Activity in Italians: A Model of Gene-Environment Interaction

SummaryThe PAI-1 gene promoter 4G/5G polymorphism was found to be associated with plasma PAI-1 activity in Northern and Central Europe populations, but no data are available on the association between this polymorphism and PAI-1 levels in Southern Europe countries (such as Italy) where the incidence of ischemic disorders is lower. This study shows that among populations with different incidence of atherothrombotic disorders the 4G/5G PAI-1 gene promoter polymorphism has the same importance in the regulation of plasma PAI-1 activity.Moreover, we have analysed some gene-environmental interactions: the correlation between PAI-1 and cholesterol in non dyslipidemic subjects and the correlation between PAI-1 activity and tryglicerides in dyslipidemic subjects differed according to the 4G/5G genotype class. Thus, our findings suggest that, among subjects with or without metabolic disorders such as dyslipidemia, completely different gene-environment interactions may occur.

CORRIGENDUM

In the paper by Sin-Chieu Liu, Stanley P. Kowalski, Tien-Hung Lan, Kenneth A. Feldmann and Andrew H. Paterson (Genetics 142: 247–258; January, 1996) entitled “Genome-wide high-resolution mapping by recurrent intermating using Arabidopsis thaliana as a model,” several errors should be corrected. On page 248, right column, the last line should read “distribute as a χ12 distribution …” On page 249, left column, first paragraph, line 6 should be read “… in which R = r, …” On page 249, left column, the second line from the bottom should read “… R 0 = r.” On page 253, right colum, the fourth line from the bottom should read “(t R>0.4)…” On page 253, the first line of the Figure 6 legend should read “Relationship between r (adjusted …” On page 258, the first text sentence should begin “The 1mj(dmjdr)2 term for each genotype class …,” the second text sentence should begin “The mean amount of information (ir) is the sum of the 1mj(dmjdr)2 term from each genotype class,” and the last text sentence should read “Because a = (1 − 2r) and b = (1 − r)1, …”