Blood and brain gene expression trajectories mirror neuropathology and clinical deterioration in neurodegeneration.

Most prevalent neurodegenerative disorders take decades to develop and their early detection is challenged by confounding non-pathological ageing processes. For all neurodegenerative conditions, we continue to lack longitudinal gene expression data covering their large temporal evolution, which hinders the understanding of the underlying dynamic molecular mechanisms. Here, we overcome this key limitation by introducing a novel gene expression contrastive trajectory inference (GE-cTI) method that reveals enriched temporal patterns in a diseased population. Evaluated on 1969 subjects in the spectrum of late-onset Alzheimer's and Huntington's diseases (from ROSMAP, HBTRC and ADNI datasets), this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak, amyloid and Vonsattel stages). Furthermore, when applied to in vivo blood samples at baseline (ADNI), it significantly predicts clinical deterioration and conversion to advanced disease stages, supporting the identification of a minimally invasive (blood-based) tool for early clinical screening. This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. Eighty-five to ninety per cent of the most predictive molecular pathways identified in the brain are also top predictors in the blood. These pathways support the importance of studying the peripheral-brain axis, providing further evidence for a key role of vascular structure/functioning and immune system response. The GE-cTI is a promising tool for revealing complex neuropathological mechanisms, with direct implications for implementing personalized dynamic treatments in neurology.

Mean-field solution of structural balance dynamics in nonzero temperature.

In signed networks with simultaneous friendly and hostile interactions, there is a general tendency to a global structural balance, based on the dynamical model of links status. Although the structural balance represents a state of the network with a lack of contentious situations, there are always tensions in real networks. To study such networks, we generalize the balance dynamics in nonzero temperatures. The presented model uses elements from Boltzmann-Gibbs statistical physics to assign an energy to each type of triad, and it introduces the temperature as a measure of tension tolerance of the network. Based on the mean-field solution of the model, we find out that the model undergoes a first-order phase transition from an imbalanced random state to structural balance with a critical temperature T_{c}, where in the case of T>T_{c} there is no chance to reach the balanced state. A main feature of the first-order phase transition is the occurrence of a hysteresis loop crossing the balanced and imbalanced regimes.

Evolution of Communities in the Medical Sciences: Evidence from the Medical Words Network.

BACKGROUND: Classification of medical sciences into its sub-branches is crucial for optimum administration of healthcare and specialty training. Due to the rapid and continuous evolution of medical sciences, development of unbiased tools for monitoring the evolution of medical disciplines is required. METHODOLOGY/PRINCIPAL FINDINGS: Network analysis was used to explore how the medical sciences have evolved between 1980 and 2015 based on the shared words contained in more than 9 million PubMed abstracts. The k-clique percolation method was used to extract local research communities within the network. Analysis of the shared vocabulary in research papers reflects the trends of collaboration and splintering among different disciplines in medicine. Our model identifies distinct communities within each discipline that preferentially collaborate with other communities within other domains of specialty, and overturns some common perceptions. CONCLUSIONS/SIGNIFICANCE: Our analysis provides a tool to assess growth, merging, splitting and contraction of research communities and can thereby serve as a guide to inform policymakers about funding and training in healthcare.

The effect of endotoxin on the controllability of cardiac rhythm in rats.

Decreased heart rate variability (HRV) has both diagnostic and prognostic value in patients with sepsis. However, it is not known whether reduced HRV in sepsis reflects an altered input from the autonomic nervous system or a remodeling of the cardiac pacemaker cells by inflammatory mediators. The present study aimed to investigate the effect of endotoxin on the heart rate dynamics of a denervated isolated heart in rats. Saline or endotoxin was injected into rats and their hearts were isolated and perfused. Atrial electrical activity was recorded and memory length in the time-series was assessed using inverse statistical analysis. Memory was defined as a statistical feature that lasts for a period of time and distinguishes the time-series from a random process. Endotoxaemic hearts exhibited a prolonged memory compared to the controls with respect to observing rare events. This indicates that a sudden decelerating event could potentially affect the cardiac rhythm of an endotoxaemic heart for a longer time than the controls. The prolongation of memory is indirectly linked to a reduced controllability in a complex system; therefore our data may provide evidence for a reduced controllability in cardiac rhythm following endotoxaemia.

Quantifying memory in complex physiological time-series.

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.