Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks

Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEPapp) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEPtot) is the sum of PEEPapp and intrinsic PEEP (PEEPi), a hidden component. To measure PEEPtot, ventilation must be discontinued with an end-expiratory hold maneuver (EEHM). We hypothesized that artificial neural networks (ANN) could estimate the PEEPtot from flow and pressure tracings during ongoing mechanical ventilation. Ten pigs were mechanically ventilated, and the time constant of their respiratory system (τRS) was measured. We shortened their expiratory time (TE) according to multiples of τRS, obtaining different respiratory patterns (Rpat). Pressure (PAW) and flow (V′AW) at the airway opening during ongoing mechanical ventilation were simultaneously recorded, with and without the addition of external resistance. The last breath of each Rpat included an EEHM, which was used to compute the reference PEEPtot. The entire protocol was repeated after the induction of ALI with i.v. injection of oleic acid, and 382 tracings were obtained. The ANN had to extract the PEEPtot, from the tracings without an EEHM. ANN agreement with reference PEEPtot was assessed with the Bland–Altman method. Bland Altman analysis of estimation error by ANN showed −0.40 ± 2.84 (expressed as bias ± precision) and ±5.58 as limits of agreement (data expressed as cmH2O). The ANNs estimated the PEEPtot well at different levels of PEEPapp under dynamic conditions, opening up new possibilities in monitoring PEEPi in critically ill patients who require ventilator treatment

Computational Models of Timing Mechanisms in the Cerebellar Granular Layer

A long-standing question in neuroscience is how the brain controls movement that requires precisely timed muscle activations. Studies using Pavlovian delay eyeblink conditioning provide good insight into this question. In delay eyeblink conditioning, which is believed to involve the cerebellum, a subject learns an interstimulus interval (ISI) between the onsets of a conditioned stimulus (CS) such as a tone and an unconditioned stimulus such as an airpuff to the eye. After a conditioning phase, the subject’s eyes automatically close or blink when the ISI time has passed after CS onset. This timing information is thought to be represented in some way in the cerebellum. Several computational models of the cerebellum have been proposed to explain the mechanisms of time representation, and they commonly point to the granular layer network. This article will review these computational models and discuss the possible computational power of the cerebellum

Coral microbiome composition along the northern Red Sea suggests high plasticity of bacterial and specificity of endosymbiotic dinoflagellate communities

Background The capacity of reef-building corals to tolerate (or adapt to) heat stress is a key factor determining their resilience to future climate change. Changes in coral microbiome composition (particularly for microalgal endosymbionts and bacteria) is a potential mechanism that may assist corals to thrive in warm waters. The northern Red Sea experiences extreme temperatures anomalies, yet corals in this area rarely bleach suggesting possible refugia to climate change. However, the coral microbiome composition, and how it relates to the capacity to thrive in warm waters in this region, is entirely unknown. Results We investigated microbiomes for six coral species (Porites nodifera, Favia favus, Pocillopora damicornis, Seriatopora hystrix, Xenia umbellata, and Sarcophyton trocheliophorum) from five sites in the northern Red Sea spanning 4° of latitude and summer mean temperature ranges from 26.6 °C to 29.3 °C. A total of 19 distinct dinoflagellate endosymbionts were identified as belonging to three genera in the family Symbiodiniaceae (Symbiodinium, Cladocopium, and Durusdinium). Of these, 86% belonged to the genus Cladocopium, with notably five novel types (19%). The endosymbiont community showed a high degree of host-specificity despite the latitudinal gradient. In contrast, the diversity and composition of bacterial communities of the surface mucus layer (SML)—a compartment particularly sensitive to environmental change—varied significantly between sites, however for any given coral was species-specific. Conclusion The conserved endosymbiotic community suggests high physiological plasticity to support holobiont productivity across the different latitudinal regimes. Further, the presence of five novel algal endosymbionts suggests selection of certain genotypes (or genetic adaptation) within the semi-isolated Red Sea. In contrast, the dynamic composition of bacteria associated with the SML across sites may contribute to holobiont function and broaden the ecological niche. In doing so, SML bacterial communities may aid holobiont local acclimatization (or adaptation) by readily responding to changes in the host environment. Our study provides novel insight about the selective and endemic nature of coral microbiomes along the northern Red Sea refugia