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Ischemic stroke (IS) is a vascular disease group concomitant with high morbidity and mortality. Berberine is a bioactive substance and it has been known to improve stroke, but its mechanism is yet to be proven. Mice were fed with BBR for 14 days. Then, the mice were made into MCAO/R models. Neurological score, infarct volume, neuronal damage and markers associated with inflammation were detected. We tested the changes in intestinal flora in model mice after BBR administration using 16SrRNA sequencing. Chromatography–mass spectrometry was used to detect butyrate chemically. Tissue immunofluorescence was used to detect the changes in the microglia and astroglia in the mice brains. Our findings suggest that berberine improves stroke outcomes by modulating the gut microbiota. Specifically, after MCAO/R mice were given berberine, the beneficial bacteria producing butyric acid increased significantly, and the mice also had significantly higher levels of butyric acid. The administration of butyric acid and an inhibitor of butyric acid synthesis, heptanoyl-CoA, showed that butyric acid improved the stroke outcomes in the model mice. In addition, butyric acid could inhibit the activation of the microglia and astrocytes in the brains of model mice, thereby inhibiting the generation of pro-inflammatory factors IL-6, IL-1β and TNF-α as well as improving stroke outcomes. Our results suggest that berberine may improve stroke outcomes by modulating the gut flora to increase the abundance of butyric acid. These findings elucidate the mechanisms by which berberine improves stroke outcomes and provide some basis for clinical treatment. [ABSTRACT FROM AUTHOR]

Previous studies suggested that tropical sea surface salinity (SSS) can influence tropical Pacific sea surface temperature (SST) through mixing and entrainment and thus it may be a signal for El Niño‐Southern Oscillation (ENSO) prediction. This paper explores the influence of SSS on ENSO spring predictability barrier (SPB) using an empirical dynamic model ‐ Linear Inverse Model (LIM). By coupling and decoupling SSS in the LIM, we find that tropical Pacific SSS plays a significant role in weakening both Central‐Pacific and Eastern‐Pacific ENSO SPB. The evolution of optimal initial structure also shows the importance of SSS dynamics in ENSO. We found an SSS mode that plays the dominant role in SSS impacting ENSO prediction. By the analysis of lead‐lag correlation, we find that this mode can induce easterlies during the spring, which finally leads to a La Niña‐like SST pattern in the winter through zonal advective and thermocline feedbacks. Plain Language Summary: The spring predictability barrier (SPB) is a phenomenon of forecast skill reduction of the El Niño‐Southern Oscillation (ENSO) when it comes to the boreal spring, regardless of the initial month. Sea surface salinity (SSS) can influence sea surface temperature by altering sea surface density and thus it may be a signal for ENSO prediction. Using a linear dynamical model, we find that SSS plays an important role in improving the forecast skill of ENSO and weakening SPB. We further find that SSS can induce the easterlies during the spring, which finally leads to a La Niña‐like SST pattern. Our study suggests that SSS can be used to predict ENSO about 1 year later. Key Points: A linear dynamical model suggests that taking sea surface salinity (SSS) into consideration can strongly weaken Central‐Pacific and Eastern‐Pacific El Niño‐Southern Oscillation (ENSO) spring predictability barrier (SPB)A new SSS mode is found to be important for weakening ENSO SPBThe SSS mode can predict ENSO events about 1 year earlier by inducing sea surface temperature and wind anomalies in the early spring [ABSTRACT FROM AUTHOR]

The National Center for Atmospheric Research Community Earth System Model is used to study the 'spring predictability barrier' (SPB) problem for El Niño events from the perspective of initial error growth. By conducting perfect model predictability experiments, we obtain two types of initial sea temperature errors, which often exhibit obvious season-dependent evolution and cause a significant SPB when predicting the onset of El Niño events bestriding spring. One type of initial errors possesses a sea surface temperature anomaly (SSTA) pattern with negative anomalies in the central-eastern equatorial Pacific, plus a basin-wide dipolar subsurface temperature anomaly pattern with negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The other type consists of an SSTA component with positive anomalies over the southeastern equatorial Pacific, plus a large-scale zonal dipole pattern of the subsurface temperature anomaly with positive anomalies in the upper layers of the eastern equatorial Pacific and negative anomalies in the lower layers of the central-western equatorial Pacific. Both exhibit a La Niña-like evolving mode and cause an under-prediction for Niño-3 SSTA of El Niño events. For the former initial error type, the resultant prediction errors grow in a manner similar to the behavior of the growth phase of La Niña; while for the latter initial error type, they experience a process that is similar to El Niño decay and transition to a La Niña growth phase. Both two types of initial errors cause negative prediction errors of Niño-3 SSTA for El Niño events. The prediction errors for Niño-3 SSTA are mainly due to the contribution of initial sea temperature errors in the large-error-related regions in the upper layers of the eastern tropical Pacific and/or in the lower layers of the western tropical Pacific. These regions may represent ''sensitive areas'' for El Niño-Southern Oscillation (ENSO) predictions, thereby providing information for target observations to improve the forecasting skill of ENSO. [ABSTRACT FROM AUTHOR]