Pou4f3 plays an important role in the development of hair cells in the inner ear sensory epithelia. Autophagy is related to the auditory damage. However, the role and mechanism of Pou4f3 on ...drug-induced ototoxicity are incompletely understood. Hence, this study aimed to explore the effects of Pou4f3 on the apoptosis of cochlear hair cells (CHCs) and to explore whether autophagy was involved in this process. The cisplatin was used to produce a loss of CHCs to create a murine model of deafness. The AAV vectors were delivered into the scala media through the lateral wall. Compared with the control mice, the cisplatin-treated mice exhibited significantly enhanced apoptosis and autophagy in the cochleae, accompanied by a notably decreased Pou4f3 levels. Both mutation and knockdown of Pou4f3 promoted the apoptosis- and autophagy-related protein levels, and enhanced the cisplatin-induced levels of apoptosis- and autophagy-related proteins. Furthermore, the autophagy activator rapamycin promoted the apoptosis and autophagy in the cochlea. In addition, the autophagy inhibitor 3-MA overturned the promoting effect of Pou4f3 knockdown on the apoptosis and autophagy. Collectively, in cisplatin-induced deafness mice, the Pou4f3 gene mutation facilitated apoptosis of cochlear hair cells, at least partially, through inducing autophagy.
By conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model, which participates in the Sub-seasonal to Seasonal (S2S) Prediction Project, we ...systematically evaluate the model’s capability in forecasting MJO and its main deficiencies. In the original S2S hindcast set, MJO forecast skill is about 16 days. Such a skill shows significant seasonal-to-interannual variations. It is found that the model-dependent MJO forecast skill is more correlated with the Indian Ocean Dipole (IOD) than with the El Niño–Southern Oscillation. The highest skill is achieved in autumn when the IOD attains its maturity. Extended skill is found when the IOD is in its positive phase. MJO forecast skill’s close association with the IOD is partially due to the quickly strengthening relationship between MJO amplitude and IOD intensity as lead time increases to about 15 days, beyond which a rapid weakening of the relationship is shown. This relationship transition may cause the forecast skill to decrease quickly with lead time, and is related to the unrealistic amplitude and phase evolutions of predicted MJO over or near the equatorial Indian Ocean during anomalous IOD phases, suggesting a possible influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21–22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere–western Indian Ocean area and across the eastern Indian Ocean–Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the well-known Maritime Continent predictability barrier.
This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal ...hindcasts of the Beijing Climate Center Climate System Model, BCC CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model’s forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts.
Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Ni˜no3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Ni˜no–Southern Oscillation.
El Niño–Southern Oscillation (ENSO) retrospective ensemble-based probabilistic predictions were performed for the period of 1856–2003 using the Lamont-Doherty Earth Observatory, version 5 (LDEO5), ...model. To obtain more reliable and skillful ENSO probabilistic predictions, first, four ensemble construction strategies were investigated: (i) the optimal initial perturbation with singular vector of sea surface temperature anomaly (SSTA), (ii) the realistic high-frequency anomalous winds, (iii) the stochastic optimal pattern of anomalous winds, and (iv) a combination of the first and the third strategy. Second, verifications were conducted to examine the reliability and resolution of the probabilistic forecasts provided by the four methods. Results suggest that reliability of ENSO probabilistic forecast is more sensitive to the choice of ensemble construction strategy than the resolution, and a reliable and skillful ENSO probabilistic prediction system may not necessarily have the best deterministic prediction skills. Among these ensemble construction methods, the fourth strategy produces the most reliable and skillful ENSO probabilistic prediction, benefiting from the joint contributions of the stochastic optimal winds and the singular vector of SSTA. In particular, the stochastic optimal winds play an important role in improving the ENSO probabilistic predictability for the LDEO5 model.
In this study, El Niño–Southern Oscillation (ENSO) retrospective forecasts were performed for the 120 yr from 1881 to 2000 using three realistic models that assimilate the historic dataset of sea ...surface temperature (SST). By examining these retrospective forecasts and corresponding observations, as well as the oceanic analyses from which forecasts were initialized, several important issues related to ENSO predictability have been explored, including its interdecadal variability and the dominant factors that control the interdecadal variability.
The prediction skill of the three models showed a very consistent interdecadal variation, with high skill in the late nineteenth century and in the middle–late twentieth century, and low skill during the period from 1900 to 1960. The interdecadal variation in ENSO predictability is in good agreement with that in the signal of interannual variability and in the degree of asymmetry of ENSO system. A good relationship was also identified between the degree of asymmetry and the signal of interannual variability, and the former is highly related to the latter. Generally, the high predictability is attained when ENSO signal strength and the degree of asymmetry are enhanced, and vice versa. The atmospheric noise generally degrades overall prediction skill, especially for the skill of mean square error, but is able to favor some individual prediction cases. The possible reasons why these factors control ENSO predictability were also discussed.
At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the ...identification accuracy is low. Second, the traditional identification methods usually include an offline training stage and online identifying stage, which cannot achieve the real-time identification of the radar emitter. Aimed at the above problems, this paper proposes a radar emitter identification method based on transfer learning and online learning. First, for the case where the target domain contains only a small number of labeled samples, the TrAdaBoost method is used as the basic learning framework to train a support vector machine, which can obtain useful knowledge from the source domain to aid in the identification of the target domain. Then, for the case where the target domain does not contain labeled samples, the Expectation-Maximization algorithm is used to filter the unlabeled samples in the target domain to generate the available training data. Finally, to make the identification quickly and accurately, we propose a radar emitter identification method, based on online learning to ensure real-time updating of the model. Simulation experiments show that the proposed method, based on transfer learning and online learning, has higher identification accuracy and good timeliness.
The El Niño-Southern Oscillation (ENSO) ensemble prediction skills of the Beijing Climate Center (BCC) climate prediction system version 2 (BCC-CPS2) are examined for the period from 1991 to 2018. ...The upper-limit ENSO predictability of this system is quantified by measuring its “potential” predictability using information-based metrics, whereas the actual prediction skill is evaluated using deterministic and probabilistic skill measures. Results show that: (1) In general, the current operational BCC model achieves an effective 10-month lead predictability for ENSO. Moreover, prediction skills are up to 10–11 months for the warm and cold ENSO phases, while the normal phase has a prediction skill of just 6 months. (2) Similar to previous results of the intermediate coupled models, the relative entropy (RE) with a dominating ENSO signal component can more effectively quantify correlation-based prediction skills compared to the predictive information (PI) and the predictive power (PP). (3) An evaluation of the signal-dependent feature of the prediction skill scores suggests the relationship between the “Spring predictability barrier (SPB)” of ENSO prediction and the weak ENSO signal phase during boreal spring and early summer.
Using hindcasts of the Beijing Climate Center Climate System Model, the relationships between interannual variability (IAV) and intraseasonal variability (ISV) of the Asian-western Pacific summer ...monsoon are diagnosed. Predictions show reasonable skill with respect to some basic characteristics of the ISV and IAV of the western North Pacific summer monsoon (WNPSM) and the Indian summer monsoon (ISM). However, the links between the seasonally averaged ISV (SAISV) and seasonal mean of ISM are overestimated by the model. This deficiency may be partially attributable to the overestimated frequency of long breaks and underestimated frequency of long active spells of ISV in normal ISM years, although the model is capable of capturing the impact of ISV on the seasonal mean by its shift in the probability of phases.
Furthermore, the interannual relationships of seasonal mean, SAISV, and seasonally averaged long-wave variability (SALWV; i.e., the part with periods longer than the intraseasonal scale) of the WNPSM and ISM with SST and low-level circulation are examined. The observed seasonal mean, SAISV, and SALWV show similar correlation patterns with SST and atmospheric circulation, but with different details. However, the model presents these correlation distributions with unrealistically small differences among different scales, and it somewhat overestimates the teleconnection between monsoon and tropical central-eastern Pacific SST for the ISM, but underestimates it for the WNPSM, the latter of which is partially related to the too-rapid decrease in the impact of El Niño-Southern Oscillation with forecast time in the model.
In this study, singular vector analysis was performed for the period from 1856 to 2003 using the latest Zebiak-Cane model version LDEO5. The singular vector, representing the optimal growth pattern ...of initial perturbations/errors, was obtained by perturbing the constructed tangent linear model of the Zebiak-Cane model. Variations in the singular vector and singular value, as a function of initial time, season, ENSO states, and optimal period, were investigated. Emphasis was placed on exploring relative roles of linear and nonlinear processes in the optimal perturbation growth of ENSO, and deriving statistically robust conclusions using long-term singular vector analysis. It was found that the first singular vector is dominated by a west-east dipole spanning most of the equatorial Pacific, with one center located in the east and the other in the central Pacific. Singular vectors are less sensitive to initial conditions, i.e., independence of seasons and decades; while singular values exhibit a strong sensitivity to initial conditions. The dynamical diagnosis shows that the total linear and nonlinear heating terms play opposite roles in controlling the optimal perturbation growth, and that the linear optimal perturbation is more than twice as large as the nonlinear one. The total linear heating causes a warming effect and controls two positive perturbation growth regions: one in the central Pacific and the other in the eastern Pacific; whereas the total linearized nonlinear advection brings a cooling effect controlling the negative perturbation growth in the central Pacific.
Widely varied compounds, including certain plasticizers, hypolipidemic drugs (e.g., ciprofibrate, fenofibrate, WY-14643, and clofibrate), agrochemicals, and environmental pollutants, are peroxisome ...proliferators (PPs). Appropriate dose of PPs causes a moderate increase in the number and size of peroxisomes and the expression of genes encoding peroxisomal lipid-metabolizing enzymes. However, high-dose PPs cause varied harmful effects. Chronic administration of PPs to mice and rats results in hepatomegaly and ultimately carcinogenesis. Nuclear receptor protein peroxisome proliferator-activated receptor-α (Pparα) was shown to be required for this process. However, biological adaptations to minimize this risk are poorly understood. In this study, we found that miR-181a2 expression was induced by the Pparα agonist WY-14643. Moreover, exogenous expression of miR-181a-5p dramatically alleviated the cell toxicity caused by overactivation of Pparα. Further studies showed that miR-181a-5p directly targeted the Pparα 3′ untranslated region and depressed the Pparα protein level. This study identified a feedback loop between miR-181a-5p and Pparα, which allows biological systems to approach a balance when Pparα is overactivated.
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