Antidepressants are extensively used during pregnancy and associated with severe outcomes, including innate malformations, prematurity, and low birth weight, etc.

Precise and correct estimation of streamflow is important for the operative progression in water resources systems. The artificial intelligence approaches; such as artificial neural networks (ANN) have been applied for efficiently tackling the hydrological matters like streamflow forecasting in this study at upper Yangtze River. The objective is to investigate the certainty of monthly streamflow by applying artificial neural networks including Generalized Regression Network (GRNN). To overcome the non-linearity problem of streamflow, artificial neural networks integrated with discrete wavelet transform (DWT). Data has been analyzed by comparing the simulation outputs of the models with the correlation coefficient (R) root mean square errors (RMSE). It is found that the decomposition technique DWT has ability to improve the forecasting results as compare to single applied artificial neural networks. Moreover, all applied models are separately applies on the peak values as well which also have showed that intergrated model has more ability to catch the peak values

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Autism spectrum disorders and epilepsies are heterogeneous human disorders that have miscellaneous etiologies and pathophysiology. There is considerable risk of frequent epilepsy in autism that facilitates amplified morbidity and mortality. Several biological pathways appear to be involved in disease progression, including gene transcription regulation, cellular growth, synaptic channel function, and maintenance of synaptic structure. Here, abnormalities in excitatory/inhibitory (E/I) balance ratio are reviewed along with part of an epileptiform activity that may drive both overconnectivity and genetic disorders where autism spectrum disorders and epilepsy frequently co-occur. The most current ideas concerning common etiological and molecular mechanisms for co-occurrence of both autism spectrum disorders and epilepsy are discussed along with the powerful pharmacological therapies that protect the cognition and behavior of patients. Better understanding is necessary to identify a biological mechanism that might lead to possible treatments for these neurological disorders.

This research investigates the spatial and temporal trend analysis of precipitation time series. Precise predictions of precipitation trends can play an imperative role in economic growth of a country. This study examined precipitation inconsistency for 23 stations at Dongting Lake, China, over a 52-year study period (1961-2012). Statistical, nonparametric Mann-Kendall (MK) and Spearman's rho tests were applied to identify trends within monthly, seasonal, and annual precipitation. The trend-free pre-whitening method was used to exclude sequential correlation in the precipitation time series. The performance of the Mann-Kendall (MK) and Spearman's rho tests was steady at the tested significance levels. The results showed a fusion of increasing and decreasing trends at different stations within monthly and seasonal time scales. The results obtained with the Mann-Kendall and Spearman's rho tests showed agreement in their assessments of monthly, seasonal, and annual precipitation trends. The variability of negative and positive trends at various stations points to the need for more detailed studies on the climate change of this region. In the case of whole Dongting basin on the monthly time scale, a significant positive trend is found, while at Yuanjiang River and Xianjiag River both positive and negative significant trends are identified. Only Yuanjiang River has shown a significant trend on the seasonal time scale. No significant trends have been exhibited on the annual time scale in any case. In the case of monthly, Nanxian station exhibited the maximum positive increase in monthly precipitation during the months of July and September. In the case of seasonal, only Tongren station showed a positive trend on the monthly level, and no significant negative trends were detected in both spring and autumn seasons.

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Although potassium channelopathies have been linked to a wide range of neurological con- ditions, the underlying pathogenic mechanism is not always clear, and a systematic summary of clini- cal manifestation is absent. Several neurological disorders have been associated with alterations of calcium-activated potassium channels (KCa channels), such as loss- or gain-of-function mutations, post-transcriptional modification, etc. Here, we outlined the current understanding of the molecular and cellular properties of three subtypes of KCa channels, including big conductance KCa channels (BK), small conductance KCa channels (SK), and the intermediate conductance KCa channels (IK). Next, we comprehensively reviewed the loss- or gain-of-function mutations of each KCa channel and described the corresponding mutation sites in specific diseases to broaden the phenotypic-genotypic spectrum of KCa-related neurological disorders. Moreover, we reviewed the current pharmaceutical strategies targeting KCa channels in KCa-related neurological disorders to provide new directions for drug discovery in anti-seizure medication.