Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1–45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.
Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP 0 ), response index to side-dress N based on harvested yield (RI Harvest ), and side-dress N agronomic efficiency (AE NS ). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015)(2016)(2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP 0 (R 2 = 0.44-0.78) than only using NDVI or RVI (R 2 = 0.26-0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AE NS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AE NS . The INFOA-based PNM strategies could increase marginal returns by 212 $ ha −1 and 70 $ ha −1 , reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%-40% and 11%-65% compared with farmer's typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.Sustainability 2019, 11, 706 2 of 26 as climate change and tropospheric ozone pollution, can also negatively affect crop yields [7,8]. N fertilizer related greenhouse gas (GHG) emissions (nitrous oxide under wet conditions and ammonia under hot conditions) have exceeded the corresponding gains in soil carbon related to its effect on increased biomass by 700% in China [9]. Therefore, optimizing N management for maize production to minimize the adverse environmental impacts is crucially important for sustainable development of agriculture [5,10].The optimum N rate depends on crop N demand and soil N supply. The crop N demand is determined by the plant growth status and grain yield potential, while the soil N supply is a net result of mineralization, immobilization and losses of soil N. They are both influenced by many factors such as seasonal temperature, precipitation, physical and biogeochemical soil properties, and management history [11]. The interactions between soil water and N determine the growth, development and yield of maize. Efficient utilization of water and N can only be realized if they are closely matched [12]. Climate conditions can affect optimum N via various processes in soil such as nitrification, denitrification, leaching, and mineralization, which will modula...
Electroporation-based therapy (EBT), as a high-voltage-pulse technology has been prevalent with favorable clinical outcomes in the treatment of various solid tumors. The aim of this review paper is to promote the clinical translation of EBT for brain tumors. First, we briefly introduced the mechanism of pore formation in a cell membrane activated by external electric fields using a single cell model. Then, we summarized and discussed the current in vitro and in vivo preclinical studies, in terms of (1) the safety and effectiveness of EBT for brain tumors in animal models, and (2) the blood-brain barrier (BBB) disruption induced by EBT. Two therapeutic effects could be achieved in EBT for brain tumors simultaneously, i.e., the tumor ablation induced by irreversible electroporation (IRE) and transient blood-brain barrier (BBB) disruption induced by reversible electroporation (RE). The BBB disruption could potentially improve the uptake of anti-tumor drugs thereby enhancing brain tumor treatment. The challenges that hinder the application of EBT in the treatment of human brain tumors are discussed in the review paper as well.
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