New isoforms and assembly of glutamine synthetase in the leaf of wheat (Triticum aestivum L.).

Glutamine synthetase (GS; EC 6.3.1.2) plays a crucial role in the assimilation and re-assimilation of ammonia derived from a wide variety of metabolic processes during plant growth and development. Here, three developmentally regulated isoforms of GS holoenzyme in the leaf of wheat (Triticum aestivum L.) seedlings are described using native-PAGE with a transferase activity assay. The isoforms showed different mobilities in gels, with GSII>GSIII>GSI. The cytosolic GSI was composed of three subunits, GS1, GSr1, and GSr2, with the same molecular weight (39.2kDa), but different pI values. GSI appeared at leaf emergence and was active throughout the leaf lifespan. GSII and GSIII, both located in the chloroplast, were each composed of a single 42.1kDa subunit with different pI values. GSII was active mainly in green leaves, while GSIII showed brief but higher activity in green leaves grown under field conditions. LC-MS/MS experiments revealed that GSII and GSIII have the same amino acid sequence, but GSII has more modification sites. With a modified blue native electrophoresis (BNE) technique and in-gel catalytic activity analysis, only two GS isoforms were observed: one cytosolic and one chloroplastic. Mass calibrations on BNE gels showed that the cytosolic GS1 holoenzyme was ~490kDa and likely a dodecamer, and the chloroplastic GS2 holoenzyme was ~240kDa and likely a hexamer. Our experimental data suggest that the activity of GS isoforms in wheat is regulated by subcellular localization, assembly, and modification to achieve their roles during plant development

Neural circuitry mechanisms in decision-making

The brain is the exclusive organ that makes decisions for humans and the society. In this thesis, I will discuss recent advances in the understanding of neuroscientific mechanisms in decision-making. Decision-making is not a new topic in the human history, but it has existed for thousands of years. We made numerous decisions over centuries, and the consequences of those decisions transformed the landscape of the Earth, established the norms for our society, and revolutionized our way of thinking. To understand the concepts and frameworks for decision-making, I will review significant intellectual advances in the history, start with several simple enough models to describe and predict decisionmaking behaviors. However, the models, concepts, and logical deduction do not provide enough understanding of the decision-making process. We should also aware limitations, which determine our choice processes and outcomes, such as how much information we have, how much cognitive power we can put into a problem. After the established the models that sufficiently contain the errors and limitations of decision-making, the central question is to understand the brain, which operates the whole process. As the brain is specialized into functional regions, it is easier to build hypothesis in decision-making process if we conceptually break down the decision-making process into discrete stages. Firstly, attention is the foremost important mechanism controls our actions and choices. Only with attention allocated to the problem, one can then represent the problem to related brain areas, mobilize memory and the affective system to retrieve internal status, start evaluating different choices, plan and take action, reevaluate the outcome and update the original memory and representation of values. To further dissect the decisionmaking mechanism in the brain, particularly in this thesis, we examined and discussed neural circuits that are regulated by local interneurons and long-range neuromodulators. Moreover, such knowledge can be robustly translated into an understanding of various types of mental disorders. In this thesis, three studies are included to illustrate how different neural circuits could alter animals' decision-making process and performance. In Paper I, the prefrontal fast-spiking interneurons were recorded and manipulated in a task measuring a goal-directed behavior and top-down attention. The neuronal activities of fast-spiking cells in the medial prefrontal cortex were significantly regulated during the attentional process, and such pattern defined the firing of the principal neurons with a phase-locking mechanism. We further showed enhanced gamma synchrony characterized the successful allocation of attention. Moreover, modulation of gamma synchrony using optogenetics can significantly change the animals' performance in top-down attention. In Paper II, we investigated the functions of fast-spiking NMDA glutamate receptors in depressive-like behavior. Using a genetically modified animal model, we compared the phenotypes between the fast-spiking NMDA receptor knockout animals and controls. There was no significant difference between two groups in response to non-competitive NMDA receptor antagonist in expressing depressive-like symptoms or in anhedonia. In Paper III, we investigated the role of the long-range modulatory serotonergic system in impulsive behaviors. Activation of the ascending serotonergic population with optogenetics slightly alleviate the level of impulsiveness in both impulsive action and impulsive choice. Conversely, optogenetic inhibition of the ascending serotonergic population significantly increased impulsive action and impulsive choice. Furthermore, using optical calcium imaging, our results illustrated that the neuronal activities of the ascending serotonergic population strongly responded to the delivery of reward. In summary, the work of this thesis provides a further understanding and new insights of functional roles of particular neuronal subpopulations in different discrete stages of decision-making

Combining a Fuzzy Matter-Element Model with a Geographic Information System in Eco-Environmental Sensitivity and Distribution of Land Use Planning

Sustainable ecological and environmental development is the basis of regional development. The sensitivity classification of the ecological environment is the premise of its spatial distribution for land use planning. In this paper, a fuzzy matter-element model and factor-overlay method were employed to analyze the ecological sensitivity in Yicheng City. Four ecological indicators, including soil condition,, water condition,, atmospheric conditions and biodiversity were used to classify the ecological sensitivity. The results were categorized into five ranks: insensitive, slightly sensitive, moderately sensitive, highly sensitive and extremely sensitive zones. The spatial distribution map of environmental sensitivity for land use planning was obtained using GIS (Geographical Information System) techniques. The results illustrated that the extremely sensitive and highly sensitive areas accounted for 14.40% and 30.12% of the total area, respectively, while the moderately sensitive and slightly sensitive areas are 25.99% and 29.49%, respectively. The results provide the theoretical foundation for land use planning by categorizing all kinds of land types in Yicheng City

Study on Disposal and Destruction of Abandoned Chemical Weapons by the Japanese Army in China

AbstractAbandoned chemical weapons (ACW) by the Japanese Army in China belong to old chemical weapons, which were produced in World WarII, buried underground or underwater. There are significant differences between old chemical weapons and chemical weapons in stock. The aim of the paper is the investigation and study on the disposal and destruction of ACW. We present the methods how to recognize and identify ACW, how to distinguish what kind of chemical warfare agents inside it. Its destruction principle and basic program of ACW, the operation technological processes for destruction of yellow munitions, red munitions, irregular munitions, contaminated solid material and water, other wastewater are specially emphasized

Human posture recognition based on multiple features and rule learning

The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random sub-Weili Ding space methods to create different samples and features for improved classification of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs