Quasi-deterministic generation of entangled atoms in a cavity

We present a scheme to generate a maximally entangled state of two three-level atoms in a cavity. The success or failure of the generation of the desired entangled state can be determined by detecting the polarization of the photon leaking out of the cavity. With the use of an automatic feedback, the success probability of the scheme can be made to approach unity.Comment: 10 pages, 3 figure

Npas4 regulates IQSEC3 expression in hippocampal somatostatin interneurons to mediate anxiety-like behavior

Activity-dependent GABAergic synapse plasticity is important for normal brain functions, but the underlying molecular mechanisms remain incompletely understood. Here, we show that Npas4 (neuronal PAS-domain protein 4) transcriptionally regulates the expression of IQSEC3, a GABAergic synapse-specific guanine nucleotide-exchange factor for ADP-ribosylation factor (ARF-GEF) that directly interacts with gephyrin. Neuronal activation by an enriched environment induces Npas4-mediated upregulation of IQSEC3 protein specifically in CA1 stratum oriens layer somatostatin (SST)-expressing GABAergic interneurons. SST+ interneuron-specific knockout (KO) of Npas4 compromises synaptic transmission in these GABAergic interneurons, increases neuronal activity in CA1 pyramidal neurons, and reduces anxiety behavior, all of which are normalized by the expression of wild-type IQSEC3, but not a dominant-negative ARF-GEF-inactive mutant, in SST+ interneurons of Npas4-KO mice. Our results suggest that IQSEC3 is a key GABAergic synapse component that is directed by Npas4 and ARF activity, specifically in SST+ interneurons, to orchestrate excitation-to-inhibition balance and control anxiety-like behavior.1

Chiral-selective aggregation of serine and glucose in the gas phase

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Specific and Nonspecific Bindings of Alkaline-Earth Metal Ions to Guanine-Quadruplex Thrombin-Binding Aptamer DNA

Specific and nonspecific bindings of alkaline-earth metal ions to the thrombin-binding aptamer (TBA) DNA d(GGTTGGTGTGGTTGG) were studied using electrospray ionization (ESI) mass spectrometry (MS). A single-stranded DNA d(AATTAATGTAATTAA) was used as a control for nonspecific binding. Both 1:1 and 2:1 metal-DNA complexes formed in 3:1 water/isopropanol were detected with ESI-MS and their binding constants were determined by the titration method. The 1:1 binding constant (K-1) of Sr2+ or Ba2+ for TBA was three orders of magnitude greater than their K-1 for the control DNA, whereas K-1 of Mg2+ or Ca2+ for TBA was comparable to their K-1 for the control DNA. To the contrary, the second binding constant (K-2) was nearly the same independent of the metal ion and DNA. To see if addition of metal ion led to a structural change in DNA, the circular dichroism (CD) spectra of DNA in ESI solvent were obtained in the presence and absence of metal ion. TBA showed characteristic bands of the chair-type guanine-quadruplex (G4) structure. Significantly, addition of Sr2+ or Ba2+ enhanced the G4 bands of TBA, but neither Mg2+ nor Ca2+ induced a change, indicating that Sr2+ and Ba2+ interact strongly with G4 TBA and stabilize it. Control DNA showed no special motif and none of the four metal ions affected its CD spectra. Binding constants and CD results suggest that only the first binding of Sr2+ and Ba2+ to G4 TBA is specific and the first binding of Mg2+ and Ca2+ as well as the second binding of all four metal ions are nonspecific. The specific and nonspecific bindings of alkaline-earth metal ions to G4 TBA are further rationalized in terms of ionic radius, coordination number, and Gibbs free energy of dehydration of the metal ion. (c) 2012 Elsevier B.V. All rights reserved.X1143sciescopu

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This study estimated water quality constituents especially in CBOD, TN, TP, and Chlorophyll-a in Sayeun reservoir by using CEQUAL- W2 model. With water quality data in surface, middle, and bottom of water body, the model calibration was implemented by changing water quality parameters in the model. Using the calibrated model, we performed scenario analysis to investigate the variation of water quality in respond to different elevations. CBOD, TN, and Chlorophyll-a concentration predicted by the model showed a good agreement with the measured the trend of the concentrations. However, TP estimated was relatively low tendency by the model. We found that water quality (i.e., CBOD, TN, TP, and chlorophyll-a) at both water surface and middle layer was degraded in respond to the decrease of water level by 6.8m. Although results of CBOD in both surface and middle layers decreased to about 2% and 1%, respectively, in the surface layer, TN, TP, and Chlorophyll-a increased to about 4%, 3%, and 51% and, in the middle, TN and TP increased up to 12% and 3%, respectively. In the middle layer, especially, water quality degraded mainly due to increased organic matter from growth, mortality, decay, and sedimentation of algae and anaerobic release of nutrients from sediment. This study demonstrated that water quality could be influenced by controlling water surface elevation, implying that there is a need to control nutrient inflow and re-suspension from sediment in the reservoir.clos

Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning

Harmful algal blooms (HABs) have been frequently occurred with releasing toxic substances, which typically lead to water quality degradation and health problems for humans and aquatic animals. Hence, accurate quantitative analysis and prediction of HABs should be implemented to detect, monitor, and manage severe algal blooms. However, the traditional monitoring required sufficient expense and labor while numerical models were restricted in terms of their ability to simulate the algae dynamic. To address the challenging issue, this study evaluates the applicability of deep learning to simulate chlorophyll-a (Chl-a) and phycocyanin (PC) with the internet of things(loT) system. Our research adopted LSTM models for simulating Chl-a and PC. Among LSTM models, the attention LSTM model achieved superior performance by showing 0.84 and 2.35 (g/L) of the correlation coefficient and root mean square error. Among preprocessing methods, the z-score method was selected as the optimal method to improve model performance. The attention mechanism highlighted the input data from July to October, indicating that this period was the most influential period to model output. Therefore, this study demonstrated that deep learning with loT system has the potential to detect and quantify cyanobacteria, which can improve the eutrophication management schemes for freshwater reservoirs

Drone-borne sensing of major and accessory pigments in algae using deep learning modeling

Intensive algal blooms increasingly degrade the inland water quality. Hence, this study aimed to analyze the algal phenomena quantitatively and qualitatively using synoptic monitoring, algal pigment analysis, and a deep learning model. Water surface reflectance was measured using field monitoring and drone hyperspectral image sensing. The algal experiment conducted on the water samples provided data on major pigments including chlorophyll-a and phycocyanin, accessory pigments including lutein, fucoxanthin, and zeaxanthin, and absorption coefficients. Based on the reflectance and absorption coefficient spectral inputs, a one-dimensional convolutional neural network (1D-CNN) was developed to estimate the concentrations of the major and minor pigments. The 1D-CNN could model periodic trends of chlorophyll-a, phycocyanin, lutein, fucoxanthin, and zeaxanthin compared to the observed ones, with R-2 values of 0.87, 0.71, 0.76, 0.78, and 0.74, respectively. In addition, major and secondary pigment maps developed by applying the trained 1D-CNN model to the processed drone hyperspectral image inputs successfully provided spatial information regarding the spots of interest. The model provided explicit algal biomass information using the estimated major pigments and implicit taxonomical information using accessory pigments such as green algae, diatoms, and cyanobacteria. Therefore, we provide strong evidence of the extendibility of deep learning models for analyzing various algal pigments to gain a better understanding of algal blooms