Time-Resolved in Situ Visualization of the Structural Response of Zeolites During Catalysis

Zeolites are three-dimensional aluminosilicates having unique properties from the size and connectivity of their sub-nanometer pores, the Si/Al ratio of the anionic framework, and the charge-balancing cations. The inhomogeneous distribution of the cations affects their catalytic performances because it influences the intra-crystalline diffusion rates of the reactants and products. However, the structural deformation regarding inhomogeneous active regions during the catalysis is not yet observed by conventional analytical tools. Here we employ in situ X-ray free electron laser-based time-resolved coherent X-ray diffraction imaging to investigate the internal deformations originating from the inhomogeneous Cu ion distributions in Cu-exchanged ZSM-5 zeolite crystals during the deoxygenation of nitrogen oxides with propene. We show that the interactions between the reactants and the active sites lead to an unusual strain distribution, confirmed by density functional theory simulations. These observations provide insights into the role of structural inhomogeneity in zeolites during catalysis and will assist the future design of zeolites for their applications

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Molecular characterization of Drosophila genes encoding a G protein alpha subunit and a phospholipase C that are expressed in the central nervous system

Two Drosophila genes (dgo and p21) have been isolated and characterized. The dgo gene encoding a G protein $\alpha$ subunit has been isolated by screening genomic and adult head cDNA libraries using bovine transducin $\alpha$ subunit cDNA as probe, and the p21 gene encoding a PLC has been isolated by screening a genomic library using Drosophila norpA (a PLC gene involved in phototransduction) cDNA as probe. The dgo gene, which maps to 47A on the second chromosome, encodes two proteins which are both 354 amino acids long but differ in seven amino acids in the amino terminal region. The deduced amino-acid sequences of the two proteins are 81% identical to that of a rat G\sb{\rm o} \alpha subunit. Analysis of genomic clones revealed that there are eight coding exons and that the putative transcripts for the two proteins differ in the 5\sp\prime-noncoding regions and the first coding exons but share the remaining six coding exons. The arrangement of two different 5\sp\prime-noncoding regions on the gene suggests that two different promoters regulate the expression of the transcripts encoding the two proteins. RNA blot analysis detected three transcripts: a 3.9 kb transcript found at all stages of development, a 5.4 kb transcript present predominantly in adult heads, and a 3.4 kb transcript present only in adult bodies. In situ hybridizations of a cDNA probe to adult tissue sections showed that the gene is expressed abundantly in neuronal cell bodies in the brain, optic lobe, and thoracic ganglia. The p21 gene, which maps to 21C on the second chromosome, encodes two proteins which are 1305 and 1312 amino acids long. The two deduced protein sequences are similar in sequence and overall structure to the $\beta$-class of mammalian PLCs and differ only by seven consecutive amino acids present near C-terminus of one of the proteins, but not the other. Partial analysis of genomic clones provided the evidence for alternative splicing responsible for this difference between two deduced protein sequences. RNA blot analysis detected two prominent transcripts: a 5.6 kb transcript expressed throughout development, and a 7.0 kb transcript found only in adult heads. In situ hybridization of a cDNA probe to adult tissue sections showed that the gene is expressed abundantly in neuronal cell bodies in the brain, optic lobe, and thoracic ganglia. The developmental profiles of the dgo and p21 transcripts were very similar to each other. Furthermore, in situ hybridizations of dgo and p21 cDNAs to adjacent sections of adult and larvae showed nearly identical distributions of the transcripts in the nervous system. The results suggest that dgo and p21 genes are involved in the same signal transduction pathways of Drosophila

Determination and Quantification of Heavy Metals in Sediments through Laser-Induced Breakdown Spectroscopy and Partial Least Squares Regression

Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method—namely, the Savitzky–Golay (SG) derivative—was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (R2), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the R2 and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers

Preference-based serial decisions are counterintuitively influenced by emotion regulation and conscientiousness.

Our decisions have a temporally distributed order, and different choice orders (e.g., choosing preferred items first or last) can lead to vastly different experiences. We previously found two dominant strategies (favorite-first and favorite-last) in a preference-based serial choice setting (the 'sushi problem'). However, it remains unclear why these two opposite behavioral patterns arise: i.e., the mechanisms underlying them. Here we developed a novel serial-choice task, using pictures based on attractiveness, to test for a possible shared mechanism with delay discounting, the 'peak-end' bias (i.e., preference for experienced sequences that end well), or working-memory capacity. We also collected psychological and clinical metric data on personality, depression, anxiety, and emotion regulation. We again found the two dominant selection strategies. However, the results of the delay, peak-end bias, and memory capacity tasks were not related to serial choice, while two key psychological metrics were: emotion regulation and conscientiousness (with agreeableness also marginally related). Favorite-first strategists actually regulated emotions better, suggesting better tolerance of negative outcomes. Whereas participants with more varied strategies across trials were more conscientious (and perhaps agreeable), suggesting that they were less willing to settle for a single, simpler strategy. Our findings clarify mechanisms underlying serial choice and show that it may reflect a unique ability to organize choices into sequences of events

Applying Deep Learning to the Heat Production Planning Problem in a District Heating System

District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In the second planning phase, the deep learning module with trained parameters predicts production plan for the test scenarios. The computational experiments show that after the training process is completed, it has the characteristic of quickly deriving results appropriate to the situation. By combining optimization and deep learning modules in a solution framework, it is expected that the proposed algorithm could be applied to online optimization of district heating systems

Tcf7l2 plays crucial roles in forebrain development through regulation of thalamic and habenular neuron identity and connectivity

The thalamus acts as a central integrator for processing and relaying sensory and motor information to and from the cerebral cortex, and the habenula plays pivotal roles in emotive decision making by modulating dopaminergic and serotonergic circuits. These neural compartments are derived from a common developmental progenitor domain, called prosomere 2, in the caudal forebrain. Thalamic and habenular neurons exhibit distinct molecular profile, neurochemical identity, and axonal circuitry. However, the mechanisms of how their progenitors in prosomere 2 give rise to these two populations of neurons and contribute to the forebrain circuitry remains unclear. In this study, we discovered a previously unrecognized role for Tcf7l2, a transcription factor known as the canonical Wnt nuclear effector and diabetes risk-conferring gene, in establishing neuronal identity and circuits of the caudal forebrain. Using genetic and chemical axon tracers, we showed that efferent axons of the thalamus, known as the thalamocortical axons (TCAs), failed to elongate normally and strayed from their normal course to inappropriate locations in the absence of Tcf7l2. Further experiments with thalamic explants revealed that the pathfinding defects of Tcf7l2-deficient TCAs were associated at least in part with downregulation of guidance receptors Robo1 and Robo2 expression. Moreover, the fasciculus retroflexus, the main habenular output tract, was missing in embryos lacking Tcf7l2. These axonal defects may result from dysregulation of Nrp2 guidance receptor. Strikingly, loss of Tcf7l2 caused a post-mitotic identity switch between thalamic and habenular neurons. Despite normal acquisition of progenitor identity in prosomere 2, Tcf7l2-deficient thalamic neurons adopted a molecular profile of a neighboring forebrain derivative, the habenula. Conversely, habenular neurons failed to maintain their normal post-mitotic neuronal identity and acquired a subset of thalamic neuronal features in the absence of Tcf7l2. Our findings suggest a unique role for Tcf7l2 in generating distinct neuronal phenotypes from homogeneous progenitor population, and provide a better understanding of the mechanism underlying neuronal specification, differentiation, and connectivity of the developing caudal forebrain

Quick assessment with controlled attenuation parameter for hepatic steatosis in children based on MRI-PDFF as the gold standard

Abstract Background Controlled attenuation parameter (CAP) is a recently introduced, non-invasive and quantitative method to evaluate hepatic steatosis demonstrated in adults, but limited in obesity and not well evaluated in children. The aim of this study was to investigate the diagnostic performance for assessing hepatic steatosis grades using CAP in children based on MR proton density fat fraction (PDFF). Methods Children evaluated for non-alcoholic fatty liver disease (NAFLD) who were assessed for PDFF and CAP were enrolled retrospectively. Hepatic steatosis grades 0–3 were classified according to PDFF using cutoff values of 6, 17.5, and 23.3%. Subgroup analyses were performed in non-obese and obese groups using the 95th percentile body mass index (BMI) as a cutoff and BMI30 group when BMI > 30 kg/m2. Pearson’s correlations between variables were also analyzed. Results In a total of 86 children, there were 53 in the obese group including 17 of the BMI30 group. CAP demonstrated 98.7% sensitivity and 80% specificity for diagnosing grades 1–3 vs. grade 0 using a cutoff value of 241 dB/m (area under the curve = 0.941, p  30 kg/m2

Single-Trace Attacks on the Message Encoding of Lattice-Based KEMs

We propose single-trace side-channel attacks against lattice-based KEMs, the current candidates of the NIST\u27s standardization project. More specifically, we analyze the message encoding in the encapsulation of lattice-based KEMs to obtain the ephemeral session keys, concluding that a single trace leakage allows a whole key recovery: our implementation on a ChipWhisperer UFO STM32F3 target board shows 100% success rates for Crystals-Kyber and Saber regardless of optimization level, and more than a 79% success rate for FrodoKEM. We further show that our attack methodologies are not restricted to the above algorithms but widely applicable to other NIST PQC candidates, including LAC, NewHope, NTRU Prime, and NTRU

New Insight into the Reaction Mechanism for Exceptional Capacity of Ordered Mesoporous SnO2 Electrodes via Synchrotron-Based X-ray Analysis

Tin oxide-based materials, operating via irreversible conversion and reversible alloying reaction, are promising lithium storage materials due to their higher capacity. Recent studies reported that nanostructured SnO2 anode provides higher capacity beyond theoretical capacity based on the alloying reaction mechanism; however, their exact mechanism remains still unclear. Here, we report the detailed lithium storage mechanism of an ordered mesoporous SnO2 electrode material. Synchrotron X-ray diffraction and absorption spectroscopy reveal that some portion of Li2O decomposes upon delithiation and the resulting oxygen reacts with Sn to form the SnOx phase along with dealloying of LixSn, which are the main reasons for unexpected high capacity of an ordered mesoporous SnO2 material. This finding will not only be helpful in a more complete understanding of the reaction mechanism of Sn-based oxide anode materials but also will offer valuable guidance for developing new anode materials with abnormal high capacity for next generation rechargeable batteries.