Synthesis and characterization of tantalum(V) boronate clusters : multifunctional Lewis acid cages for binding guests

Open and shut cases: Tantalum(V) boronate clusters [(Cp*Ta)3(μ2‐η2‐RBO2)3(μ2‐O)2(μ2‐OH)(μ3‐OH)] (Cp*=η5‐C5Me5; 1: R=Ph, 2: R=iBu) with Lewis acidic cavities were prepared. Whereas the cavity of 2 is blocked by the iBu groups, that of 1 is open and can bind Lewis basic guests such as ketones (see picture) by interaction with one boronate and one μ3‐OH ligand

Clinicopathological significance of SOX4 expression in primary gallbladder carcinoma

<p>Abstract</p> <p>Aim</p> <p>SOX4, as a member of the SRY-related HMG-box (SOX) transcription factor family, has been demonstrated to be involved in tumorigenesis of many human malignancies; however, its role in primary gallbladder carcinoma (PGC) is still largely unknown. The aim of this study was to investigate SOX4 expression in PGC and its prognostic significance.</p> <p>Methods</p> <p>From 1997 to 2006, 136 patients underwent resection for PGC. The median follow-up was 12.8 months. Immunostainings for SOX4 were performed on these archival tissues. The correlation of SOX4 expression with clinicopathological features including survival was analyzed.</p> <p>Results</p> <p>SOX4 was expressed in 75.0% (102/136) of PGC but not in the normal epithelium of the gallbladder. In addition, the over-expression of SOX4 was significantly associated with low histologic grade (<it>P </it>= 0.02), low pathologic T stage (<it>P </it>= 0.02), and early clinical stage (<it>P </it>= 0.03). The levels of SOX4 immunostainings in PGC tissues with positive nodal metastasis were also significantly lower than those without (<it>P </it>= 0.01). Moreover, Kaplan-Meier curves showed that SOX4 over-expression was significantly related to better overall (<it>P </it>= 0.008) and disease-free survival (<it>P </it>= 0.01). Furthermore, multivariate analyses showed that SOX4 expression was an independent risk factor for both overall (<it>P </it>= 0.03, hazard ratio, 3.682) and disease-free survival (<it>P </it>= 0.04, hazard ratio, 2.215).</p> <p>Conclusion</p> <p>Our data indicate for the first time that the over-expression of SOX4 in PGC was significantly correlated with favorable clinicopathologic features and was an independent prognostic factor for better overall and disease-free survival in patients. Therefore, SOX4 might be an auxiliary parameter for predicting malignant behavior for PGC.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/1534825818694957</url>.</p

A systematic review of factors influencing signage salience in indoor environments

Wayfinding signage is an intermediary public facility that coordinates the relationship between space and people, and it is crucial to help people find their way in complex indoor environments. In people’s cognitive behaviour towards wayfinding signs, the visual salience of the signs is the prerequisite and key to ensuring their effective operation. This paper aims to review published research articles on the effect of indoor environments on the saliency of wayfinding signs. The literature review was conducted by PICO methodology to formulate the research question and develop search strategies. Relevant research articles were identified by systematically searching electronic databases, including Web of Science, ScienceDirect, ProQuest, and EBSCO. This paper summarises two categories of factors influencing signage salience: (1) floor plan factors and (2) environmental factors. This study examined and condensed the attributes of wayfinding signage and their impact on how pedestrians perceive visuals while navigating. Exploring the elements that influence the visual prominence of indoor signs enhances our comprehension of how pedestrians engage with visually guided information indoors. Furthermore, this offers a theoretical foundation for the realm of indoor wayfinding

Shedding light on X17: community report

The workshop “Shedding light on X17” brings together scientists looking for the existence of a possible new light particle, often referred to as X17. This hypothetical particle can explain the resonant structure observed at ∼ 17 MeV in the invariant mass of electron-positron pairs, produced after excitation of nuclei such as 8Be and 4He by means of proton beams at the Atomki Laboratory in Debrecen. The purpose of the workshop is to discuss implications of this anomaly, in particular theoretical interpretations as well as present and future experiments aiming at confirming the result and/or at providing experimental evidence for its interpretation

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Deep learning-based design model for suction caisson foundation in clay

Suction caissons have been used extensively for anchoring and supporting the offshore installations like oil platforms and wind turbines. These foundations are normally subjected to complex combinations of the vertical, horizontal and moment loads (i.e. V, H, M) from the self-weight, wind, wave and currents. In the past decades, extensive studies have been conducted to investigate the combined V-H-M loading behaviour of suction caissons in clay. However, most existing studies are focused on the ultimate bearing capacity, while the deflection response is more critical in foundation design for recent infrastructures like offshore wind turbines. Due to the complex load conditions, predicting the three-dimensional (3D) deflection response of the foundation is still challenging. Machine learning (ML) appears on the research horizon due to its excellent capacity of solving nonlinear problems with desired speed and accuracy. However, conventional machine learning approaches were limited in their capacity to analyze raw natural data without artificial interventions. Meanwhile, the deep learning technique (DL), as a branch of machine learning, allows a machine to be fed with raw data, automatically extract the features, and discover intricate structures in high-dimensional data. The deep learning technique has been used in many fields like language translation, auto-pilot and image recognition. And Deep neural networks, including deep learning algorithms and architectures, are gradually being developed. In light of these backgrounds, this study proposed to develop a deep learning based surrogate model to predict the 3D deflection response of suction caissons under combined V-H-M loading. The advanced three-dimensional nonlinear finite element (FE) simulations under complex V-H-M loading paths were performed on suction caissons of different geometric configurations and in clay soils with different stiffness and strength properties. The 3D FE simulation data was then used to train the deep learning based design model. Three popular neural network structures, i.e., Feed forward Neural Network (FNN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN) have been employed to develop the hybrid surrogate design model. In this study, two different training strategies were proposed for this geotechnical problem. In the first category, the 3D load-deflection behaviour of suction caisson is idealized as a point-to-point mapping problem, i.e. mapping between the deflections (i.e. displacement and rotation) with loads (i..e force and moment). This task was achieved by Fully-Connected Neural Network model (FC-NN) based on FNN, One Dimension Convolution Neural Network model (1D-CNN) based on CNN and Long Short Term Memory model (LSTM) based on RNN. In the second training strategy, the load-deflection response was idealized as a time series process, a line-to-line mapping problem, mapping between the past loading paths (i.e. 10 groups of forces and moments) with future loading paths (i.e. 90 groups of forces and moments). Besides the three neural network models mentioned before, another two complex and advanced models, LSTM Model combined with convolution neural network (1D-CNN+LSTM) and Temporal Convolutional Network model (TCN), are also applied for temporal prediction. The performance and training efficiency of these models were also systematically evaluated by interpolation and extrapolation experiments. Basically, all the models can well capture the 3D deflection response of the foundation with significantly high accuracy (i.e., root mean squared error is smaller than 0.05 and coefficient of determination is near 1.000) than the traditional design approach (such as macro-elements model), and with greater efficiency than the 3D FE simulations. Among all the models, the TCN model has the highest prediction accuracy and robustness. However, the FC-NN model has the simplest model structure and highest computational efficient in learning the non-linear relationship between deflection response and V-H-M load. Besides capturing the relationship between input and output, the deep learning model can also assist to identify the intrinsic failure mechanism. By observing the fluctuation of generalisation ability, the evolution of the failure mechanism of suction caisson with embedment depth was revealed.Civil Engineerin

<inline-formula><graphic file="1687-1847-2011-437842-i1.gif"/></inline-formula>-Stability of Impulsive Neural Networks with Unbounded Time-Varying Delays and Continuously Distributed Delays

This paper is concerned with the problem of -stability of impulsive neural systems with unbounded time-varying delays and continuously distributed delays. Some -stability criteria are derived by using the Lyapunov-Krasovskii functional method. Those criteria are expressed in the form of linear matrix inequalities (LMIs), and they can easily be checked. A numerical example is provided to demonstrate the effectiveness of the obtained results.</p

α- and β-tricalcium phosphate: A density functional study

Biologically important α- and β-tricalcium phosphates (TCPs) have been investigated using ab initio density functional calculations. α- and β-TCP have particularly large unit cells amounting to 312 and 273 atoms, respectively. The relationship between α-TCP and its three subcells, as well as the influence of the distribution of the Ca vacancies apparently existing in β-TCP have been studied. The calculated structural parameters for all the TCP phases, are in substantial agreement with experiment. The Ca-O distance varies continuously, while the P-O bonds distribute over a very narrow range. Oxygens hold the majority of the bonding electrons which reflects the ionic nature of the α and β phases. The results suggest that β-TCP is more stable than the α phase, and that β-TCP with uniformly distributed Ca vacancies is the most stable structure. The three 1/3 α-TCP subcells relax to similar structures, and we found that the full α-TCP cell and its subcells have very similar stability, electronic and structural properties, suggesting that the subcell can be a good approximation for studying α-TCP.The authors gratefully acknowledge the support of the NSERC of Canada and Millenium Biologix, Inc. A.R. was supported by the European Community research and training networks COMELCAN (HPRN-CT-2000-00128) and NANOPHASE (HPRN-CT-2000-00169), MCyT of Spain Grant No. MAT2001-09, and Basque Country University.Peer reviewe