Mutation analysis of the WNT4 gene in Han Chinese women with premature ovarian failure

BACKGROUND: The WNT4 gene plays an important role in female sex determination and differentiation. It also contributes to maintaining of the ovaries and the survival of follicles. METHODS: We sequenced the coding region and splice sites of WNT4 in 145 Han Chinese women with premature ovarian failure (POF) and 200 healthy controls. RESULTS: Only one novel variation, in Exon 2 (195C > T), was detected among the women with POF. However, this synonymous variation did not result in a change in amino acid sequence (65 Asp > Asp). No further variants were found in any of the samples. CONCLUSION: Although we cannot provide any evidence that it is a possible disease-causing gene, this study is the first attempt to investigate the possible role of WNT4 in Han Chinese women with POF

From Speech to Data: Unraveling Google's Use of Voice Data for User Profiling

Smart home voice assistants enable users to conveniently interact with IoT devices and perform Internet searches; however, they also collect the voice input that can carry sensitive personal information about users. Previous papers investigated how information inferred from the contents of users' voice commands are shared or leaked for tracking and advertising purposes. In this paper, we systematically evaluate how voice itself is used for user profiling in the Google ecosystem. To do so, we simulate various user personas by engaging with specific categories of websites. We then use \textit{neutral voice commands}, which we define as voice commands that neither reveal personal interests nor require Google smart speakers to use the search APIs, to interact with these speakers. We also explore the effects of the non-neutral voice commands for user profiling. Notably, we employ voices that typically would not match the predefined personas. We then iteratively improve our experiments based on observations of profile changes to better simulate real-world user interactions with smart speakers. We find that Google uses these voice recordings for user profiling, and in some cases, up to 5 out of the 8 categories reported by Google for customizing advertisements are altered following the collection of the voice commands.Comment: 11 pages, 1 figure, 7 table

An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Three-dimensional convolutional neural networks (3DCNNs), a rapidly evolving modality of deep learning, has gained popularity in many fields. For oral cancers, CT images are traditionally processed using two-dimensional input, without considering information between lesion slices. In this paper, we established a 3DCNNs-based image processing algorithm for the early diagnosis of oral cancers, which was compared with a 2DCNNs-based algorithm. The 3D and 2D CNNs were constructed using the same hierarchical structure to profile oral tumors as benign or malignant. Our results showed that 3DCNNs with dynamic characteristics of the enhancement rate image performed better than 2DCNNS with single enhancement sequence for the discrimination of oral cancer lesions. Our data indicate that spatial features and spatial dynamics extracted from 3DCNNs may inform future design of CT-assisted diagnosis system

Quantization of the minimal nilpotent orbits and the quantum Hikita conjecture

We show that the specialized quantum D-module of the equivariant quantum cohomology ring of the minimal resolution of an ADE singularity is isomorphic to the D-module of graded traces on the minimal nilpotent orbit in the Lie algebra of the same type. This generalizes a recent result of Shlykov [Hikita conjecture for the minimal nilpotent orbit, to appear in Proc. AMS, https://doi.org/10.1090/proc/15281] and hence verifies in this case the quantum version of Hikita's conjecture, proposed by Kamnitzer, McBreen and Proudfoot [The quantum Hikita conjecture, Advances in Mathematics 390 (2021) 107947]. We also show analogous isomorphisms for singularities of BCFG type

Serviceability behaviour of ultra-high performance fibre-reinforced concrete composite slabs

This thesis presents a comprehensive study of composite slabs comprised of a steel profiled deck and ultra-high-performance fibre-reinforced concrete (UHPFRC). The study begins by examining the local bond properties of composite slabs at the contact surface, investigating the impact of concrete mix designs and profiled deck rib openings on the longitudinal bond behaviour. Subsequently, an experimental study is conducted to analyse the behaviour of simply-supported and continuous composite slabs with varying fibre content, focusing on flexural behaviour, load-slip performance and cracking resistance at both the serviceability and ultimate limit states. These experimental results are then used to validate a new numerical model that allows for changing fibre contents and time-dependent behaviour as well as the partial interaction (PI) behaviour between concrete and steel components (for both internal reinforcements and profiled deck). This numerical model is a displacement-based approach that models the serviceability flexural behaviour of simply-supported composite slabs. Finally, based on the same theory as numerical model, an analytical solution which considers non-linear shrinkage strains is developed to provide a quick and rational solution for predicting the load-deflection behaviour of composite slabs at the serviceability limit state. In the first chapter, the characteristics of UHPFRC and the significant benefits of its application as part of steel concrete composites are introduced. The current studies that investigate the behaviour of steel concrete composite structures in experimental and numerical studies and approaches used to evaluate the performance of composites are summarised. Learning from these studies, research gaps and significances of these studies covered in this thesis are identified. The second chapter focuses on the study of local bond properties conducting by a new single-lap test apparatus. This new test apparatus is designed to avoid material pre-failure (such as buckling of profiled deck and concrete crushing) and reduce the friction impact from additional clamping force. Six trial tests are conducted to study the impact of various bonded length and then the shortest one is selected for the following 48 tests to study bond behaviour of steel concrete composites in terms of two different steel decks (dove-tailed and trapezoidal types), varying fibre contents and the presence/absence of coarse aggregate in concrete mixing. This interfacial bond-slip property is important because longitudinal shear transfer dominates the behaviour of composites such as ultimate capacity and flexural stiffness. The third chapter reports on an experimental investigation aimed at quantifying the benefits of application of UHPFRC to composite slabs with profiled steel decks. Steel concrete composites comprising of steel fibre reinforced concrete (SFRC) and high strength concrete (HSC) have been extensively investigated in the previous studies while the application of UHPFRC as potential substitute because of its high compressive strength and ductility, tensile strain-hardening behaviour and superior post-crack characteristics as one component of composite structures have not been given enough attention. This experimental study focuses on the performance of six simply-supported composite slabs and three continuous composite slabs with fibre content of 0%, 1% and 2%. These tests aims to evaluate the influence of fibre content on the behaviour of composite slabs in terms of deformation, stiffness, crack control and moment redistribution. Furthermore, a rotational displacement-based numerical model is proposed to evaluate the behaviour of composite slab in serviceability with considering long-term effects. The development of this unified approach is necessary because there are numerous combinations for fibre contents and fibre types in cementitious matrix when designing a composite slab. Therefore, a general and applicable model for predicting behaviour of different composite slab designs is required. This model only requires fundamental material properties obtained from small scale tests instead of full-scale slabs loading test results which provides a financial benefit. With application of PI theory in a tension stiffening analysis to determine relative slip between concrete and steel components (local behaviour) and in a mechanics segmental analysis to determine moment/rotation/strain (M//ɛ) properties (global behaviour) for the non-debonded and debonded region, this model predicts crack behaviour, longitudinal slip and flexural performance of profiled slab at the serviceability limit state. This numerical model is validated by comparing with a total of six simply-supported experimental test results with applying their corresponding tested material properties as shown in Chapter 3. Finally, on the basis of numerical model, in order to allow for realistic nonlinear shrinkage strains and relative slip occurring between concrete and steel decking, a closed-form analytical solution is developed to estimate the load-deflection behaviour of composites slab at the serviceability limit state up until the formation of macro-cracking. In this approach, two loading configurations are considered, a central point load and uniform distributed load (UDL), and their corresponding elemental curvature expressions in segmental analysis are demonstrated separately. To simplify the process, all the material properties are assumed to be linear-elastic while concrete tensile strain-stress relationship is bi-linear. This approach is validated by comparing with numerical model results. Therefore, this analytical solutions can be used as design guideline for evaluating the load-deflection performance of profiled slabs in serviceability behaviour.Thesis (Ph.D.) -- University of Adelaide, School of Architecture and Civil Engineering, 202

SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty

Generating stable and robust grasps on arbitrary objects is critical for dexterous robotic hands, marking a significant step towards advanced dexterous manipulation. Previous studies have mostly focused on improving differentiable grasping metrics with the assumption of precisely known object geometry. However, shape uncertainty is ubiquitous due to noisy and partial shape observations, which introduce challenges in grasp planning. We propose, SpringGrasp planner, a planner that considers uncertain observations of the object surface for synthesizing compliant dexterous grasps. A compliant dexterous grasp could minimize the effect of unexpected contact with the object, leading to more stable grasp with shape-uncertain objects. We introduce an analytical and differentiable metric, SpringGrasp metric, that evaluates the dynamic behavior of the entire compliant grasping process. Planning with SpringGrasp planner, our method achieves a grasp success rate of 89% from two viewpoints and 84% from a single viewpoints in experiment with a real robot on 14 common objects. Compared with a force-closure based planner, our method achieves at least 18% higher grasp success rate

Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation

Developing robot controllers in a simulated environment is advantageous but transferring the controllers to the target environment presents challenges, often referred to as the "sim-to-real gap". We present a method for continuous improvement of modeling and control after deploying the robot to a dynamically-changing target environment. We develop a differentiable physics simulation framework that performs online system identification and optimal control simultaneously, using the incoming observations from the target environment in real time. To ensure robust system identification against noisy observations, we devise an algorithm to assess the confidence of our estimated parameters, using numerical analysis of the dynamic equations. To ensure real-time optimal control, we adaptively schedule the optimization window in the future so that the optimized actions can be replenished faster than they are consumed, while staying as up-to-date with new sensor information as possible. The constant re-planning based on a constantly improved model allows the robot to swiftly adapt to the changing environment and utilize real-world data in the most sample-efficient way. Thanks to a fast differentiable physics simulator, the optimization for both system identification and control can be solved efficiently for robots operating in real time. We demonstrate our method on a set of examples in simulation and show that our results are favorable compared to baseline methods

One-Shot Transfer of Long-Horizon Extrinsic Manipulation Through Contact Retargeting

Extrinsic manipulation, the use of environment contacts to achieve manipulation objectives, enables strategies that are otherwise impossible with a parallel jaw gripper. However, orchestrating a long-horizon sequence of contact interactions between the robot, object, and environment is notoriously challenging due to the scene diversity, large action space, and difficult contact dynamics. We observe that most extrinsic manipulation are combinations of short-horizon primitives, each of which depend strongly on initializing from a desirable contact configuration to succeed. Therefore, we propose to generalize one extrinsic manipulation trajectory to diverse objects and environments by retargeting contact requirements. We prepare a single library of robust short-horizon, goal-conditioned primitive policies, and design a framework to compose state constraints stemming from contacts specifications of each primitive. Given a test scene and a single demo prescribing the primitive sequence, our method enforces the state constraints on the test scene and find intermediate goal states using inverse kinematics. The goals are then tracked by the primitive policies. Using a 7+1 DoF robotic arm-gripper system, we achieved an overall success rate of 80.5% on hardware over 4 long-horizon extrinsic manipulation tasks, each with up to 4 primitives. Our experiments cover 10 objects and 6 environment configurations. We further show empirically that our method admits a wide range of demonstrations, and that contact retargeting is indeed the key to successfully combining primitives for long-horizon extrinsic manipulation. Code and additional details are available at stanford-tml.github.io/extrinsic-manipulation.Comment: 8 pages, 6 figure

Crystal structure and morphology of CeO₂ doped stabilized zirconia ceramics under high-frequency microwave field sintering

AbstractUnder high-frequency microwave irradiation, zirconia ceramics were prepared by sintering nano-CeO₂ (Ce = 7 mol%) doped zirconia powder. The different effects of temperature environment on the phase structure transformation, surface functional groups, microstructure, growth process, and density of doped zirconia were analyzed, and the optimized microwave sintering process for zirconia was determined. The experimental results reveal that the tetragonal phase of zirconia is positively correlated with the temperature when the temperature reaches about 1100 °C in the studied range. The reason is that the grain grows with the increase of sintering temperature, and the surface energy of grain decreases, which leads to the fluctuation of tetragonal phase content. The density of zirconia reaches 98.03% at 1300 °C, and the growth activation energy is 27.40 kJ/mol. There is no abnormal growth of zirconia particles, and the phase transition temperature decreases, which is attributed to the efficient heating of microwave and the incorporation of nano-ceria stabilizer.Abstract Under high-frequency microwave irradiation, zirconia ceramics were prepared by sintering nano-CeO₂ (Ce = 7 mol%) doped zirconia powder. The different effects of temperature environment on the phase structure transformation, surface functional groups, microstructure, growth process, and density of doped zirconia were analyzed, and the optimized microwave sintering process for zirconia was determined. The experimental results reveal that the tetragonal phase of zirconia is positively correlated with the temperature when the temperature reaches about 1100 °C in the studied range. The reason is that the grain grows with the increase of sintering temperature, and the surface energy of grain decreases, which leads to the fluctuation of tetragonal phase content. The density of zirconia reaches 98.03% at 1300 °C, and the growth activation energy is 27.40 kJ/mol. There is no abnormal growth of zirconia particles, and the phase transition temperature decreases, which is attributed to the efficient heating of microwave and the incorporation of nano-ceria stabilizer

Source Localization for Cross Network Information Diffusion

Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks, where one network's functionality depends on the other. Source localization on cross-networks entails locating diffusion sources on the source network by only giving the diffused observation in the target network. The task is challenging due to challenges including: 1) diffusion sources distribution modeling; 2) jointly considering both static and dynamic node features; and 3) heterogeneous diffusion patterns learning. In this work, we propose a novel method, namely CNSL, to handle the three primary challenges. Specifically, we propose to learn the distribution of diffusion sources through Bayesian inference and leverage disentangled encoders to separately learn static and dynamic node features. The learning objective is coupled with the cross-network information propagation estimation model to make the inference of diffusion sources considering the overall diffusion process. Additionally, we also provide two novel cross-network datasets collected by ourselves. Extensive experiments are conducted on both datasets to demonstrate the effectiveness of \textit{CNSL} in handling the source localization on cross-networks.Comment: Code and data are available at: https://github.com/tanmoysr/CNSL