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X-chromosome inactivation (XCI) is the form of dosage compensation in mammalian female cells to balance X-linked gene expression levels of the two sexes. Many diseases are related to XCI due to inactivation escape and skewing, and the symptoms and severity of these diseases also largely depend on the status of XCI. They can be divided into 3 types: X-linked diseases, diseases that are affected by XCI escape, and X-chromosome aneuploidy. Here, we review representative diseases in terms of their definition, symptoms, and XCI’s role in the pathogenesis of these diseases.

The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were ( 27.8 ± 30.6 ) μm and ( 29.1 ± 30.9 ) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was ( − 6.31 ± 2.82 ) μm, and that in group B was ( − 6.45 ± 3.06 ) μm. The 95% confidence interval of the difference between the two is −7.66~−5.43 μm. In patients with severe dry eye, the average CCT was ( − 3.78 ± 1.13 ) μm in group A and ( − 7.09 ± 2.05 ) μm in group B ( P < 0.05 ). The 95% confidence interval of the difference between the two is −7.05~ −5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe ( P < 0.05 ). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome.

A novel method is proposed based on the transmissibility concept and matrix regularization for indirectly measuring the structural responses. The inputs are some measured responses that are obtained via physical sensors. The outputs are the structural responses corresponding to some critical locations where no physical sensors are installed. Firstly, the transmissibility concept is introduced for expressing the relationship between the measured responses and the indirectly measured ones. Herein, a transmissibility matrix is formulated according to the theory of force identification under unknown initial conditions. Then, in order to reduce the size of the transmissibility matrix, structural responses are reshaped in a form of a matrix by using the concept of moving time windows. According to the matrix form of input-output relationship, indirect reconstruction of responses is boiled down to an optimization equation. Since inverse problem may be ill-conditioned, matrix regularization such as F-norm regularization is then recommended for improving the optimization problem. Herein, the penalty function is defined by using a weighted sum of two F-norm values, which correspond to the estimated responses of physical sensors and the ones of the concerned critical locations, respectively. Numerical simulations and experimental studies are finally carried out for verifying the effectiveness and feasibility of the proposed method. Some results show that the proposed method can be applied for indirectly measuring the responses with good robustness.

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

Compressive sensing theory can be applied to reconstruct the signal with far fewer measurements than what is usually considered necessary, while in many scenarios, such as spectrum detection and modulation recognition, we only expect to acquire useful characteristics rather than the original signals, where selecting the feature with sparsity becomes the main challenge. With the aim of digital modulation recognition, the paper mainly constructs two features which can be recovered directly from compressive samples. The two features are the spectrum of received data and its nonlinear transformation and the compositional feature of multiple high-order moments of the received data; both of them have desired sparsity required for reconstruction from subsamples. Recognition of multiple frequency shift keying, multiple phase shift keying, and multiple quadrature amplitude modulation are considered in our paper and implemented in a unified procedure. Simulation shows that the two identification features can work effectively in the digital modulation recognition, even at a relatively low signal-to-noise ratio.

The graphene double-walled carbon nanotube (DWCNT) hybrid films were prepared by vacuum filtration and screen printing. Their electron field emission properties have been studied systematically. The electron emission properties of the hybrid films are much better than those of pure DWCNT films and pure graphene films. Comparing with the screen printed films, the vacuum filtered films have many advantages, such as lower turn-on field, higher emission current density, better uniformity, better long-term stability, and stronger adhesive strength with conductive substrates. The optimized hybrid films with 20% weight ratio of graphene, which were fabricated by vacuum filtration, show the best electron emission performances with a low turn-on field of 0.50 Vμm−1(at 1 μAcm−2) and a high field enhancement factorβof 27000.

Heterogeneous wireless networks based on varieties of radio access technologies (RATs) and standards will coexist in the future. In order to exploit this potential multiaccess gain, it is required that different RATs are managed in a cooperative fashion. This paper proposes two advanced functional architecture supporting the functionalities of interworking between WiMAX and 3GPP networks as a specific case: Radio Control Server- (RCS-) and Access Point- (AP-) based centralized architectures. The key technologies supporting the interworking are then investigated, including proposing the Generic Link Layer (GLL) and researching the multiradio resource management (MRRM) mechanisms. This paper elaborates on these topics, and the corresponding solutions are proposed with preliminary results.

The kinetics, thermodynamics, and isotherms during electrical removal of Cu2+by carbon nanotube and carbon nanofiber (CNT-CNF) electrodes in CuCl2solution were studied under different solution temperatures, initial Cu2+concentrations, and applied voltages. The result shows that Langmuir isotherm can describe experimental data well, indicating monolayer adsorption, and higher Cu2+removal and rate constant are achieved at higher voltage, lower initial Cu2+concentration, and higher solution temperature. Meanwhile, the thermodynamics analyses indicate that the electrical removal of Cu2+onto CNT-CNF electrodes is mainly driven by a physisorption process.