MTR4 drives liver tumorigenesis by promoting cancer metabolic switch through alternative splicing.

The metabolic switch from oxidative phosphorylation to glycolysis is required for tumorigenesis in order to provide cancer cells with energy and substrates of biosynthesis. Therefore, it is important to elucidate mechanisms controlling the cancer metabolic switch. MTR4 is a RNA helicase associated with a nuclear exosome that plays key roles in RNA processing and surveillance. We demonstrate that MTR4 is frequently overexpressed in hepatocellular carcinoma (HCC) and is an independent diagnostic marker predicting the poor prognosis of HCC patients. MTR4 drives cancer metabolism by ensuring correct alternative splicing of pre-mRNAs of critical glycolytic genes such as GLUT1 and PKM2. c-Myc binds to the promoter of the MTR4 gene and is important for MTR4 expression in HCC cells, indicating that MTR4 is a mediator of the functions of c-Myc in cancer metabolism. These findings reveal important roles of MTR4 in the cancer metabolic switch and present MTR4 as a promising therapeutic target for treating HCC

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

AC Electrodeposition of PEDOT Films in Protic Ionic Liquids for Long-Term Stable Organic Electrochemical Transistors

Poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS)-based organic electrochemical transistors (OECTs) are widely utilized to construct highly sensitive biosensors. However, the PSS phase exhibits insulation, weak acidity, and aqueous instability. In this work, we fabricated PEDOT OECT by alternating current electrodeposition in protic ionic liquids. The steady-state characteristics were demonstrated to be stable in long-term tests. In detail, the maximum transconductance, the on/off current ratio, and the hysteresis were stable at 2.79 mS, 504, and 0.12 V, respectively. Though the transient behavior was also stable, the time constant could reach 218.6 ms. Thus, the trade-off between switching speed and stability needs to be considered in applications that require a rapid response

Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method

Pig weight and body size are important indicators for producers. Due to the increasing scale of pig farms, it is increasingly difficult for farmers to quickly and automatically obtain pig weight and body size. Due to this problem, we focused on a multiple output regression convolutional neural network (CNN) to estimate pig weight and body size. DenseNet201, ResNet152 V2, Xception and MobileNet V2 were modified into multiple output regression CNNs and trained on modeling data. By comparing the estimated performance of each model on test data, modified Xception was selected as the optimal estimation model. Based on pig height, body shape, and contour, the mean absolute error (MAE) of the model to estimate body weight (BW), shoulder width (SW), shoulder height (SH), hip width (HW), hip width (HH), and body length (BL) were 1.16 kg, 0.33 cm, 1.23 cm, 0.38 cm, 0.66 cm, and 0.75 cm, respectively. The coefficient of determination (R2) value between the estimated and measured results was in the range of 0.9879–0.9973. Combined with the LabVIEW software development platform, this method can estimate pig weight and body size accurately, quickly, and automatically. This work contributes to the automatic management of pig farms

Tensor-based Basis Function Learning for Three-dimensional Sound Speed Fields

Basis function learning is the stepping stone towards effective three-dimensional (3D) sound speed field (SSF) inversion for various acoustic signal processing tasks, including ocean acoustic tomography, underwater target localization/tracking, and underwater communications. Classical basis functions include the empirical orthogonal functions (EOFs), Fourier basis functions, and their combinations. The unsupervised machine learning method, e.g., the K-SVD algorithm, has recently tapped into the basis function design, showing better representation performance than the EOFs. black However, existing methods do not consider basis function learning approaches that treat 3D SSF data as a third-order tensor}, and thus cannot fully utilize the 3D interactions/correlations therein. To circumvent such a drawback, basis function learning is linked to tensor decomposition in this paper, which is the primary drive for recent multi-dimensional data mining. In particular, a tensor-based basis function learning framework is proposed, which can include the classical basis functions (using EOFs and/or Fourier basis functions) as its special cases. This provides a unified tensor perspective for understanding and representing 3D SSFs. Numerical results using the South China Sea 3D SSF data have demonstrated the excellent performance of the tensor-based basis functions

Highly Sensitive Magnetoelastic Biosensor for Alpha2-Macroglobulin Detection Based on MnFe2O4@chitosan/MWCNTs/PDMS Composite

The need for Alpha2-Macroglobulin (α2-M) detection has increased because it plays an important role in the diagnosis of diabetic nephropathy (DN). However, few sensors can realize the high-sensitive detection for α2-M with characteristics of being fast, flexible, wearable and portable. Herein, a biosensor based on a MnFe2O4@chitosan/MWCNTs/PDMS composite film was developed for α2-M detection. Due to the excellent magnetoelastic effect of MnFe2O4 nanoparticles, the stress signal of the biosensor surface induced by the specific antibody–antigen binding was transformed into the electrical and magnetic signal. Chitosan-coated MnFe2O4 particles were used to provide biological modification sites for the α2-M antibody, which simplified the conventional biological functionalization modification process. The MnFe2O4@chitosan particles were successfully prepared by a chemical coprecipitation method and the property was studied by TEM, FT-IR and XRD. MWCNTs were employed to enhance electrical conductivity and the sensitivity of the biosensor. The detection limit (LOD) was reduced to 0.1299 ng·mL−1 in the linear range from 10 ng∙mL−1 to 100 µg·mL−1, which was significantly lower than the limit of health diagnostics. The biosensor is fabricated by a simple method, with advantages of being rapid and highly-sensitive, and having selective detection of α2-M, which provides a novel method for the early diagnosis of DN, and it has potential in the point of care (PoC) field

Review of applications and surface smoothing mechanisms of optical waveguide devices

Optical waveguide devices are widely used in many fields and have good development prospects. But surface roughness of waveguide device induces a passive effect on the light transmission loss and the Q value of ring cavity, which restricts the development and applications of optical waveguide devices. Currently, the common used surface and side wall smoothing methods for waveguide devices are the thermal oxidation method, laser beam method, and hydrogen annealing method, and the surface hydrogen annealing method has better smoothing effect. However, the mechanism of hydrogen annealing method is still not clear so far, thus the experimental parameters cannot be further optimized to obtain optimal experimental result. Based on the review of the contents mentioned above, the hydrogen annealing mechanism is primarily studied through the simulation analysis by Materials Studio, which provides theoretical foundation and guidance for smoothing of waveguide device by hydrogen annealing technology

Catalyst-Assisted Large-Area Growth of Single-Crystal β-Ga2O3 Nanowires on Sapphire Substrates by Metal–Organic Chemical Vapor Deposition

In this work, we have achieved synthesizing large-area high-density β-Ga2O3 nanowires on c-plane sapphire substrate by metal–organic chemical vapor deposition assisted with Au nanocrystal seeds as catalysts. These nanowires exhibit one-dimensional structures with Au nanoparticles on the top of the nanowires with lengths exceeding 6 μm and diameters ranging from ~50 to ~200 nm. The β-Ga2O3 nanowires consist of a single-crystal monoclinic structure, which exhibits strong ( 2 ¯ 01) orientation, confirmed by transmission electronic microscopy and X-ray diffraction analysis. The PL spectrum obtained from these β-Ga2O3 nanowires exhibits strong emissions centered at ~360 and ~410 nm, respectively. The energy band gap of the β-Ga2O3 nanowires is estimated to be ~4.7 eV based on an optical transmission test. A possible mechanism for the growth of β-Ga2O3 nanowires is also presented

A Flexible Capacitive Paper-Based Pressure Sensor Fabricated Using 3D Printing

Flexible pressure sensors usually exhibit high sensitivity, excellent resolution, and can be mass-produced. Herein, a high-resolution, capacitive, paper-based, 3D-printed pressure sensor with a simple, low-cost preparation method is proposed. The sensor has a wide detection range (300–44,000 Pa), a short response time (<50 ms), and high mechanical stability during repeated loading/unloading (3750 Pa). It can measure the weight of an object precisely, from which the shape of the object can be predicted. The sensor can also perform gait detection. The advantages presented by low-cost, high sensitivity, wide detection range, and the ability to be mass-produced make these sensors potential candidates for applications in contact detection and wearable medical devices