Transcriptome-wide high-throughput deep m6A-seq reveals unique differential m6A methylation patterns between three organs in Arabidopsis thaliana

Proportion of two types of m6A distributing feature in mRNA. (DOC 30 kb

Hysteresis linearization control of a novel hybrid vibration isolator

The undesired hysteresis exists widely in smart-material actuators, which significantly reduces the accuracy and response speed of the actuators. In this paper, the static experiment of our previously developed hybrid vibration isolator (HVI) employing piezoelectric actuator is implemented to choose appropriate preload of the HVI. The preload-dependent hysteresis of the HVI is also proved in the static experiment. To achieve the hysteresis linearization control of the HVI, two linearization methods named the feedforward linearization and feedforward compensation and PI feedback hybrid linearization control are presented, respectively, which are based on the Bouc-Wen model and corresponding parameter identification model. To evaluate the effectiveness of the proposed linearization controllers for the HVI, the experiments are implemented. The experiment results demonstrate that both linearization controllers for the HVI can linearize the hysteresis characteristics and improve the HVI control accuracy. In addition, the feedforward compensation and PI feedback hybrid linear controller can achieve higher linearity than feedforward compensation controller

Musca domestica Cecropin (Mdc) Alleviates Salmonella typhimurium-Induced Colonic Mucosal Barrier Impairment: Associating With Inflammatory and Oxidative Stress Response, Tight Junction as Well as Intestinal Flora

Salmonella typhimurium, a Gram-negative food-borne pathogen, induces impairment in intestinal mucosal barrier function frequently. The injury is related to many factors such as inflammation, oxidative stress, tight junctions and flora changes in the host intestine. Musca domestica cecropin (Mdc), a novel antimicrobial peptide containing 40 amino acids, has potential antibacterial, anti-inflammatory, and immunological functions. It remains unclear exactly whether and how Mdc reduces colonic mucosal barrier damage caused by S. typhimurium. Twenty four 6-week-old male mice were divided into four groups: normal group, control group (S. typhimurium-challenged), Mdc group, and ceftriaxone sodium group (Cs group). HE staining and transmission electron microscopy (TEM) were performed to observe the morphology of the colon tissues. Bacterial load of S. typhimurium in colon, liver and spleen were determined by bacterial plate counting. Inflammatory factors were detected by enzyme linked immunosorbent assay (ELISA). Oxidative stress levels in the colon tissues were also analyzed. Immunofluorescence analysis, RT-PCR, and Western blot were carried out to examine the levels of tight junction and inflammatory proteins. The intestinal microbiota composition was assessed via 16s rDNA sequencing. We successfully built and evaluated an S. typhimurium-infection model in mice. Morphology and microcosmic change of the colon tissues confirmed the protective qualities of Mdc. Mdc could inhibit colonic inflammation and oxidative stress. Tight junctions were improved significantly after Mdc administration. Interestingly, Mdc ameliorated intestinal flora imbalance, which may be related to the improvement of tight junction. Our results shed a new light on protective effects and mechanism of the antimicrobial peptide Mdc on colonic mucosal barrier damage caused by S. typhimurium infection. Mdc is expected to be an important candidate for S. typhimurium infection treatment

Subsequent monitoring of ferric ion and ascorbic acid using graphdiyne quantum dots-based optical sensors

Graphdiyne (GDY) as an emerging carbon nanomaterial has attracted increasing attention because of its uniformly distributed pores, highly π-conjugated, and tunable electronic properties. These excellent characteristics have been widely explored in the fields of energy storage and catalysts, yet there is no report on the development of sensors based on the outstanding optical property of GDY. In this paper, a new sensing mechanism is reported built upon the synergistic effect between inner filter effect and photoinduced electron transfer. We constructed a novel nanosensor based upon the newly-synthesized nanomaterial and demonstrated a sensitive and selective detection for both Fe3+ ion and ascorbic acid, enabling the measurements in real clinical samples. For the first time fluorescent graphdiyne oxide quantum dots (GDYO-QDs) were prepared using a facile ultrasonic protocol and they were characterized with a range of techniques, showing a strong blue-green emission with 14.6% quantum yield. The emission is quenched efficiently by Fe3+ and recovered by ascorbic acid (AA). We have fabricated an off/on fluorescent nanosensors based on this unique property. The nanosensors are able to detect Fe3+ as low as 95 nmol L−1 with a promising dynamic range from 0.25 to 200 μmol L−1. The LOD of AA was 2.5 μmol L−1, with range of 10–500 μmol L−1. It showed a promising capability to detect Fe3+ and AA in serum samples

Optimization of Fault-Insertion Test and Diagnosis of Functional Failures

<p>Advances in semiconductor technology and design automation methods have introduced a new era for electronic products. With design sizes in millions of logic gates and operating frequencies in GHz, defects-per-million rates continue to increase, and defects are manifesting themselves in subtle ways. Traditional test methods are not sufficient to guarantee product quality and diagnostic programs cannot rapidly locate the root cause of failure in large systems. Therefore, there is a need for efficient fault diagnosis methods that can provide quality assurance, accelerate new product release, reduce manufacturing cost, and increase product yield.</p><p>This thesis research is focused on fault-insertion test (FIT) and fault diagnosis at the board and system levels. FIT is a promising technique to evaluate system reliability and facilitate fault diagnosis. The error-handling mechanism and system reliability can be assessed in the presence of intentionally inserted faults, and artificial faulty scenarios can be used as references for fault diagnosis. However, FIT needs to be deployed under constraints of silicon area, design effort, availability of equipment, and what is actually possible to test from one design to the next. In this research, physical defect modeling is developed to provide an efficient solution for fault-insertion test. Artificial faults at the pin level are created to represent physical defects inside devices. One pin-level fault is able to mimic the erroneous behaviors caused by multiple internal defects. Therefore, system reliability can be evaluated in a more efficient way.</p><p>Fault diagnosis is a major concern in the semiconductor industry. As the density and complexity of systems increase relentlessly and the subtle effects of defects in nanometer technologies become more pronounced, fault diagnosis becomes difficult, time-consuming, and ineffective. Diagnosis of functional failure is especially challenging. Moreover, the cost associated with board-level diagnosis is escalating rapidly. Therefore, this thesis presents a multi-pronged approach to improve the efficiency and accuracy of fault diagnosis, including the construction of a diagnostic framework with FIT and Bayesian inference, the extraction of an effective fault syndrome (error flow), the selection of diagnosis-oriented fault-insertion points, and the application of machine learning for intelligent diagnosis.</p><p>First, in the inference-based diagnosis framework, FIT is used to create a large number of faulty samples and derive the probabilities needed for the application of Bayes' theorem; next the probability of a fault candidate being the root cause can be inferred based on the given fault syndromes. Results on a case study using an open-source RISC system-on-chip demonstrate the feasibility and effectiveness of the proposed approach. Second, the concept of error flow is proposed to mimic actual data propagation in a circuit, and thus it reflects the logic functionality and timing behavior of circuits. With this additional information, more fault syndromes are distinguishable. Third, diagnosis-oriented fault-insertion points are defined and selected to create the representative and distinguishable syndromes. Finally, machine learning approaches are used to facilitate the debug and repair process. Without requiring the need to understand the complex functionality of the boards, an intelligent diagnostic system is designed to automatically exploit the diagnostic knowledge available from past cases and make decisions on new cases.</p><p>In summary, this research has investigated efficient means to perform fault-insertion test and developed automated and intelligent diagnosis methods targeting functional failures at the board level. For a complex circuit board currently in production, the first-time success rate for diagnosis has been increased from 35.63% to 72.64%. It is expected to contribute to quality assurance, product release acceleration, and manufacturing-cost reduction in the semiconductor industry.</p>Dissertatio

The Magneto–Mechanical Hyperelastic Property of Isotropic Magnetorheological Elastomers with Hybrid-Size Magnetic Particles

Isotropic magnetorheological elastomers (MREs) with hybrid-size particles are proposed to tailor the zero-field elastic modulus and the relative magnetorheological rate. The hyperelastic magneto–mechanical property of MREs with hybrid-size CIPs (carbonyl iron particles) was experimentally investigated under large strain, which showed differential hyperelastic mechanical behavior with different hybrid-size ratios. Quasi-static magneto–mechanical compression tests corresponding to MREs with different hybrid size ratios and mass fractions were performed to analyze the effects of hybrid size ratio, magnetic flux density, and CIP mass fraction on the magneto–mechanical properties. An extended Knowles magneto–mechanical hyperelastic model based on magnetic energy, coupling the magnetic interaction, is proposed to predict the influence of mass fraction, hybrid size ratio, and magnetic flux density on the magneto–mechanical properties of isotropic MRE. Comparing the experimental and predicted results, the proposed model can accurately evaluate the quasi-static compressive magneto–mechanical properties, which show that the predicted mean square deviations of the magneto–mechanical constitutive curves for different mass fractions are all in the range of 0.9–1. The results demonstrate that the proposed hyperelastic magneto–mechanical model, evaluating the magneto–mechanical properties of isotropic MREs with hybrid-size CIPs, has a significant stress–strain relationship. The proposed model is important for the characterization of magneto–mechanical properties of MRE-based smart devices