An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error

An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the required information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process, respectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting error

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods

Pilot Scale Production of Highly Efficacious and Stable Enterovirus 71 Vaccine Candidates

BACKGROUND: Enterovirus 71 (EV71) has caused several epidemics of hand, foot and mouth diseases (HFMD) in Asia and now is being recognized as an important neurotropic virus. Effective medications and prophylactic vaccine against EV71 infection are urgently needed. Based on the success of inactivated poliovirus vaccine, a prototype chemically inactivated EV71 vaccine candidate has been developed and currently in human phase 1 clinical trial. PRINCIPAL FINDING: In this report, we present the development of a serum-free cell-based EV71 vaccine. The optimization at each step of the manufacturing process was investigated, characterized and quantified. In the up-stream process development, different commercially available cell culture media either containing serum or serum-free was screened for cell growth and virus yield using the roller-bottle technology. VP-SFM serum-free medium was selected based on the Vero cell growth profile and EV71 virus production. After the up-stream processes (virus harvest, diafiltration and concentration), a combination of gel-filtration liquid chromatography and/or sucrose-gradient ultracentrifugation down-stream purification processes were investigated at a pilot scale of 40 liters each. Although the combination of chromatography and sucrose-gradient ultracentrifugation produced extremely pure EV71 infectious virus particles, the overall yield of vaccine was 7-10% as determined by a VP2-based quantitative ELISA. Using chromatography as the downstream purification, the virus yield was 30-43%. To retain the integrity of virus neutralization epitopes and the stability of the vaccine product, the best virus inactivation was found to be 0.025% formalin-treatment at 37 °C for 3 to 6 days. Furthermore, the formalin-inactivated virion vaccine candidate was found to be stable for >18 months at 4 °C and a microgram of viral proteins formulated with alum adjuvant could induce strong virus-neutralizing antibody responses in mice, rats, rabbits, and non-human primates. CONCLUSION: These results provide valuable information supporting the current cell-based serum-free EV71 vaccine candidate going into human Phase I clinical trials

Recapitulation of Fibromatosis Nodule by Multipotential Stem Cells in Immunodeficient Mice

Musculoskeletal fibromatosis remains a disease of unknown etiology. Surgical excision is the standard of care, but the recurrence rate remains high. Superficial fibromatosis typically presents as subcutaneous nodules caused by rapid myofibroblast proliferation followed by slow involution to dense acellular fibrosis. In this study, we demonstrate that fibromatosis stem cells (FSCs) can be isolated from palmar nodules but not from cord or normal palm tissues. We found that FSCs express surface markers such as CD29, CD44, CD73, CD90, CD105, and CD166 but do not express CD34, CD45, or CD133. We also found that FSCs are capable of expanding up to 20 passages, that these cells include myofibroblasts, osteoblasts, adipocytes, chondrocytes, hepatocytes, and neural cells, and that these cells possess multipotentiality to develop into the three germ layer cells. When implanted beneath the dorsal skin of nude mice, FSCs recapitulated human fibromatosis nodules. Two weeks after implantation, the cells expressed immunodiagnostic markers for myofibroblasts such as α-smooth muscle actin and type III collagen. Two months after implantation, there were fewer myofibroblasts and type I collagen became evident. Treatment with the antifibrogenic compound Trichostatin A (TSA) inhibited the proliferation and differentiation of FSCs in vitro. Treatment with TSA before or after implantation blocked formation of fibromatosis nodules. These results suggest that FSCs are the cellular origin of fibromatosis and that these cells may provide a promising model for developing new therapeutic interventions

Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan

AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities

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 <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

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level.Comment: 24 pages, 5 figure