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Background: Bevacizumab (Avastin®) is a monoclonal antibody targeting the vascular endothelial growth factor (VEGF). Used alone or in combination with chemotherapy and/or immunotherapy, Avastin® has shown promising efficacy in many cancers. This study compared the efficacy and safety of TAB008 with Avastin® sourced from the EU (bevacizumab-EU), in patients with non-squamous non-small cell lung cancer (nsNSCLC). Method: In this randomized, double-blind, multicenter, phase III similarity study, treatment naïve for metastatic lung cancer., EGFR wild-type, locally advanced, metastatic, or recurrent non-squamous, non-small cell, lung cancer (nsNSCLC) patients were enrolled and randomized (1:1) into TAB008 or Avastin® groups. Patients received TAB008 or Avastin® 15 mg/kg intravenously plus paclitaxel/carboplatin for 4–6 cycles followed by TAB008 or Avastin® 7.5 mg/kg until disease progression, unacceptable toxicity or death. The primary endpoint compared the objective response rate (ORR) within 6 cycles as read by an independent radiological review committee (IRRC). Secondary endpoints compared disease control rate (DCR) Within 6 cycles, duration of response (DoR), progression-free survival (PFS), a year overall survival rate (OSR), overall survival (OS), safety, immunogenicity, and steady-state pharmacokinetics. Results: A total of 549 nsNSCLC patients were enrolled (277 in TAB008 group and 272 in Avastin® group). In the full analysis set, ORRs were 55.957% for TAB008 and 55.720% for Avastin®, and the ORR ratio was 1 (90% CI 0.89–1.14), well within the predefined equivalence margin of 0.75–1.33. No significant differences were found in DCR within 6 cycles (95.703% vs 95.367%, p = 0.8536), DoR (8.17 vs 7.3 months, p = 0.3526), PFS (9.10 vs. 7.97 months, p = 0.9457), 1 year overall survival rate (66.2% vs 68%, p = 0.6793), or OS (20.4 vs 17.6 months, p = 0.6549). Serious adverse events (SAEs) occurred in 37.55% (104/277) of patients in the TAB008 group and 34.32% (93/271) in the Avastin® group. Anti-drug antibodies were reported in 3 of 277 (1.08%) TAB008 patients, and 5 of 271 (1.85%) Avastin® patients, neutralizing antibody (Nab) was positive in 1 patient on Avastin®, which became negative upon follow-up. The steady-state trough concentrations (Cssmin) were 106.13 μg/mL in TAB008 group and 96.03 μg/mL in Avastin® groups, with the treatment group ratio of LS geometric means fully contained within the bioequivalence limits of 80.00–125.00% (90% CI was 101.74–120.05%). Conclusions: TAB008 is similar to Avastin® in terms of efficacy, safety, and pharmacokinetic parameters, with comparable immunogenicity. Trial registration: ClinicalTrials.gov number; NCT05427305. [ABSTRACT FROM AUTHOR]

Gradual switching between multiple resistance levels is desirable for analog in-memory computing using resistive random-access memory (RRAM). However, the filamentary switching of HfOx-based conventional RRAM often yields only two stable memory states instead of gradual switching between multiple resistance states. Here, we demonstrate that a thermal barrier of Ge2Sb2Te5 (GST) between HfOx and the bottom electrode (TiN) enables wider and weaker filaments, by promoting heat spreading laterally inside the HfOx. Scanning thermal microscopy suggests that HfOx + GST devices have a wider heating region than control devices with only HfOx, indicating the formation of a wider filament. Such wider filaments can have multiple stable conduction paths, resulting in a memory device with more gradual and linear switching. The thermally enhanced HfOx + GST devices also have higher on/off ratio (>103) than control devices (<102) and a median set voltage lower by approximately 1 V (∼35%), with a corresponding reduction of the switching power. Our HfOx + GST RRAM shows 2× gradual switching range using fast (∼ns) identical pulse trains with amplitude less than 2 V. [ABSTRACT FROM AUTHOR]

Although the Lasso method has been popular for variable selection in regression modeling, it has been known to yield very different model structures with minor perturbations of the training data. A consequence is that, when cross-validation (CV) is used to determine the hyperparameter λ , seemingly heterogeneous model structures among the CV-folds are resulted for the same λ. In this paper, we propose a new stable Lasso method for model structure learning of static and dynamic models. We begin with building consensus Lasso models with a grid of λ values using all training data. Then the CV-fold models are optimized to conform with the consensus model structures with a modified Lasso objective. In addition, we propose a stable criterion that uses CV errors jointly with a stability measure to select the most stable model with near minimum CV errors. The proposed method is applied to inferential modeling of a chemical plant at DOW Chemical and dynamic modeling of an industrial boiler. • A stable Lasso algorithm is proposed to select variables for inferential sensor modeling. • The algorithm enhances consistent structures in the cross-validation step. • A stable selection criterion of MSE jointly the Jaccard stability measure is proposed. • Successful case studies are presented on inferential modeling of two industrial processes. [ABSTRACT FROM AUTHOR]

Background: Traditional videometric method can not be used in the measurement of large flexible cable-net structure for its large overall size and small partial size. Objective: A videometrics technique was proposed in this work to measure the topography and deformation of a large cable net structure. Methods: Tiny spots with high brightness (and large gray gradient) are used to mark the cable net nodes. By arranging the imaging light path properly, the light spot markers can be enlarged and accurately identified in the captured images. Results: The relationship between the imaging parameters and the gray distribution of the light spot markers were derived and verified. And a topographical measurement experiment of a cable net structure was carried out with the proposed videometrics technique. Conclusions: The topography/deformation of the cable net can be measured with tiny-light spot markers, and the effectiveness and robustness of the technique on topography and deformation measurement of large cable-net structures are demonstrated. [ABSTRACT FROM AUTHOR]

A physical model is derived to create a biological cell separator that is based on controlling the entropy in a microfluidic system having conjoined spherical structures. A one-dimensional simplified model of this three-dimensional problem in terms of the corresponding effects of entropy on the Brownian motion of particles is presented. This dynamic mechanism is based on the Langevin equation from statistical thermodynamics and takes advantage of the characteristics of the Fokker-Planck equation. This mechanism can be applied to manipulate biological particles inside a microfluidic system with identical, conjoined, spherical compartments. This theoretical analysis is verified by performing a rapid and a simple technique for separating yeast cells in these conjoined, spherical microfluidic structures. The experimental results basically match with our theoretical model and we further analyze the parameters which can be used to control this separation mechanism. Both numerical simulations and experimental results show that the motion of the particles depends on the geometrical boundary conditions of the microfluidic system and the initial concentration of the diffusing material. This theoretical model can be implemented in future biophysics devices for the optimized design of passive cell sorters. [ABSTRACT FROM AUTHOR]

This paper is concerned with data science and analytics as applied to data from dynamic systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in industrial operation data. Therefore, we focus on latent variable methods that achieve dimension reduction and collinearity removal. We present a new dimension reduction expression of state space framework to unify dynamic latent variable analytics for process data, dynamic factor models for econometrics, subspace identification of multivariate dynamic systems, and machine learning algorithms for dynamic feature analysis. We unify or differentiate them in terms of model structure, objectives with constraints, and parsimony of parameterization. The Kalman filter theory in the latent space is used to give a system theory foundation to some empirical treatments in data analytics. We provide a unifying review of the connections among the dynamic latent variable methods, dynamic factor models, subspace identification methods, dynamic feature extractions, and their uses for prediction and process monitoring. Both unsupervised dynamic latent variable analytics and the supervised counterparts are reviewed. Illustrative examples are presented to show the similarities and differences among the analytics in extracting features for prediction and monitoring. [ABSTRACT FROM AUTHOR]

Shape accuracy is an important quality measure of finished parts built by Additive Manufacturing (AM) processes. Previous work has established a generic and prescriptive methodology to represent, predict and compensate in-plane (x – y plane) shape deviation of AM built products using a limited number of test cases. However, extension to the out-of-plane (z-plane) shape deviation faces a major challenge due to intricate inter-layer interactions and error accumulation. One direct manifestation of such complication is that products of the same shape exhibit different deviation patterns when varying product sizes. This work devises an economic experimental plan and a data analytical approach to model out-of-plane deviation for improving the understanding of inter-layer interactions using a small set of training shapes. The key strategy is to discover the transition of deviation patterns from a smaller shape with fewer layers to a bigger one with more layers. This transition is established through the effect equivalence principle, which enables the model predicting a smaller shape to digitally "reproduce" the bigger shape by identifying the equivalent amount of design adjustment. In addition, a Bayesian approach is established to infer the deviation models capable of predicting deviation of complex shapes along the z-direction. Furthermore, prediction and compensation of out-of-plane deviation for two-dimensional freeform shapes are accomplished with experimental validation in a stereolithography process. [ABSTRACT FROM AUTHOR]

In order to investigate the interactive effects of simulated acid rain and copper (Cu) on macroalgae, Sargassum horneri was cultured under pH of 8.2, 5.6, 4.5 and Cu concentrations of 0, 25, 100 µg L-1. Under the medium concentration of Cu, moderate pH mitigated the toxicity of Cu on S. horneri, while the low pH and high Cu concentration reduced the contents of chlorophyll (Chl) a and carotenoids. Furthermore, pH had no interaction with Cu on maximal Chl fluorescence or electron transport efficiency, while effective PSII quantum yield, maximum electron transport rate, and saturating irradiance were obviously affected by pH and Cu, and the interactive effects were significant. Additionally, with the increasing Cu concentration and decreasing pH, nonphotochemical quenching and regulated energy dissipation dropped sharply, while nonregulated energy dissipation increased. Therefore, the high concentration of Cu and low pH had synergistic effects on the photosynthetic performance and growth of S. horneri. [ABSTRACT FROM AUTHOR]

Foot pad dermatitis (FPD) is a serious problem of the modern poultry industry, negatively affecting birds' welfare and health status, walking and feeding activity, growth performance, carcass quality, and economic performance of meat production. The gut microbiome in poultry with FPD has not been previously investigated. Therefore, we compared the cecal microbiomes of 8 breeding ducks with FPD to 8 control ducks (breeders with apparently healthy feet) by pyrosequencing the bacterial 16S ribosomal RNA gene. The results showed a significant β-diversity (P < 0.05) of cecal microbiota presented between healthy and FPD-affected breeder ducks. The plasma endotoxins, interleukin 1β (IL-1β), IL-17, IL-6, IL-10, and tumor necrosis factor-α concentration, and the abundance of class Clostridia in FPD-affected ducks was markedly higher (P < 0.05), however, the abundance of genus Prevotella, Lactobacillus, Lachnospiraceae UCG-008, and the Firmicutes to Bacteroidetes ratio in FPD-affected ducks was significantly lower (P < 0.05) when compared to healthy ducks. These findings suggest when duck breeders are affected with FPD, ducks show an increased inflammatory response and a difference of structure and composition of the cecal microbiome. [ABSTRACT FROM AUTHOR]

Background: Clinical evidence suggests that curcuminoids, as a natural polyphenol, can provide support for cardioprotection and glucose metabolism. This meta‐analysis assessed the efficacy and safety of curcumin with respect to improving glucose metabolism in patients with cardiovascular risk factors. Methods: Four databases (PubMed, Cochrane Library, Web of Science and Embase) were searched up to June 2018. The inclusion criteria included (i) randomised controlled trials (RCT) and (ii) subjects with risk factors for cardiovascular disease supplemented with curcumin and curcuminoids. A random‐effects model and a standardised mean difference with a 95% confidence interval were used to perform quantitative data synthesis. Sensitivity and subgroup analyses were conducted to assess the effects. Results: Fourteen eligible RCT with 1277 subjects were included. In the overall analyses, curcumin led to significant decreases in fasting blood glucose (FBG), glycated haemoglobin (HbA1c) and homeostatic model assessment of insulin resistance (HOMA‐IR). The subgroup analyses suggested that curcumin or combined curcuminoids were more effective at reducing FBG and HbA1c in type 2 diabetes patients than in individuals with metabolic syndrome. Supplementation with curcuminoids at doses ≥300 mg day−1 showed significant decreases in FBG, HbA1c and HOMA‐IR. The effects of supplementation on FBG, HbA1c and HOMA‐IR were more significant over long periods (≥12 weeks) than short periods. Curcumin and curcuminoids were well tolerated, with no serious adverse events. Conclusions: Curcumin or combined curcuminoids could exert cardioprotective effects in patients at risk for cardiovascular disease by improving glucose metabolism. However, further high‐quality studies and larger sample sizes are required to confirm these results. [ABSTRACT FROM AUTHOR]

The objective of the present study was to investigate the effect of dietary graded raw potato starch (RPS) levels on growth performance, plasma cytokines concentration, ileal barrier function, and cecal short-chain fatty acids (SCFA) concentration in meat ducks from 1 to 35 D of age. This study included 2 experiments. In experiment (Exp.) 1, sixteen 35-day-old meat ducks were used to evaluate the AME of RPS by orogastric administration. Results showed the AME value of RPS on ducks is 2.76 kcal/g. In Exp. 2, a total of 600 one-day-old ducklings were randomly assigned to 5 isonitrogenous and isoenergetic dietary treatments that included 0 (control), 6, 12, 18, and 24% RPS, respectively. Samples were collected at both of 14 and 35 D. Neither growth performance nor ileal parameters (length, weight, and pH) at both of 14 and 35 D was affected by dietary RPS. However, the mucosal thickness (14 D), villus height (except for 18% RPS at 14 D), and the villus height: crypt depth ratio (14 and 35 D) of the ileum were increased in the 12 and 18% RPS diets when compared to 0% RPS diet. Meanwhile, proinflammatory factors such as plasma interleukin (IL)-1β and IL-6 (14 D) reduced in 12% RPS diet and tumor necrosis factor α decreased in 12% (except for 14 D) and 18% RPS groups. When compared with the control group, diets with 18% RPS significantly increased mucin 2 gene expression at 14 D, and 12% RPS elevated the mRNA expression of tight junction proteins including Zonula occludens-1 and Claudin 1 (except for 14 D) in the ileal mucosa of birds. Furthermore, ducks fed 12% RPS diet had higher concentrations of acetate, propionate, and butyrate in cecal digesta than other groups. These findings indicated that diets with 12 and/or 18% RPS increased the cecal SCFA concentration, which subsequently enhanced the barrier function and improved intestinal health in the ileum for 14 and 35-day-old meat ducks. [ABSTRACT FROM AUTHOR]

To study the feasibility and safety for patients with high-risk (HR) and very high-risk (VHR) prostate cancer treated with adaptive ultra-hypofractionated whole-pelvic radiotherapy (UHF-WPRT) on 1.5 magnetic resonance (MR)-Linac. Sevenpatients with clinical stage T3a-4N0-1M0-1c consecutively treated with UHF-WPRT on a 1.5-T MR-Linac were recruited prospectively in a phase II trial (NCT05183074, ChiCTR2000033382). A 36.25 Gy dose in five fractions was delivered every other day with a boost of 40 Gy to the whole prostate, as well as 25 Gy to whole pelvic nodal area with a concomitant boost of 35 Gy to metastatic regional nodes. To estimate the delivered dose, we collected data by 3D-MR for the following stages: pre-MR, position verification-MR (PV-MR) in the Adapt-To-Shape (ATS) workflow, and 3D-MR during the beam-on phase (Bn-MR) and at the end of RT (post-MR). The target and organ-at-risk contours in the PV-MR, Bn-MR, and post-MR stages were projected from the pre-MR data by deformable image registration and manually adapted by the physician, followed by dose recalculation for the ATS plan. The cumulative acute genitourinary (GU) and gastrointestinal (GI) toxicities were evaluated as per NCI-CTCAE 5.0 criteria. The primary endpoints were acute ≥grade 3 genitourinary (GU) and gastrointestinal (GI) toxicities during the first 3 months. Overall, 133 MR scans were collected (35 pre-MR, 35 PV-MR, 31 Bn-MR and 32 post-MR scans). With a median on-couch time of 61 minutes, the mean prostate and pelvic planning target volume (PTV)-V95% of all scans was 96.98 ± 3.06% and 96.44 ± 2.85%, respectively. The corresponding mean prostate clinical target volume (CTV)-V100% was 99.89 ± 0.32%, 98.71 ± 1.90%, 97.77 ± 2.89%, and 98.56 ± 1.72%, and the mean pelvic CTV-V100% was 97.57% ± 3.70%, 96.54 ± 3.80%, 95.43 ± 4.31%, and 94.39 ± 4.47% on pre-MR, PV-MR, Bn-MR and post-MR scans, respectively. For the 4 patients with positive nodes, the mean V100% of metastatic regional nodes was 99.89 ± 0.81%. The median V29 Gy change in the rectal wall was -1% (-18%–20%). The V29 Gy of the rectal wall increased by >15% was observed in one scan. A slight increase in the high dose of bladder wall was noted due to gradual bladder growth during the workflow. With median follow-up time of 7.3 (4.6-12.2) months, all patients were followed-up for more than 3 months. No patient was observed with acute CTCAE grade 2 or more severe GU or GI toxicities (0%). UHF-RT to prostate and pelvic with ATS workflow is well tolerated by patients with HR and VHR prostate cancer, with only mild GU and GI toxicities. The 3D-MR–based dosimetry analysis demonstrated clinically acceptable estimated dose coverage of target volumes during the beam-on period. [ABSTRACT FROM AUTHOR]

In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory over the last 20 years and commercial successes by leading big data companies. Then we discuss the characteristics of process manufacturing systems and briefly review the data analytics research and development in the last three decades. We give three attributes for process data analytics to make machine learning techniques applicable in the process industries. Next we provide a perspective on the currently active topics in machine learning that could be opportunities for process data analytics research and development. Finally we address the importance of a data analytics culture. Issues discussed range from technology development to workforce education and from government initiatives to curriculum enhancement. [ABSTRACT FROM AUTHOR]

The diffusion couples of Co-Cr-Mo ternary alloys were prepared and annealed at 1473 K for 72 h in order to determine the interdiffusion coefficients by using the electron probe microanalysis combined with Whittle and Green method. The experimental interdiffusion coefficients were critically assessed to the atomic mobilities by means of DICTRA software package. Good agreements were obtained from comprehensive comparison between the model-predicted diffusion properties and the experimental data, meanwhile, considered that the calculated diffusion coefficients were within the acceptable uncertainty of the experimentally measured diffusion coefficients, which verified the reliability of the atomic mobilities. The diffusion phenomena, such as diffusion paths and the concentration-distance profiles in the Co-Cr-Mo ternary system can be reasonably described by the presently obtained atomic mobilities. [ABSTRACT FROM AUTHOR]

Partial least squares (PLS) regression is widely used to capture the latent relationship between inputs and outputs in static system modeling. Several dynamic PLS algorithms have been proposed to capture the characteristics of dynamic data. However, none of these algorithms provides an explicit expression for the dynamic inner and outer models. In this paper, a dynamic inner PLS algorithm is proposed for dynamic data modeling. The proposed algorithm provides an explicit dynamic inner model that is ensured in deriving the outer model. Several examples are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Presents a study that employed a dislocation dipole model to stimulate the nucleation of microvoids at the interface between a thin film and a substrate. Formulation; Results and discussion; Conclusion.

The medicinal plant Sophora tonkinensis is a characteristic Chinese shrub of karst areas. The arid climate in karst areas produces high‐quality S. tonkinensis; however, the mechanisms of drought tolerance are not clear, which restricts sustainable plantings of S. tonkinensis.This study involved a 20‐day drought stress experiment with potted S. tonkinensis and threee soil water regimes: control (CK), mild drought (MDT), and severe drought (SDT). Plant morphology, biomass, physiological indicators, alkaloid content, and other changes under drought stress were monitored.The content of soluble sugars and proteins, and activity of antioxidant enzymes in leaves and roots were higher under drought than CK, indicating that S. tonkinensis is tolerant to osmotic stress in early drought stages. Content of matrine and oxymatrine increased gradually with increasing drought duration in the short term. The epidermis of S. tonkinensis leaves have characteristics of desert plants, including upper epidermal waxy layer, lower epidermal villi, and relatively sunken stomata, suggesting that S. tonkinensis has strong drought tolerance.In conclusion, drought stress changed the cell structure of S. tonkinensis, induced antioxidant enzyme activity and increased its resistance to drought. [ABSTRACT FROM AUTHOR]

Deep learning models such as the long short-term memory (LSTM) network have been applied for dynamic inferential modeling. However, many studies apply LSTM as a black-box approach without examining the necessity and usefulness of the internal LSTM gates for inferential modeling. In this paper, we use LSTM as a state space realization and compare it with linear state space modeling and statistical learning methods, including N4SID, partial least squares, the Lasso, and support vector regression. Two case studies on an industrial 660 MW boiler and a debutanizer column process indicate that LSTM underperforms all other methods. LSTM is shown to be capable of outperforming linear methods for a simulated reactor process with severely excited nonlinearity in the data. By dissecting the sub-components of a simple LSTM model, the effectiveness of the LSTM gates and nonlinear activation functions is scrutinized. • LSTM is implemented as a nonlinear Kalman filter for dynamic inferential modeling. • LSTM is benchmarked with N4SID, PLS, Lasso, and SVR statistical learning methods. • Results on a boiler emission and debutanizer and a CSTR process are reported. • The results show that LSTM underperform N4SID and statistical learning methods. • With severe nonlinearity in the simulated CSTR data, LSTM underperforms SVR. [ABSTRACT FROM AUTHOR]

In this Letter, a field programmable gate array (FPGA)-based synchronising control system design for spinning disk confocal image scanning microscope is described. Based on the analysis on the condition of laser triggering, the author simplifies the design by transforming the floating-point calculation formula to a very simple form of integer calculation, which can then be implemented by fixed-point calculation on FPGA without trimming any bit, for which there is no computational precision loss. The testing of simulation and experiment shows that the design is very accurate and reliable. [ABSTRACT FROM AUTHOR]

In this Letter, a field programmable gate array (FPGA)‐based synchronising control system design for spinning disk confocal image scanning microscope is described. Based on the analysis on the condition of laser triggering, the author simplifies the design by transforming the floating‐point calculation formula to a very simple form of integer calculation, which can then be implemented by fixed‐point calculation on FPGA without trimming any bit, for which there is no computational precision loss. The testing of simulation and experiment shows that the design is very accurate and reliable. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been widely applied for data modeling and process monitoring. However, it is not appropriate to directly apply PCA to data from a dynamic process, since PCA focuses on variance maximization only and pays no attention to whether the components contain dynamics or not. In this paper, a novel dynamic PCA (DiPCA) algorithm is proposed to extract explicitly a set of dynamic latent variables with which to capture the most dynamic variations in the data. After the dynamic variations are extracted, the residuals are essentially uncorrelated in time and static PCA can be applied. The new models generate a subspace of principal time series that are most predictable from their past data. Geometric properties are explored to give insight into the new dynamic model structure. For the purpose of process monitoring, fault detection indices based on DiPCA are developed based on the proposed model. Case studies on simulation data, data from an industrial boiler process, and the Tennessee Eastman process are presented to illustrate the effectiveness of the proposed dynamic models and fault detection methods. [ABSTRACT FROM AUTHOR]

Traditional multivariate statistical process monitoring techniques usually assume measurements follow a multivariate Gaussian distribution so that T 2 can be used for monitoring. The assumption usually does not hold in practice. Many efforts have been spent on redefining a proper boundary of control region for non-Gaussian distributed processes. These efforts lead to new models such as independent component analysis (ICA), statistical pattern analysis (SPA), and new techniques such as kernel density estimation (KDE), support vector data description (SVDD). However, it has not been stated clearly how a latent structure will affect monitoring performance. In this paper, most of main stream methods for non-Gaussian process monitoring are recalled and categorized. The essential problem formulation of process monitoring is summarized from a general case and then explained in both Gaussian and non-Gaussian distribution, respectively. According to this formulation, KDE and SVDD methods are effective but time-consuming to extract proper control region of non-Gaussian distributed processes. Dimension reduction models are more beneficial to overcome the curse of dimensionality, rather than extracting non-Gaussian data structure. Besides, the monitoring of non-Gaussian processes can be converted into the monitoring of Gaussian processes according to central limitation theorem. [ABSTRACT FROM AUTHOR]

After introducing process data analytics using latent variable methods and machine learning, this paper briefly review the essence and objectives of latent variable methods to distill desirable components from a set of measured variables. These latent variable methods are then extended to modeling high dimensional time series data to extract the most dynamic latent time series, of which the current values are best predicted from the past values of the extracted latent variables. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods. The extracted features reveal hidden information in the data that is valuable for understanding process variability. [ABSTRACT FROM AUTHOR]

Background: Arginine depletion is a putative target in hepatocellular carcinoma (HCC). HCC often lacks argininosuccinate synthetase, a citrulline to arginine-repleting enzyme. ADI-PEG 20 is a cloned arginine degrading enzyme--arginine deiminase--conjugated with polyethylene glycol. The goal of this study was to evaluate this agent as a potential novel therapeutic for HCC after first line systemic therapy. Methods and patients: Patients with histologically proven advanced HCC and Child-Pugh up to B7 with prior systemic therapy, were randomized 2: 1 to ADI-PEG 20 18 mg/m2 versus placebo intramuscular injection weekly. The primary end point was overall survival (OS), with 93% power to detect a 4-5.6 months increase in median OS (one-sided α=0.025). Secondary end points included progression-free survival, safety, and arginine correlatives. Results: A total of 635 patients were enrolled: median age 61, 82% male, 60% Asian, 52% hepatitis B, 26% hepatitis C, 76% stage IV, 91% Child-Pugh A, 70% progressed on sorafenib and 16% were intolerant. Median OS was 7.8 months for ADI-PEG 20 versus 7.4 for placebo (P=0.88, HR=1.02) and median progression-free survival 2.6 months versus 2.6 (P=0.07, HR=1.17). Grade 3 fatigue and decreased appetite occurred in <5% of patients. Two patients on ADI-PEG 20 had≤grade 3 anaphylactic reaction. Death rate within 30 days of end of treatment was 15.2% on ADI-PEG 20 versus 10.4% on placebo, none related to therapy. Post hoc analyses of arginine assessment at 4, 8, 12 and 16 weeks, demonstrated a trend of improved OS for those with more prolonged arginine depletion. Conclusion: ADI-PEG 20 monotherapy did not demonstrate an OS benefit in second line setting for HCC. It was well tolerated. Strategies to enhance prolonged arginine depletion and synergize the effect of ADI-PEG 20 are underway. [ABSTRACT FROM AUTHOR]

Summary: This study investigated the effect of dietary fibre (DF) supplementation on hepatic fat deposition of Cherry Valley meat ducks. A total of 600 1‐d‐old ducks were randomly allotted to five dietary fibre diets (1.46 (basal diet), 3.09, 4.15, 6.18 and 7.52% (analysed)) for 14 days. Growth performance decreased with increasing DF concentrations, when the DF level more than 6.18% the average daily gain (ADG) and feed conversion ratio (F/G) markedly decreased in meat ducks. Meat ducks supplemented over 4.15% DF had lower percentage of liver reported to live weight, liver lipid contents, hepatic triglyceride and total cholesterol concentrations as well as decreased serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) concentrations at 14 days. The transcription of fatty acid synthase (FAS), acetyl‐CoA carboxylase (ACC) and sterol‐regulatory element‐binding protein‐1 (SREBP‐1) in liver was significantly reduced by DF, while no any significant difference was found about the effect of DF on hepatic peroxisome proliferator‐activated receptor α (PPARα), adipose triglyceride lipase (ATGL), transcription as well as caecal digesta short‐chain fatty acids (SCFA). Data suggested that dietary fibre supplementation decreased hepatic lipid deposition and improved characteristics of liver health via inhibiting hepatic FAS, ACC and SREBP‐1 expression in meat ducks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a potential-based integral equation solver for low-frequency electromagnetic (EM) problems. In this formulation, the scalar potential ( \Phi ) equation is solved in tandem with the vector potential ( \textbf {A} ) equation. The resulting system is immune to low-frequency catastrophe and accurate in capturing the electrostatic and magnetostatic physics. The fast convergence of the new \textbf {A}$ - $\Phi $ system, which is a typical symmetric saddle point problem, is made possible through the design of an appropriate left constraint preconditioner. Numerical examples validate the efficiency and stability of the novel formulation in solving both EM scattering and circuit problems over a wide frequency range up to very low frequencies. [ABSTRACT FROM PUBLISHER]

This study was conducted to investigate the effects of dietary rapeseed meal ( RSM) inclusion levels on growth performance, organ health and standardized ileal amino acid digestibility ( SIAAD) in meat ducks from 15 to 35 days of age. Six hundred and eighty 15-days-old ducks were randomly allotted to five treatments based on body weight. Five isonitrogenous and isoenergetic diets were formulated on a digestible amino acid basis by replacing 0% (the control), 25%, 50%, 75% and 100% (based on fresh) of protein from soya bean meal ( SBM) with protein from RSM. The corresponding levels of RSM in experimental diets were 0%, 6.66%, 13.32%, 19.98% and 26.64% respectively. With increasing dietary RSM levels, body weight ( BW) and average daily gain ( ADG) linearly decreased (p < 0.001), whereas feed-to-gain ratio (F: G) linearly increased (p = 0.0078). Ducks fed the diets with 13.32% or more RSM had significantly lower (p < 0.05) BW, ADG and ADFI, or higher F: G than ducks fed the control diet. The maximum limit of dietary RSM supplementation was estimated to range from 4.27% to maximize ADG for 15 to 35 days to 11.69% to maintain feed intake for 15 to 35 days on the basis of a broken-line model. At day35, the 4th primary wing feather length and SIAAD (except for Met, Thr and Val) linearly decreased (p < 0.001), and the thyroid glands weight (% of BW) linearly increased (p < 0.05) with increasing dietary RSM levels. Ducks fed the RSM inclusion diets had significantly lower (p < 0.0001) serum aspartate aminotransferase ( AST) and alanine transaminase ( ALT) activities than ducks fed the control diet. These results suggested that the maximum limit of dietary RSM containing 7.57 μmol/g glucosinolates was estimated to be 4.27% to avoid growth reduction. [ABSTRACT FROM AUTHOR]

Data driven fault detection and diagnosis methods become more and more attractive in modern industries especially process industries. They can not only guarantee safe operation but also greatly improve product quality. For example, dynamic principal component analysis models and reconstruction based contribution are widely applicable in many occasions. However, there is one issue which does not receive enough attention, namely locating the root cause of a fault when it occurs. In this paper, a framework of root cause location is proposed to address this issue, including both stationary faults and nonstationary faults. A case study on Tennessee Eastman process is used to demonstrate the usage and effectiveness of these approaches. Results show the proposed framework is valid. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a promising direct manufacturing technology, and the geometric accuracy of AM built products is crucial to fulfill the promise of AM. Prediction and control of three-dimensional (3D) shape deformation, particularly out-of-plane geometric errors of AM built products, have been a challenging task. Although finite-element modeling has been extensively applied to predict 3D deformation and distortion, improving part accuracy based purely on such simulation still needs significant methodology development. We have been establishing an alternative strategy that can be predictive and transparent to specific AM processes based on a limited number of test cases. Successful results have been accomplished in our previous work to control in-plane (x-y plane) shape deformation through offline compensation. In this study, we aim to establish an offline out-of-plane shape deformation control approach based on limited trials of test shapes. We adopt a novel spatial deformation formulation in which both in-plane and out-of-plane geometric errors are placed under a consistent mathematical framework to enable 3D accuracy control. Under this new formulation of 3D shape deformation, we develop a prediction and offline compensation method to reduce out-of-plane geometric errors. Experimental validation is successfully conducted to validate the developed 3D shape accuracy control approach. [ABSTRACT FROM AUTHOR]

Effects of ZnO-BO (ZB) addition on the densification, phase evolution and microwaves dielectric properties of BaSmTiO (BST) ceramics for low-temperature fired applications have been investigated. The sintering temperature of BST ceramics can be effectively lowered to about 1000°C with introduction of ZB. Tungsten bronze like single phase is observed in the BST ceramics with 0.5 and 1.0% ZB. However, SmTiO secondary phase appears when ZB addition reaches 2%, and SmTiO phase gradually increases with the increase ZB addition. Microwave dielectric properties of the present ceramics are strongly dependent on phase constitution and density. Optimal microwave dielectric properties of ε = 63.4, Qf = 2830 GHz, τ =-8.8 ppm/°C is obtained for BST ceramics with 1% ZB addition. [ABSTRACT FROM AUTHOR]

A series of functional microcapsules for electrophoretic display (EPD) prepared by complex coacervation technology utilising gelatin and arabic gum as a wall material are presented. The authors have investigated the Isopar M electrophoretic fluid containing blue-white, red-white and yellow-white particles modified by the physical coating as core materials of microcapsules, respectively. The mass ratio of wall/core material is a key factor influencing the yield of microcapsules. The surface of microcapsule was characterised by optical microscopy and scanning electron microscopy. The display prototypes based on the red-white, blue-white and yellow-white microencapsulated electrophoretic fluids have been prepared. The contrast ratios of prototypes were found to be 1.62, 1.67 and 1.22, respectively. [ABSTRACT FROM AUTHOR]

Conventional electric field integral equation-based theory is susceptible to the spurious internal resonance problem when the characteristic modes (CMs) of closed perfectly conducting objects are computed iteratively. In this paper, we present a combined field integral equation-based theory to remove the difficulty of internal resonances in CMs analysis. The electric and magnetic field integral operators are shown to share a common set of nontrivial characteristic pairs (values and modes), leading to a generalized eigenvalue problem which is immune to the internal resonance corruption. Numerical results are presented to validate the proposed formulation. This work may offer efficient solutions to CM analysis which involves electrically large closed surfaces. [ABSTRACT FROM PUBLISHER]

Pulsed electric field (PEF) treatment can be used for non-thermal inactivation of microorganisms. The aim of this paper is to investigate PEF treatment of yeast, Saccharomyces cerevisiae, using three different field waveforms: square; non-oscillating exponential and oscillating exponential. The PEF system used in this paper consists of a pulsed power supply and a parallel-plane metallic electrodes treatment cell located in an air-pressurised chamber. PEF treatment of the yeast was conducted using electric field impulses with magnitudes of 67 kV/cm and 80 kV/cm. The efficacy of the PEF treatment for inactivation of the yeast cells was assessed by comparison of the PEFtreated and untreated yeast populations. Results showed that ~3-log10 reduction in the yeast population can be achieved with 100 impulses using all tested waveforms. Amongst all three tested waveforms non-oscillating exponential impulses demonstrated improved PEF performance. The effect of duration of treatment and peak magnitude of the field on the PEF process is discussed. [ABSTRACT FROM PUBLISHER]

Dynamic principal component analysis (DPCA) is widely used in the monitoring of dynamic multivariate processes. In traditional DPCA where a time window is used, the dynamic relations among process variables are implicit and difficult to interpret in terms of variables. To extract explicit latent variables that are dynamically correlated, a dynamic latent-variable model is proposed in this paper. The new structure can improve the modeling and the interpretation of dynamic processes and enhance the performance of monitoring. Fault detection strategies are developed, and contribution analysis is available for the proposed model. The case study on the Tennessee Eastman Process is used to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

In this paper, a data-driven multiblock concurrent projection to latent structures (CPLS) method is proposed for monitoring large-scale manufacturing lines, particularly for cold rolling continuous annealing processes (CAPs) fault diagnosis. The proposed method provides decentralized process monitoring and helps localize faults in both input variables and output variables concurrently. First, the CPLS-based process monitoring method is briefly reviewed. Second, a multiblock CPLS algorithm, which incorporates process block partition, is proposed to diagnose faults relevant to process inputs or outputs with a decentralized structure. For the CAP line application, tension-specific variations, roll-specific variations, and tension–roll covariations are analyzed in each partitioned block. Furthermore, within the roll-specific subspace of an abnormal block, a delay-alignment scheme based on strip transportation delay is proposed to diagnose defective processing materials. [ABSTRACT FROM PUBLISHER]

This paper addresses the convergence of the electric-field integral equation (EFIE)-based matrix systems with the right-hand-side effect. The role of the right-hand-side excitation in determining the convergence rate of the iterative solvers is found to be important or even crucial at low frequencies. The weighted contributions from different singular vectors are decided by not only the corresponding singular values but also the right-hand side. Based on this understanding, we investigate the low-frequency stabilized form of both EFIE and Calderón multiplicative preconditioner EFIE (CMP-EFIE) on capacitive problems. For the parallel-plate capacitor excited by the delta-gap source, the singular vectors with small singular values cannot be excited, and the charge currents on the capacitive surface dominate. Thus, the stability of the EFIE-based system can be achieved at low frequencies. Detailed spectral analysis and convergent results are carried out in order to capture the physical nature of the problems. [ABSTRACT FROM PUBLISHER]

Most existing subspace identification methods use steady-state Kalman filter (SKF) in parameterization, hence, infinite data horizons are implicitly assumed to allow the Kalman gain to reach steady state. However, using infinite horizons requires collecting infinite data which is unrealistic in practice. In this paper, a subspace framework with non-steady state Kalman filter (NKF) parameterization is established to provide exact parameterization for finite data horizon identification problems. Based on this we propose a novel subspace identification method with NKF parameterization which can handle closed-loop data and avoid assumption on infinite horizons. It is shown that with finite data, the proposed parameterization method provides more accurate and consistent solutions than existing SKF based methods. The paper also reveals why it is often beneficial in practice to estimate a bank of ARX models over a single ARX model. [ABSTRACT FROM AUTHOR]

This paper presents an application case study of an economic model predictive control (EMPC) method for optimizing the building demand and energy cost under the time-of-use price policy. The control strategy is comprised of an economic objective function that accounts for the combination of energy and demand costs with a time-of-use rate structure, a dynamic thermal process and power model of the building thermal mass dynamics, and a set of constraints to ensure the building is operated properly. The optimization is a min–max optimization problem and is converted to a linear program. The EMPC method is implemented in a commercial office building located in Milwaukee, Wisconsin, USA. An internet-based control architecture is developed to carry out tests with the EMPC controller at a remote location. The test results show that the EMPC strategy is capable of shifting the peak demand to off-peak hours and reducing energy costs compared to a baseline case for the building. [ABSTRACT FROM AUTHOR]