Detecting Deterministic Chaotic Inter-arrival Times in Material Flow Systems

Automated, modular, asynchronous and locally controlled material flow systems promise high routing flexibility in production lines because their conveying modules can be reconfigured without reprogramming PLCs. However, if such material flow systems comprise cycles and different routes, they may exhibit undesirable deterministic chaotic inter-arrival times, which can lead to conveying bottlenecks when approaching maximum capacity. Since existing analytical models have not been practically adopted for planning material flow systems, an approach for detecting deterministic chaotic inter-arrival times during production is proposed. It employs the Hough transform to identify trajectories in inter-arrival time phase space. The approach is tested with a laboratory double belt conveyor system, in which non-deterministic behavior is minimized. Results are compared with a previously published analytical model. It is shown that the proposed approach is able to detect deterministic chaotic inter-arrival times for the test cases. Phase trajectories are only partly identified. Future research should test and compare different line detection algorithms for their influence on the approach’s robustness in practical production environments

Impact of different alginate lyases on combined cellulase–lyase saccharification of brown seaweed

Alginate attack characteristics and impact on cellulase–lyase catalyzed saccharification of brown seaweed were compared for three microbial PL7 alginate lyases (EC 4.2.2.-) two of them heterologously expressed in Escherichia coli as part of the work.</p

Industrial Human Activity Prediction and Detection Using Sequential Memory Networks

Prediction of human activity and detection of subsequent actions is crucial for improving the interaction between humans and robots during collaborative operations. Deep-learning techniques are being applied to recognize human activities, including industrial applications. However, the lack of sufficient dataset in the industrial domain and complexities of some industrial activities such as screw driving, assembling small parts, and others affect the model development and testing of human activities. The InHard dataset (Industrial Human Activity Recognition Dataset) was recently published to facilitate industrial human activity recognition for better human-robot collaboration, which still lacks extended evaluation. We propose an activity recognition method using a combined convolutional neural network (CNN) and long short-term memory (LSTM) techniques to evaluate the InHard dataset and compare it with a new dataset captured in a lab environment. This method improves the success rate of activity recognition by processing temporal and spatial information. Accordingly, the accuracy of the dataset is tested using labeled lists of activities from IMU and video data. A model is trained and tested for nine low-level activity classes with approximately 400 samples per class. The test result shows 88% accuracy for IMU-based skeleton data, 77% for RGB spatial video, and 63% for RGB video-based skeleton. The result has been verified using a previously published region-based activity recognition. The proposed approach can be extended to push the cognition capability of robots in human-centric workplaces

Donor Screening for Human T-cell Lymphotrophic Virus 1/2: Changing Paradigms for Changing Testing Capacity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74865/1/j.1600-6143.2009.02867.x.pd

Interventions for reducing sedentary behaviour in community-dwelling older adults

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To identify the effects and assess the effectiveness of interventions to reduce sedentary behaviour (total sedentary time and the pattern of accumulation of sedentary time) in older adults. To summarise the effects of interventions to reduce sedentary behaviour on quality of life, depression, and health status in older adults. To summarise any evidence on the cost-effectiveness of interventions that reduce sedentary behaviour in older adults

Pathophysiological implications of urinary peptides in hepatocellular carcinoma

SIMPLE SUMMARY: In this study, the application of capillary electrophoresis mass spectrometry enabled identification of 31 urinary peptides significantly associated with hepatocellular carcinoma diagnosis and prognosis. Further assessment of these peptides lead to prediction of cellular proteases involved in their development namely Meprin A subunit α and Kallikrein-6. Subsequent identification of the proteases was verified by immunohistochemistry in normal liver, cirrhosis and hepatocellular carcinoma. Histopathological assessment of the proteases revealed numerical gradient staining signifying their involvement in liver fibrosis and hepatocellular carcinoma formation. The discovered urinary peptides offered a potential noninvasive tool for diagnosis and prognosis of hepatocellular carcinoma. ABSTRACT: Hepatocellular carcinoma (HCC) is known to be associated with protein alterations and extracellular fibrous deposition. We investigated the urinary proteomic profiles of HCC patients in this prospective cross sectional multicentre study. 195 patients were recruited from the UK (Coventry) and Germany (Hannover) between 1 January 2013 and 30 June 2019. Out of these, 57 were HCC patients with a background of liver cirrhosis (LC) and 138 were non-HCC controls; 72 patients with LC, 57 with non-cirrhotic liver disease and 9 with normal liver function. Analysis of the urine samples was performed by capillary electrophoresis (CE) coupled to mass spectrometry (MS). Peptide sequences were obtained and 31 specific peptide markers for HCC were identified and further integrated into a multivariate classification model. The peptide model demonstrated 79.5% sensitivity and 85.1% specificity (95% CI: 0.81–0.93, p < 0.0001) for HCC and 4.1-fold increased risk of death (95% CI: 1.7–9.8, p = 0.0005). Proteases potentially involved in HCC progression were mapped to the N- and C-terminal sequence motifs of the CE-MS peptide markers. In silico protease prediction revealed that kallikrein-6 (KLK6) elicits increased activity, whilst Meprin A subunit α (MEP1A) has reduced activity in HCC compared to the controls. Tissue expression of KLK6 and MEP1A was subsequently verified by immunohistochemistry

Clinical Relevance of Transjugular Liver Biopsy in Comparison with Percutaneous and Laparoscopic Liver Biopsy

Background. Transjugular liver biopsy (TJLB) is frequently used to obtain liver specimens in high-risk patients. However, TJLB sample size possibly limits their clinical relevance. Methods. 102 patients that underwent TJLB were included. Clinical parameters and outcome of TJLB were analyzed. Control samples consisted of 112 minilaparoscopic liver biopsies (mLLBs) and 100 percutaneous liver biopsies (PLBs). Results. Fewer portal tracts were detected in TJLB (4.3 ± 0.3) in comparison with PLB (11.7 ± 0.5) and mLLB (11.0 ± 0.6). No difference regarding the specification of indeterminate liver disease and staging/grading of chronic hepatitis was observed. In acute liver failure (n = 32), a proportion of hepatocellular necrosis beyond 25% was associated with a higher rate of death or liver transplantation. Conclusions. Despite smaller biopsy samples the impact on the clinical decision process was found to be comparable to PLB and mLLB. TJLB represents a helpful tool to determine hepatocellular necrosis rates in patients with acute liver failure

Crude fucoidan content in two North Atlantic kelp species, Saccharina latissima and Laminaria digitata - seasonal variation and impact of environmental factors

Fucoidans are sulphated fucose-rich polysaccharides predominantly found in the cell walls of brown algae. The bioactive properties of fucoidans attract increasing interest from the medico-pharmaceutical industries and may drive an increase in demand of brown algae biomass. In nature, the biochemical composition of brown algae displays a seasonal fluctuation driven by environmental factors and endogenous rhythms. To cultivate and harvest kelps with high yields of fucoidans, knowledge is needed on seasonal variation and impact of environmental conditions on the fucoidan content of brown algae. The relations between the fucoidan content and key environmental factors (irradiance, nutrient availability, salinity and exposure) were examined by sampling natural populations of the common North Atlantic kelps, Saccharina latissima and Laminaria digitata, over a full year at Hanstholm in the North Sea and Aarhus in the Kattegat. In addition, laboratory experiments were carried out isolating the effects of the single factors. The results demonstrated that (1) seasonal variation alters the fucoidan content by a factor of 2–2.6; (2) interspecific differences exist in the concentrations of crude fucoidan (% of dry matter): L. digitata (11%) > S. latissima (6%); and (3) the effects of single environmental factors were not consistent between species or between different conspecific populations. The ambiguous response to single environmental factors complicates prospective directions for manipulating an increased content of fucoidan in a cultivation scenario and emphasizes the need for knowledge on performance of local kelp ecotypes.This study was carried out as part of the MacroAlgae Biorefinery (MAB3), the MacroAlgae Biorefinery 4 (MAB4) and the Macrofuels projects, funded by The Danish Council for Strategic Research, the Innovation Fund Denmark and the European Union's Horizon 2020 research and innovation programme under grant agreement no. 654010, respectively