Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Lean Methodologies in Knowledge Creation: A Case in the Energy Sector

: This study aims to explore the value of using lean methodologies (LM) on knowledge creation (KC) and consequently business processes. The need for a proper, efficient, and effective mechanism to create knowledge is crucial for organizations’ continuous improvement; this triggers stakeholder to utilize new technological solutions, like LM to boost knowledge creation and consequently sustain a continuous improvement in business processes. This study utilized semi-structured interview method with several open-ended and close-ended questions to get in-depth understanding of this stud objective. Five participants (lean and knowledge workers) of a leading organization in the energy sector were interviewed. Collected data analysed using thematic analysis. Results indicated that several LM add value to knowledge creation in the energy organization; Value Stream Mapping (VSM), Six Sigma and the A3 are the most frequently used as well as the most added value. The findings also indicated that the application of LM in KC, enhances business processes’ efficiency and effectiveness, but limited on innovation. This study provides insights for researchers and practitioners to applying the Lean Methodologies in KM to enhance the effectiveness, efficiency, and innovation of the business processes.
Scrutinizing of a Liquid Prebiotic on Growth Performance and Internal Organs of Japanese Quail

An experiment has been conducted to evaluate the effects of a liquid prebiotic (LP) on the carcass, digestive tract, and productive traits of Japanese quail. Eighty 7-day-old Japanese quails were randomly divided into 2 treatments. Each treatment consisted of 4 replicates (pens) of 10 birds each. The first treatment (control) contained a standard recommended diet with no added prebiotic. The second treatment was similar to the first diet accompanied by LP in the drinking water (0.5 milliliter LP per liter of water through the entire study). Feed and water were offered ad libitum from days 7 to 42. Feed intake (FI), body weight gain (BWG), and feed conversion ratio (FCR) were measured weekly. Utilization of LP did not have a significant effect on the relative weight of internal organs and carcass. The mortality rate was not influenced by treatments. In overall, the FI of birds receiving LP was significantly decreased, while BWG was considerably increased as compared to the control group. Consequently, overall FCR was dramatically improved.
Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest Regression

Uncertainty measures estimate the reliability of a predictive model. Especially in the field of molecular property prediction as part of drug design, model reliability is crucial. Besides other techniques, Random Forests have a long tradition in machine learning related to chemoinformatics and are widely used. Random Forests consist of an ensemble of individual regression models, namely, decision trees and, therefore, provide an uncertainty measure already by construction. Regarding the disagreement of single-model predictions, a narrower distribution of predictions is interpreted as a higher reliability. The standard deviation of the decision tree ensemble predictions is the default uncertainty measure for Random Forests. Due to the increasing application of machine learning in drug design, there is a constant search for novel uncertainty measures that, ideally, outperform classical uncertainty criteria. When analyzing Random Forests, it appears obvious to consider the variance of the dependent variables within each terminal decision tree leaf to obtain predictive uncertainties. Hereby, predictions that arise from more leaves of high variance are considered less reliable. Expectedly, the number of such high-variance leaves yields a reasonable uncertainty measure. Depending on the dataset, it can also outperform ensemble uncertainties. However, small-scale comparisons, i.e., considering only a few datasets, are insufficient, since they are more prone to chance correlations. Therefore, large-scale estimations are required to make general claims about the performance of uncertainty measures. On several chemoinformatic regression datasets, high-variance leaves are compared to the standard deviation of ensemble predictions. It turns out that high-variance leaf uncertainty is meaningful, not superior to the default ensemble standard deviation. A brief possible explanation is offered.
Multiscale analysis of large-strain deformation behaviour of random cross-linked elastomers

ABSTRACT The hyperelastic behaviour of elastomers is characterised by a complex and strongly non-linear response at large deformations, which leads to several modelling challenges. While the existing macroscopic-strain-invariants based models can qualitatively capture the trend of elastomer’s deformation behaviour, incorporating network scale dynamics of polymeric chains becomes important in order to account for lower length-scale phenomena like chemical cross-linking and Langevin chain dynamics. In this work, the mechanical behaviour of SBR-1502 (unfilled) cross-linked elastomer has been simulated using a multiscale modelling approach. All atomistic MD simulations were performed to compute the equilibrium density, glass transition temperature and two lower length scale parameters – volumetric segment density of polymeric chains () and the average number of Kuhn segments between two cross-linking points (). These parameters are fed into a constitutive modelling framework based on the 8-chain network concept of Arruda and Boyce to obtain the bulk equilibrium stress-strain response up to large values of strain. Hyperelastic stress-strain plots are obtained for uniaxial, biaxial and shear modes of deformation (up to ∼750% strain) and limiting chain extensibility is investigated for the random network structure. Coupled model predictions are compared with available experimental data for SBR-1502 and the results are encouraging.
Right Coronary Artery Flow Velocity and Volume Assessment with Spiral K-Space Sampled Breath-Hold Velocity-Encoded Magnetic Resonance Imaging at 3Tesla: Accuracy and Reproducibility

Purpose—To evaluate accuracy and reproducibility of flow velocity and volume measurements in a phantom and in human coronary arteries using breath-hold velocity-encoded (VE) MRI with spiral k-space sampling at 3Tesla. Materials and Methods—Flow velocity assessment was performed using VE MRI with spiral k-space sampling. Accuracy of VE MRI was tested in vitro at five constant flow rates. Reproducibility was investigated in 19 healthy subjects (mean age 25.4± 1.2 years, 11 men,) by repeated acquisition in the right coronary artery (RCA). Results—MRI-measured flow rates correlated strongly with volumetric collection (Pearson correlation r=0.99; p<0.01). Due to limited sample resolution, VE MRI overestimated the flow rate by 47% on average when non-constricted region-of-interest segmentation was used. Using constricted region-of-interest segmentation with lumen size equal to ground-truth luminal size, less than 13% error in flow rate was found. In vivo RCA flow velocity assessment was successful in 82% of the applied studies. High interscan, intraand inter-observer agreement was found for almost all indices describing coronary flow velocity. Reproducibility for repeated acquisitions varied by less than 16% for peak velocity values and by less than 24% for flow volumes. Conclusion—3T breath-hold VE MRI with spiral k-space sampling enables accurate and reproducible assessment of RCA flow velocity.
Consumer Resource Allocation Models at the Individual Level

A methodology for the derivation of individual-level resource allocation functions is described. This methodology can be used to derive individual-level multiattribute preference functions that predict the share of some resource an individual consumer will allocate to competing alternatives. The methodology is illustrated in a pilot application in which subjects estimated the proportion of 100 minutes they would spend listening to different music groups.
How to Train Your T Cells: Overcoming Immune Dysfunction in Multiple Myeloma

The progression of multiple myeloma, a hematologic malignancy characterized by unregulated plasma cell growth, is associated with increasing innate and adaptive immune system dysfunction, notably in the T-cell repertoire. Although treatment advances in multiple myeloma have led to deeper and more durable clinical responses, the disease remains incurable for most patients. Therapeutic strategies aimed at overcoming the immunosuppressive tumor microenvironment and activating the host immune system have recently shown promise in multiple myeloma, particularly in the relapsed and/or refractory disease setting. As the efficacy of T-cell–dependent immuno-oncology therapy is likely affected by the health of the endogenous T-cell repertoire, these therapies may also provide benefit in alternate treatment settings (e.g., precursor disease; after stem cell transplantation). This review describes T-cell–associated changes during the evolution of multiple myeloma and provides an overview of T-cell–dependent immuno-oncology approaches under investigation. Vaccine and checkpoint inhibitor interventions are being explored across the multiple myeloma disease continuum; treatment modalities that redirect patient T cells to elicit an anti–multiple myeloma response, namely, chimeric antigen receptor (CAR) T cells and bispecific antibodies [including BiTE (bispecific T-cell engager) molecules], have been primarily evaluated to date in the relapsed and/or refractory disease setting. CAR T cells and bispecific antibodies/antibody constructs directed against B-cell maturation antigen have generated excitement, with clinical data demonstrating deep responses. An increased understanding of the complex interplay between the immune system and multiple myeloma throughout the disease course will aid in maximizing the potential for T-cell–dependent immuno-oncology strategies in multiple myeloma.
Influence of Compost Supplemented with Jatropha Cake on Growth, Dry Matter Accumulation and Nutrient Uptake of Maize (Zeamays L.)

The potentials of compost supplemented with jatropha cake as soil amendments were evaluated under screenhouse at Kwara State University. Maize (variety EVDT-W99 STR) was used as test crop. The experiment consisted of two consecutive experiments in which residual effect was observed in the second experiment. The treatment were; control (Zero addition), NPK 15 –15-15 at 60 kg N/ha, 30% Grade B + 70% Jatropha cake at 1.5, 2.0 and 2.5 t /ha, 40% Grade B + 60% Jatropha cake at 1.5, 2.0 and 2.5 t /ha, 50% Grade B + 50% Jatropha cake at 1.5, 2.0 and 2.5 t /ha, 60% Grade B + 40% Jatropha cake at 1.5, 2.0 and 2.5 t /ha . The design was completely randomized design replicated three times. Results obtained showed that 50% Grade B + 50% Jatropha cake greatly influenced growth parameters of maize at first and second cropping. The 50% Grade B + 50% Jatropha cake treatment gave the highest Nitrogen, phosphorus and potassium uptake value; 13.28, 0.32 and 14.60 mg/pot respectively at second planting which differed significantly from all other treatments. The study showed that jatropha cake supplemented with compost at 2.5 t/ha gave higher growth parameters and nutrient uptake when compared with NPK at second planting.
Twenty years of West Nile virus spread and evolution in the Americas visualized by Nextstrain

It has been 20 years since West Nile virus first emerged in the Americas, and since then, little progress has been made to control outbreaks caused by this virus. After its first detection in New York in 1999, West Nile virus quickly spread across the continent, causing an epidemic of human disease and massive bird die-offs. Now the virus has become endemic to the United States, where an estimated 7 million human infections have occurred, making it the leading mosquito-borne virus infection and the most common cause of viral encephalitis in the country. To bring new attention to one of the most important mosquito-borne viruses in the Americas, we provide an interactive review using Nextstrain: a visualization tool for real-time tracking of pathogen evolution (nextstrain.org/WNV/NA). Nextstrain utilizes a growing database of more than 2,000 West Nile virus genomes and harnesses the power of phylogenetics for students, educators, public health workers, and researchers to visualize key aspects of virus spread and evolution. Using Nextstrain, we use virus genomics to investigate the emergence of West Nile virus in the U S, followed by its rapid spread, evolution in a new environment, establishment of endemic transmission, and subsequent international spread. For each figure, we include a link to Nextstrain to allow the readers to directly interact with and explore the underlying data in new ways. We also provide a brief online narrative that parallels this review to further explain the data and highlight key epidemiological and evolutionary features (nextstrain.org/narratives/twenty-years-of-WNV). Mirroring the dynamic nature of outbreaks, the Nextstrain links provided within this paper are constantly updated as new West Nile virus genomes are shared publicly, helping to stay current with the research. Overall, our review showcases how genomics can track West Nile virus spread and evolution, as well as potentially uncover novel targeted control measures to help alleviate its public health burden.
Brainstem serotonergic, catecholaminergic, and inflammatory adaptations during chronic hypercapnia in goats

Despite the prevalence of CO2 retention in human disease, little is known about the adaptive neurobiological effects of chronic hypercapnia. We have recently shown 30‐d exposure to increased inspired CO2 (InCO2) leads to a steady‐state ventilation that exceeds the level predicted by the sustained acidosis and the acute CO2/H+ chemoreflex, suggesting plasticity within respiratory control centers. Based on data showing brainstem changes in aminergic and inflammatory signaling during carotid body denervation‐induced hypercapnia, we hypothesized chronic hypercapnia per se will lead to similar changes. We found that: 1) increased InCO2 increased IL‐1β in the medullary raphe (MR), ventral respiratory column, and cuneate nucleus after 24 h, but not after 30 d of hypercapnia; 2) the number of serotonergic and total neurons were reduced within the MR and ventrolateral medulla following 30 d of increased InCO2; 3) markers of tryptophan metabolism were altered following 24 h, but not 30 d of InCO2; and 4) there were few changes in brainstem amine levels following 24 h or 30 d of increased InCO2. We conclude that these changes may contribute to initiating or maintaining respiratory neuroplasticity during chronic hypercapnia but alone do not account for ventilatory acclimatization to chronic increased InCO2. —Burgraff, N. J., Neumueller, S. E. , Buchholz, K. J., LeClaire, J., Hodges, M. R. , Pan, L. , Forster, H. V. Brainstem serotonergic, catecholaminergic, and inflammatory adaptations during chronic hypercapnia in goats. FASEB J. 33, 14491‐14505 (2019). www.fasebj. org
Leadership

This paper reviews about the human leadership behaviour. Leadership is a process of social influence in which one person can enlist the aid and support of others in the accomplishment of a common task. A leader’s style is the result of philosophy, personality and experience of the leader. Some organizations have adopted group leadership. Group leadership have specific characteristics.
Lokale Applikation von Kortikosteroiden verhindert Halsentzündung

Halsentzündungen nach endotrachealer Intubation sind unangenehm für die Patienten und können die Regeneration negativ beeinflussen. Zur Prävention werden daher häufig Kortikosteroide über den Tubus appliziert und sind gängigen Schmerzmitteln in vielen Studien überlegen. Kuriyama und Mitarbeiter haben nun in einer Übersichtsarbeit 20 randomisierte Studien zum Thema zusammengetragen und systematische ausgewertet.
A GRU-based Meta-learning Model Based on Active Learning

In the realities of machine learning, labeling a data set may be expensive, tedious, or extremely difficult and it is often not easy to choose the common criteria for active learning to select samples for different data sets. In order to solve these difficulties, this paper introduces a Gated Recurrent Unit (GRU)-based meta-learner model, which combines active learning with reinforcement learning and uses it in a stream-based one-shot learning task. Based on the uncertainty of the instances, the model learns an action strategy that determines when to predict or request the label of each instance. Through the experiments on Omniglot dataset, the model shows its ability to achieve a good prediction accuracy with few label requests. Keywords-active learning; meta learning; reinforcement learning; GRU.
Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.
Combining Convolutional Neural Networks and In-line Near-infrared Spectroscopy for Real-time Monitoring of the Chromatographic Elution Process in Commercial Production of Notoginseng Total Saponins.

The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in-line near-infrared spectroscopy for notoginsenoside R1 , ginsenosides Rg1 , Re, Rb1 and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28 and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in-line near-infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products. This article is protected by copyright. All rights reserved.
SCIENCE DIPLOMACY: NEW DAY OR FALSE DAWN

Nueva Zelanda, lugar de edición de esta obra, es un país relevante en espacios multilaterales que se ocupan de deporte, género, cultura indígena y fuentes de energía renovable. También es un país destacado en una nueva área de la política internacional poco estudiada en América Latina y el Caribe: la diplomacia científica, actividad que reune dos términos aparentemente incompatibles. Por una parte, la ciencia cuyos actores motivados por su curiosidad y la opción racional de aceptar cambios, principalmente a través de la implementación de métodos de investigación científica. Por otra parte, la diplomacia movilizada por actores que expresan los intereses nacionales y objetivos de la política exterior de los estados (A. Cooper, Heine, & Thakur, 2013; Jacobs & Page, 2005). La diplomacia científica es un mecanismo de diálogo entre científicos y políticos destinado a promover intereses nacionales en áreas del conocimiento, significado éste que es asumido por los compiladores Davis y Patman. Ellos, en efecto, reconocen las contribuciones de la evidencia científica en la formulación de política exterior para brindar respuestas a problemas complejos de la agenda mundial como, por ejemplo, enfermedades infecciosas, escasez de alimentos y cambio climático.
Does tumescent liposuction damage the lymph vessels in lipoedema patients?

Background Lymphatic insufficiency might play a significant role in the pathophysiology of lipoedema. Liposuction is up to now the best treatment. As liposuction is invasive, the technique could destruct parts of the lymphatic system and by this aggravate the lymphatic component and/or induce lymphoedema. We investigated the function of the lymphatic system in lipoedema patients before and after tumescent liposuction and thus whether tumescent liposuction can be regarded as a safe treatment. Methods Lymphoscintigraphy was performed to quantify the lymph outflow of 117 lipoedema patients. Mean clearance percentages of radioactive protein loaded after 1 min with respect to the total injected dose and corrected for decay of the radiopharmaceutical in the subcutaneous lymphatics were used as functional quantitative parameters as well as the clearance percentages and inguinal uptake 2 h post injection. The results of lymphatic function in lipoedema patients were compared with values obtained from normal healthy volunteers. We also compared 50 lymphoscintigraphies out of the previous 117 lipoedema patients before and six months after tumescent liposuction. Results In 117 lipoedema patients clearance 2 h post injection in the right and left foot was disturbed in 79.5 and 87.2% respectively. The inguinal uptake 2 h post injection in the right and left groin was disturbed in 60.3 and 64.7% respectively. In 50 lipoedema patients mean clearance and inguinal uptake after tumescent liposuction were slightly improved, 0.01 (p = 0.37) versus 0.02 (p = 0.02), respectively. This is statistically not relevant in clearance. Conclusion Lipoedema legs have a delayed lymph transport. Tumescent liposuction does not diminish the lymphatic function in lipoedema patients, thus tumescent liposuction can be regarded as a safe treatment.
Running Waters

Citation for published version (APA): Pedersen, M. L. , Kronvang, B. , Sand-Jensen, K. , & Hoffmann, C. C. (2006). Lowland River Systems Processes, Form and Function. In K. Sand-Jensen, N. Friberg, & J. Murphy (Eds.), Running Waters: Historical Development and Restoration of Danish Lowland Streams (pp. 12-25). National Environmental Research Institute. http://www2.dmu. dk/1_viden/2_Publikationer/3_Ovrige/rapporter/RW_web. pdf