bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sequencing of Norovirus in Southern, Nigeria: Prevalent Genotypes and Putative GII.4 Novel Recombinants among Children
Authors: Osazuwa Favour 
Published: December 2020

Norovirus is now known to be the leading cause of gastroenteritis among children worldwide. This present report highlights the genetic diversity of norovirus among children less than 5 years in Southern, Nigeria. Stool specimens were collected from 300 children with diarrhea and analyzed for norovirus using conventional reverse transcriptase-Polymerase Chain Reaction. Sequencing of the capsid region was performed to genotype the strains. Norovirus was detected in 45 (11.1%) of children with diarrhea. Genogroup II norovirus was detected in 38/45 (84.4%) patients, while genogroup I (GI) noroviruses were identified in 7/38 (15.6%) patients. Genotype diversity was large, as demonstrated by the nine identified genotypes (2 GI and 7 GII). GII.4 was the most predominant genotype. Two norovirus GII.4 variants, New Orleans_2009 and Sydney_2012 were identified in this study. A putative novel GII.4 recombinant was also detected. This study report for the first time the detection of norovirus GII.17 Kawasaki strain in South–South, region of Nigeria.
The Resistance of New Kind of High-Strength Cement after 5 Years Exposure to Sulfate Solution
Authors: Bačuvčík Michal , Martauz Pavel , Janotka Ivan , Cvopa Branislav 
Published: December 2020

This article deals with the determination of technically important properties, the recognition of microstructure and pore structure, and the mortar resistance of a new cement kind NONRIVAL CEM I 52.5 N containing 7.94% wt. of C3A to 5% sodium sulfate solution. Both reference types of cement were industrially manufactured: 1) ordinary Portland cement CEM I 42.5 R and 2) Portland cement CEM I 42.5 R – SR 0, declared as sulfate resistant because of C3A = 0%. The research was carried out at standardized mortars. The used sodium sulfate solution, which contained 33802.8 mg of aggressive SO42− per liter, exceeded approximately 5 to 10 times the concentration of the third degree of aggressiveness of the XA chemical environment according to STN EN 206 + A1. The reference medium was drinking water. The 5-year results of non-destructive and destructive physical-mechanical tests as well as the formed microstructure and pore structure in both liquid media were evaluated. The cause of the NONRIVAL CEM I 52.5 N sulfate resistance was explained, despite the manufacturer’s declared C3A content of up to 8% by weight. Sulfate resistance of NONRIVAL CEM I 52.5 N is found comparable to that of sulfate resistant CEM I 42.5 R – SR 0.
Caccia e bracconaggio come conflitti socio-ambientali in Africa: violenza, ineguaglianze e politiche (neo)coloniali
Authors: Pegorini Marta 
Published: December 2020

Starting from a reflection about the role held by political ecology, anthropology, critical theories in a possible dialogue between social and environmental dimension, I would like to demonstrate how strong is the connection between them, how much nature is a category culturally created and a translation of specific politics, specific social and power relations: social environmental conflicts totally represent this condition and, in particular, poaching proves to be a context of widespread violence, organized criminality and social economical decay in local contexts, as well as example of cruelty against non human endangered species. Environmental crimes as social environmental conflicts prove the urgency to formulate international politics, create collaborations between governments, NGO, academics, activists and review epistemological categories for a holistic knowledge and interconnected fields of study.
Pesticides
Authors: Rubino Federico Maria , Mandić‐Rajčević Stefan , Colosio Claudio 
Published: December 2020

AbstractPesticides are substances or mixtures used to control living organisms that damage crops or goods, transmit or cause diseases and other unwanted events, such as the uncontrolled expansion of endogenous living species, or infestation by non‐native ones. Unwanted living organisms include animals, such as insects and rodents, invading plants, such as weeds and ornamental flowers, shrubs and trees, microorganisms and viruses that cause transmittable diseases to plants, animals, and humans.
NKG2D and MICA/B shedding: a ‘tag game’ between NK cells and malignant cells
Authors: Xing Samantha , Ferrari de Andrade Lucas 
Published: December 2020

AbstractNatural killer (NK) cells are innate lymphocytes with cytotoxic functions and recognise target cells with the NK group 2D (NKG2D) receptor. Tumor cells are marked for NK‐cell‐mediated destruction upon expression of MICA and MICB (MICA/B), which are NKG2D ligands upregulated by many human cancers in response to cellular stress pathways associated with malignant transformation such as DNA damage and accumulation of misfolded proteins. However, MICA/B proteins are downregulated by tumor cells via intriguing molecular mechanisms, such as post‐translational modifications in which the external domains of MICA/B are proteolytically cleaved by surface proteases and shed into the extracellular space. MICA/B shedding by cancer cells causes effective escape from NKG2D recognition and allows the development of cancers. Patients frequently have increased concentrations of soluble MICA/B molecules shed in the blood plasmas and sera, thus indicating that MICA/B shedding is a therapeutic target in immune‐oncology. Here, we review the clinical significance of MICA/B shedding in cancer as well as novel immunotherapeutic approaches that aim to restore NKG2D‐mediated surveillance. We also briefly discuss potential roles of MICA/B shedding beyond oncology, such as in viral infections and immune tolerance. This review will help to inform the future developments of NKG2D‐based immunotherapies.
Icaritin inhibits PD‐L1 expression by Targeting Protein IκB Kinase α
Authors: Mo Dongliang , Zhu Hai , Wang Jun , Hao Haibang , Guo Yuming , Wang Jiaojiao , Han Xu , Zou Liangfeng , Li Zhongwan , Yao Hua , Zhu Jinsong , Zhou Junma , Peng Yong , Li Jian , Meng Kun 
Published: December 2020

AbstractIcaritin, a small molecule currently being investigated in phase III clinical trials in China (NCT03236636 and NCT03236649) for treatment of advanced hepatocellular carcinoma (HCC), is a prenylflavonoid derivative obtained from the Epimedium genus. Previously, it was found that Icaritin decreased the expression of PD‐L1, but its direct molecular targets and the underlying mechanisms have not been identified. In this study, we report the identification of IKK‐α as the protein target of Icaritin by biotin‐based affinity binding assay. The further mutagenesis assay has provided evidence that C46 and C178 in IKK‐α were essential amino acids for Icaritin binding to IKK‐α, revealing the binding sites of Icaritin to IKK‐α for the first time. Functionally, Icaritin inhibited the NF‐κB signalling pathway by blocking IKK complex formation, which led to decreased nuclear translocation of NF‐κB p65, and subsequent downregulation of PD‐L1 expression in a dose–dependent manner. More importantly, PD‐L1‐positive patients exhibited longer overall survival upon Icaritin therapy. Finally, Icaritin in combination with checkpoints antibodies, such as α‐PD‐1, has demonstrated much better efficacy than any single therapy in animal models. This is the first report that anticancer effects of Icaritin are mediated, at least in part, by impairing functions of IKK‐α.
In vitro dry matter and crude protein rumen degradation and abomasal digestibility of soybean meal treated with chestnut and quebracho wood extracts
Authors: Lavrenčič Andrej , Levart Alenka 
Published: December 2020

AbstractThe effects of commercial chestnut (CWE) and quebracho (QUE) extract at different inclusion levels to soybean meal (SBM) on the in vitro degradability and digestibility of dry matter (DM) and crude protein (CP) were evaluated. Samples were prepared by mixing 0 (CON), 15, 30, and 60 g/kg of CWE and QUE with SBM, soaked in water overnight at room temperature, dried, and ground. Samples were incubated in duplicate in buffered rumen fluid for 24 hr at 39°C. In vitro rumen degradability of DM and CP of tannin‐treated SBM decreased with increasing quantities of tannins, especially with CWE‐treated SBM. In vitro abomasal (pepsin‐HCl) digestibility of the DM and CP was only slightly suppressed. As a result, rumen by‐pass protein (BP‐CP) increased with increased quantities of tannins, especially with CWE‐treated SBM. In comparison with nontreated SBM, the BP‐CP digestibility did not decrease, except with the highest quantity of QUE. Treatment of the SBM with tannins, especially with CWE, increased flow of the undegraded protein to the abomasum, suggesting the better supply of the ruminant animal with amino acids.
The proportion of computed tomography kidneys, ureters and bladder (CTKUB) scans that comply with scan extent protocol in an emergency department: a clinical audit and dose ramification study
Authors: Kasi Arianne , Steffens Tom , Starkey Deborah , Braithwaite Vicki 
Published: December 2020

AbstractIntroductionTo assess computed tomography kidneys, ureters and bladder (CTKUB) scan extent protocol compliance and associated doses in the Emergency Department (ED) of an Australian tertiary hospital.MethodsA retrospective clinical audit of 150 consecutive ED CTKUB cases was completed. For each patient, scan extent compliance at the superior (kidneys) and inferior (pubic symphysis) borders, in reference to the protocol was recorded. Compliance and non‐compliance (over‐/under‐scanning) was identified, described (superior/inferior), quantified (via IMPAX measurements) and recorded via a purpose‐built audit tool. In addition, a PBU40 phantom was scanned to assess the percentage of dose (DLP) increase per centimetre of over‐scanning to contextualise results.ResultsA notable non‐compliance with department protocol was noted. Eight cases (5.3%) demonstrated overall CT scan extent compliance. The remaining 142 cases (94.7%) demonstrated some form of non‐compliance; superiorly, inferiorly or both. Analysing the 150 superior and 150 inferior data points independently, the most common non‐compliance was over‐scanning at the kidneys by 4 cm to5 cm (19 cases, ~10% extra DLP) beyond tolerance and over‐scanning inferiorly at the pubic symphysis by 1 cm to 2 cm (29 cases, ~6.4% extra DLP). Estimated dose increases of up to 35% to 45% were found when clinical audit results were simulated using a PBU40.ConclusionsOver‐scanning is a predominant occurrence in CTKUB scans in this department. Reasons for over‐scanning weren't investigated. It's anticipated this audit will lead to greater awareness of scan extent compliance and dose ramifications of non‐compliance. The usage of more easily identified anatomical landmarks and a follow‐up audit is suggested.
Energy Profile Prediction of Milling Processes Using Machine Learning Techniques
Authors: Mühlbauer Matthias , Würschinger Hubert , Polzer Dominik , Hanenkamp Nico 
Published: December 2020

AbstractThe prediction of the power consumption increases the transparency and the understanding of a cutting process, this delivers various potentials. Beside the planning and optimization of manufacturing processes, there are application areas in different kinds of deviation detection and condition monitoring. Due to the complicated stochastic processes during the cutting processes, analytical approaches quickly reach their limits. Since the 1980s, approaches for predicting the time or energy consumption use empirical models. Nevertheless, most of the existing models regard only static snapshots and are not able to picture the dynamic load fluctuations during the entire milling process. This paper describes a data-driven way for a more detailed prediction of the power consumption for a milling process using Machine Learning techniques. To increase the accuracy we used separate models and machine learning algorithms for different operations of the milling machine to predict the required time and energy. The merger of the individual models allows finally the accurate forecast of the load profile of the milling process for a specific machine tool. The following method introduces the whole pipeline from the data acquisition, over the preprocessing and the model building to the validation.
Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks
Authors: Specht Felix , Otto Jens 
Published: December 2020

AbstractCondition monitoring systems based on deep neural networks are used for system failure detection in cyber-physical production systems. However, deep neural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, e.g. sensor signals, are able to mislead a deep neural network into misclassification. A consequence of such an attack may be the manipulation of the physical production process of a cyber-physical production system without being recognized by the condition monitoring system. This can result in a serious threat for production systems and employees. This work introduces an approach named CyberProtect to prevent misclassification caused by adversarial example attacks. The approach generates adversarial examples for retraining a deep neural network which results in a hardened variant of the deep neural network. The hardened deep neural network sustains a significant better classification rate (82% compared to 20%) while under attack with adversarial examples, as shown by empirical results.
Detection and localization of an underwater docking station in acoustic images using machine learning and generalized fuzzy hough transform
Authors: Karimanzira Divas , Renkewitz Helge 
Published: December 2020

AbstractLong underwater operations with autonomous battery charging and data transmission require an Autonomous Underwater Vehicle (AUV) with docking capability, which in turn presume the detection and localization of the docking station. Object detection and localization in sonar images is a very difficult task due to acoustic image problems such as, non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation and multipath problems. As for detection methods which are invariant to rotations, scale and shifts, the Generalized Fuzzy Hough Transform (GFHT) has proven to be a very powerful tool for arbitrary template detection in a noisy, blurred or even a distorted image, but it is associated with a practical drawback in computation time due to sliding window approach, especially if rotation and scaling invariance is taken into account. In this paper we use the fact that the docking station is made out of aluminum profiles which can easily be isolated using segmentation and classified by a Support Vector Machine (SVM) to enable selective search for the GFHT. After identification of the profile locations, GFHT is applied selectively at these locations for template matching producing the heading and position of the docking station. Further, this paper describes in detail the experiments that validate the methodology.
Deployment architecture for the local delivery of ML-Models to the industrial shop floor
Authors: Backhaus Andreas , Herzog Andreas , Adler Simon , Jachmann Daniel 
Published: December 2020

AbstractInformation processing systems with some form of machine-learned component are making their way into the industrial application and offer high potentials for increasing productivity and machine utilization. However, the systematic engineering approach to integrate and manage these machine-learned components is still not standardized and no reference architecture exist. In this paper we will present the building block of such an architecture which is developed with the ML4P project by Fraunhofer IFF.
Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis
Authors: Graniero Paolo , Gärtler Marco 
Published: December 2020

AbstractBatch runs corresponding to the same recipe usually have different duration. The data collected by the sensors that equip batch production lines reflects this fact: time series with different lengths and unsynchronized events. Dynamic Time Warping (DTW) is an algorithm successfully used, in batch monitoring too, to synchronize and map to a standard time axis two series, an action called alignment. The online alignment of running batches, although interesting, gives no information on the remaining time frame of the batch, such as its total runtime, or time-to-end. We notice that this problem is similar to the one addressed by Survival Analysis (SA), a statistical technique of standard use in clinical studies to model time-to-event data. Machine Learning (ML) algorithms adapted to survival data exist, with increased predictive performance with respect to classical formulations. We apply a SA-ML-based system to the problem of predicting the time-to-end of a running batch, and show a new application of DTW. The information returned by openended DTW can be used to select relevant data samples for the SA-ML system, without negatively affecting the predictive performance and decreasing the computational cost with respect to the same SA-ML system that uses all the data available. We tested the system on a real-world dataset coming from a chemical plant.
Information modeling and knowledge extraction for machine learning applications in industrial production systems
Authors: Windmann Stefan , Kühnert Christian 
Published: December 2020

AbstractIn this paper, a new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360 component descriptions. To model sensor data, components of the OGC SensorThings model such as data streams and observations have been incorporated in this approach. Machine learning models can be integrated into the information model in terms of existing model serving frameworks like PMML or Tensorflowgraph. Based on the proposed information model, a tool chain for automatic knowledge extraction is introduced and the automatic classification of unstructured text is investigated as a particular application case for the proposed tool chain.
Explanation Framework for Intrusion Detection
Authors: Burkart Nadia , Franz Maximilian , Huber Marco F. 
Published: December 2020

AbstractMachine learning and deep learning are widely used in various applications to assist or even replace human reasoning. For instance, a machine learning basedintrusion detection system (IDS)monitors a network for malicious activity or specific policy violations. We propose that IDSs should attach a sufficiently understandable report to each alert to allow the operator to review them more efficiently. This work aims at complementing an IDS by means of a framework to create explanations. The explanations support the human operator in understanding alerts and reveal potential false positives. The focus lies on counterfactual instances and explanations based on locally faithful decision-boundaries.
A remark on the non-uniqueness in $$L^\infty $$ of the solutions to the two-dimensional Stokes problem in exterior domains
Authors: Maremonti Paolo 
Published: December 2020

AbstractThe paper is concerned with the IBVP in exterior domains of the two-dimensional Stokes equations. The goal was to investigate the well-posedness in the set of solutions assuming an initial data $$u_0\in L^\infty (\Omega )$$

u
0

∈

L
∞

(
Ω
)

, divergence free, and enjoying the property "Equation missing" for all $$t>0$$

t
>
0

and c independent of u. For all $$u_0\in L^\infty $$

u
0

∈

L
∞

, divergence-free one shows examples of non-uniqueness in the above set of solutions.
Liouville results for fully nonlinear equations modeled on Hörmander vector fields: I. The Heisenberg group
Authors: Bardi Martino , Goffi Alessandro 
Published: December 2020

AbstractThis paper studies Liouville properties for viscosity sub- and supersolutions of fully nonlinear degenerate elliptic PDEs, under the main assumption that the operator has a family of generalized subunit vector fields that satisfy the Hörmander condition. A general set of sufficient conditions is given such that all subsolutions bounded above are constant; it includes the existence of a supersolution out of a big ball, that explodes at infinity. Therefore for a large class of operators the problem is reduced to finding such a Lyapunov-like function. This is done here for the vector fields that generate the Heisenberg group, giving explicit conditions on the sign and size of the first and zero-th order terms in the equation. The optimality of the conditions is shown via several examples. A sequel of this paper applies the methods to other Carnot groups and to Grushin geometries.
Stochastic Dynamic Programming with Non-linear Discounting
Authors: Bäuerle Nicole , Jaśkiewicz Anna , Nowak Andrzej S. 
Published: December 2020

AbstractIn this paper, we study a Markov decision process with a non-linear discount function and with a Borel state space. We define a recursive discounted utility, which resembles non-additive utility functions considered in a number of models in economics. Non-additivity here follows from non-linearity of the discount function. Our study is complementary to the work of Jaśkiewicz et al. (Math Oper Res 38:108–121, 2013), where also non-linear discounting is used in the stochastic setting, but the expectation of utilities aggregated on the space of all histories of the process is applied leading to a non-stationary dynamic programming model. Our aim is to prove that in the recursive discounted utility case the Bellman equation has a solution and there exists an optimal stationary policy for the problem in the infinite time horizon. Our approach includes two cases: (a) when the one-stage utility is bounded on both sides by a weight function multiplied by some positive and negative constants, and (b) when the one-stage utility is unbounded from below.
Horizontal annular flow through orifice studied by X-ray microtomography
Authors: Porombka Paul , Boden Stephan , Lucas Dirk , Hampel Uwe 
Published: December 2020

AbstractAn X-ray microtomography (µCT) system was adapted so that 3D scans of fixed horizontal or vertical test sections can be performed. The mobile µCT system has been applied to measure the local, time-averaged volume fraction distribution of developing annular air-water flow in a horizontal pipe with µm spatial resolution. Based on the volume fraction data the liquid film thickness profile is computed and the accumulation, stripping and renewal of the annular liquid film at a circular orifice is studied. The development length of the annular flow downstream of the orifice is evaluated based on the integral volume fraction and the change of the film thickness profile along the pipe axis. Both parameters give a consistent result, indicating that liquid film renewal can be judged based on integral measurement techniques in this case. Further, the detailed 3D data enables the validation of computational fluid dynamics codes based on phase-averaged variables such as the Euler-Euler approach.
Graphic abstract