Miracle-dz/UniFAR-FTUs huggingface dataset:
Knockout of Tobacco Homologs of Arabidopsis Multi-Antibiotic Resistance 1 Gene Confers a Limited Resistance to Aminoglycoside Antibiotics

To explore a possible recessive selective marker for future DNA-free genome editing by direct delivery of a CRISPR/Cas9-single guide RNA (sgRNA) ribonucleoprotein complex, we knocked out homologs of the Arabidopsis Multi-Antibiotic Resistance 1 (MAR1)/RTS3 gene, mutations of which confer aminoglycoside resistance, in tobacco plants by an efficient Agrobacterium-mediated gene transfer. A Cas9 gene was introduced into Nicotiana tabacum and Nicotiana sylvestris together with an sgRNA gene for one of three different target sequences designed to perfectly match sequences in both S- and T-genome copies of N. tabacum MAR1 homologs (NtMAR1hs). All three sgRNAs directed the introduction of InDels into NtMAR1hs, as demonstrated by CAPS and amplicon sequencing analyses, albeit with varying efficiency. Leaves of regenerated transformant shoots were evaluated for aminoglycoside resistance on shoot-induction media containing different aminoglycoside antibiotics. All transformants tested were as sensitive to those antibiotics as non-transformed control plants, regardless of the mutation rates in NtMAR1hs. The NtMAR1hs–knockout seedlings of the T1 generation showed limited aminoglycoside resistance but failed to form shoots when cultured on shoot-induction media containing kanamycin. The results suggest that, like Arabidopsis MAR1, NtMAR1hs have a role in plants’ sensitivity to aminoglycoside antibiotics, and that tobacco has some additional functional homologs.
Airport Ground Movement: Real World Data Sets and Approaches to Handling Uncertainty

1 Abstract Two related topics are considered in this presentation, both concerning the ground movement of aircraft. The first describes the collection of data from publicly available websites and the second discusses the issue of uncertainty in this problem. The airport ground movement problem [1] connects together the problems of runway scheduling and gate allocation, which are often tackled separately in the research literature. The overall problem involves allocating routes for aircraft to take as they proceed along the taxiways between the runways and the gates (stands), and timings or orders for them to take them. The aim is to find a schedule that reduces delays, reduces the fuel burn associated with taxiing, and is resilient to last-minute changes. This represents a challenging problem because there are typically several pinch points where congestion is more likely to occur, and the uncertainty inherent in aircraft landing times, pushback times and taxi speeds means that routes need to be constantly updated to reflect the current situation. In addition, any solution method must be efficient enough that it can be executed within a couple of minutes, at most, to accommodate incoming, changing data. Furthermore, once an aircraft has had a route allocated and commenced its movement, it is undesirable (and
Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.
Towards a Floating Plastic Waste Early Warning System

Plastic waste in living waters is a worldwide problem. One particular variant of this problem is floating plastic waste, e.g. plastic bottles or bags. Rivers often carry large amount of floating plastic waste, due to unauthorized or not properly maintained waste dumps installed in the rivers’ flood plain. It is of utmost importance that environmental protection agencies be aware of such large-scale plastic pollutions so that they can initiate appropriate countermeasures. This paper presents two iterations of an early warning system designed to alert environmental protection agencies of plastic waste pollution. These systems are based on processing camera images but while the first iteration uses motion detection for identifying relevant images, the second iteration adopted a machine learning algorithm deployed in edge computing architecture. Better selectivity of the machine learning-based solution significantly eases the burden on the operators of the early warning system.
Noninvasive Detection, Tracking, and Characterization of Aerogel Implants Using Diagnostic Ultrasound

Medical implants are routinely tracked and monitored using different techniques, such as MRI, X-ray, and ultrasound. Due to the need for ionizing radiation, the two former methods pose a significant risk to tissue. Ultrasound imaging, however, is non-invasive and presents no known risk to human tissue. Aerogels are an emerging material with great potential in biomedical implants. While qualitative observation of ultrasound images by experts can already provide a lot of information about the implants and the surrounding structures, this paper describes the development and study of two simple B-Mode image analysis techniques based on attenuation measurements and echogenicity comparisons, which can further enhance the study of the biological tissues and implants, especially of different types of biocompatible aerogels.
Anatomical and Phytochemical Characteristics of Different Parts of Hypericum scabrum L. Extracts, Essential Oils, and Their Antimicrobial Potential

Hypericum (Hypericaceae) is a genus that comprises a high number of species around the world. In this study, the roots, aerial parts, flowers, fruits, and aerial parts with flowers from Hypericum scabrum were macerated separately by methanol and water and then fractionated by different solvents of, such as ethyl acetate, n-hexane, butanol, dichloromethane, aqueous residue sub-extracts, and ethnobotanical use. All the extracts, sub-extracts and essential oils of H. scabrum were investigated for the first time in detail for their antimicrobial, total phenolics, and antioxidant activities. Anatomical structures of the root, stem, leaf, upper and lower leaf surface, stamen, sepal, and petal of H. scabrum were examined. The biochemical layout of essential oils was determined by GC and GC/MS. The antioxidant activity was determined by free radical scavenging activity (by DPPH). Antimicrobial activity was applied against Candida albicans ATCC 10231, Escherichia coli ATCC 8739, Staphylococcus aureus ATCC 6538, Bacillus subtilis ATCC 19659, and C. tropicalis ATCC 750 using microdilution methods. The essentials of the aerial parts, flower, and fruit are characterized by the presence of monoterpene hydrocarbons, whereas roots oil include alkanes. The GC-FID and GC-MS analysis showed that major components of roots, aerial parts, flowers, and fruits oils were undecane (66.1%); α-pinene (17.5%), γ-terpinene (17.4%), and α-thujene (16.9%); α-pinene (55.6%), α-thujene (10.9%), and γ-terpinene (7.7%); α-pinene (85.2%), respectively. The aerial part sub-extracts indicated a greater level of total phenolics and antioxidant potential. The n-hexane sub-extracts (from aerial part, flower, and aerial part with flower) showed the best activity against B. subtilis, with 39.06 µg/mL MIC value. The presented research work indicates that H. scabrum can be a novel promising resource of natural antioxidant and antimicrobial compounds.
Deployment Strategies for Target Monitoring and Coverage Improvement in Mobile Sensor Networks

Efficient sensor deployment strategies are developed in this work for target monitoring and coverage improvement in collaborative wireless mobile sensor networks. The objective of the target monitoring problem is to compute the desired sensing and communication radii of sensors as well as their location at every time instant such that a set of prescribed specifications such as connectivity preservation and low energy consumption are satisfied. An energy-efficient strategy is also proposed for tracking a moving target in a sensing field, using a grid of sufficiently small rectangular cells. The grid is converted to a graph with properly weighted edges. A shortest-path algorithm is subsequently used to route information from target to destination by a subset of sensors. In the problem of coverage improvement in mobile sensor networks, on the other hand, the objective is to place each sensor in the field using available local information about its neighbors in such a way that the area covered by sensors is as large as possible, while some important criteria are taken into consideration. Both cases of identical and nonidentical sensors (in terms of sensing radii) are considered, and different iterative algorithms are developed which are shown to be convergent. The relocation algorithms are based on the relative position of each sensor w.r. t. the boundaries of its cell or the corresponding corner point. The algorithms are extended to the case of limited communication range of sensors (leading to inaccurate Voronoi cells), an environment with prioritized sensing (mathematically characterized by a weighting function for different points), and an environment with obstacles (leading to some invisible areas). Simulation results are provided to validate the effectiveness of the proposed algorithms.
Electricity production from the management of municipal solid waste leachate with microbial fuel cells

Microbial fuel cells are a new technology that can be used for treating landfill leachate and simultaneously producing electricity. Three designs were tested in batch cycles using landfill leachate (908-3200 mg/L COD): a Square (995 mL), Circle (934 mL) and a Large Scale MFC (18.3 L). A total of seven cycles were completed for each the Square MFC and Circle MFC and two cycles for the Larger Scale MFC. Maximum power densities of 24-31 mW/m2 (653 mW/m3-824 mW/m3) were achieved using the Circle MFC and a maximum voltage of 635 mV was produced using the Larger Scale MFC. BOD, TOC, and Ammonia were removed at 50-72%, 17-53 %, and 7-69%, respectively. The Larger Scale MFC achieved 47-86% BOD removal, 51 % TOC removal and 60% ammonia reduction while operating over 52 days. These results demonstrate MFCs can be used to treat landfill leachate with the benefit of power generation.
Mitigating the Dilution Effect in Auditors’ Judgments Using a Frequency Response Mode

This paper investigates the potential of using a frequency response mode to reduce the dilution effect of non-diagnostic evidence on auditors’ fraud risk assessments. We test one hypothesis and examine a research question related to the dilution effect where response mode (frequency vs. probability) and type of non-diagnostic information (neutral vs. favorable vs. unfavorable) are manipulated in a between-subjects experiment with professional auditors as participants. Results of the hypothesis test show that auditors’ fraud risk judgments demonstrate a significantly lower dilution effect when they evaluate diagnostic and non-diagnostic evidence using a frequency response mode, as compared to the probability response mode. This effect is most pronounced when auditors are provided with favorable non-diagnostic evidence.
A Novel Microstrip-to-Microstrip Vertical Via Transition in X-Band Multilayer Packages

This paper investigates a novel X-band microstrip-to-microstrip vertical via transition with matching pads loaded signal via. This design has been proposed for a multilayer substrate package. The matching pads, which are located in the center of the signal via on each ground layer, are adopted to further improve the impedance matching level and thus attain better signal transition performance. A physics-based equivalent circuit modeling approach has been employed for this research. The right angle MS-to-MS via transition was also designed using this technique. The simulated -parameters indicate that the match-pad design made a breakthrough in achieving an approximate −15 dB wide-band return loss reduction. The measured -parameter of MS-to-MS transition showed that return loss with the matching pads is better than that without the matching pads.
Fine-grained Action Recognition using Attribute Vectors

: Modelling the subtle interactions between human and objects is crucial in ﬁne-grained action recognition. However, the existing methodologies that employ deep networks for modelling the interactions are highly supervised, computationally expensive, and need a vast amount of annotated data for training. In this paper, a framework for an efﬁcient representation of ﬁne-grained actions is proposed. First, spatio-temporal features, namely, histogram of optical ﬂow (HOF), and motion boundary histogram (MBH) are extracted for each input video as these features are more robust to irregular motions and capture the motion information in videos efﬁciently. Then a large Gaussian mixture model (GMM) is trained using the maximum a posterior (MAP) adaption, to capture the attributes of ﬁne-grained actions. The adapted means of all mixtures are concatenated to form an attribute vector for each ﬁne-grained action video. This attribute vector is of large dimension and contains redundant attributes that may not contribute to the particular ﬁne-grained action. So, factor analysis is used to decompose the high-dimensional attribute vector to a low-dimension in order to retain only the attributes which are responsible for that ﬁne-grained action. The efﬁcacy of the proposed approach is demonstrated on three ﬁne-grained action datasets, namely, JIGSAWS, KSCGR, and MPII cooking2.
Experimental and Numerical Modelling of Turbulent Flow over an Inclined Backward-Facing Step in an Open Channel

the water tank with a pump driven by a motor equipped with a frequency converter. The pump with the maximum ﬂow rate of Q (cid:2) 7.2 m 3 /min. enabled the maximum speed in the channel of 3 m/s. To stabilize the inﬂow rate, a wire mesh screens and a honeycomb were installed at the entry of the straight inﬂow part. The channel is made of transparent plastic material allowing the use of contactless optical measuring tech-niques. The laser Doppler anemometry (LDA) for measurements of one and/or two velocity components in selected points of the ﬂow ﬁeld and the particle image velocimetry method (PIV) were used for measurements of the velocity ﬁeld in selected channel sections. The extended uncertainty guess of ﬂow velocity determination at reliability level of 95% was less than 5% for LDA and 15% for PIV techniques. The sketch of the test section of the channel with an inclined backward-facing step with the inclination angle α (cid:2) 20° is given in Fig. 1. The distance of the edge of the inclined step from the channel entry is 0.9 m. The height of the inclined step is H (cid:2) 100 mm. The whole length of the investigated section is 2.1 m. Boundary con-EXPERIMENTAL The contribution deals with the experimental and numerical modelling of the turbulent flow over an inclined backward-facing step in an open water channel. The modelling was carried out in the wide range of the Froude number covering the subcritical, supercritical and near critical regimes as well. The study was concentrated particularly on the development of flow separation and on changes of free surface. Numerical results obtained using the commercial software ANSYS CFX 12.0 for the two-equation SST model and the EARSM model were compared with experimental data obtained by means of PIV and LDA measuring techniques.
Usage of Dynamic Analysis to Determine Force Interactions Between Components of Rolling Bearings

aim is to detail the creation of a roller bearing model in the Adams program suite for subsequent dynamic analysis and to obtain information about different parts of the bearing during the simulation. The bearing model was made so as to closely resemble its real-life counterpart, which allows us to estimate load conditions, dynamic conditions of individual bearing parts and interactions between them.
Nine-Strain Bacterial Synbiotic Improves Crying and Lowers Fecal Calprotectin in Colicky Babies—An Open-Label Randomized Study

The aim of this study (ClinicalTrials.gov registration NCT04666324) was to determine the effects of a nine-strain synbiotic and simethicone on the duration of crying and the gut inflammation marker calprotectin in colicky babies aged 3–6 weeks, diagnosed using the Wessel criteria. The open-label study comprised a control group of non-colicky babies (n = 20) and two parallel treatment groups (each n = 50) to which colicky babies were randomly and equally assigned to receive the multi-strain synbiotic or simethicone orally for 28 days. Primary outcome measures were the change in daily crying duration and the level of fecal calprotectin on days 1 and 28 of the study. Administration of the synbiotic resulted in a rechange of crying duration of −7.18 min/day of treatment, while simethicone had a significantly smaller effect (−5.74 min/day). Fecal calprotectin levels in colicky babies were significantly elevated compared to those in non-colicky babies. Treatment with the nine-strain synbiotic resulted in a significant lowering of fecal calprotectin at the end of the study, while no such effect was found for simethicone. No adverse effects were reported. Study results confirm earlier findings of crying duration reductions in colicky babies by the synbiotic, an effect that might be linked to its anti-inflammatory properties.
Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions

Understanding human intentions during interactions has been a long-lasting theme, that has applications in human-robot interaction, virtual reality and surveillance. In this study, we focus on full-body human interactions with large-sized daily objects and aim to predict the future states of objects and humans given a sequential observation of human-object interaction. As there is no such dataset dedicated to full-body human interactions with large-sized daily objects, we collected a large-scale dataset containing thousands of interactions for training and evaluation purposes. We also observe that an object's intrinsic physical properties are useful for the object motion prediction, and thus design a set of object dynamic descriptors to encode such intrinsic properties. We treat the object dynamic descriptors as a new modality and propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network to better generalize to unseen objects. We also demonstrate the predicted results are useful for human-robot collaborations.
Elucidating Antiangiogenic Potential of Rauwolfia serpentina: VEGFR-2 Targeting-Based Molecular Docking Study

Angiogenesis plays a critical role in tumorigenesis as it provides the necessary blood supply to the newly grown solid tumor. It helps maintain the tumor microenvironment, promotes tumor development, progression, and metastasis. The vascular epithelial growth factor (VEGF), interacting with the tyrosine kinase receptor VEGFR-2 on endothelial cells, exerts its proangiogenic activity. Hence, targeting the VEGFR-2 signaling is considered a promising strategy to inhibit angiogenesis and thus cancer treatment. This study aims to identify the bioactive compounds derived from the medicinal herb Rauwolfia serpentina that effectively binds with VEGFR-2. The bioactive compounds of R. serpentina were first screened for their physicochemical properties using the DataWarrior program (version 5.5.0). Finally, 17 compounds that obeyed Lipinski's rule of five and showed good drug-likeness were selected for molecular docking studies. Molecular docking results showed that the ligands ajmalicidine, 1, 2-dihydrovomilenine, rauwolscine, yohimbine, ajmaline, and papaverine interact strongly with the target VEGFR-2 receptor. Hydrogen bonds and hydrophobic interactions stabilized the interactions of these compounds with VEGFR-2. These compounds showed favourable drug-like properties and possess no significant toxicity. Therefore, the findings of this study indicate that the compounds derived from R. serpentina can be considered for the development of antiangiogenic drug candidates by targeting VEGFR-2.
A high-performance MAC protocol for consumer broadband satellite systems

In this paper, an extended version of the CRDSA protocol proposed in and is presented. The enhanced CRDSA protocol, dubbed CRDSA++, extends the original CRDSA concept to more than two replicas (e.g. 4 or 5) and exploits power fluctuations in the received signal to further boost the performance of the CRDSA protocol. The paper provides a wide range of performance and sensitivity analysis with respect to the number of replicas and power unbalance. It is shown that a packet loss ratio (PLR) below 10-4 can be achieved with a normalized throughput in excess of 1 packet per slot. This represents a remarkable performance for an open loop random access (RA) scheme without any kind of access control over a medium shared by a large population of terminals. Finally, the paper also provides a preliminary discussion on the implementation aspects of the CRDSA++ RA scheme, as well as on the integration aspects with a DAMA protocol to support a wider range of service scenarios with improved MAC layer performance. (10 pages)
A VR Application for the Analysis of Human Responses to Collaborative Robots

The increasing number of robots performing certain tasks in our society, especially in the industrial environment, introduces more scenarios where a human must collaborate with a robot to achieve a common goal which, in turn, raises the need to study how safe and natural this interaction is and how it can be improved. Virtual reality is an excellent tool to simulate these interactions, as it allows the user to be fully immersed in the world while being safe from a possible robot malfunction. In this work, a simulation was created to study how effective virtual reality is in the studies of human-robot interaction. It is then used in an experiment where the participants must collaborate with a simulated Baxter to place objects delivered by the robot in the correct place, within a time limit. During the experiment, the electrodermal activity and heart rate of the participants are measured, allowing for the analysis of reactions to events occurring within the simulation. At the end of each experiment, participants fill a user experience questionnaire (UEQ) and a Flow Short Scale questionnaire to evaluate their sense of presence and the interaction with the robot.