bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of a Chain Mower Performance for Weed Control under Tree Rows in an Alley Cropping Farming System
Authors: Gagliardi Lorenzo , Fontanelli Marco , Frasconi Christian , Sportelli Mino , Antichi Daniele , Tramacere Lorenzo Gabriele , Rallo Giovanni , Peruzzi Andrea , Raffaelli Michele
Published: November 2022

In the area under tree rows of alley cropping systems, coarse plant material as well as pruning material or stones may be present, so the use of a mower equipped with chains as cutting a tool could be advantageous. A mower designed for under-row weed control in orchards, equipped with an automatic tree-skipping mechanism, was modified by replacing blades with chains with the aim of evaluating its performance in an alley cropping system. A first trial was carried out in an open field to preliminarily compare the chain mower with the version equipped with blades in relation to different settings of working speed (1.6 and 2.4 km·h−1) and rotation speed of the cutting tool (1830 and 2500 rpm). Weed biomass reduction, weed cover reduction, weed height reduction, weed biomass regrowth, and clipping size were assessed. In a second trial, the performance of the mowers with different setting configurations was assessed in an alley cropping system under a more critical environmental condition for mowing, i.e., the presence of dew. Weed biomass reduction, weed cover reduction, weed height reduction, and the mowers’ field capacity with different working speed settings were assessed. No major differences emerged between the mowers and the chain mower performance was comparable to that of the standard blade mower. The setting with the high working speed and high rotation speed of the cutting tool turns out to be the best compromise, obtaining a weed biomass reduction of 59.6%, a weed cover reduction of 40.9%, and a higher field capacity compared to the setting with the low working speed, with an increase of 47.9%.
An Approach for Autonomous Feeding Robot Path Planning in Poultry Smart Farm
Authors: Zhang Yanjun , Sun Weiming , Yang Jian , Wu Weiwei , Miao Hong , Zhang Shanwen
Published: November 2022

In order to solve the problems of poor feeding environment, untimely feeding and high labor demand in poultry smart farms, the development of feeding robots is imminent, while the research on path planning algorithms is an important part of developing feeding robots. The energy consumption of the feeding robot is one of the important elements of concern in the process of path planning. In this study, the shortest path does not mean that the feeding robot consumes the least energy, because the total mass of the feeding robot keeps changing during the feeding process. It is necessary to find the most suitable path so that the feeding robot consumes the lowest amount of energy during the feeding process. A branch and bound algorithm to calculate the minimum energy consumption travel path for small-scale buckets lacking feed is proposed. The lower bound of the branch and bound on the energy consumption is obtained by the approach of preferred selection of the set of shortest edges combined with the sequence inequality, and the upper bound could be obtained based on Christofides’s Heuristic algorithm. A double-crossover operator genetic algorithm based on an upper bound on energy consumption for large-scale buckets lacking feed is proposed, and different crossover operations are performed according to the relationship between the fitness value and the upper bound of energy consumption in order to find a better path. The experiment results show that the approach proposed in this study is efficient; for small-scale buckets lacking feed, a branch and bound algorithm could calculate the minimum energy consumption path of 17 points in 300 s, and for large-scale buckets lacking feed, a double-crossover operator genetic algorithm based on an upper bound on energy consumption could calculate the minimum energy consumption travel path within 30 points in 60 s. The result is more accurate compared to the genetic algorithm with a single crossover operator.
Evaluation of Strategies to Reduce Equine Strongyle Infective Larvae on Pasture and Study of Larval Migration and Overwintering in a Nordic Climate
Authors: Osterman-Lind Eva , Hedberg Alm Ylva , Hassler Hillevi , Wilderoth Hanna , Thorolfson Helena , Tydén Eva
Published: November 2022

Horses, as grazing animals, are inadvertently exposed to intestinal parasites that, if not controlled, may cause disease. However, the indiscriminate use of anthelmintic drugs has led to drug resistance, highlighting the need for pasture-management practices to reduce the level of parasitic exposure and lessen reliance on drugs. The efficacy of such methods depends both on the epidemiology of the parasites and the prevailing weather conditions. The aim of the study was to investigate the effect of faecal removal and harrowing on reducing the number of parasite larvae in herbage. Moreover, the migratory and survival ability of strongyle larvae in a Nordic climate was studied. Faeces from horses naturally infected with strongyle nematodes were used to contaminate pastures and grass samples were collected to harvest larvae. Twice-weekly faecal removal significantly reduced larval yields, whereas harrowing on a single occasion under dry weather conditions in the summer did not. Strongyle larvae were able to migrate 150 cm from the faecal pats, but most larvae were found within 50 cm. Both Cyathostominae and S. vulgaris survived the winter months with larvae harvested up to 17–18 months after faecal placement. Resting of pastures for one year greatly reduced the parasite level, but two years of rest were required for parasite-free pasture.
The Use of Star Anise-Cinnamon Essential Oil as an Alternative Antibiotic in Prevention of Salmonella Infections in Yellow Chickens
Authors: Li Changcheng , Xu Ziheng , Chen Wenyan , Zhou Chenyu , Wang Can , Wang Min , Liang Jingzhen , Wei Ping
Published: November 2022

Salmonella is capable of harming human and animal health, and its multidrug resistance (MDR) has always been a public health problem. In addition, antibiotic-free or antibiotic-reduced policies have been implemented in poultry production. Therefore, the search for antibiotic alternatives is more urgent than ever before. The aim of this study was to assess the antibacterial activity of star anise-cinnamon essential oil (SCEO) in vitro and its prophylactic effect against the infections of Salmonella pullorum, Salmonella give, and Salmonella kentucky in vivo. The results demonstrated that SCEO is effective against Salmonella pullorum, Salmonella give, and Salmonella kentucky in vitro. Supplementation with SCEO could significantly decrease the infections of Salmonella pullorum and Salmonella give, whereas it could slightly but not significantly decrease the infection of Salmonella kentucky, while also significantly alleviating the body weight (BW) loss caused by the infections of Salmonella pullorum, Salmonella give, and Salmonella kentucky in Yellow chickens. The SCEO had the best prophylactic effect against the infection of Salmonella give in Yellow chickens, followed by the infection of Salmonella pullorum and the infection of Salmonella kentucky. The SCEO, used as an antibiotic alternative, could be an effective prevention strategy against the infections of Salmonella pullorum, Salmonella give, and Salmonella kentucky in Yellow chickens.
Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
Authors: Islam Md. Kamrul , Reza Imran , Gazder Uneb , Akter Rocksana , Arifuzzaman Md , Rahman Muhammad Muhitur
Published: November 2022

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.
The Effect of Human Blood Plasma Conditioning Films on Platelet Transfusion Bag Surface Properties
Authors: Wilson-Nieuwenhuis Joels , Dempsey-Hibbert Nina , Liauw Christopher M. , Whitehead Kathryn A.
Published: November 2022

Transfusion-associated bacterial infections continue to occur which may be due to the formation of bacterial biofilms on the inner surface of the blood bag. Plasticized poly (vinyl chloride) (p-PVC) platelet storage bags in three surface roughness states (rough, smooth and flattened) were used to determine the effect that a conditioning film (CF) of human plasma had on surface properties and its interaction with Staphylococcus epidermidis and Serratia marcescens. SEM and optical profilometry determined changes in surface roughness, whilst EDX and ATR-FTIR determined surface chemistry. The physicochemistry of the surfaces and bacteria was assessed using contact angle measurements and MATH assays respectively. When applied to a rougher surface, the CF reduced the surface topography, masked certain surface chemistry features and made the surfaces more hydrophilic. The CF reduced the adhesion of the bacteria to most of the hydrocarbons. When human plasma was combined with bacteria, most of the physicochemical properties changed similarly to those of human plasma alone, with the most significant changes observed after 24 h especially with Ser. marcescens. The results demonstrated that the presence of human plasma had a significant effect on the surface properties of the platelet bags and also on microbial interactions with the bag surface.
A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts
Authors: Zhao Yijiang , Wei Xingcai , Liu Yizhi , Liao Zhuhua
Published: November 2022

Due to their status as non-professionals, the reputations of Volunteered Geographic Information (VGI) contributors have a very important impact on data quality. In the process of contributor reputation evaluation in OpenStreetMap (OSM), it is very difficult to calculate the semantic similarity between object versions contributed by volunteers. Aimed at this issue, this paper proposes a model of contributor’s reputation based on semantic similarity of ontology concepts. Firstly, contributors are classified into three categories based on an improved WPCA and classification method. Then, an initial reputation is set for every OSM user in each class according to these categories and related research. Secondly, the related concept ontology is constructed for OSM entities; then, the semantic similarity of the object version is calculated according to the similarity of concept attributes and the semantic distance of concept. The contributor’s evaluation reputation is computed by synthesizing the semantic similarity, geometric similarity, and topological similarity of object versions. Thirdly, the contributor’s evaluation reputation and the initial reputation is aggregated to obtain the contributor’s reputation; finally, the OSM data of Rutland, England, is used as an example to verify the validity of our model. The experimental results show that the proposed model can obtain a more comprehensive contributor’s evaluation by fusing with the semantic similarity of ontology concept. The evaluation bias caused only by the semantic change between versions can be eliminated. Moreover, the obtained user’s reputation is positively correlated with the data quality. The contributor’s reputation evaluation method proposed in this paper is an effective method for evaluating the contributor’s reputation in OSM-like systems.
The Reactive Strength Index in Unilateral Hopping for Distance and Its Relationship to Sprinting Performance: How Many Hops Are Enough for a Comprehensive Evaluation?
Authors: Šarabon Nejc , Milinović Ivan , Dolenec Aleš , Kozinc Žiga , Babić Vesna
Published: November 2022

The reactive strength index (RSI) is used to assess reactive strength in the lower limbs. Since previous studies have mostly focused on vertical RSI (derived from drop jumps), we investigated the RSI across eight consecutive unilateral horizontal hops and its associations with sprint performance. A sample of 104 male kinesiology students (age: 19.2 ± 1.1 years) performed unilateral hops and 100 m sprints with split times recorded. RSI was determined as the ratio between contact time and subsequent flight time for each hop. On a group level, the horizontal RSI was statistically significantly (p < 0.001; ηp2 = 0.49) and increased from hop 1 (1.04 ± 0.17) to hop 5 (1.41 ± 0.22), but then plateaued (1.43–1.44) for hops 6–8. However, on an individual level, variations in RSI were present all the way to the last hop. All sprint split times were in small-to-moderate correlation with RSI variables (r = 0.25–0.40), implying that individuals with higher hopping RSI presented with shorter sprint times. Future studies should expand the research on the relationship between horizontal hopping RSI and sports performance, and examine if unilateral RSI tests can provide practitioners with valuable information when performed alongside more common vertical RSI tests.
Simulation Method and Application of Three-Dimensional DFN for Rock Mass Based on Monte-Carlo Technique
Authors: Li Ang , Li Yaodong , Wu Feng , Shao Guojian , Sun Yang
Published: November 2022

In this study, the authors simulate a polygonal discrete fracture network (DFN) in rock masses. The probability models of the relevant geological parameters, including the orientation, trace length, volume density, and coordinates of the centroid, are firstly developed as fractures are in the shape of rectangles. In the process, the probability distribution of rectangular fractures with side lengths as random variables is introduced and described in terms of mean trace lengths on the basis of the probability model of disk-shaped fracture with the diameter as the random variable. The relationship between the volume density and the linear density of rectangular fractures is given for a negative exponential distribution. Following this, the coordinates of the vertices of fractures are derived based on spatial algebraic geometry, and the data for the three-dimensional DFN model are generated using the Monte-Carlo technique. The resulting three-dimensional DFN is visualized by calling the Open GL graphics database in the environment of Visual C, and the process of implementation of the DFN simulation is given. Finally, the validity of the simulation is verified by applying it to engineering practice.
Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network
Authors: Liu Yanzhang , Cai Jinqi , Tan Guirong
Published: November 2022

Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products.
Certain Geometric Properties of the Fox–Wright Functions
Authors: Kumar Anish , Mondal Saiful R. , Das Sourav
Published: November 2022

The primary objective of this study is to establish necessary conditions so that the normalized Fox–Wright functions possess certain geometric properties, such as convexity and pre-starlikeness. In addition, we present a linear operator associated with the Fox–Wright functions and discuss its k-uniform convexity and k-uniform starlikeness. Furthermore, some sufficient conditions were obtained so that this function belongs to the Hardy spaces. The results of this work are presumably new and illustrated by several consequences, remarks, and examples.
Existence Results for an m-Point Mixed Fractional-Order Problem at Resonance on the Half-Line
Authors: Imaga Ogbu F. , Iyase Samuel A. , Ogunniyi Peter O.
Published: November 2022

This work considers the existence of solutions for a mixed fractional-order boundary value problem at resonance on the half-line. The Mawhin’s coincidence degree theory will be used to prove existence results when the dimension of the kernel of the linear fractional differential operator is equal to two. An example is given to demonstrate the main result obtained.
Cell Behavioral Dynamics as a Cue in Optimizing Culture Stabilization in the Bioprocessing of Pluripotent Stem Cells
Authors: Thanuthanakhun Naruchit , Kim Mee-Hae , Kino-oka Masahiro
Published: November 2022

Pluripotent stem cells (PSCs) are important for future regenerative medicine therapies. However, in the production of PSCs and derivatives, the control of culture-induced fluctuations in the outcome of cell quality remains challenging. A detailed mechanistic understanding of how PSC behaviors are altered in response to biomechanical microenvironments within a culture is necessary for rational bioprocessing optimization. In this review, we discuss recent insights into the role of cell behavioral and mechanical homeostasis in modulating the states and functions of PSCs during culture processes. We delineate promising ways to manipulate the culture variability through regulating cell behaviors using currently developed tools. Furthermore, we anticipate their potential implementation for designing a culture strategy based on the concept of Waddington’s epigenetic landscape that may provide a feasible solution for tuning the culture quality and stability in the bioprocessing space.
Genetic Relationship, SPAD Reading, and Soluble Sugar Content as Indices for Evaluating the Graft Compatibility of Citrus Interstocks
Authors: Wang Tie , Deng Lijun , Huang Shengjia , Xiong Bo , Ihtisham Muhammad , Zheng Zhendong , Zheng Wei , Qin Zeyu , Zhang Mingfei , Sun Guochao , Wang Jun , Wang Zhihui
Published: November 2022

The interstock, a stock between the rootstock and scion, has a significant regulatory effect on the stock and scion, and its function is highly dependent on graft compatibility. To assess the graft compatibility of the interstock and scion, ‘Yuanxiaochun’ was top grafted onto ‘Ponkan’, ‘Shiranuhi’, ‘Harumi’, ‘Tarocco’, and ‘Kumquat’. The results showed significant differences in the survival ratio and preservation ratio among different combinations. Grafting compatibility in the early stages of grafting was associated with the genetic relationship. The biomass accumulation revealed that the interstock could influence both the rootstock and the scion. The physiological and biochemical traits analysis suggested that SPAD reading and soluble sugar could be employed as preselected indices to evaluate graft compatibility in the late stage of grafting. These results indicated that the evaluation of graft compatibility was a dynamic process. The findings provided a new approach for studying the stock and scion interaction mechanisms mediated by interstock, and directly provided a theoretical and practical basis for the high-grafting of ‘Yuanxiaochun’ citrus.
Genetic Structure of an East Asian Minnow (Toxabramis houdemeri) in Southern China, with Implications for Conservation
Authors: Chen Weitao , Li Yuefei , Cai Xingwei , Xiang Denggao , Gao Shang , Li Ce , Lan Chun , Zhu Shuli , Yang Jiping , Li Xinhui , Li Jie
Published: November 2022

River dynamics have been hypothesized to substantially influence the genetic structure of freshwater fish taxa. Southern China harbors abundant independent river systems, which have undergone historical rearrangements. This river system is thus an excellent model with which to test the abovementioned hypothesis. In this study, a cyprinid widespread in many independent rivers in southern China, Toxabramis houdemeri, was chosen as an exemplar species with which to explore the effects of river configuration changes on spatial genetic structure using mitochondrial and nuclear markers. The results indicated that the T. houdemeri populations fell into four mitochondrial haplotype groups, each genetically endemic to a single river or two adjacent river systems. The mitochondrial haplotype network recovered a clear genetic boundary between Hainan Island populations and mainland populations. Notable genetic differentiation was observed within populations from distinct river systems in both mitochondrial and nuclear loci. River system separation, mountain barriers, and mobility were the key factors shaping the genetic structure of T. houdemeri populations. Late Pleistocene divergence and historical immigration were identified within the four mitochondrial haplotype groups, indicating that river rearrangements triggered by the Late Pleistocene glacial cycles were important drivers of the complex genetic structure and demographic history of T. houdemeri. Historical demographics suggested that T.houdemeri populations expanded during the Late Pleistocene. The present study has important consequences for the management and conservation of T. houdemeri.
Analysis of Different Binding Modes for Tiagabine within the GAT-1 Transporter
Authors: Łątka Kamil , Bajda Marek
Published: November 2022

The recently obtained cryo-electron microscopy structure (PDB code: 7SK2) of the human γ-aminobutyric acid transporter type 1 (hGAT-1) in complex with the antiepileptic drug, tiagabine, revealed a rather unexpected binding mode for this inhibitor in an inward-open state of the transporter. The simultaneously released crystal structures of the modified dopamine transporter with mutations mimicking hGAT-1 indicated an alternative binding mode for the tiagabine analogues that were found to block the transporter in an outward-open state, which is more consistent with the results of previous biological and molecular modeling studies. In view of the above discrepancies, our study compares different hypothetical tiagabine binding modes using classical and accelerated molecular dynamics simulations, as well as MM-GBSA free binding energy (dG) calculations. The results indicate that the most stable and energetically favorable binding mode of tiagabine is the one where the nipecotic acid fragment is located in the main binding site (S1) and the aromatic rings are arranged within the S2 site of the hGAT-1 transporter in an outward-open state, confirming the previous molecular modelling findings. The position of tiagabine bound to hGAT-1 in an inward-open state, partially within the intracellular release pathway, was significantly less stable and the dG values calculated for this complex were higher. Furthermore, analysis of the cryo-electron map for the 7SK2 structure shows that the model does not appear to fit into the map optimally at the ligand binding site. These findings suggest that the position of tiagabine found in the 7SK2 structure is rather ambiguous and requires further experimental verification. The identification of the main, high-affinity binding site for tiagabine and its analogues is crucial for the future rational design of the GABA transporter inhibitors.
Atypical Substrates of the Organic Cation Transporter 1
Authors: Redeker Kyra-Elisa Maria , Jensen Ole , Gebauer Lukas , Meyer-Tönnies Marleen Julia , Brockmöller Jürgen
Published: November 2022

The human organic cation transporter 1 (OCT1) is expressed in the liver and mediates hepatocellular uptake of organic cations. However, some studies have indicated that OCT1 could transport neutral or even anionic substrates. This capability is interesting concerning protein-substrate interactions and the clinical relevance of OCT1. To better understand the transport of neutral, anionic, or zwitterionic substrates, we used HEK293 cells overexpressing wild-type OCT1 and a variant in which we changed the putative substrate binding site (aspartate474) to a neutral amino acid. The uncharged drugs trimethoprim, lamivudine, and emtricitabine were good substrates of hOCT1. However, the uncharged drugs zalcitabine and lamotrigine, and the anionic levofloxacin, and prostaglandins E2 and F2α, were transported with lower activity. Finally, we could detect only extremely weak transport rates of acyclovir, ganciclovir, and stachydrine. Deleting aspartate474 had a similar transport-lowering effect on anionic substrates as on cationic substrates, indicating that aspartate474 might be relevant for intra-protein, rather than substrate-protein, interactions. Cellular uptake of the atypical substrates by the naturally occurring frequent variants OCT1*2 (methionine420del) and OCT1*3 (arginine61cysteine) was similarly reduced, as it is known for typical organic cations. Thus, to comprehensively understand the substrate spectrum and transport mechanisms of OCT1, one should also look at organic anions.
Rapid Bacteria Detection from Patients’ Blood Bypassing Classical Bacterial Culturing
Authors: Huber François , Lang Hans Peter , Heller Stefanie , Bielicki Julia Anna , Gerber Christoph , Meyer Ernst , Egli Adrian
Published: November 2022

Sepsis is a life-threatening condition mostly caused by a bacterial infection resulting in inflammatory reaction and organ dysfunction if not treated effectively. Rapid identification of the causing bacterial pathogen already in the early stage of bacteremia is therefore vital. Current technologies still rely on time-consuming procedures including bacterial culturing up to 72 h. Our approach is based on ultra-rapid and highly sensitive nanomechanical sensor arrays. In measurements we observe two clearly distinguishable distributions consisting of samples with bacteria and without bacteria respectively. Compressive surface stress indicates the presence of bacteria. For this proof-of-concept, we extracted total RNA from EDTA whole blood samples from patients with blood-culture-confirmed bacteremia, which is the reference standard in diagnostics. We determined the presence or absence of bacterial RNA in the sample through 16S-rRNA hybridization and species-specific probes using nanomechanical sensor arrays. Via both probes, we identified two clinically highly-relevant bacterial species i.e., Escherichia coli and Staphylococcus aureus down to an equivalent of 20 CFU per milliliter EDTA whole blood. The dynamic range of three orders of magnitude covers most clinical cases. We correctly identified all patient samples regarding the presence or absence of bacteria. We envision our technology as an important contribution to early and sensitive sepsis diagnosis directly from blood without requirement for cultivation. This would be a game changer in diagnostics, as no commercial PCR or POCT device currently exists who can do this.
Biosensors in Rehabilitation and Assistance Robotics
Authors: Ubeda Andres , Garcia Gabriel J. , Jara Carlos A. , Morell Vicente
Published: November 2022

Robotic developments in the field of rehabilitation and assistance have seen a significant increase in the last few years [...]
Evaluation and Prediction of Pavement Deflection Parameters Based on Machine Learning Methods
Authors: Chen Xueqin , Dong Qiao , Dong Shi
Published: November 2022

The deflection measurements made using Falling Weight Deflectometers (FWDs) are widely used in the back-calculation of pavement layer moduli. Pavement structural characteristics, changes in temperature, and other related factors exert a significant effect on the deflection measurements. Therefore, three machine learning methods—Classification and Regression Tree (CART), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were used to evaluate the importance of influencing factors including FWD test conditions, pavement structural parameters, climatic factors, traffic level, rehabilitation level, and service age, on the FWD measurements of deflection basin in this study. The results indicated that structural number was an important feature for all FWD measurements but its importance on lg(D0–D20) and lg(D0–D30) was smaller than other FWD measurements. The relative feature importance of the asphalt layer, base, and subbase on lg(D0–D20) and lg(D0–D30) was asphalt layer > subbase > base; their relative importance on lg(D20–D60), lg(D30–D60), and lg(D30–D90) was asphalt layer > base > subbase; and their relative importance on lg(D90−D120) and lg(D60–D120) was base > subbase > asphalt layer. Among the FWD test condition variables, drop load was the most significant factor influencing deflection measurements. The second-layer temperature was also important for lg(D0–D20), lg(D0–D30), and lg(D0–D45). The importance of precipitation was greater than the freeze index. The prediction results shown that the accuracy of GBDT was as high as 99%. Besides, GBDT outperformed RF, and RF outperformed CART. The analyses between FWD deflection parameters and influencing factors, especially the structural characteristics of the pavement, provide theoretical evidence for the evaluation of pavement layer strength on the basis of FWD data.
Customized AutoML: An Automated Machine Learning System for Predicting Severity of Construction Accidents
Authors: Toğan Vedat , Mostofi Fatemeh , Ayözen Yunus Emre , Behzat Tokdemir Onur
Published: November 2022

Construction companies are under pressure to enhance their site safety condition, being constantly challenged by rapid technological advancements, growing public concern, and fierce competition. To enhance construction site safety, literature investigated Machine Learning (ML) approaches as risk assessment (RA) tools. However, their deployment requires knowledge for selecting, training, testing, and employing the most appropriate ML predictor. While different ML approaches are recommended by literature, their practicality at construction sites is constrained by the availability, knowledge, and experience of data scientists familiar with the construction sector. This study develops an automated ML system that automatically trains and evaluates different ML to select the most accurate ML-based construction accident severity predictors for the use of construction professionals with limited data science knowledge. A real-life accident dataset is evaluated through automated ML approaches: Auto-Sklearn, AutoKeras, and customized AutoML. The investigated AutoML approaches offer higher scalability, accuracy, and result-oriented severity insight due to their simple input requirements and automated procedures.
Effects of Hybrid PVA–Steel Fibers on the Mechanical Performance of High-Ductility Cementitious Composites
Authors: Li Lianghui , Li Bixiong , Wang Zhiwen , Zhang Zhibo , Alselwi Othman
Published: November 2022

Producing high-ductility cementitious composites (HDCC) increased in parallel with concrete demand in China recently. However, the high cost of manufacturing cementitious composites (HDCC) persists. To reduce the cost of HDCC, steel fibers, polyvinyl alcohol (PVA), and river sand were used to produce HDCC concrete in the present study. A total fiber content of 2% was formed with five different proportions of PVA fiber and steel fiber. Within the scope of the experimental studies, mechanical (workability, compressive strength, tensile, and bending properties), and microstructural (scanning electron microscopy) tests were carried out to investigate the properties of the hybrid fiber-reinforced composites. The results showed that the fluidity of HDCC increased with increasing steel fiber substitution. The compressive strength of the mixture containing 0.5% steel fiber and 1.5% PVA fiber exhibited a better compressive strength of 31.3 MPa. The tensile performance of the mixture was improved due to the incorporation of steel fiber. The initial cracking strength was about 2.32 MPa, 25.4% higher than that of the reference group, and the ultimate tensile strength was 3.36–3.56 MPa. However, reducing the content of PVA fiber impacts the flexural rigidity of the matrix.
Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy
Authors: Strijbis Victor I. J. , Dahele Max , Gurney-Champion Oliver J. , Blom Gerrit J. , Vergeer Marije R. , Slotman Berend J. , Verbakel Wilko F. A. R.
Published: November 2022

Depending on the clinical situation, different combinations of lymph node (LN) levels define the elective LN target volume in head-and-neck cancer (HNC) radiotherapy. The accurate auto-contouring of individual LN levels could reduce the burden and variability of manual segmentation and be used regardless of the primary tumor location. We evaluated three deep learning approaches for the segmenting individual LN levels I–V, which were manually contoured on CT scans from 70 HNC patients. The networks were trained and evaluated using five-fold cross-validation and ensemble learning for 60 patients with (1) 3D patch-based UNets, (2) multi-view (MV) voxel classification networks and (3) sequential UNet+MV. The performances were evaluated using Dice similarity coefficients (DSC) for automated and manual segmentations for individual levels, and the planning target volumes were extrapolated from the combined levels I–V and II–IV, both for the cross-validation and for an independent test set of 10 patients. The median DSC were 0.80, 0.66 and 0.82 for UNet, MV and UNet+MV, respectively. Overall, UNet+MV significantly (p < 0.0001) outperformed other arrangements and yielded DSC = 0.87, 0.85, 0.86, 0.82, 0.77, 0.77 for the combined and individual level I–V structures, respectively. Both PTVs were also significantly (p < 0.0001) more accurate with UNet+MV, with DSC = 0.91 and 0.90, respectively. The accurate segmentation of individual LN levels I–V can be achieved using an ensemble of UNets. UNet+MV can further refine this result.
Characteristics, Patterns of Care and Predictive Geriatric Factors in Elderly Patients Treated for High-Grade IDH-Mutant Gliomas: A French POLA Network Study
Authors: Montégut Coline , Guillamo Jean-Sébastien , Ducray François , Dehais Caroline , Cohen-Jonathan Moyal Elisabeth , Desenclos Christine , Petit Antoine , Seizeur Romuald , Bekaert Lien , Gaultier Claude , Motuo Fotso Marie Jeannette , Blonski Marie , Frenel Jean-Sébastien , Vauléon Elodie , Langlois Olivier , Noel Georges , Carpentier Antoine F. , Di Stefano Anna Luisa , Bronnimann Charlotte , Figarella-Branger Dominique , Chinot Olivier , Tabouret Emeline
Published: November 2022

Background: Describe the characteristics, patterns of care, and predictive geriatric factors of elderly patients with IDHm high-grade glioma (HGG) included in the French POLA network. Material and Methods: The characteristics of elderly (≥70 years) patients IDHm HGG were compared to those of younger patients IDHm HGG (<70 years) and of elderly patients IDHwt HGG. Geriatric features were collected. Results: Out of 1433 HGG patients included, 119 (8.3%) were ≥70 years. Among them, 39 presented with IDHm HGG. The main characteristics of elderly IDHm HGG were different from those of elderly IDHwt HGG but similar to those of younger IDHm HGG. In contrast, their therapeutic management was different from those of younger IDHm HGG with less frequent gross total resection and radiotherapy. The median progression-free survival (PFS) and overall survival (OS) were longer for elderly patients IDHm HGG (29.3 months and 62.1 months) than elderly patients IDHwt HGG (8.3 months and 13.3 months) but shorter than those of younger patients IDHm HGG (69.1 months and not reached). Geriatric factors associated with PFS and OS were mobility, neuropsychological disorders, body mass index, and autonomy. Geriatric factors associated with PFS and OS were mobility, neuropsychological disorders, and body mass index, and autonomy. Conclusion: the outcome of IDHm HGG in elderly patients is better than that of IDHwt HGG. Geriatric assessment may be particularly important to optimally manage these patients.
Green Dynamic Kinetic Resolution—Stereoselective Acylation of Secondary Alcohols by Enzyme-Assisted Ruthenium Complexes
Authors: Heba Monika , Wolny Anna , Kastelik-Hryniewiecka Anna , Stradomska Dominika , Jurczyk Sebastian , Chrobok Anna , Kuźnik Nikodem
Published: November 2022

Dynamic kinetic resolution allows for the synthesis of enantiomerically pure asymmetric alcohols. Cyclopentadienyl-derived ruthenium catalysts were immobilized with an ionic liquid, [BMIM][NTf2], on multiwall carbon nanotubes and used for the racemization of chiral secondary alcohols. This successful approach was combined with the enantioselective enzymatic acylation of secondary alcohols (1-phenylethanol and 1-(1-naphthyl)ethanol) using Novozyme® 435. The resulting catalytic system of the ruthenium racemization catalysts and enzymatic acylation led to chiral esters being obtained by dynamic kinetic resolution. The immobilized catalytic system in the ionic liquid gave the same activity of >96% yield within 6 h and a selectivity of 99% enantiomeric excess as the homogeneous system, while allowing for the convenient separation of the desired products from the catalyst. Additionally, the process can be regarded as green, since the efficient reuse of the catalytic system was demonstrated.
Enzymatic Synthesis of Modified Nucleoside 5′-Monophosphates
Authors: Koplūnaitė Martyna , Butkutė Kamilė , Špelveris Dominykas , Urbelienė Nina , Meškys Rolandas
Published: November 2022

There is an extensive list of applications for nucleosides, nucleotides, and their analogues that spans from substrates and inhibitors in enzymatic research to anticancer and antiviral drugs. Nucleoside phosphates are often obtained by chemical phosphorylation reactions, although enzymatic nucleoside phosphorylation is a promising green alternative. In this work two nucleoside kinases, D. melanogaster deoxynucleoside kinase and B. subtilis deoxycytidine kinase, have been employed for the phosphorylation of various canonical and modified nucleosides, and the results between the two enzymes have been compared. It was determined that both kinases are suitable candidates for enzymatic nucleoside 5′-monophosphate synthesis, as the reaction yields are often in the 40–90% range. Deoxynucleoside kinase, however, often outperforms deoxycytidine kinase and accepts a wider range of nucleoside analogues as substrates. Hence, deoxynucleoside kinase and deoxycytidine kinase were active towards 43 and 34 of 57 tested compounds, respectively. Both nucleoside kinases have been also tested for a larger-scale synthesis of nucleoside monophosphates in the presence of a GTP regeneration system using acetate kinase from E. coli.
Computational Insight into Defective Boron Nitride Supported Double-Atom Catalysts for Electrochemical Nitrogen Reduction
Authors: Cao Rong , Xia Jie-Zhen , Wu Qi
Published: November 2022

Designing highly selective and efficient double-atom electrocatalysts (DACs) is essential for achieving a superior nitrogen-reduction reaction (NRR) performance. Herein, we explored the defective boron nitride–supported cage-like double-atom catalysts to rummage the qualified NRR catalysts. Based on a systematic evaluation of the stability, N2 adsorption, NRR selectivity and activity of 10 DACs of TM1-TM2@VB-BN, we predicted Ru-Ti@VB-BN to be the NRR candidate with a limiting potential of −0.40 V. Compared to the corresponding single-atom catalysts, the introduction of Ti/Mo modulates the d-band center of the active metal atom, which improves the NRR performance. Moreover, the magnetic Ru-Ti dimer can facilitate the transfer of charge to molecular N2, ensuring a significant activation of the inert N≡N bond. This research not only opens up new avenues for designing boron nitride–supported DACs for NRR, but also deepens the understanding of DACs in N2 activation.
Microglia–Neutrophil Interactions Drive Dry AMD-like Pathology in a Mouse Model
Authors: Boyce Maeve , Xin Ying , Chowdhury Olivia , Shang Peng , Liu Haitao , Koontz Victoria , Strizhakova Anastasia , Nemani Mihir , Hose Stacey , Zigler J. Samuel , Campbell Matthew , Sinha Debasish , Handa James T. , Kaarniranta Kai , Qian Jiang , Ghosh Sayan
Published: November 2022

In dry age-related macular degeneration (AMD), inflammation plays a key role in disease pathogenesis. Innate immune cells such as microglia and neutrophils infiltrate the sub-retinal space (SRS) to induce chronic inflammation and AMD progression. But a major gap in our understanding is how these cells interact with each other in AMD. Here, we report a novel concept of how dynamic interactions between microglia and neutrophils contribute to AMD pathology. Using well-characterized genetically engineered mouse models as tools, we show that in the diseased state, retinal pigmented epithelial (RPE) cells trigger pro-inflammatory (M1) transition in microglia with diminished expression of the homeostatic marker, CX3CR1. Activated microglia localize to the SRS and regulate local neutrophil function, triggering their activation and thereby inducing early RPE changes. Ligand receptor (LR)-loop analysis and cell culture studies revealed that M1 microglia also induce the expression of neutrophil adhesion mediators (integrin β1/α4) through their interaction with CD14 on microglia. Furthermore, microglia-induced neutrophil activation and subsequent neutrophil-mediated RPE alterations were mitigated by inhibiting Akt2 in microglia. These results suggest that the Akt2 pathway in microglia drives M1 microglia-mediated neutrophil activation, thereby triggering early RPE degeneration and is a novel therapeutic target for early AMD, a stage without treatment options.
Dopaminergic Signalling Enhances IL-2 Production and Strengthens Anti-Tumour Response Exerted by Cytotoxic T Lymphocytes in a Melanoma Mouse Model
Authors: Chovar-Vera Ornella , López Ernesto , Gálvez-Cancino Felipe , Prado Carolina , Franz Dafne , Figueroa Diego A. , Espinoza Alexandra , Figueroa Claudio , Lladser Alvaro , Pacheco Rodrigo
Published: November 2022

Dopamine has emerged as an important regulator of immunity. Recent evidence has shown that signalling through low-affinity dopamine receptors exerts anti-inflammatory effects, whilst stimulation of high-affinity dopamine receptors potentiates immunity in different models. However, the dopaminergic regulation of CD8+ T-cells in anti-tumour immunity remains poorly explored. Here, we studied the role of dopamine receptor D3 (DRD3), which displays the highest affinity for dopamine, in the function of CD8+ T-cells and its consequences in the anti-tumour immune response. We observed that the deficiency of Drd3 (the gene encoding DRD3) in CD8+ T-cells limits their in vivo expansion, leading to an impaired anti-tumour response in a mouse melanoma model. Mechanistic analyses suggest that DRD3 stimulation favours the production of interleukin 2 (IL-2) and the surface expression of CD25, the α-chain IL-2 receptor, which are required for expansion and effector differentiation of CD8+ T-cells. Thus, our results provide genetic and pharmacologic evidence indicating that DRD3 favours the production of IL-2 by CD8+ T-cells, which is associated with higher expansion and acquisition of effector function of these cells, promoting a more potent anti-tumour response in a melanoma mouse model. These findings contribute to understanding how dopaminergic signalling affects the cellular immune response and represent an opportunity to improve melanoma therapy.
The Impaired Neurodevelopment of Human Neural Rosettes in HSV-1-Infected Early Brain Organoids
Authors: D’Aiuto Leonardo , Caldwell Jill K. , Wallace Callen T. , Grams Tristan R. , Wesesky Maribeth A. , Wood Joel A. , Watkins Simon C. , Kinchington Paul R. , Bloom David C. , Nimgaonkar Vishwajit L.
Published: November 2022

Intrauterine infections during pregnancy by herpes simplex virus (HSV) can cause significant neurodevelopmental deficits in the unborn/newborn, but clinical studies of pathogenesis are challenging, and while animal models can model some aspects of disease, in vitro studies of human neural cells provide a critical platform for more mechanistic studies. We utilized a reductionist approach to model neurodevelopmental outcomes of HSV-1 infection of neural rosettes, which represent the in vitro equivalent of differentiating neural tubes. Specifically, we employed early-stage brain organoids (ES-organoids) composed of human induced pluripotent stem cells (hiPSCs)-derived neural rosettes to investigate aspects of the potential neuropathological effects induced by the HSV-1 infections on neurodevelopment. To allow for the long-term differentiation of ES-organoids, viral infections were performed in the presence of the antiviral drug acyclovir (ACV). Despite the antiviral treatment, HSV-1 infection caused organizational changes in neural rosettes, loss of structural integrity of infected ES-organoids, and neuronal alterations. The inability of ACV to prevent neurodegeneration was associated with the generation of ACV-resistant mutants during the interaction of HSV-1 with differentiating neural precursor cells (NPCs). This study models the effects of HSV-1 infection on the neuronal differentiation of NPCs and suggests that this environment may allow for accelerated development of ACV-resistance.