bluuebunny/crossref_metadata_2025 huggingface dataset:
Soil Particle Modeling and Parameter Calibration for Use with Discrete Element Method
Authors: Yang Yukun , Wen Baoqin , Ding Longpeng , Li Liqiao , Chen Xinghua , Li Jingbin
Published: December 2021

Highlights

Soil particle shapes were statistically analyzed, and four representative particles were obtained.

A particle model was established using three-dimensional non-contact surface topography.

This study used a response surface design method to calibrate significant soil parameters.

The simulation parameters were verified by rotary tiller experiment.

Abstract. The discrete element method (DEM) has broad prospects for application in soil-tool simulations. To ensure the reliability of simulations, appropriate simulation parameters and particle modeling are essential. Therefore, in this article, a method combining simulation and actual tests is proposed to calibrate the critical soil parameters. First, the effect of soil particle shape on particle contact was considered. Soil particle shapes were statistically analyzed using an improved GrabCut algorithm and k-means algorithm. Four representative soil particles were obtained. Second, a soil particle model was established by microscope and three-dimensional non-contact surface topography. Finally, taking the angle of repose as the response value, the three parameters with significant effects on the angle of repose, i.e., soil shear modulus, Hertz-Mindlin with Johnson-Kendall-Roberts contact model (JKR), and soil-soil restitution coefficient, were obtained via a Plackett-Burman experiment. The optimal value intervals of the significant parameters were determined by the steepest climbing test. A polynomial regression model between the angle of repose and the three significant parameters was established with a Box-Behnken experiment using three factors and three levels. The interactions between the three significant parameters were not significant, as revealed by response surface analysis. The optimal values of the significant parameters were obtained by taking the actual angle of repose as the target and resulted in a soil shear module of 9.8 MPa, JKR of 0.063, and soil-soil restitution coefficient of 0.478. To verify the reliability of the calibrated parameters, the soil angles of repose from the simulation and from actual tests were compared and analyzed. For a simulated angle of repose of 38.5°, the actual angle of repose was 38.6°, and the relative error was 0.26%. DEM was also used to simulate a rotary tiller with the calibrated parameters. The maximum error of the simulated soil throwing angle was less than 10% when compared with the actual throwing angle. The experimental results showed that the calibrated parameters were accurate and can provide a reference for the selection of soil discrete element parameters. Keywords: Angle of repose, Numerical simulation, Parameter calibration, Shape survey, Soil.
Improved Canopy Characterization with Laser Scanning Sensor for Greenhouse Spray Applications
Authors: Nair Uchit , Ling Peter P. , Zhu Heping
Published: December 2021

Highlights

An algorithm was developed to process laser sensor data to make more accurate measurements of canopy dimensions.

The algorithm isolated individual canopies, removed distortion, and estimated the occluded portions of the dataset.

The algorithm reduced measured error by 46% in terms of root mean square error (RMSE).

The RMSE was higher for sensor heights below and above a calculated optimal sensor height.

Abstract. Laser-guided intelligent spray technology for greenhouse applications requires sensors that can accurately measure plant dimensions. This study proposed a new method to overcome current limitations by introducing a processing algorithm that manipulates the noisy dataset and determines the optimal sensor height to produce better measurements of the canopy width. The processing algorithm involves a combination of registration, clustering, and mirroring. Registration aligns multiple scans of the same scene to improve resolution. Clustering isolates individual plant canopies from the dataset to enable further processing. Mirroring is used to resolve the problems of distortion and occlusion and predict missing information in the dataset. The performance of the processing algorithm was evaluated by calculating the root mean square error (RMSE) in the canopy width measurements. Its results were compared with the measurements reported in earlier research, where there was limited processing of the laser sensor data. The processing algorithm reduced RMSE values by 46% compared to the earlier research, and the largest improvements were seen for objects placed beyond 1.5 m from the sensor. The sensor height was observed to be inversely proportional to the RMSE values. The average RMSE of the processing algorithm was 25 mm, compared to 47 mm in the earlier research when the laser sensor was at a height of 1 m. Another experimental setup was used to test the limits of the relationship between sensor height and algorithm performance while using objects that were more representative of plant canopy shapes. The accuracy of the processing algorithm decreased when the sensor height was either above or below the optimal sensor height, which was derived from calculations made in earlier research. The processing algorithm has potential to improve spray efficiencies. Keywords: Automation, Clustering, LiDAR, Point cloud data processing, Variable-rate spray.
Comparison of Machine Learning Methods for Leaf Nitrogen Estimation in Corn Using Multispectral UAV Images
Authors: Barzin Razieh , Kamangir Hamid , Bora Ganesh C.
Published: December 2021

Highlights

Leaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.

Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.

The most influential vegetation indices were determined for estimation of leaf nitrogen.

Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.
Coupling a Pest and Disease Damage Module with CSM-NWheat: A Wheat Crop Simulation Model
Authors: Berton Ferreira Thiago , Pavan Willingthon , Cunha Fernandes José Maurício , Asseng Senthold , Antunes de Oliveira Fabio Augusto , Amaral Hölbig Carlos , Noleto Luz Pequeno Diego , Dalmago Genei Antônio , Lazaretti Zanatta Alexandre , Hoogenboom Gerrit
Published: December 2021

Highlights

CSM-NWheat, a DSSAT wheat crop model, was coupled with a pest module named PEST.

The coupled model can simulate the impact of pest and disease damage on wheat crops.

Pest damage is expressed in daily steps by communication links called coupling points.

Coupling points are linked with state variables at which pest damage can be applied.

Field pest-scouting reports and linear interpolation are used to compute damage rates.

Abstract. Wheat is one of the most important global staple crops and is affected by numerous pests and diseases. Depending on their intensity, pests and diseases can cause significant economic losses and even crop failures. Pest models can assist decision-making, thus helping reduce crop losses. Most wheat simulation models account for abiotic stresses such as drought and nutrients, but they do not account for biotic stresses caused by pests and diseases. Therefore, the objective of this study was to couple a dynamic pest and disease damage module to the DSSAT model CSM-NWheat. Coupling points were integrated into the CSM-NWheat model for applying daily damage to all plant components, including leaves, stems, roots, and grains, the entire plant, and to the assimilate supply. The coupled model was tested by simulating a wheat crop with virtual damage levels applied at each coupling point. Measured foliar damage caused by tan spot (Pyrenophora tritici-repentis) was also simulated. The modified model accurately estimated the reduction in leaf area growth and the yield loss when compared with observed data. With the incorporation of the pest module, CSM-NWheat can now predict the potential impact of pests and diseases on wheat growth and development, and ultimately economic yield. Keywords: Biotic stress, Decision support, DSSAT, Model coupling, Yield loss.
Comparing evidence on the effectiveness of reading resources from expert ratings, practitioner judgements, and research repositories
Authors: Hollands Fiona M. , Pan Yilin , Kieffer Michael J. , Holmes Venita R. , Wang Yixin , Escueta Maya , Head Laura , Muroga Atsuko
Published: December 2021

Background:

Education decision makers are increasingly expected to use evidence to inform their actions. However, the majority of educational interventions have not yet been studied and it is challenging to produce high quality research evidence quickly enough to influence policy questions.

Aims and objectives:

We set out to gather evidence on the efficacy of reading resources implemented at 23 struggling elementary schools in a large, urban district in the US. The schools were at risk of closure by the state.

Methods:

For each reading resource, we searched for existing effectiveness studies and collected professional judgements by surveying practitioners. We also used an expert survey to collect judgements from three reading experts. We compared the ratings among experts and between practitioners and experts. We also compared practitioner and expert judgements to evidence summaries from research repositories.

Findings:

We found evidence summaries in research repositories for only five of 23 reading resources used in the 23 schools. Experts showed poor to good agreement on ten questions about each resource. Agreement between practitioners and experts was low with practitioners generally rating resources more positively than reading experts.

Discussion and conclusions:

Practitioners may be overly optimistic about the efficacy of educational materials while experts have difficulty assessing how well the materials serve an unfamiliar population. In the absence of rigorous evaluations of locally-implemented programmes, district decision makers can review the consistency of evidence collected from practitioners and experts, along with external research evidence, to inform actions towards supporting and guiding struggling schools.
A constrained method for lensless coherent imaging of thin samples
Authors: Soulez Ferréol , Rostykus Manon , Moser Christophe , Unser Michael
Published: December 2021

Lensless inline holography can produce high-resolution images over a
large field of view (FoV). In a previous work [Appl. Opt.
60, B38 (2021)APOPAI0003-693510.1364/AO.414976], we
showed that (i) the actual FoV can be extrapolated outside of the
camera FoV and (ii) the effective resolution of the setup can be
several times higher than the resolution of the camera. In this paper,
we present a reconstruction method to recover high resolution with an
extrapolated FoV image of the phase and the amplitude of a sample from
aliased intensity measurements taken at a lower resolution.
Optical phase modulation by natural eye movements: application to time-domain FF-OCT image retrieval
Authors: Mazlin Viacheslav , Xiao Peng , Irsch Kristina , Scholler Jules , Groux Kassandra , Grieve Kate , Fink Mathias , Boccara A. Claude
Published: December 2021

Eye movements are commonly seen as an obstacle to high-resolution ophthalmic imaging. In this context we study the natural axial movements of the in vivo human eye and show that they can be used to modulate the optical phase and retrieve tomographic images via time-domain full-field optical coherence tomography (TD-FF-OCT). This approach opens a path to a simplified ophthalmic TD-FF-OCT device, operating without the usual piezo motor-camera synchronization. The device demonstrates in vivo human corneal images under the different image retrieval schemes (2-phase and 4-phase) and different exposure times (3.5 ms, 10 ms, 20 ms). Data on eye movements, acquired with a spectral-domain OCT with axial eye tracking (180 B-scans/s), are used to study the influence of ocular motion on the probability of capturing high-signal tomographic images without phase washout. The optimal combinations of camera acquisition speed and amplitude of piezo modulation are proposed and discussed.
Multi-step deep neural network for identifying subfascial vessels in a dorsal skinfold window chamber model
Authors: Xu Xuelin , Shen Yi , Lin Li , Lin Lisheng , Li Buhong
Published: December 2021

Automatic segmentation of blood vessels in the dorsal skinfold window chamber (DWSC) model is a prerequisite for the evaluation of vascular-targeted photodynamic therapy (V-PDT) biological response. Recently, deep learning methods have been widely applied in blood vessel segmentation, but they have difficulty precisely identifying the subfascial vessels. This study proposed a multi-step deep neural network, named the global attention-Xnet (GA-Xnet) model, to precisely segment subfascial vessels in the DSWC model. We first used Hough transform combined with a U-Net model to extract circular regions of interest for image processing. GA step was then employed to obtain global feature learning followed by coarse segmentation for the entire blood vessel image. Secondly, the coarse segmentation of blood vessel images from the GA step and the same number of retinal images from the DRIVE datasets were combined as the mixing sample, inputted into the Xnet step to learn the multiscale feature predicting fine segmentation maps of blood vessels. The data show that the accuracy, sensitivity, and specificity for the segmentation of multiscale blood vessels in the DSWC model are 96.00%, 86.27%, 96.47%, respectively. As a result, the subfascial vessels could be accurately identified, and the connectedness of the vessel skeleton is well preserved. These findings suggest that the proposed multi-step deep neural network helps evaluate the short-term vascular responses in V-PDT.
Coherent Doppler wind lidar with real-time wind processing and low signal-to-noise ratio reconstruction based on a convolutional neural network
Authors: Kliebisch Oliver , Uittenbosch Hugo , Thurn Johann , Mahnke Peter
Published: December 2021

Multi-classification using a convolutional neural network (CNN) is proposed as a denoising method for coherent Doppler wind lidar (CDWL) data. The method is intended to enhance the usable range of a CDWL beyond the atmospheric boundary layer (ABL). The method is implemented and tested in an all-fiber pulsed CWDL system operating at 1550 nm wavelength with 20 kHz repetition rate, 300 ns pulse length and 180 µJ of laser energy. Real-time pre-processing using a field programmable gate array (FPGA) is implemented producing averaged lidar spectrograms. Real-world measurement data is labeled using conventional frequency estimators and mixed with simulated spectrograms for training of the CNN. First results of this methods show that the CNN can outperform conventional frequency estimations substantially in terms of maximum range and delivers reasonable output in very low signal-to-noise (SNR) situations while still delivering accurate results in the high-SNR regime. Comparing the CNN output with radiosonde data shows the feasibility of the proposed method.
D-band signal generation and transmission without optical filter based on optical carrier suppression and single-sideband modulation
Authors: Li Yitong , Yu Jianguo , Song Ruiliang , Liu Ning
Published: December 2021

We experimentally demonstrated a novel and simple scheme to generate D-band millimeter-wave (mm-wave) signal without optical filter based on optical carrier suppression (OCS) and single-sideband (SSB). One intensity modulator (IM) driven by radio frequency (RF) signal at 50 GHz is firstly employed to generate two tones with channel spacing of 2 x RF frequency based on OCS. Another subsequent in-phase/quadrature (I/Q) modulator driven by RF signal at 30 GHz is then applied to generate SSB signal by using independent sideband (ISB). No optical filter is needed so that the whole system can be simplified. After using a D-band photomixer for detection, we finally generated the vector mm-wave at 130 GHz. Based on the proposed system, 4-Gbaud/8-Gbaud quadrature phase shift keying (QPSK) information carried by the generated D-band mm-wave signals were transmitted over 22.5-km single model fiber (SMF) and 1-m wireless distance radio-over-fiber (ROF) link. Bit-error-ratio (BER) performances under hard/soft-decision forward-error-correction (FEC) threshold are shown respectively in cases of two different signals transmission rates.
Flatfield ultrafast imaging with single-shot non-synchronous array photography
Authors: Sheinman Matthew , Erramilli Shyamsunder , Ziegler Lawrence , Hong Mi K. , Mertz Jerome
Published: December 2021

We present a method for acquiring a sequence of time-resolved images in a single shot, called single-shot non-synchronous array photography (SNAP). In SNAP, a pulsed laser beam is split by a diffractive optical element into an array of angled beamlets whose illumination fronts remain perpendicular to the optical axis. Different time delays are imparted to each beamlet by an echelon, enabling them to probe ultrafast dynamics in rapid succession. The beamlets are imaged onto different regions of a camera by a lenslet array. Because the illumination fronts remain flat (head-on) independently of beamlet angle, the exposure time in SNAP is fundamentally limited only by the laser pulse duration, akin to a “global shutter” in conventional imaging. We demonstrate SNAP by capturing the evolution of a laser induced plasma filament over 20 frames at an average rate of 4.2 trillion frames per second (Tfps) and a peak rate of 5.7 Tfps.
Rubidium copper chloride scintillator for X-ray imaging screen
Authors: Naewthong Worakit , Juntapo Waridsaraporn , Amarit Ratthasart , Duangkanya Kamonchanok , Sumriddetchkajorn Sarun , Rungseesumran Thiti , Kamwang Natthaporn , Tariwong Yaowaluk , Kaewkhao Jakrapong , Kopwitthaya Atcha
Published: December 2021

Metal halide-based perovskite materials have received great attention in scintillating applications because they can emit strong visible light when interacting with X-ray particles. Here, we report a scintillator based on rubidium copper chlorine incorporated in the polydimethylsiloxane matrix. The scintillator shows a bright violet emission under ultraviolet and ionizing radiation. The temperature-dependent photoluminescence and radioluminescence shows maximum thermal enhancement at 80°C and 60°C, respectively. Moreover, the first X-ray image from this material reveals the detailed information of the object captured by a commercially available digital camera, indicating a potent scintillator for creating X-ray imaging screens.
Vaccination with SARS-CoV-2 variants of concern protects mice from challenge with wild-type virus
Authors: Amanat Fatima , Strohmeier Shirin , Meade Philip S. , Dambrauskas Nicholas , Mühlemann Barbara , Smith Derek J. , Vigdorovich Vladimir , Sather D. Noah , Coughlan Lynda , Krammer Florian
Published: December 2021

Vaccines against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have been highly efficient in protecting against Coronavirus Disease 2019 (COVID-19). However, the emergence of viral variants that are more transmissible and, in some cases, escape from neutralizing antibody responses has raised concerns. Here, we evaluated recombinant protein spike antigens derived from wild-type SARS-CoV-2 and from variants B.1.1.7, B.1.351, and P.1 for their immunogenicity and protective effect in vivo against challenge with wild-type SARS-CoV-2 in the mouse model. All proteins induced high neutralizing antibodies against the respective viruses but also induced high cross-neutralizing antibody responses. The decline in neutralizing titers between variants was moderate, with B.1.1.7-vaccinated animals having a maximum fold reduction of 4.8 against B.1.351 virus. P.1 induced the most cross-reactive antibody responses but was also the least immunogenic in terms of homologous neutralization titers. However, all antigens protected from challenge with wild-type SARS-CoV-2 in a mouse model.
Mutations in LRRK2 linked to Parkinson disease sequester Rab8a to damaged lysosomes and regulate transferrin-mediated iron uptake in microglia
Authors: Mamais Adamantios , Kluss Jillian H. , Bonet-Ponce Luis , Landeck Natalie , Langston Rebekah G. , Smith Nathan , Beilina Alexandra , Kaganovich Alice , Ghosh Manik C. , Pellegrini Laura , Kumaran Ravindran , Papazoglou Ioannis , Heaton George R. , Bandopadhyay Rina , Maio Nunziata , Kim Changyoun , LaVoie Matthew J. , Gershlick David C. , Cookson Mark R.
Published: December 2021

Mutations in leucine-rich repeat kinase 2 (LRRK2) cause autosomal dominant Parkinson disease (PD), while polymorphic LRRK2 variants are associated with sporadic PD. PD-linked mutations increase LRRK2 kinase activity and induce neurotoxicity in vitro and in vivo. The small GTPase Rab8a is a LRRK2 kinase substrate and is involved in receptor-mediated recycling and endocytic trafficking of transferrin, but the effect of PD-linked LRRK2 mutations on the function of Rab8a is poorly understood. Here, we show that gain-of-function mutations in LRRK2 induce sequestration of endogenous Rab8a to lysosomes in overexpression cell models, while pharmacological inhibition of LRRK2 kinase activity reverses this phenotype. Furthermore, we show that LRRK2 mutations drive association of endocytosed transferrin with Rab8a-positive lysosomes. LRRK2 has been nominated as an integral part of cellular responses downstream of proinflammatory signals and is activated in microglia in postmortem PD tissue. Here, we show that iPSC-derived microglia from patients carrying the most common LRRK2 mutation, G2019S, mistraffic transferrin to lysosomes proximal to the nucleus in proinflammatory conditions. Furthermore, G2019S knock-in mice show a significant increase in iron deposition in microglia following intrastriatal LPS injection compared to wild-type mice, accompanied by striatal accumulation of ferritin. Our data support a role of LRRK2 in modulating iron uptake and storage in response to proinflammatory stimuli in microglia.
Prevalence and inequality in persistent undiagnosed, untreated, and uncontrolled hypertension: Evidence from a cohort of older Mexicans
Authors: Dieteren C. M. , O’Donnell O. , Bonfrer I.
Published: December 2021

Hypertension is the leading risk factor for cardiovascular diseases (CVDs) and substantial gaps in diagnosis, treatment and control signal failure to avert premature deaths. Our aim was to estimate the prevalence and assess the socioeconomic distribution of hypertension that remained undiagnosed, untreated, and uncontrolled for at least five years among older Mexicans and to estimate rates of transition from those states to diagnosis, treatment and control. We used data from a cohort of Mexicans aged 50+ in two waves of the WHO Study on Global AGEing and adult health (SAGE) collected in 2009 and 2014. Blood pressure was measured, hypertension diagnosis and treatment self-reported. We estimated prevalence and transition rates over five years and calculated concentration indices to identify socioeconomic inequalities using a wealth index. Using probit models, we identify characteristics of those facing the greatest barriers in receiving hypertension care. More than 60 percent of individuals with full item response (N = 945) were classified as hypertensive. Over one third of those undiagnosed continued to be in that state five years later. More than two fifths of those initially untreated remained so, and over three fifths of those initially uncontrolled failed to achieve continued blood pressure control. While being classified as hypertensive was more concentrated among the rich, missing diagnosis, treatment and control were more prevalent among the poor. Men, singles, rural dwellers, uninsured, and those with overweight were more likely to have persistent undiagnosed, untreated, and uncontrolled hypertension. There is room for improvement in both hypertension diagnosis and treatment in Mexico. Clinical and public health attention is required, even for those who initially had their hypertension controlled. To ensure more equitable hypertension care and effectively prevent premature deaths, increased diagnosis and long-term treatment efforts should especially be directed towards men, singles, uninsured, and those with overweight.
Offspring of first-generation hatchery steelhead trout (Oncorhynchus mykiss) grow faster in the hatchery than offspring of wild fish, but survive worse in the wild: Possible mechanisms for inadvertent domestication and fitness loss in hatchery salmon
Authors: Blouin Michael S. , Wrey Madeleine C. , Bollmann Stephanie R. , Skaar James C. , Twibell Ronald G. , Fuentes Claudio
Published: December 2021

Salmonid fish raised in hatcheries often have lower fitness (number of returning adult offspring) than wild fish when both spawn in the wild. Body size at release from hatcheries is positively correlated with survival at sea. So one explanation for reduced fitness is that hatcheries inadvertently select for trait values that enhance growth rate under the unnatural environment of a hatchery, but that are maladaptive in the wild environment. A simple prediction of this hypothesis is that juveniles of hatchery origin should grow more quickly than fish of wild origin under hatchery conditions, but should have lower survival under wild conditions. We tested that hypothesis using multiple full sibling families of steelhead (Oncorhynchus mykiss) that were spawned using either two wild parents (WxW) or two first-generation hatchery (HxH) parents. Offspring from all the families were grown together under hatchery conditions and under semi-natural conditions in artificial streams. HxH families grew significantly faster in the hatchery, but had significantly lower survival in the streams. That we see this tradeoff after only a single generation of selection suggests that the traits involved are under very strong selection. We also considered one possible alteration to the hatchery environment that might reduce the intensity of selection among families in size at release. Here we tested whether reducing the fat content of hatchery feed would reduce the variance among families in body size. Although fish raised under a low-fat diet were slightly smaller, the variation among families in final size was unchanged. Thus, there is no evidence that reducing the fat content of hatchery feed would reduce the opportunity for selection among families on size at release.
Wisconsin dairy farm worker perceptions and practices related to antibiotic use, resistance, and infection prevention using a systems engineering framework
Authors: Kates Ashley E. , Knobloch Mary Jo , Konkel Ali , Young Amanda , Steinberger Andrew , Shutske John , Ruegg Pamela L. , Sethi Ajay K. , Goldberg Tony , Leite de Campos Juliana , Suen Garret , Safdar Nasia
Published: December 2021

We studied farmworker practices and beliefs potentially contributing to transmission of bacteria and their associated antibiotic resistance genes (ARGs) among animals and farm workers to identify potential behavioral interventions to reduce the risk of bacterial transmission. Ten focus groups were conducted on eight Wisconsin dairy farms to assess potentially high-risk practices and farmworker knowledge and experiences with antibiotic use and resistance using the Systems Engineering in Patient Safety (SEIPS) framework. Farmworkers were asked to describe common on-farm tasks and the policies guiding these practices. We found workers demonstrated knowledge of the role of antibiotic stewardship in preventing the spread of ARGs. Worker knowledge of various forms of personal protective equipment was higher for workers who commonly reported glove-use. Additionally, workers knowledge regarding the importance of reducing ARG transmission varied but was higher than we had hypothesized. Programs to reduce ARG spread on dairy farms should focus on proper hand hygiene and personal protective equipment use at the level of knowledge, beliefs, and practices.
The first report of porcine parvovirus 7 (PPV7) in Colombia demonstrates the presence of variants associated with modifications at the level of the VP2-capsid protein
Authors: Vargas-Bermudez Diana S. , Rendon-Marin Santiago , Ruiz-Saenz Julian , Mogollón Dario , Jaime Jairo
Published: December 2021

There are a wide variety of porcine parvoviruses (PPVs) referred to as PPV1 to PPV7. The latter was discovered in 2016 and later reported in some countries in America, Asia, and Europe. PPV7 as a pathogenic agent or coinfection with other pathogens causing disease has not yet been determined. In the present study, we report the identification of PPV7 for the first time in Colombia, where it was found retrospectively since 2015 in 40% of the provinces that make up the country (13/32), and the virus was ratified for 2018 in 4/5 provinces evaluated. Additionally, partial sequencing (nucleotides 380 to 4000) was performed of four Colombian strains completely covering the VP2 and NS1 viral genes. A sequence identity greater than 99% was found when comparing them with reference strains from the USA and China. In three of the four Colombian strains, an insertion of 15 nucleotides (five amino acids) was found in the PPV7-VP2 capsid protein (540–5554 nt; 180–184 aa). Based on this insertion, the VP2 phylogenetic analysis exhibited two well-differentiated evolutionarily related groups. To evaluate the impact of this insertion on the structure of the PPV7-VP2 capsid protein, the secondary structure of two different Colombian strains was predicted, and it was determined that the insertion is located in the coil region and not involved in significant changes in the structure of the protein. The 3D structure of the PPV7-VP2 capsid protein was determined by threading and homology modeling, and it was shown that the insertion did not imply a change in the shape of the protein. Additionally, it was determined that the insertion is not involved in suppressing a potential B cell epitope, although the increase in length of the epitope could affect the interaction with molecules that allow a specific immune response.
Industrial robot selection using a multiple criteria group decision making method with individual preferences
Authors: Zhao Jinling , Sui Yubing , Xu Yang , Lai K. K.
Published: December 2021

This paper proposes a multiple criteria group decision making with individual preferences (MCGDM-IP) to address the robot selection problem (RSP). Four objective criteria elicitation approaches, namely, Shannon entropy approach, CRITIC approach, distance-based approach, and ideal-point approach, are proposed to indicate individual decision makers. A preliminary group decision matrix is therefore formulated. Both preferential differences representing the preference degrees among different robots, and preferential priorities representing the favorite ranking of robots for each individual decision maker, are analyzed to propose a revised group decision matrix. A satisfaction index is developed to manifest the merits of the proposed MCGDM-IP. An illustrative example using the data drawn from previous literature is conducted to indicate the effectiveness and validity of MCGDM-IP. The results demonstrate that the MCGDM-IP could generate a more satisfactory scheme to evaluate and select industrial robots, with an improvement of group satisfactory level as 2.12%.
Biological laterality and peripheral nerve DTI metrics
Authors: Holmes Scott A. , Staffa Steven J. , Karapanagou Anastasia , Lopez Natalia , Karian Victoria , Borra Ronald , Zurakowski David , Lebel Alyssa , Borsook David
Published: December 2021

Background and purpose

Clinical comparisons do not usually take laterality into account and thus may report erroneous or misleading data. The concept of laterality, well evaluated in brain and motor systems, may also apply at the level of peripheral nerves. Therefore, we sought to evaluate the extent to which we could observe an effect of laterality in MRI-collected white matter indices of the sciatic nerve and its two branches (tibial and fibular).

Materials and methods

We enrolled 17 healthy persons and performed peripheral nerve diffusion weighted imaging (DWI) and magnetization transfer imaging (MTI) of the sciatic, tibial and fibular nerve. Participants were scanned bilaterally, and findings were divided into ipsilateral and contralateral nerve fibers relative to self-reporting of hand dominance. Generalized estimating equation modeling was used to evaluate nerve fiber differences between ipsilateral and contralateral legs while controlling for confounding variables. All findings controlled for age, sex and number of scans performed.

Results

A main effect of laterality was found in radial, axial, and mean diffusivity for the tibial nerve. Axial diffusivity was found to be lateralized in the sciatic nerve. When evaluating mean MTR, a main effect of laterality was found for each nerve division. A main effect of sex was found in the tibial and fibular nerve fiber bundles.

Conclusion

For the evaluation of nerve measures using DWI and MTI, in either healthy or disease states, consideration of underlying biological metrics of laterality in peripheral nerve fiber characteristics need to considered for data analysis. Integrating knowledge regarding biological laterality of peripheral nerve microstructure may be applied to improve how we diagnosis pain disorders, how we track patients’ recovery and how we forecast pain chronification.
Characterising the dynamics of cerebral metabolic dysfunction following traumatic brain injury: A microdialysis study in 619 patients
Authors: Guilfoyle Mathew R. , Helmy Adel , Donnelly Joseph , Stovell Matthew G. , Timofeev Ivan , Pickard John D. , Czosnyka Marek , Smielewski Peter , Menon David K. , Carpenter Keri L. H. , Hutchinson Peter J.
Published: December 2021

Traumatic brain injury (TBI) is a major cause of death and disability, particularly amongst young people. Current intensive care management of TBI patients is targeted at maintaining normal brain physiology and preventing secondary injury. Microdialysis is an invasive monitor that permits real-time assessment of derangements in cerebral metabolism and responses to treatment. We examined the prognostic value of microdialysis parameters, and the inter-relationships with other neuromonitoring modalities to identify interventions that improve metabolism. This was an analysis of prospective data in 619 adult TBI patients requiring intensive care treatment and invasive neuromonitoring at a tertiary UK neurosciences unit. Patients had continuous measurement of intracranial pressure (ICP), arterial blood pressure (ABP), brain tissue oxygenation (PbtO2), and cerebral metabolism and were managed according to a standardized therapeutic protocol. Microdialysate was assayed hourly for metabolites including glucose, pyruvate, and lactate. Cerebral perfusion pressure (CPP) and cerebral autoregulation (PRx) were derived from the ICP and ABP. Outcome was assessed with the Glasgow Outcome Score (GOS) at 6 months. Relationships between monitoring variables was examined with generalized additive mixed models (GAMM). Lactate/Pyruvate Ratio (LPR) over the first 3 to 7 days following injury was elevated amongst patients with poor outcome and was an independent predictor of ordinal GOS (p<0.05). Significant non-linear associations were observed between LPR and cerebral glucose, CPP, and PRx (p<0.001 to p<0.05). GAMM models suggested improved cerebral metabolism (i.e. reduced LPR with CPP >70mmHg, PRx <0.1, PbtO2 >18mmHg, and brain glucose >1mM. Deranged cerebral metabolism is an important determinant of patient outcome following TBI. Variations in cerebral perfusion, oxygenation and glucose supply are associated with changes in cerebral LPR and suggest therapeutic interventions to improve cerebral metabolism. Future prospective studies are required to determine the efficacy of these strategies.
Senotherapeutic-like effect of Silybum marianum flower extract revealed on human skin cells
Authors: Woo Jieun , Shin Seoungwoo , Cho Eunae , Ryu Dehun , Garandeau David , Chajra Hanane , Fréchet Mathilde , Park Deokhoon , Jung Eunsun
Published: December 2021

Cellular senescence causes irreversible growth arrest of cells. Prolonged accumulation of senescent cells in tissues leads to increased detrimental effects due to senescence associated secretory phenotype (SASP). Recent findings suggest that elimination of senescent cells has a beneficial effect on organismal aging and lifespan. In this study, using a validated replicative senescent human dermal fibroblasts (HDFs) model, we showed that elimination of senescent cells is possible through the activation of an apoptotic mechanism. We have shown in this replicative senescence model, that cell senescence is associated with DNA damage and cell cycle arrest (p21, p53 markers). We have shown that Silybum marianum flower extract (SMFE) is a safe and selective senolytic agent targeting only senescent cells. The elimination of the cells is induced through the activation of apoptotic pathway confirmed by annexin V/propidium iodide and caspase-3/PARP staining. Moreover, SMFE suppresses the expression of SASP factors such as IL-6 and MMP-1 in senescent HDFs. In a co-culture model of senescent and young fibroblasts, we demonstrated that senescent cells impaired the proliferative capacities of young cells. Interestingly, when the co-culture is treated with SMFE, the cell proliferation rate of young cells is increased due to the decrease of the senescent burden. Moreover, we demonstrated in vitro that senescent fibroblasts trigger senescent process in normal keratinocytes through a paracrine effect. Indeed, the conditioned medium of senescent HDFs treated with SMFE reduced the level of senescence-associated beta-galactosidase (SA-β-Gal), p16INK4A and SASP factors in keratinocytes compared with CM of senescent HDFs. These results indicate that SMFE can prevent premature aging due to senescence and even reprograms aged skin. Indeed, thanks to its senolytic and senomorphic properties SMFE is a candidate for anti-senescence strategies.
RASE: Modeling cumulative disadvantage due to marginalized group status in academia
Authors: Shandera Sarah , Matsick Jes L. , Hunter David R. , Leblond Louis
Published: December 2021

We propose a framework of Resources, Achievement, Status, and Events (RASE) that allows the many disparate but well-documented phenomena affecting underrepresented groups in STEM to be assembled into a story of career trajectories, illuminating the possible cumulative impact of many small inequities. Our framework contains a three-component deterministic cycle of (1) production of Achievements from Resources, (2) updated community Status due to Achievements, and (3) accrual of additional Resources based on community Status. A fourth component, stochastic Events, can influence an individual’s level of Resources or Achievements at each time step of the cycle. We build a specific mathematical model within the RASE framework and use it to investigate the impact of accumulated disadvantages from multiple compounding variables. We demonstrate that the model can reproduce data of observed disparities in academia. Finally, we use a publicly available visualization and networking tool to provide a sandbox for exploring career outcomes within the model. The modeling exercise, results, and visualization tool may be useful in the context of training STEM faculty to recognize and reduce effects of bias.
Research on optimization of real-time efficient storage algorithm in data information serialization
Authors: Huang Bin , Tang You
Published: December 2021

Background

Along with the vigorous development of Internet technology, increasing the functions of the various types of equipment, network communication easy and diversity, at the same time, the amount of data is very huge, under the network bandwidth limitations, through long lead to a data need to be cut into more, one by one, transfer times, information delay problems.

Results

Aiming at the problems of poor data integrity, low efficiency and poor serialization efficiency of traditional data storage information, this article introduces Protobuf technology to study the serialization of data storage information. The serpentine gap method is used to complete the allocation interval of the sequence nodes, so that the working state and the resting state always maintain a dynamic balance. According to the first-level rules, the storage data of the completed target node is obtained, and the grammatical structure and the semantics of the target data are analyzed, Meanwhile corresponding correspondences are established, and the data storage information is serialized. In order to verify the effectiveness of Protobuf’s data storage information serialization method, a comparative experiment is designed. By using three methods of HDVM, Redis and Protobuf to serialize JSON data, the comparative analysis shows that HDVM has the longest processing time and Protobuf has the shortest processing time, and the data integrity is not affected. The simulation data shows that the Protobuf serialization method has short conversion time, high space utilization, and the Obvious advantages in correctness and integrity. It is vary suitable for serialization of JSON data in the case of limited bandwidth.
Study protocol for a two-site clinical trial to validate a smartphone-based artificial intelligence classifier identifying cervical precancer and cancer in HPV-positive women in Cameroon
Authors: Baleydier Inès , Vassilakos Pierre , Viñals Roser , Wisniak Ania , Kenfack Bruno , Tsuala Fouogue Jovanny , Enownchong Enow Orock George , Lemoupa Makajio Sophie , Foguem Tincho Evelyn , Undurraga Manuela , Cattin Magali , Makohliso Solomzi , Schönenberger Klaus , Gervaix Alain , Thiran Jean-Philippe , Petignat Patrick
Published: December 2021

Introduction

Cervical cancer remains a major public health challenge in low- and middle-income countries (LMICs) due to financial and logistical issues. WHO recommendation for cervical cancer screening in LMICs includes HPV testing as primary screening followed by visual inspection with acetic acid (VIA) and treatment. However, VIA is a subjective procedure dependent on the healthcare provider’s experience. Its accuracy can be improved by computer-aided detection techniques. Our aim is to assess the performance of a smartphone-based Automated VIA Classifier (AVC) relying on Artificial Intelligence to discriminate precancerous and cancerous lesions from normal cervical tissue.

Methods

The AVC study will be nested in an ongoing cervical cancer screening program called “3T-study” (for Test, Triage and Treat), including HPV self-sampling followed by VIA triage and treatment if needed. After application of acetic acid on the cervix, precancerous and cancerous cells whiten more rapidly than non-cancerous ones and their whiteness persists stronger overtime. The AVC relies on this key feature to determine whether the cervix is suspect for precancer or cancer. In order to train and validate the AVC, 6000 women aged 30 to 49 years meeting the inclusion criteria will be recruited on a voluntary basis, with an estimated 100 CIN2+, calculated using a confidence level of 95% and an estimated sensitivity of 90% +/-7% precision on either side. Diagnostic test performance of AVC test and two current standard tests (VIA and cytology) used routinely for triage will be evaluated and compared. Histopathological examination will serve as reference standard. Participants’ and providers’ acceptability of the technology will also be assessed. The study protocol was registered under ClinicalTrials.gov (number NCT04859530).

Expected results

The study will determine whether AVC test can be an effective method for cervical cancer screening in LMICs.
MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes
Authors: Zhang Xiaoxiao , Kschischo Maik
Published: December 2021

Translatingin vitroresults from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test setF1score >90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology.