bluuebunny/crossref_metadata_2025_split huggingface dataset:
Myeloid-resident neuropilin-1 influences brown adipose tissue in obesity
Authors: Diaz-Marin Roberto , Crespo-Garcia Sergio , Wilson Ariel M. , Buscarlet Manuel , Dejda Agnieszka , Fournier Frédérik , Juneau Rachel , Alquier Thierry , Sapieha Przemyslaw 
Published: August 2021

AbstractThe beneficial effects of brown adipose tissue (BAT) on obesity and associated metabolic diseases are mediated through its capacity to dissipate energy as heat. While immune cells, such as tissue-resident macrophages, are known to influence adipose tissue homeostasis, relatively little is known about their contribution to BAT function. Here we report that neuropilin-1 (NRP1), a multiligand single-pass transmembrane receptor, is highly expressed in BAT-resident macrophages. During diet-induced obesity (DIO), myeloid-resident NRP1 influences interscapular BAT mass, and consequently vascular morphology, innervation density and ultimately core body temperature during cold exposure. Thus, NRP1-expressing myeloid cells contribute to the BAT homeostasis and potentially its thermogenic function in DIO.
Kinetic modelling for concentration and toxicity changes during the oxidation of 4-chlorophenol by UV/H2O2
Authors: Ferreiro Cristian , Sanz Josu , Villota Natalia , de Luis Ana , Lombraña José Ignacio 
Published: August 2021

AbstractThis work develops a kinetic model that allow to predict the water toxicity and the main degradation products concentration of aqueous solutions containing 4-chlorophenol oxidised by UV/H2O2. The kinetic model was developed grouping degradation products of similar toxicological nature: aromatics (hydroquinone, benzoquinone, 4-chlorocatechol and catechol), aliphatics (succinic, fumaric, maleic and malonic acids) and mineralised compounds (oxalic, acetic and formic acids). The degradation of each group versus time was described as a mathematical function of the rate constant of a second-order reaction involving the hydroxyl radical, the quantum yield of lump, the concentration of the hydroxyl radicals and the intensity of the emitted UV radiation. The photolytic and kinetic parameters characterising each lump were adjusted by experimental assays. The kinetic, mass balance and toxicity equations were solved using the Berkeley Madonna numerical calculation tool. Results showed that 4-chlorophenol would be completely removed during the first hour of the reaction, operating with oxidant molar ratios higher than R = 200 at pH  6.0 and UV = 24 W. Under these conditions, a decrease in the rate of total organic carbon (TOC) removal close to 50% from the initial value was observed. The solution colour, attributed to the presence of oxidation products as p-benzoquinone and hydroquinone, were oxidised to colourless species, that resulted in a decrease in the toxicity of the solutions (9.95 TU) and the aromaticity lost.
Optimizing nutrient use efficiency, productivity, energetics, and economics of red cabbage following mineral fertilization and biopriming with compatible rhizosphere microbes
Authors: Sarkar Deepranjan , Sankar Ardith , Devika O. Siva , Singh Sonam , Shikha  , Parihar Manoj , Rakshit Amitava , Sayyed R. Z. , Gafur Abdul , Ansari Mohammad Javed , Danish Subhan , Fahad Shah , Datta Rahul 
Published: August 2021

AbstractConventional agricultural practices and rising energy crisis create a question about the sustainability of the present-day food production system. Nutrient exhaustive crops can have a severe impact on native soil fertility by causing nutrient mining. In this backdrop, we conducted a comprehensive assessment of bio-priming intervention in red cabbage production considering nutrient uptake, the annual change in soil fertility, nutrient use efficiency, energy budgeting, and economic benefits for its sustainable intensification, among resource-poor farmers of Middle Gangetic Plains. The compatible microbial agents used in the study includeTrichoderma harzianum,Pseudomonas fluorescens, andBacillus subtilis. Field assays (2016–2017 and 2017–2018) of the present study revealed supplementing 75% of recommended NPK fertilizer with dual inoculation ofT.harzianumandP.fluorescensincreased macronutrient uptake (N, P, and K), root length, heading percentage, head diameter, head weight, and the total weight of red cabbage along with a positive annual change in soil organic carbon. Maximum positive annual change in available N and available P was recorded under 75% RDF + P.fluorescens + B.subtilisand 75% RDF + T.harzianum + B.subtilis, respectively. Bio-primed plants were also higher in terms of growth and nutrient use efficiency (agronomic efficiency, physiological efficiency, apparent recovery efficiency, partial factor productivity). Energy output (26,370 and 26,630 MJ ha−1), energy balance (13,643 and 13,903 MJ ha−1), maximum gross return (US $ 16,030 and 13,877 ha−1), and net return (US $ 15,966 and 13,813 ha−1) were considerably higher inT.harzianum,andP.fluorescenstreated plants. The results suggest the significance of the bio-priming approach under existing integrated nutrient management strategies and the role of dual inoculations in producing synergistic effects on plant growth and maintaining the soil, food, and energy nexus.
Multiwell Raman plate reader for high-throughput biochemical screening
Authors: Kawagoe Hiroyuki , Ando Jun , Asanuma Miwako , Dodo Kosuke , Miyano Tetsuya , Ueda Hiroshi , Sodeoka Mikiko , Fujita Katsumasa 
Published: August 2021

AbstractAlthough Raman spectroscopy has been used for the quantitative analysis of samples in many fields, including material science, biomedical, and pharmaceutical research, its low sensitivity hindered the application of the analytical capability for high-throughput screening. Here, we developed a high-throughput Raman screening system that can analyze hundreds of specimens in a multiwell plate simultaneously. Multiple high numerical aperture (NA) lenses are assembled under each well in the multiwell plate to detect Raman scattering simultaneously with high sensitivity. The Raman spectrum of 192 samples loaded on a standard 384-well plate can be analyzed simultaneously. With the developed system, the throughput of Raman measurement was significantly improved (about 100 times) compared to conventional Raman instruments based on a single-point measurement. By using the developed system, we demonstrated high-throughput Raman screening to investigate drug polymorphism and identify a small-molecule binding site in a protein. Furthermore, the same system was used to demonstrate high-speed chemical mapping of a centimeter-sized pork slice.
Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
Authors: Mellone Alberto , Gong Zilong , Scarciotti Giordano 
Published: August 2021

AbstractThe implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical “hybrid” approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.
Investigation on CT characterization of pore structure in nylon-uncured rubber composite from a microscopic view
Authors: Li Yong , Chi Yanmeng , Han Shanling , Miao Yanan , Chen Long 
Published: August 2021

AbstractIn order to construct the geometric models characterizing the real micro pore-fracture structures of nylon-uncured rubber composite, and further compare the distribution law in the pore-fracture of solid (nylon)-gas (pore) two-phases with that of solid (nylon)-viscoelastic body (rubber)-gas (pore) three-phases composite, in this paper, the X-ray three-dimensional (3D) microscope is applied for the nylon material and nylon-rubber composite respectively. By employing the 3D visualization software (Avizo), three-dimensional reconstruction and pore-fracture network model is realized, where the quantitative statistics and comparative analysis are carried out. The results demonstrate that the pore/throat number of nylon material accounting for 20.8%/33.9% are the largest when the pore/throat radius is in the range of 3–4 μm/1–2 μm, respectively, however, the pore/throat number of nylon-rubber composite with the radius 3–4 μm/1–2 μm occupies merely 5.49%/11.3%. Furthermore, the average pore radius of nylon material is believed as larger than that of nylon-rubber composite based on the pore network model, where the pore/throat surface area and pore/throat volume have perfect consistent patterns with that of pore radius. This work will offer a theoretical basis for the investigation of gas seepage capability discrepancy between the solid (nylon) one-phase and solid (nylon)-viscoelastic body (rubber) two-phases.
Finding successful strategies in a complex urban sustainability game
Authors: Monechi Bernardo , Ubaldi Enrico , Gravino Pietro , Chabay Ilan , Loreto Vittorio 
Published: August 2021

AbstractThe adverse effects of unsustainable behaviors on human society are leading to an increasingly urgent and critical need to change policies and practices worldwide. This requires that citizens become informed and engaged in participatory governance and measures leading to sustainable futures. Citizens’ understanding of the inherent complexity of sustainable systems is a necessary (though generally not sufficient) ingredient for them to understand controversial public policies and maintain the core principles of democratic societies. In this work, we present a novel, open-ended experiment where individuals had the opportunity to solve model urban sustainability problems in a purposeful game. Participants were challenged to interact with familiar LEGO blocks representing elements in a complex generative urban economic indicators model. Players seeks to find a specific urban configuration satisfying particular sustainability requirements. We show that, despite the intrinsic complexity and non-linearity of the problems, participants’ ability to make counter-intuitive actions helps them find suitable solutions. Moreover, we show that through successive iterations of the experiment, participants can overcome the difficulties linked to non-linearity and increase the probability of finding the correct solution to the problem. We contend that this kind ofwhat-ifplatforms could have a crucial role in future approaches to sustainable developments goals.
Associative learning shapes visual discrimination in a web-based classical conditioning task
Authors: Stegmann Yannik , Andreatta Marta , Pauli Paul , Wieser Matthias J. 
Published: August 2021

AbstractThreat detection plays a vital role in adapting behavior to changing environments. A fundamental function to improve threat detection is learning to differentiate between stimuli predicting danger and safety. Accordingly, aversive learning should lead to enhanced sensory discrimination of danger and safety cues. However, studies investigating the psychophysics of visual and auditory perception after aversive learning show divergent findings, and both enhanced and impaired discrimination after aversive learning have been reported. Therefore, the aim of this web-based study is to examine the impact of aversive learning on a continuous measure of visual discrimination. To this end, 205 participants underwent a differential fear conditioning paradigm before and after completing a visual discrimination task using differently oriented grating stimuli. Participants saw either unpleasant or neutral pictures as unconditioned stimuli (US). Results demonstrated sharpened visual discrimination for the US-associated stimulus (CS+), but not for the unpaired conditioned stimuli (CS−). Importantly, this finding was irrespective of the US’s valence. These findings suggest that associative learning results in increased stimulus salience, which facilitates perceptual discrimination in order to prioritize attentional deployment.
Evolution of NETosis markers and DAMPs have prognostic value in critically ill COVID-19 patients
Authors: Huckriede Joram , Anderberg Sara Bülow , Morales Albert , de Vries Femke , Hultström Michael , Bergqvist Anders , Ortiz-Pérez José T. , Sels Jan Willem , Wichapong Kanin , Lipcsey Miklos , van de Poll Marcel , Larsson Anders , Luther Tomas , Reutelingsperger Chris , de Frutos Pablo Garcia , Frithiof Robert , Nicolaes Gerry A. F. 
Published: August 2021

AbstractCoronavirus disease 19 (COVID-19) presents with disease severities of varying degree. In its most severe form, infection may lead to respiratory failure and multi-organ dysfunction. Here we study the levels and evolution of the damage associated molecular patterns (DAMPS) cell free DNA (cfDNA), extracellular histone H3 (H3) and neutrophil elastase (NE), and the immune modulators GAS6 and AXL in relation to clinical parameters, ICU scoring systems and mortality in patients (n = 100) with severe COVID-19. cfDNA, H3, NE, GAS6 and AXL were increased in COVID-19 patients compared to controls. These measures associated with occurrence of clinical events and intensive care unit acquired weakness (ICUAW). cfDNA and GAS6 decreased in time in patients surviving to 30 days post ICU admission. A decrease of 27.2 ng/mL cfDNA during ICU stay associated with patient survival, whereas levels of GAS6 decreasing more than 4.0 ng/mL associated with survival. The presence of H3 in plasma was a common feature of COVID-19 patients, detected in 38% of the patients at ICU admission. NETosis markers cfDNA, H3 and NE correlated well with parameters of tissue damage and neutrophil counts. Furthermore, cfDNA correlated with lowest p/f ratio and a lowering in cfDNA was observed in patients with ventilator-free days.
Thioesterase induction by 2,3,7,8-tetrachlorodibenzo-p-dioxin results in a futile cycle that inhibits hepatic β-oxidation
Authors: Cholico Giovan N. , Fling Russell R. , Zacharewski Nicholas A. , Fader Kelly A. , Nault Rance , Zacharewski Timothy R. 
Published: August 2021

Abstract2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), a persistent environmental contaminant, induces steatosis by increasing hepatic uptake of dietary and mobilized peripheral fats, inhibiting lipoprotein export, and repressing β-oxidation. In this study, the mechanism of β-oxidation inhibition was investigated by testing the hypothesis that TCDD dose-dependently repressed straight-chain fatty acid oxidation gene expression in mice following oral gavage every 4 days for 28 days. Untargeted metabolomic analysis revealed a dose-dependent decrease in hepatic acyl-CoA levels, while octenoyl-CoA and dicarboxylic acid levels increased. TCDD also dose-dependently repressed the hepatic gene expression associated with triacylglycerol and cholesterol ester hydrolysis, fatty acid binding proteins, fatty acid activation, and 3-ketoacyl-CoA thiolysis while inducing acyl-CoA hydrolysis. Moreover, octenoyl-CoA blocked the hydration of crotonyl-CoA suggesting short chain enoyl-CoA hydratase (ECHS1) activity was inhibited. Collectively, the integration of metabolomics and RNA-seq data suggested TCDD induced a futile cycle of fatty acid activation and acyl-CoA hydrolysis resulting in incomplete β-oxidation, and the accumulation octenoyl-CoA levels that inhibited the activity of short chain enoyl-CoA hydratase (ECHS1).
Patrolling human SLE haematopoietic progenitors demonstrate enhanced extramedullary colonisation; implications for peripheral tissue injury
Authors: Kokkinopoulos Ioannis , Banos Aggelos , Grigoriou Maria , Filia Anastasia , Manolakou Theodora , Alissafi Themis , Malissovas Nikolaos , Mitroulis Ioannis , Verginis Panayotis , Boumpas Dimitrios T. 
Published: August 2021

AbstractSystemic lupus erythematosus (SLE) is an autoimmune disease where bone-marrow-derived haematopoietic cells have a key role in its pathogenesis with accumulating evidence suggesting an aberrant function of haematopoietic stem/progenitor cells (HSPCs). We examined whether patrolling HSPCs differ from bone-marrow HSPCs both in SLE and healthy individuals, and how they participate in peripheral tissue injury. By employing next-generation RNA sequencing, the transcriptomes of CD34+HSPCs deriving from the bone marrow and those patrolling the bloodstream of both healthy and individuals with SLE were compared. Patrolling SLE and Healthy human HSPC kinetics were examined through their inoculation into humanised mice. Patrolling and bone-marrow HSPCs have distinct molecular signatures, while patrolling SLE HSPCs showed an enhanced extramedullary gene expression profile. Non-mobilised, SLE-derived circulating HSPCs demonstrated altered homing capacities. Xenotransplantation of circulating HSPCs in humanised mice showed that human peripheral blood HSPCs possess the ability for extramedullary organ colonisation to the kidneys. Circulating and bone marrow-derived HSPCs are distinct in steady and diseased states. Patrolling SLE CD34+HSPCs are able to home at extramedullary sites such as the spleen and kidneys, potentially participating in peripheral tissue injury.
Localized management of non-indigenous animal domesticates in Northwestern China during the Bronze Age
Authors: Vaiglova Petra , Reid Rachel E. B. , Lightfoot Emma , Pilaar Birch Suzanne E. , Wang Hui , Chen Guoke , Li Shuicheng , Jones Martin , Liu Xinyi 
Published: August 2021

AbstractThe movements of ancient crop and animal domesticates across prehistoric Eurasia are well-documented in the archaeological record. What is less well understood are the precise mechanisms that farmers and herders employed to incorporate newly introduced domesticates into their long-standing husbandry and culinary traditions. This paper presents stable isotope values (δ13C, δ15N) of humans, animals, and a small number of plants from the Hexi Corridor, a key region that facilitated the movement of ancient crops between Central and East Asia. The data show that the role of animal products in human diets was more significant than previously thought. In addition, the diets of domestic herbivores (sheep/goat, and cattle) suggest that these two groups of domesticates were managed in distinct ways in the two main ecozones of the Hexi Corridor: the drier Northwestern region and the wetter Southeastern region. Whereas sheep and goat diets are consistent with consumption of naturally available vegetation, cattle exhibit a higher input of C4 plants in places where these plants contributed little to the natural vegetation. This suggests that cattle consumed diets that were more influenced by human provisioning, and may therefore have been reared closer to the human settlements, than sheep and goats.
A novel method for peanut variety identification and classification by Improved VGG16
Authors: Yang Haoyan , Ni Jiangong , Gao Jiyue , Han Zhongzhi , Luan Tao 
Published: August 2021

AbstractCrop variety identification is an essential link in seed detection, phenotype collection and scientific breeding. This paper takes peanut as an example to explore a new method for crop variety identification. Peanut is a crucial oil crop and cash crop. The yield and quality of different peanut varieties are different, so it is necessary to identify and classify different peanut varieties. The traditional image processing method of peanut variety identification needs to extract many features, which has defects such as intense subjectivity and insufficient generalization ability. Based on the deep learning technology, this paper improved the deep convolutional neural network VGG16 and applied the improved VGG16 to the identification and classification task of 12 varieties of peanuts. Firstly, the peanut pod images of 12 varieties obtained by the scanner were preprocessed with gray-scale, binarization, and ROI extraction to form a peanut pod data set with a total of 3365 images of 12 varieties. A series of improvements have been made to VGG16. Remove the F6 and F7 fully connected layers of VGG16. Add Conv6 and Global Average Pooling Layer. The three convolutional layers of conv5 have changed into Depth Concatenation and add the Batch Normalization(BN) layers to the model. Besides, fine-tuning is carried out based on the improved VGG16. We adjusted the location of the BN layers. Adjust the number of filters for Conv6. Finally, the improved VGG16 model's training test results were compared with the other classic models, AlexNet, VGG16, GoogLeNet, ResNet18, ResNet50, SqueezeNet, DenseNet201 and MobileNetv2 verify its superiority. The average accuracy of the improved VGG16 model on the peanut pods test set was 96.7%, which was 8.9% higher than that of VGG16, and 1.6–12.3% higher than that of other classical models. Besides, supplementary experiments were carried out to prove the robustness and generality of the improved VGG16. The improved VGG16 was applied to the identification and classification of seven corn grain varieties with the same method and an average accuracy of 90.1% was achieved. The experimental results show that the improved VGG16 proposed in this paper can identify and classify peanut pods of different varieties, proving the feasibility of a convolutional neural network in variety identification and classification. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification.
An integrated approach of field, weather, and satellite data for monitoring maize phenology
Authors: Nieto Luciana , Schwalbert Raí , Prasad P. V. Vara , Olson Bradley J. S. C. , Ciampitti Ignacio A. 
Published: August 2021

AbstractEfficient, more accurate reporting of maize (Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013–2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation—identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain.