bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of biological and organic fertilizers on growth processes, productivity and quality of melon fruits under Southeastern Kazakhstan
Authors: Aitbayeva A T , Zorzhanov B D , Kossanov S U , Koshmagambetova M Zh , Balgabayeva R K 
Published: June 2022

Abstract
In Kazakhstan, melon growing is cultivated using chemical fertilizers. Organic production is not practiced where there being no research. Our country, possessing a great high-quality variety, can be the focus of world community attention as a producer and supplier of ecological melon. Besides economic interests, changeover from traditional methods of cultivation to biological one is of great importance in ecological aspect. Unregulated and too high agrochemicals application causes a number of negative consequences, upwards from soil pollution with severe salts to poisoning caused using low-quality products. The most popular and regulated elements of biological melon cultivation in production include organic fertilizers instead of chemical ones. Therefore, our research is aimed to study bioorganic fertilizers on melon culture under Southeastern Kazakhstan. Scientific experiments were held at the experimental sites of the «Fruit and Vegetable Research Institute» LLP. We experimented using classical research methods generally adopted in melon cultivation. The findings showed that bioorganic fertilizers improve the growth parameters and quality of melon culture, and increase fruit productivity. The yield growth on the studied options for control ranged from 31.23% to 41.71%, depending on the nutritional conditions.
Agrochemical characteristics of soils in the vicinity of the village of Mishkino, Mishkinsky district of the Republic of Bashkortostan
Authors: Minina N N , Makhmutov A R , Sinelnikova O V 
Published: June 2022

Abstract
The article is devoted to the analysis of the dynamics of the content of such important elements of fertility as phosphorus and potassium and the acidity of arable soils in the vicinity of the Mishkino Mishkinsky district of the Republic of Bashkortostan is determined. The data on the content of phosphorus and potassium and the determination of acidity in the soils of the Mishkinsky district of the Republic of Bashkortostan from 1976 to 2000 were compared. Studies on the phosphorus content in the soils of the Mishkino district conducted in 1976 showed an average level of phosphorus availability. Studies conducted in 1992 showed a slight increase in the availability of phosphorus in the soil. Studies in 2015 and 2020 showed a continuing trend of increasing the availability of phosphorus in the soil. Studies conducted to study the potassium content showed a fairly low potassium content in 1976. In 1992, in general, there were changes in the classes of security and in the amount of potassium content in the soil. Studies in 2015 and 2020 showed a steady trend towards an increase in potassium content in the soils of the district and the presence of soils only with an increased and high potassium content. Studies on soil acidity conducted in 1976 showed that the area of arable soils is mainly represented by medium-acid and slightly acidic soils. Studies in 1992 showed an increase in soil acidity. In the following years, the farm undertook methods of agrochemical effects on soils in order to reduce acidity. Research 2015. and 2020 showed a significant decrease in the acidity of arable soils. It has been established that due to the use of methods of rational nature management of territories, the area of agricultural soils with an average and increased content of mobile phosphorus and exchangeable potassium increases, processes of neutralization of strongly acidic soils occur.
Improving biomedical named entity recognition by dynamic caching inter-sentence information
Authors: Tong Yiqi , Zhuang Fuzhen , Zhang Huajie , Fang Chuyu , Zhao Yu , Wang Deqing , Zhu Hengshu , Ni Bin 
Published: June 2022

AbstractMotivationBiomedical Named Entity Recognition (BioNER) aims to identify biomedical domain-specific entities (e.g. gene, chemical and disease) from unstructured texts. Despite deep learning-based methods for BioNER achieving satisfactory results, there is still much room for improvement. Firstly, most existing methods use independent sentences as training units and ignore inter-sentence context, which usually leads to the labeling inconsistency problem. Secondly, previous document-level BioNER works have approved that the inter-sentence information is essential, but what information should be regarded as context remains ambiguous. Moreover, there are still few pre-training-based BioNER models that have introduced inter-sentence information. Hence, we propose a cache-based inter-sentence model called BioNER-Cache to alleviate the aforementioned problems.ResultsWe propose a simple but effective dynamic caching module to capture inter-sentence information for BioNER. Specifically, the cache stores recent hidden representations constrained by predefined caching rules. And the model uses a query-and-read mechanism to retrieve similar historical records from the cache as the local context. Then, an attention-based gated network is adopted to generate context-related features with BioBERT. To dynamically update the cache, we design a scoring function and implement a multi-task approach to jointly train our model. We build a comprehensive benchmark on four biomedical datasets to evaluate the model performance fairly. Finally, extensive experiments clearly validate the superiority of our proposed BioNER-Cache compared with various state-of-the-art intra-sentence and inter-sentence baselines.Availabilityand implementationCode will be available at https://github.com/zgzjdx/BioNER-Cache.Supplementary informationSupplementary data are available at Bioinformatics online.
6-Sulfo LacNAc monocytes are quantitatively and functionally disturbed in systemic sclerosis patients
Authors: Ricard Laure , Eshagh Déborah , Siblany Lama , de Vassoigne Frédéric , Malard Florent , Laurent Charlotte , Beurier Pauline , Jachiet Vincent , Rivière Sébastien , Fain Olivier , Mohty Mohamad , Gaugler Béatrice , Mekinian Arsène 
Published: June 2022

Abstract
Systemic sclerosis (SSc) is an autoimmune disease characterized by fibrosis, microangiopathy, and autoantibodies. We previously reported that circulating follicular helper T (cTfh) cells are increased in SSc and induce plasmablast differentiation. However, mechanisms leading to cTfh cell expansion and activation in SSc remain to be established. Tfh cells require IL-12 for their expansion and differentiation. 6-Sulfo LacNAc monocytes (slanMo), a subset of monocytes, have a higher capacity to produce IL-12 and to induce CD4+ T cell proliferation in comparison with dendritic cells (DC) or classical monocytes. The aim of this study was to perform a quantitative and functional analysis of monocytes and DC and to correlate them with cTfh cell expansion and clinical manifestations in SSc. Using flow cytometry, we analyzed different monocyte subsets including slanMo and DC from 36 SSc patients and 26 healthy controls (HC). In vitro culture experiments of sorted slanMo were performed for functional analysis and cytokine production. We observed that slanMo, intermediate and non-classical monocytes were increased in SSc in comparison with HC. Furthermore, the increase in slanMo cells was more potent in patients with diffuse SSc. We observed a significant positive correlation between slanMo and cTfh cell levels in SSc patients but not in HC. Other monocyte subsets did not correlate with cTfh cell expansion. In addition, we observed that in vitro, slanMo cells from SSc patients produced less IL-12 than slanMo from HC. SlanMo are increased in SSc and may participate in the activation of cTfh cells in SSc.
The Evolutionary History of Peptidases Involved in the Processing of Organelle-Targeting Peptides
Authors: Garrido Clotilde , Wollman Francis-André , Lafontaine Ingrid 
Published: June 2022

Abstract
Most of the proteins present in mitochondria and chloroplasts, the organelles acquired via endosymbiotic events, are encoded in the nucleus and translated into the cytosol. Most of such nuclear-encoded proteins are specifically recognized via an N-terminal-encoded targeting peptide (TP) and imported into the organelles via a translocon machinery. Once imported, the TP is degraded by a succession of cleavage steps ensured by dedicated peptidases. Here, we retrace the evolution of the families of the mitochondrial processing peptidase (MPP), stromal processing peptidase (SPP), presequence protease (PreP), and organellar oligo-peptidase (OOP) that play a central role in TP processing and degradation across the tree of life. Their bacterial distributions are widespread but patchy, revealing unsurprisingly complex history of lateral transfers among bacteria. We provide evidence for the eukaryotic acquisition of MPP, OOP, and PreP by lateral gene transfers from bacteria at the time of the mitochondrial endosymbiosis. We show that the acquisition of SPP and of a second copy of OOP and PreP at the time of the chloroplast endosymbiosis was followed by a differential loss of one PreP paralog in photosynthetic eukaryotes. We identified some contrasting sequence conservations between bacterial and eukaryotic homologs that could reflect differences in the functional context of their peptidase activity. The close vicinity of the eukaryotic peptidases MPP and OOP to those of several bacterial pathogens, showing antimicrobial resistance, supports a scenario where such bacteria were instrumental in the establishment of the proteolytic pathway for TP degradation in organelles. The evidence for their role in the acquisition of PreP is weaker, and none is observed for SPP, although it cannot be excluded by the present study.
Uncertainty quantification for regularized inversion of electromagnetic geophysical data—Part I: motivation and theory
Authors: Blatter Daniel , Morzfeld Matthias , Key Kerry , Constable Steven 
Published: June 2022

SUMMARYWe present a method for computing a meaningful uncertainty quantification (UQ) for regularized inversion of electromagnetic (EM) geophysical data that combines the machineries of regularized inversion and Bayesian sampling with a ‘randomize-then-optimize’ (RTO) approach. The RTO procedure is to perturb the canonical objective function in such a way that the minimizers of the perturbations closely follow a Bayesian posterior distribution. In practice, this means that we can compute UQ for a regularized inversion by running standard inversion/optimization algorithms in a parallel for-loop with only minor modification of existing codes. Our work is split into two parts. In Part I, we review RTO and extend the methodology to estimate the regularization penalty weight on the fly, not unlike in the Occam inversion. We call the resulting algorithm the RTO-TKO and explain that it samples from a biased distribution which we numerically demonstrate to be nearby the Bayesian posterior distribution. In return for accepting this small bias, the advantage of RTO-TKO over asymptotically unbiased samplers is that it significantly accelerates convergence and leverages computational parallelism, which makes it highly scalable to 2-D and 3-D EM problems. In Part II, we showcase the versatility and computational efficiency of RTO-TKO and apply it to a variety of EM inversions in 1-D and 2-D, carefully comparing the RTO-TKO results to established UQ estimates using other methods. We further investigate scalability to 3-D, and discuss the influence of prior assumptions and model parametrizations on the UQ.
Effects of fracture-surface geometries on the third-order acoustoelastic constants for aligned fluid-saturated fractures
Authors: Fu Bo-Ye , Fu Li-Yun , Han Tongcheng , Deng Wubing 
Published: June 2022

SUMMARYThe pressure sensitivity of stiffness in fractured rocks is closely related to fracture-surface geometries. The resulting stress-dependence of stiffness can be represented by the third-order elastic constants (3oECs). Fracture surfaces are generally rough at various scales, and can significantly affect the 3oECs of pre-stressed fractures as well as the wave-induced fluid flow (WIFF) induced by the Biot slow P-wave between fractures and the background medium. The WIFF usually depends on the fracture width relative to the slow P-wavelength and the fracture-surface roughness. We generate various fracture-surface geometries at different scales of random roughnesses parametrized by the surface standard deviation (SSD) of fracture-surface heights. With theoretical analyses and numerical simulations, we investigate the effect of fracture-surface geometries on the stress- and frequency-dependent stiffness through the 3oECs for pre-stressed rocks with aligned fractures. For the elastic wave in the low-frequency regime of Biot theory with the fracture scale much less than the wavelength, the induced WIFF significantly enhances the effect of fracture-surface geometries on the 3oECs and P- and S-wave moduli. The stiffness of fractured rocks increases with increasing SSDs, yielding a high sensitivity to pre-stresses. Toward the high-frequency limit, however, the fluid diffusion between fractures and the porous background decreases, which reduces the influence of fracture-surface roughnesses with the 3oECs much less than that in the low-frequency regime. The resulting P-wave modulus of aligned fluid-saturated fractures approximates to the background value.
Bidirectional Relationships between Testosterone and Aggression: A Critical Analysis of Four Predictions
Authors: George Elizabeth M , Rosvall Kimberly A 
Published: June 2022

Abstract
Experimentally elevated testosterone (T) often leads to enhanced aggression, with examples across many different species, including both males and females. Indeed, the relationship between T and aggression is among the most well-studied and fruitful areas of research at the intersection of behavioral ecology and endocrinology. This relationship is also hypothesized to be bidirectional (i.e., T influences aggression, and aggression influences T), leading to four key predictions: (1) Individuals with higher T levels are more aggressive than individuals with lower T. (2) Seasonal changes in aggression mirror seasonal changes in T secretion. (3) Aggressive territorial interactions stimulate increased T secretion. (4) Temporary elevations in T temporarily increase aggressiveness. These predictions cover a range of timescales, from a single snapshot in time, to rapid fluctuations, and to changes over seasonal timescales. Adding further complexity, most predictions can also be addressed by comparing among individuals or with repeated sampling within individuals. In our review, we explore how the spectrum of results across predictions shapes our understanding of the relationship between T and aggression. In all cases, we can find examples of results that do not support the initial predictions. In particular, we find that Predictions 1–3 have been tested frequently, especially using an among-individual approach. We find qualitative support for all three predictions, though there are also many studies that do not support Predictions 1 and 3 in particular. Prediction 4, on the other hand, is something that we identify as a core underlying assumption of past work on the topic, but one that has rarely been directly tested. We propose that when relationships between T and aggression are individual-specific or condition-dependent, then positive correlations between the two variables may be obscured or reversed. In essence, even though T can influence aggression, many assumed or predicted relationships between the two variables may not manifest. Moving forward, we urge greater attention to understanding how and why it is that these bidirectional relationships between T and aggression may vary among timescales and among individuals. In doing so, we will move toward a deeper understanding on the role of hormones in behavioral adaptation.
Wearable Sensor-Based Detection of Influenza in Presymptomatic and Asymptomatic Individuals
Authors: Temple Dorota S , Hegarty-Craver Meghan , Furberg Robert D , Preble Edward A , Bergstrom Emma , Gardener Zoe , Dayananda Pete , Taylor Lydia , Lemm Nana-Marie , Papargyris Loukas , McClain Micah T , Nicholson Bradly P , Bowie Aleah , Miggs Maria , Petzold Elizabeth , Woods Christopher W , Chiu Christopher , Gilchrist Kristin H 
Published: June 2022

AbstractBackgroundThe COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions.MethodsTwenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors. This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semisupervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the postinoculation dataset.ResultsInoculation with this challenge virus was well-tolerated with an infection rate of 85%. With the model classification threshold set so that no alarms were recorded in the 170 healthy days recorded, the algorithm correctly identified 16 of 17 (94%) positive presymptomatic and asymptomatic individuals, on average 58 hours postinoculation and 23 hours before the symptom onset.ConclusionsThe data processing and modeling methodology show promise for the early detection of respiratory illness. The detection algorithm is compatible with data collected from smartwatches using optical techniques but needs to be validated in large heterogeneous cohorts in normal living conditions.Clinical Trials Registration. NCT04204493.
Intelligent data-driven denoising based on texture complexity
Authors: Li Jingye , Liu Xiwu , Liu Yuwei , Huo Zhizhou 
Published: June 2022

Abstract
Denoising is considered to be the most frequently technique of seismic signal processing to improve signal-to-noise ratios measured and provide a precondition for subsequent processing. Deep learning (DL), a data-driven approach, is based on statistical techniques to extract features from large amounts of training datasets to learn extremely complex functions that effectively attenuate noise, rather than assumptions about linearity, low rank or sparsity. Most of the existing strategies use all available training datasets for network training, which ignores the contribution of different training data to the results. In this case, however, the generatively huge amount of training patches inevitably increases the memory and computational overburden of DL optimization. Targeting this issue, entropy sampling strategy based on the texture complexity of the seismic data is developed to extract training patches more efficiently, reducing the training datasets while guaranteeing prediction performance. Texture complexity equates to the validity of the seismic data, with regions of higher complexity representing areas of valid signal enrichment that are sampled with higher probability. Using the proposed strategy, we can use the training dataset more efficiently. We evaluate the performance of the proposed method for denoising based on the denoising convolutional auto-encoder network. Numerical illustrations on simulation models and two real surveys show that the proposed methodology has better denoising performance with higher accuracy and efficiency compared to the conventional methods.
Improved binary solution for the gamma-ray binary 1FGL J1018.6-5856
Authors: van Soelen B , Mc Keague S , Malyshev D , Chernyakova M , Komin N , Matchett N , Monageng I M 
Published: June 2022

ABSTRACT
The gamma-ray binary 1FGL J1018.6-5856 consists of an O6V((f)) type star and an unknown compact object, and shows orbitally modulated emission from radio to very high energy gamma rays. The X-ray light curve shows a maximum around the same phase as the GeV emission, but also a secondary maximum between phases ϕ = 0.2 and 0.6. A clear solution to the binary system is important for understanding the emission mechanisms occurring within the system. In order to improve on the existing binary solution, we undertook radial velocity measurements of the optical companion using the Southern African Large Telescope, as well as analysed publicly available X-ray and GeV gamma-ray data. A search for periodicity in Fermi-LAT data found an orbital period of P = 16.5507 ± 0.0004 d. The best-fitting solution to the radial velocities, held at this new period, finds the system to be more eccentric than previous observations, e = 0.531 ± 0.033 with a longitude of periastron of 151.2 ± 5.1°, and a larger mass function f = 0.00432 ± 0.00077 M⊙. We propose that the peaks in the X-ray and gamma-ray light curves around phase 0 are due to the observation of the confined shock formed between the pulsar and stellar wind pointing towards the observer. The secondary increase or strong rapid variations of the X-ray flux at phases 0.25 to 0.75 is due to the interaction of multiple randomly oriented stellar wind clumps/pulsar wind interactions around apastron.
Mass segregation and dynamics of primordial binaries in star clusters with a radially anisotropic velocity distribution
Authors: Pavlík Václav , Vesperini Enrico 
Published: June 2022

ABSTRACT
This paper is the third in a series investigating, by means of N-body simulations, the implications of an initial radially anisotropic velocity distribution on the dynamics of star clusters. Such a velocity distribution may be imprinted during a cluster’s early evolutionary stages and several observational studies have found examples of old globular clusters in which radial anisotropy is still present in the current velocity distribution. Here we focus on its influence on mass segregation and the dynamics of primordial binary stars (disruptions, ejections, and component exchanges). The larger fraction of stars on radial/highly eccentric orbits in the outer regions of anisotropic clusters lead to an enhancement in the dynamical interactions between inner and outer stars that affects both the process of mass segregation and the evolution of primordial binaries. The results of our simulations show that the time-scale of mass segregation of the initially anisotropic cluster is longer in the core and shorter in the outer regions, when compared to the initially isotropic system. The evolution of primordial binaries is also significantly affected by the initial velocity distribution and we find that the rate of disruptions, ejections, and exchange events affecting the primordial binaries in the anisotropic clusters is higher than in the isotropic ones.