bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison between bacterial bio-formulations and gibberellic acid effects on Stevia rebaudiana growth and production of steviol glycosides through regulating their encoding genes
Authors: Abdelsattar Amal M. , El-Esawi Mohamed A. , Elsayed Ashraf , Heikal Yasmin M. 
Published: October 2024

AbstractStevia rebaudiana is associated with the production of calorie-free steviol glycosides (SGs) sweetener, receiving worldwide interest as a sugar substitute for people with metabolic disorders. The aim of this investigation is to show the promising role of endophytic bacterial strains isolated from Stevia rebaudiana Egy1 leaves as a biofertilizer integrated with Azospirillum brasilense ATCC 29,145 and gibberellic acid (GA3) to improve another variety of stevia (S. rebaudiana Shou-2) growth, bioactive compound production, expression of SGs involved genes, and stevioside content. Endophytic bacteria isolated from S. rebaudiana Egy1 leaves were molecularly identified and assessed in vitro for plant growth promoting (PGP) traits. Isolated strains Bacillus licheniformis SrAM2, Bacillus paralicheniformis SrAM3 and Bacillus paramycoides SrAM4 with accession numbers MT066091, MW042693 and MT066092, respectively, induced notable variations in the majority of PGP traits production. B. licheniformis SrAM2 revealed the most phytohormones and hydrogen cyanide (HCN) production, while B. paralicheniformis SrAM3 was the most in exopolysaccharides (EPS) and ammonia production 290.96 ± 10.08 mg/l and 88.92 ± 2.96 mg/ml, respectively. Treated plants significantly increased in performance, and the dual treatment T7 (B. paramycoides SrAM4 + A. brasilense) exhibited the highest improvement in shoot and root length by 200% and 146.7%, respectively. On the other hand, T11 (Bacillus cereus SrAM1 + B. licheniformis SrAM2 + B. paralicheniformis SrAM3 + B. paramycoides SrAM4 + A. brasilense + GA3) showed the most elevation in number of leaves, total soluble sugars (TSS), and up-regulation in the expression of the four genes ent-KO, UGT85C2, UGT74G1 and UGT76G1 at 2.7, 3.3, 3.4 and 3.7, respectively. In High-Performance Liquid Chromatography (HPLC) analysis, stevioside content showed a progressive increase in all tested samples but the maximum was exhibited by dual and co-inoculations at 264.37% and 289.05%, respectively. It has been concluded that the PGP endophytes associated with S. rebaudiana leaves improved growth and SGs production, implying the usability of these strains as prospective tools to improve important crop production individually or in consortium.
Influence of Gamma irradiation on shape memory polymer nano-composite for satellite deployment mechanism
Authors: Mousa Emad , Taha Eman O. , Lotfy Salah , Anwar Ahmad 
Published: October 2024

AbstractThis research investigates the impact of gamma irradiation on epoxy-MWCNT nanocomposites for satellite deployment mechanisms. Nanocomposites, enhanced with surfactants, were meticulously prepared and subjected to controlled gamma irradiation (250–1000 kGy) utilizing the Cobalt-60 facility Industrial Mega Gamma-1 at NCRRT in Egypt. Surface tension measurements explored surfactant effects on epoxy-MWCNT composites in acetone. Acetone reduced tension from 26.7 to be 24.2 (mN/m). Surfactants (Tween 80, SDS) effectively lowered tension (24.4 mN/m), while surfactant-free systems had higher tension (25.1 mN/m). Cationic surfactant (CTAB) slightly increased tension (25.4 mN/m) but aided MWCNT dispersion. Nonionic and anionic surfactants showed superior dispersing power, aligning with MWCNTs and enhancing dispersion. Thermogravimetric analysis (TGA) unveiled alterations in the thermal stability of epoxy-MWCNT nanocomposites induced by radiation, particularly evident at elevated doses (500 and 1000 kGy). Notably, surfactant-modified specimens exhibited discernible effects on various thermal stability parameters. DMA analysis revealed radiation-induced changes in viscoelastic properties. Unirradiated epoxy exhibited a Tg of 58 °C, while 250 kGy irradiation enhanced crosslinking (Tg: 64 °C). Higher doses (500 kGy, 1000 kGy) caused marginal Tg changes. Surfactant-modified samples showed varied effects, with Tween 80 emphasizing its role in phase separation. Results highlighted radiation’s influence on stiffness and energy dissipation. Shape memory behavior indicated increased recovery time with higher doses, except at 250 kGy. Epoxy-MWCNT exhibited a stable recovery time, suggesting a MWCNT stabilizing effect. Fixation rates consistently reached 100%, indicating improved shape recovery influenced by MWCNTs and surfactants. This study provides insights into optimizing nanocomposites for satellite deployment applications.
On the utility of cerebrospinal fluid biomarkers in canine neurological disorders
Authors: Smolek Tomas , Vince-Kazmerova Zuzana , Hanes Jozef , Stevens Eva , Palus Viktor , Hajek Ivo , Katina Stanislav , Novak Petr , Zilka Norbert 
Published: October 2024

AbstractThe cerebral biomarkers, neurofilament light chain (NfL), amyloid-β, tau, and neuron specific enolase (NSE) reflect a wide spectrum of neurological damage in the brain and spinal cord. With this study, we aimed to assess whether these biomarkers hold any potential diagnostic value for the three most common canine neurological diseases. Canines suffering from meningoencephalitis of unknown origin (MUO), brain tumors, and selected non-infectious myelopathies were included. For each diagnosis, we analyzed these biomarkers in the cerebrospinal fluid collected via cranial puncture from the cisterna magna. Elevated levels of CSF tau, NfL, and NSE were observed in MUO, with all three biomarkers being intercorrelated. Tau and NSE were increased while amyloid-β was decreased in dogs suffering from tumors. In contrast, no biomarker changes were observed in dogs with myelopathies. Covariates such as age, sex, or castration had minimal impact. CSF biomarkers may reflect molecular changes related to MUO and tumors, but not to non-infectious myelopathies. The combination of NfL, tau, and NSE may represent useful biomarkers for MUO as they reflect the same pathology and are not influenced by age.
Distinguish microphase-separated structures of diblock copolymers using local order parameters
Authors: Takano Fumiki , Hiratsuka Masaki , Takahashi Kazuaki Z. 
Published: October 2024

AbstractThe microphase-separated structures of block copolymers are inherently highly ordered local structures, commonly characterized by differences in domain width and curvature. By focusing on diblock copolymers, we propose local order parameters (LOPs) that accurately distinguish between adjacent microphase-separated structures on the phase diagram. We used the Molecular Assembly structure Learning package for Identifying Order parameters (MALIO) to evaluate the structure classification performance of 186 candidate LOPs. MALIO calculates the numerical values of all candidate LOPs for the input microphase-separated structures to create a dataset, and then performs supervised machine learning to select the best LOPs quickly and systematically. We evaluated the robustness of the selected LOPs in terms of classification accuracy against variations in miscibility and fraction of block. The minimum local area size required for LOPs to achieve their classification performances is closely related to the characteristic sizes of the microphase-separated structures. The proposed LOPs are potentially applicable over a large area on the phase diagram.
Microfacies impacts on reservoir heterogeneity of early Cretaceous Yamama carbonate reservoir in South Iraq
Authors: Mohammed Abbas , Velledits Felicitász 
Published: October 2024

Abstract
Detailed well-log interpretations, including gamma-ray, density, neutron, and resistivity, alongside petrographic analysis of 100 samples over 170 m of drill cores, have revealed factors influencing reservoir heterogeneity in the Yamama Formation, Ah’Dimah Oilfield, southern Iraq. The formation comprises four reservoir units (YA-YD) separated by four non-reservoir units (BA-BD). The reservoir units are subdivided into subunits. YB2, YB3, and YC demonstrate the best reservoir quality, while YD2 is water-bearing. Seven microfacies were identified within both reservoir and non-reservoir units, deposited in a shallow carbonate ramp. These include bioclastic wackestone, Lithocodium-Bacinella float/boundstone, peloidal cortoid intraclast grainstone, reefal bioclastic rudstone, bioclastic foraminiferal wacke/packstone, miliolidal pack/grainstone, and spiculitic foraminiferal wackestone. Despite the deep burial depth of the formation (> 4000 m), it maintained good porosity values in most intervals, reaching up to 20%. Early isopachous cement protected porosity and dissolution enhanced porosity, while cementation, compaction, and pyritization reduced it. The reservoir units correlate with depositional environments, being deposited in the shoal area, while non-reservoir units were deposited in lagoon, middle, and outer-ramp settings. The Lithocodium-Bacinella float/boundstone and reefal bioclastic rudstone facies, forming reefal patches and build-ups within the shoal, dominated in YB2 and YC. Targeting these patches northeast of Ah’Dimah Oilfield is promising for field development.
Evidence of a metacognitive illusion in stimulus-specific prospective judgments of distraction by background speech
Authors: Komar Gesa Fee , Buchner Axel , Mieth Laura , van de Vijver Ruben , Bell Raoul 
Published: October 2024

AbstractTwo experiments served to examine how people arrive at stimulus-specific prospective judgments about the distracting effects of speech on cognitive performance. The direct-access account implies that people have direct metacognitive access to the cognitive effects of sounds that determine distraction. The processing-fluency account implies that people rely on the processing-fluency heuristic to predict the distracting effects of sounds on cognitive performance. To test these accounts against each other, we manipulated the processing fluency of speech by playing speech forward or backward and by playing speech in the participants’ native or a foreign language. Forward speech and native speech disrupted serial recall to the same degree as backward speech and foreign speech, respectively. However, the more fluently experienced forward speech and native speech were incorrectly predicted to be less distracting than backward speech and foreign speech. This provides evidence of a metacognitive illusion in stimulus-specific prospective judgments of distraction by speech, supporting the processing-fluency account over the direct-access account. The difference between more and less fluently experienced speech was largely absent in the participants’ global retrospective judgments of distraction, suggesting that people gain access to comparatively valid cues when experiencing the distracting effects of speech on their serial-recall performance firsthand.
Spatial localisation and sensing in two dimensions via metasurfaces
Authors: Dima Georgiana , Stevens Christopher John 
Published: October 2024

AbstractIn this study, we introduce a two-dimensional metasurface sensor designed to detect, locate and distinguish between different objects placed in its near field. When an object is placed on the metasurface, local changes can be detected in one or more of the structure’s meta-atoms. This interaction generally modifies the inductance of the meta-atom, resulting in changes to the overall input impedance of the surface. We derive the properties of the structure and its behaviour in terms of superposition and demonstrate that observing the meta-surface from a single point is sufficient for unambiguous localisation and identification. To model these changes effectively and identify the position of an object, we employ a neural network machine learning algorithm. Our approach enables accurate localisation of all studied objects, with a precision exceeding $$98\%$$

98
%

. Additionally, the distinct signatures of the objects allow for separation between them with an accuracy of over $$97\%$$

97
%

. The potential applications of this platform extend to foreign object detection on metasurfaces for wireless power transfer, providing proximity detection for many surfaces such as clothing, car bodies and robotic carapaces. Furthermore, our research suggests the feasibility of implementing a touchscreen type interface requiring only a single waveguide connection.
Exploring seed characteristics and performance through advanced physico-chemical techniques
Authors: Vadivel Dhanalakshmi , Djemal Rania , García Jessica , Pagano Andrea , Trabelsi Rahma , Gdoura-Ben Amor Maroua , Charfeddine Safa , Ghanmi Siwar , Khalifa Ibtisem , Rekik Mariem , Amor Fatma , Ebel Chantal , Gdoura Radhouane , Elleuch Amine , Balestrazzi Alma , Macovei Anca , Hanin Moez , Dondi Daniele 
Published: October 2024

AbstractSimple physico-chemical techniques can be used to evaluate the composition, structure, and characteristics of plant seeds to determine their viability, quality, and possible uses in agriculture. Advanced analytical techniques, including thermo gravimetric analysis (TGA), electron paramagnetic resonance (EPR), and high-pressure liquid chromatography (HPLC), provide completely new insights and more precise information. They can be integrated to build up seed quality profiles, with great advantage to assess water content, organic compounds, and inorganic metals without the need to carry out many extraction procedures, as requested by more conventional methods. In this study, seed lots from three different plant species such as Triticum turgidum L. subsp. durum (wheat), Trigonella foenum graecum L. (trigonella or fenugreek), and Atriplex halimus L. (saltbush or sea orach) have been used to test the potential of TGA, EPR, and HPLC to discriminate between seed-specific features. A key finding of this study is that HPLC is essential in Principal Component Analysis (PCA) because various seeds (from the same species or other species) may contain compounds with varying polarity groups. The reported data confirm the efficacy of this approach. These data, fully available for other users, are coherently constructed and provide a proof of concept for future seed quality control studies.
StratoMod: predicting sequencing and variant calling errors with interpretable machine learning
Authors: Dwarshuis Nathan , Tonner Peter , Olson Nathan D. , Sedlazeck Fritz J. , Wagner Justin , Zook Justin M. 
Published: October 2024

AbstractDespite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their application. Currently, assessing such tradeoffs relies on intuition about how a certain pipeline will perform in a given genomic context. We present StratoMod, which addresses this problem using an interpretable machine-learning classifier to predict germline variant calling errors in a data-driven manner. We show StratoMod can precisely predict recall using Hifi or Illumina and leverage StratoMod’s interpretability to measure contributions from difficult-to-map and homopolymer regions for each respective outcome. Furthermore, we use Statomod to assess the effect of mismapping on predicted recall using linear vs. graph-based references, and identify the hard-to-map regions where graph-based methods excelled and by how much. For these we utilize our draft benchmark based on the Q100 HG002 assembly, which contains previously-inaccessible difficult regions. Furthermore, StratoMod presents a new method of predicting clinically relevant variants likely to be missed, which is an improvement over current pipelines which only filter variants likely to be false. We anticipate this being useful for performing precise risk-reward analyses when designing variant calling pipelines.
Design of stimuli-responsive minimalist heptad surfactants for stable emulsions
Authors: Li Yang , Weng Yilun , Hui Yue , Wang Jiaqi , Xu Letao , Yang Yang , Yang Guangze , Zhao Chun-Xia 
Published: October 2024

AbstractPeptide surfactants have been extensively investigated with various applications in detergents, foods, and pharmaceutics due to their biodegradability, biocompatibility, and customizable structures. Traditional peptide surfactants are often designed in a head-to-tail fashion mimicking chemical surfactants. Alternatively, a side-by-side design pattern based on heptad repeats offers an approach to designing peptide surfactants. However, minimalist peptide design using a single heptad for stabilizing interfaces remains largely unexplored. Here, we design four heptad surfactants (AM1.2, 6H, 6H7K, and HK) responsive to metal ions and compare their emulsification performance with a three-heptad peptide, AM1. Among them, the HK peptide generates emulsions exhibiting good stability over months. We further optimize factors such as buffering salts, ionic strength, and emulsion dilutions to uncover their impacts on emulsion properties. Our findings deepen the understanding of emulsion properties and provide practical insights for characterizing peptide-based emulsions, paving the way for their broader utilization in diverse applications.
Social media engagement in people and climate change
Authors: Graham Hilary , Golder Su 
Published: October 2024

Abstract‘People’ are central both to populism, an ideology in which ‘people’ are pitted against corrupt elites, and to climate science and policy, which advocate ‘people-centred’ climate action. Our Brief Communication explores references to climate change and ‘people’ on Twitter (now X). Populist tropes (the people against corrupt elites) were not restricted to climate-sceptical tweets; they extended to tweets that recognised climate change was real but expressed mistrust about climate actions.
Aligning digital biomarker definitions in psychiatry with the National Institute of Mental Health Research Domain Criteria framework
Authors: Mulinari Shai 
Published: October 2024

AbstractThe field of biological psychiatry faces a growing influx of digital biomarkers spanning self-report, social, behavioral, cognitive, and physiological indicators of various mental health conditions. However, the definition of “digital biomarker,” particularly the “bio-” component, remains unclear. This article reviews the terminology of digital biomarkers in psychiatry and argues for the reservation of the term exclusively for measures of biological parameters with a plausible pathway connecting to the disease or condition of interest to enhance terminological clarity and consistency with conventional definitions of biomarker, short for biological marker. While the distinction between biological and non-biological parameters may blur at the edges, the Research Domain Criteria (RDoC) developed by the US National Institute of Mental Health offers a valuable heuristic. The RDoC distinguishes between biological (genes, molecules, cells, neural circuits, physiology) and non-biological (broadly understood behavior and self-report) units of analysis. Aligning digital biomarker definitions in psychiatry with the RDoC framework would mark a significant shift from the current broad usage, where almost any digitally measured characteristic, when used as an indicator, qualifies as a digital biomarker.
Classification with electromagnetic waves
Authors: Simsek Ergun , Manyam Harish Reddy 
Published: October 2024

AbstractThe integration of neural networks and machine learning techniques has ushered in a revolution in various fields, including electromagnetic inversion, geophysical exploration, and microwave imaging. While these techniques have significantly improved image reconstruction and the resolution of complex inverse scattering problems, this paper explores a different question: Can near‐field electromagnetic waves be harnessed for object classification? To answer this question, we first create a dataset based on the MNIST dataset, where we transform the grayscale pixel values into relative electrical permittivity values to form scatterers and calculate the electromagnetic waves scattered from these objects using a 2D electromagnetic finite‐difference frequency‐domain solver. Then, we train various machine learning models with this dataset to classify the objects. When we compare the classification accuracy and efficiency of these models, we observe that the neural networks outperform others, achieving a 90% classification accuracy solely from the data without a need for projecting the input data into a latent space. The impacts of the training dataset size, the number of antennas, and the location of antennas on the accuracy and time spent during training are also investigated. These results demonstrate the potential for classifying objects with near‐field electromagnetic waves in a simple setup and lay the groundwork for further research in this exciting direction.
TCAD‐enabled machine learning framework for DC and RF performance evaluation of InGaAs sub‐channel DG‐HEMTs
Authors: Moses M Leeban , Kumar R Saravana , Faheem Muhammad , K Ramkumar , Ali K Shoukath , Khan Arfat Ahmad 
Published: October 2024

AbstractThis research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods.
Phosphatidylserine Topically Attenuates Imiquimod-induced Psoriasis
Through Inﬂammation Inhibition in Mice
Authors: Far Bahareh Farasati , Saffari Partow Mirzaee , Jafari Razieh Mohammad , Goudarzi Ramin , Dehpour Ahmad Reza , Partoazar Alireza 
Published: October 2024

Abstract
Background Psoriasis is a chronic skin condition that is associated with
persistent inflammation and skin lesions. Topical therapy has been a promising
approach to the alleviation of psoriasis through the application of
anti-inflammatory agents. Phosphatidylserine (PS) administration has shown
anti-inflammatory effects in the trials. Consequently, the objective of this
study was to evaluate the effects of topical PS on the potential improvement of
an imiquimod (IMQ)-induced psoriasis model. Additionally, cyclosporine A was
utilized as a comparative anti-psoriatic agent in our study.
Methods The psoriasis model was established by topically applying IMQ to
the dorsal skin of mice once daily for five consecutive days. The efficacy of
topical PS was assessed using the Psoriasis Area and Severity Index (PASI) score
to evaluate skin lesions. Subsequently, the skin samples were analyzed using
Baker’s scoring system, Masson’s trichrome staining, immunohistochemistry, and
real-time PCR analysis.
Results IMQ-induced plaque-type psoriasis resulted in a significant
increase (P<0.05) in dermal thickness, hyperkeratosis, PASI score, and
inflammatory cytokines at the lesion site. The topical PS and cyclosporine A
significantly (P<0.05) reduced PASI score and dermal thickness, while
also alleviating erythema and scaling when compared to untreated mice.
Furthermore, biomolecular assessments revealed that PS significantly
(P<0.05) inhibited the gene expression of IL-17, IL-23, and TNF-α
cytokines in the IMQ-induced lesions.
Conclusion Topical PS may pointedly alleviate psoriasis through the
inhibition of inflammation. The beneficial effects of the PS recommend further
investigation in both experimental and clinical studies in the control of skin
psoriasis.