Miracle-dz/UniFAR-FTUs huggingface dataset:
Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking, DFT, and MD Simulation Analysis

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of −5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a −4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO–LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.
The Mechanism of Unsafe Behavior of Employees in High-Risk Positions under the Adjustment Effect of Hardy Personality

In order to study the impact of human and environmental matching on the unsafe behavior of employees in high-risk positions, explore the intermediary role of work satisfaction and the adjustment role of hardy personality, and improve the safety management level of employees in high-risk positions. The data collected from 501 employees in high-risk positions were empirically analyzed from an individual personality perspective with the help of AMOS 26.0 and SPSS 25.0. The research results showed that staff matching and human-organization matching have significant negative effects on unsafe behavior; job satisfaction plays a complete intermediary role in the negative relationship between human-organization matching and unsafe behavior; hardy personality can adjust the relationship between job satisfaction and unsafe behavior.
“The doctors have more questions for us”: Geographic differences in healthcare access and health literacy among transgender and nonbinary communities

Transgender and nonbinary (TNB) individuals experience a variety of systemic barriers that impact their ability to access healthcare, often leading to negative health outcomes. Previous research has suggested that improving health literacy among marginalized communities may help reduce existing health disparities. Yet few studies have examined health literacy among TNB people and how health literacy and healthcare access may differ by urbanicity. Using the capabilities theoretical approach, the current study uses data from six focus groups (N=40) from metropolitan, urban, and rural areas in Colorado to examine geographic differences in health literacy and healthcare access. Findings show that geographic differences in accessing healthcare include transportation issues, having to travel far distances to access care, finding a TNB-affirming and competent provider, and needing to educate providers and staff. These differences were more prevalent among TNB people living in non-metropolitan areas. Additionally, our findings suggest that TNB individuals have high levels of health literacy due to having to self-educate and exist within a healthcare system that was designed for cisgender people.
GIS-Based Cropland Suitability Prediction Using Machine Learning: A Novel Approach to Sustainable Agricultural Production

The increasing global demand for food has forced farmers to produce higher crop yields in order to keep up with population growth, while maintaining sustainable production for the environment. As knowledge about natural cropland suitability is mandatory to achieve this, the aim of this paper is to provide a review of methods for suitability prediction according to abiotic environmental criteria. The conventional method for calculating cropland suitability in previous studies was a geographic information system (GIS)-based multicriteria analysis, dominantly in combination with the analytic hierarchy process (AHP). Although this is a flexible and widely accepted method, it has significant fundamental drawbacks, such as a lack of accuracy assessment, high subjectivity, computational inefficiency, and an unsystematic approach to selecting environmental criteria. To improve these drawbacks, methods for determining cropland suitability based on machine learning have been developed in recent studies. These novel methods contribute to an important paradigm shift when determining cropland suitability, being objective, automated, computationally efficient, and viable for widespread global use due to the availability of open data sources on a global scale. Nevertheless, both approaches produce invaluable complimentary benefits to cropland management planning, with novel methods being more appropriate for major crops and conventional methods more appropriate for less frequent crops.
On the Jacobian Question

The direct or algorithmic approach for the Jacobian problem, consisting of the direct construction of the inverse polynomials is proposed. The so called principle and derived Jacobi conditions are proposed and discussed. The algorithmic approach is shown to be extendable to higher dimensions by proceeding on exactly identical lines. As per the important result due to Bass, Connell, and Wright [3] it is enough to show the validity of the Jacobian conjecture for cubic polynomials of special type (BCW form) in two, three, and n variables. Firstly, the method of proof for the cases of two and three variables is discussed at length. It is then indicated that the extension to the several variables case follows automatically by just following the same steps and there is no hindrance as there is essentially no change in the basic situation and the same line of thought used for the case of two and three variables remains applicable. Thus, we show that the problem can be solved completely using the important reduction of the problem to the case of special cubic degree polynomials [3]. We have shown how to obtain inverse polynomials. We fully obtain them for two variables case and almost obtain them for three variables case.
Development and Multicenter Validation of a Novel Immune-Inflammation-Based Nomogram to Predict Survival in Western Resectable Gastric and Gastroesophageal Junction Adenocarcinoma (GEA): The NOMOGAST

Background. More than 50% of operable GEA relapse after curative-intent resection. We aimed at externally validating a nomogram to enable a more accurate estimate of individualized risk in resected GEA. Methods. Medical records of a training cohort (TC) and a validation cohort (VC) of patients undergoing radical surgery for c/uT2-T4 and/or node-positive GEA were retrieved, and potentially interesting variables were collected. Cox proportional hazards in univariate and multivariate regressions were used to assess the effects of the prognostic factors on OS. A graphical nomogram was constructed using R software’s package Regression Modeling Strategies (ver. 5.0-1). The performance of the prognostic model was evaluated and validated. Results. The TC and VC consisted of 185 and 151 patients. ECOG:PS > 0 (p < 0.001), angioinvasion (p < 0.001), log (Neutrophil/Lymphocyte ratio) (p < 0.001), and nodal status (p = 0.016) were independent prognostic values in the TC. They were used for the construction of a nomogram estimating 3- and 5-year OS. The discriminatory ability of the model was evaluated with the c-Harrell index. A 3-tier scoring system was developed through a linear predictor grouped by 25 and 75 percentiles, strengthening the model’s good discrimination (p < 0.001). A calibration plot demonstrated a concordance between the predicted and actual survival in the TC and VC. A decision curve analysis was plotted that depicted the nomogram’s clinical utility. Conclusions. We externally validated a prognostic nomogram to predict OS in a joint independent cohort of resectable GEA; the NOMOGAST could represent a valuable tool in assisting decision-making. This tool incorporates readily available and inexpensive patient and disease characteristics as well as immune-inflammatory determinants. It is accurate, generalizable, and clinically effectivex.
Strongly correlated systems, coherence and entanglement

Strongly Correlated Systems: Spin Glasses (I R Pimentel) Double Exchange Models with Disorder (V Pereira et al.) Anomalous Hall Effect in Magnetic Nanostructures (V K Dugaev et al.) Introduction to the Physics of Graphene (E Vieira de Castro et al.) Dynamics and Domain Growth in Quantum Spin Systems (V Turkowski & V Rocha Vieira) Coherence in Macroscopic Systems: Atomic Bose-Einstein Condensates: Beyond Mean Field Theory (G Nunes) Critical Fields in Superconductors with Singular Density of States (R G Dias) Chiral Symmetry and BCS (J E Ribeiro) Quantum Entanglement: Entanglement in Quantum Phase Transitions (Y Omar et al.) Macroscopic Entanglement at Finite Temperature (Y Omar et al.) and other papers.
A statistical framework for modelling migration corridors

Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. Global Positioning System (GPS) tracking data allow for mapping of corridors from directly observed movements, but such tracking data are absent for many populations. We developed a novel statistical corridor modelling approach that predicts movement corridors from cost‐distance models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer Odocoileus hemionus herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green‐up, snow‐off date and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude and confidence levels of the effects of environmental variables on corridors. Within‐herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross‐herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. In addition to the practical benefit of mapping corridors for management, our statistical corridor modelling framework sets the stage for evaluating fundamental questions about the fitness trade‐offs, navigation, learning, fidelity and movement constraints that influence migratory and other corridor‐generating behaviour. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modelling approach is also readily applied to non‐migratory animal movements.
Modeling Fraud in Residential Power Usage

Understanding patterns of power usage is fundamental to the security goals of automation in the energy sector. Security aspects include data corruption via cyber-attacks, device tampering, or bypassing meter readings in energy theft. Although the mechanisms of abuse vary, persistent corruption generates patterns that statistically deviate from the designs of normal power usage. In this paper, we study power usage using both unsupervised and supervised techniques. The clustering algorithm creates energy profiles corresponding to a threat level hierarchy. We define a test in a proper statistical context by clearly specifying the fraud model (allowing simulation) to permit the analysis of the specificity and sensitivity of the model. The variance across meter readings changes over months, and the Gamma distribution fits nicely as a statistical model. Sensitivity analysis shows that detection accuracy is generally above 70% and identifies the threat level accurately. As expected, the accuracy varies over months and is lower during the months of summer. Detecting fraud in power usage is a significant problem and is an active area of research. It is essential to use the proper statistical measures to compare tests using diverse techniques.
Space Qualification of Metalized Additive Manufactured Filters

The space industry is seeking new solutions for the development of lightweight, inexpensive and easy-to-manufacture on-board communication payload devices. One of the proposed technologies is additive manufacturing (AM) of polymeric materials that are subsequently metallized. The devices developed using this technique must be operational in the particular space environment conditions. In this work we study the integrity of filters made by additive manufacturing against the extreme conditions of mechanical stress, temperature and power management as described in the standard of the European Space Agency (ESA) for satellite payload. To carry out this qualification for space, two types of X-band filters with identical frequency responses have been designed and manufactured: a classic H-plane waveguide cavities filter, and three filters on surface-mount technology (SMT). Two of the SMT filters are low height rectangular cavities filters, while the third one is a high circular cavities filter. This selection was intended to verify that the AM technique offered good results regardless of the devices topology. Two mechanical vibration tests were performed: sinusoidal sweep and random vibration. These tests emulate the transport and launching conditions of the satellite payload. Furthermore, it was measured the mechanical natural frequency of the filters, and its variation after being exposed to the tests in order to evaluate their physical integrity. The thermal analysis was performed emulating the temperature profile experienced by the satellite payload during working conditions. Then, the power handling tests were carried out. The secondary electron emission yield of the material (metallized polymer) was firstly measured. The power levels, location and frequency at which a multipactor discharge would occur were analyzed for both, low-height and standard-height, filters. Finally, both filters were subjected to those power levels, under high vacuum conditions, to check if the said multipactor discharges occurred. The mechanical stress and temperature tests prove that the developed filters can survive the hard launching and operation conditions of commercial satellite payloads. The multipactor tests report the high power levels that the devices can handle without risk of radiofrequency (RF) breakage. All these tests allow to fully qualify the proposed manufacturing technique for spatial applications.
Carbon Nanostructure/Zeolite Y Composites as Supports for Monometallic and Bimetallic Hydrocracking Catalysts

In this study, we examine the effect of integrating different carbon nanostructures (carbon nanotubes, CNTs, graphene nanoplatelets, GNPs) into Ni- and Ni-W-based bi-functional catalysts for hydrocracking of heptane performed at 400 °C. The effect of varying the SiO2/Al2O3 ratio of the zeolite Y support (between 5 and 30) on the heptane conversion is also studied. The results show that the activity, in terms of heptane conversion, followed the order CNT/Ni-ZY5 (92%) > GNP/Ni-ZY5 (89%) > CNT/Ni-W-ZY30 (86%) > GNP/Ni-W-ZY30 (85%) > CNT/Ni-ZY30 (84%) > GNP/Ni-ZY30 (83%). Thus, the CNT-based catalysts exhibited slightly higher heptane conversion as compared to the GNP-based ones. Furthermore, bimetallic (Ni-W) catalysts possessed higher BET surface areas (725 m2/g for CNT/Ni-W-ZY30 and 612 m2/g for CNT/Ni-ZY30) and exhibited enhanced hydrocracking activity as compared to the monometallic (Ni) catalyst with the same zeolite support and type of carbon structure. It was also shown that CNT-based catalysts possessed higher regeneration capability than their GNP-based counterparts due to the slightly higher thermal stability of the CVD-grown CNTs.
Structural Insights into Mouse H-FABP

Intracellular fatty acid-binding proteins are evolutionarily highly conserved proteins. The major functions and responsibilities of this family are the regulation of FA uptake and intracellular transport. The structure of the H-FABP ortholog from mouse (Mus musculus) had not been revealed at the time this study was completed. Thus, further exploration of the structural properties of mouse H-FABP is expected to extend our knowledge of the model animal’s molecular mechanism of H-FABP function. Here, we report the high-resolution crystal structure and the NMR characterization of mouse H-FABP. Our work discloses the unique structural features of mouse H-FABP, offering a structural basis for the further development of small-molecule inhibitors for H-FABP.
Enhanced DeepLabv3+ for Urban Land Use Classification Based on UAV-Borne Images

The research on urban land use classification is of great significance to the efficient management of urban land resources and the sustainable development of cities. The rapid development of UAV industry provides rich data support for urban land use classification. In order to extract higher semantic features of UAV-borne images and improve classification accuracy, this paper proposes a classification method based on enhanced DeepLabv3+ network. In view of the complexity and large amount of information, the initial backbone network Xception is replaced by the lightweight network MobileNetv2, and the classification results are optimized by using the fully connected conditional random field (CRF). The experimental results show that using the method proposed in this paper, the overall classification accuracy can reach 84.75% and the kappa coefficient can reach 0.737, which is higher than U-Net state-of-the-art algorithm, indicating that this method is reliable for urban land use classification and the classification results are highly consistent with the actual categories.
Polyphenols Content, Antioxidant Properties and Allergenic Potency of Organic and Conventional Blue Honeysuckle Berries

Blue honeysuckle berries are a rich source of polyphenols with strong antioxidant properties. The purpose of this research was to determine the effect of organic and conventional cultivation on the polyphenols, antioxidant and allergenic potency of blue honeysuckle berry cultivars: ‘No 30’, ‘Jolanta’ and ‘Indygo’ in two growing seasons. Identification of individual polyphenols was performed using the HPLC method; the total polyphenols content and antioxidant activity were determined by spectrophotometric methods. The determination of allergic potency was tested by ELISA. In the second year of the study the total polyphenols were significantly higher in organic blue honeysuckle than in the conventional blue honeysuckle. In both growing seasons, the ‘Indygo’ cv. was characterized by the highest concentration of all bioactive compounds 3241.9 mg and 3787.2 mg per 100 g−1 D.W. A strong correlation was found between the polyphenol content and the antioxidant activity for organic fruit in both years, as well as for allergenic potency. Contrary to the best bioactive properties was ‘Indigo’ cv. , with the highest allergenic potency (108.9 and 139.2 ng g−1 D.W.). The lowest content of specific allergens was found in the ‘No 30’ cv. Since honeysuckle is still a new cultivated plant, information about its allergenic potency is insufficient.
Cost-Aware Resource Recommendation for DAG-Based Big Data Workflows: An Apache Spark Case Study

The era of personal resources being sufficient for enterprise big data computations has passed. As computations are executed in the cloud, small policy changes of cloud operators may cause considerable changes in operational costs. Carefully choosing the amount of resources for a given application is thus of great importance. This, however, requires a priori knowledge of the application's performance under different configurations. Creating a performance prediction model needs to account for the heterogeneity of resources and the diversity in application workflows. Previous approaches for heterogeneous environments consider a black-box representation of the application which results in single-purpose models. This paper addresses the problem with two gray-box prediction models using linear programming (LP) and mixed-integer linear programming (MILP). Given a set of available resources, the models consider Apache Spark applications and their Directed Acyclic Graph (DAG) of workflow running on top of a Hadoop-YARN cluster. We then propose a configuration recommendation algorithm to optimize the cost-performance trade-offs when renting machine instances. The accuracy of the proposed models is evaluated with real-world executions of several representative applications on the Wikipedia dataset and the TPC-DS benchmark. The average error of only 3.28% for the proposed prediction models demonstrates the practicality of the proposed approach in handling cost-performance trade-offs.
A composite approach towards understanding the mechanisms and driving variables of river mouth variability: A case study of the Da Dien River mouth

ABSTRACT This research aims to present a composite approach to characterizing the intra-annual and interannual variability in inlet throat width and understanding its mechanism (how, when, and why it varies). The techniques used include remote sensing imagery analysis, spectral and cross-spectral analyses, correlation analyses, and random forest variable importance. The Da Dien River mouth (DDRM), which is located in southern Central Vietnam and experiences a strong annual signal of climate regimes, was selected as a case study to demonstrate the applicability of the approach. The results show that the narrowing/widening of the DDRM throat width is highly dynamic and variable under the significant dominant influence of wave height, tide and river flow, which seasonally and interannually vary according to the monsoon regime, El Niño/Southern Oscillation and sunspot number variations (better correlation with ENSO events). The overall throat width decreases during El Niño/the positive QBO phase and increases during La Niña periods/the negative QBO phase. The difference in river flow magnitudes is the main cause for the constriction/expansion of the DDRM throat width. The successful application of the study approach to the DDRM case study demonstrates its usefulness and ability to be applied to other case studies in tropical monsoon regions.