Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation on the dispersion of carbon nanotubes in solvent media: effect of sonication energy and carbon nanotube diameters

Multi-walled carbon nanotubes (CNTs) were dispersed in ethanol solvent through ultrasonication using BYK9076 as surfactant. The effect of sonication energy and the diameter of CNTs on the dispersion are investigated. The dispersion quality was assessed by visual observation, Transmission Electron Microscopy (TEM), and UV-vis spectroscopy. Results show that the sonication energy has significant effect on the dispersion quality. Observation based on bare eye shows that increase in sonication time from 15 min to 60 min results in the darker CNT solutions reflecting more CNT exfoliation. UV-vis results demonstrate that sonication energy is efficient to improve the dispersion of CNTs within about 45 min sonication. However, TEM results reveal the excessive sonication energy can shorten the CNT length. In addition, UV-vis absorbance indicates that the CNTs with larger (40-60 nm) diameter are easier to disperse compared with the CNTs with smaller diameter (10-20 nm). This study may provide insight into further understanding of dispersing CNTs in composite matrix. quality of CNT dispersion in media. However, the effects of CNT geometry and sonication energy on quality of CNT dispersion have only received little attention so far. In this study, effect of sonication energy and CNT diameter on dispersion of CNTs in solvent media via ultrasonication is reported. To do this, visual observation, Transmission Electron Microscopy (TEM), and UV-vis spectroscopy were used to assess the dispersion quality. UV-vis spectroscopy has been accepted to observe the exfoliation progress throughout the duration of the CNT dispersion in media (Zhao and Gao 2003; Grossiord, Regev et al. 2005; Grossiord, Loos et al. 2007). Our research results show that sonication energy and CNT diameter have significant influence on CNT dispersion. 2 EXPERIMENTAL SECTION
Identification and Analysis of Axolotl Homologs for Proteins Implicated in Human Neurodegenerative Proteinopathies

Neurodegenerative proteinopathies such as Alzheimer’s Disease are characterized by abnormal protein aggregation and neurodegeneration. Neuroresilience or regenerative strategies to prevent neurodegeneration, preserve function, or restore lost neurons may have the potential to combat human proteinopathies; however, the adult human brain possesses a limited capacity to replace lost neurons. In contrast, axolotls (Ambystoma mexicanum) show robust brain regeneration. To determine whether axolotls may help identify potential neuroresilience or regenerative strategies in humans, we first interrogated whether axolotls express putative proteins homologous to human proteins associated with neurodegenerative diseases. We compared the homology between human and axolotl proteins implicated in human proteinopathies and found that axolotls encode proteins highly similar to human microtubule-binding protein tau (tau), amyloid precursor protein (APP), and β-secretase 1 (BACE1), which are critically involved in human proteinopathies like Alzheimer’s Disease. We then tested monoclonal Tau and BACE1 antibodies previously used in human and rodent neurodegenerative disease studies using immunohistochemistry and western blotting to validate the homology for these proteins. These studies suggest that axolotls may prove useful in studying the role of these proteins in disease within the context of neuroresilience and repair.
The Greek Jefferson Scale of Empathy—Medical Student Version (JSE-S): Psychometric Properties and Its Associated Factors

The present study aimed to evaluate the psychometric properties of the Greek version of the Jefferson Scale of Empathy—Student version (JSE-S) and its association with potential predictors among Greek-speaking undergraduate medical students. This study adopted a cross-sectional, comparative–descriptive research design. The study was conducted during October and November 2023. Cronbach’s α values for the JSE-S and the factors “perspective taking”, “compassionate care”, and “standing in the patient’s shoes” showed internal consistency. The intraclass correlation coefficient for the JSE-S score in the test–retest study indicated a high level of reliability. The participants showed moderate empathy levels. Females scored higher than males in the Greek version of the JSE-S. Moreover, students enrolled in the fourth academic year showed higher empathy mean scores than those enrolled in the first year. Statistically significant empathy differences by specialty preferences or faith in God/supreme power were not found. The present study provided satisfactory evidence that the Greek JSE-S is a psychometrically sound measurement instrument. Empathy differences by gender were found in line with prior literature.
MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction

Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients’ profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SUPREME, use a graph convolutional network (GCN), which fails to consider the level of importance of neighboring nodes on a particular node. To address this gap, we hypothesize that paying attention to each neighbor or providing appropriate weights to neighbors based on their importance might improve the cancer subtype prediction. The natural choice to determine the importance of each neighbor of a node in a graph is to explore the graph attention network (GAT). Here, we propose MOGAT, a novel multi-omics integration approach, leveraging GAT models that incorporate graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism to extract appropriate information for a specific sample by assigning unique attention coefficients to neighboring samples. Based on our knowledge, our group is the first to explore GAT in multi-omics integration for cancer subtype prediction. To evaluate the performance of MOGAT in predicting cancer subtypes, we explored two sets of breast cancer data from TCGA and METABRIC. Our proposed approach, MOGAT, outperforms MOGONET by 32% to 46% and SUPREME by 2% to 16% in cancer subtype prediction in different scenarios, supporting our hypothesis. Our results also showed that GAT embeddings provide a better prognosis in differentiating the high-risk group from the low-risk group than raw features.
Historical turtle population decline and community changes in an Ozark River

Turtle populations are declining worldwide yet few long term studies exist to confirm this trend. Mark-recapture data collected in 1969 and 1980 exist for the turtle community inhabiting a 4.6 km section of the North Fork of White River, Ozark County, Missouri. Using the available data, we compared the turtle communities and common map turtle (Graptemys geographica) populations observed in the research section in 1969 and 1980. Community composition changes indicated that red-eared sliders (Trachemys scripta elegans), a native species that was not observed in the research section in 1969, became established in the research section by 1980. Population estimates for G. geographica indicated that the population declined significantly (N1969 = 274, N1980 = 139, z = 3.39, P < 0.001) between 1969 and 1980 and the decline was associated with a marked decrease in the number of large adult females. The loss of large adult female G. geographica suggested the decline may have been a result of targeted harvest for the food trade as females of this species attain much larger body sizes and would therefore be preferred for the food trade. The results of this study elucidate changes that occurred in a turtle community and provide a historical baseline for comparison with future studies of this community.
Relative Importance of Water‐Quality Stressors in Predicting Fish Community Responses in Midwestern Streams

Fish, habitat, and water chemistry data were collected from 98 streams in the midwestern United States, an area dominated by intense cultivation of row crops, in order to identify important water‐quality stressors to fish communities. We focused on 10 stressors including riparian disturbance, riparian vegetative cover, instream fish cover, streambed sedimentation, streamflow variability, total nitrogen, total phosphorus, minimum dissolved oxygen, pesticides, and bed sediment contaminants. Fish community response variables included a measure of observed/expected taxonomic completeness; species‐specific tolerances to nitrogen, phosphorus, dissolved oxygen, and water temperature; the percent of species classified as macrohabitat generalists; and an index of pesticide toxicity to fish. Multivariate analysis indicated that total nitrogen was the most important stressor, signifying that fish communities were responding to total nitrogen despite relatively high levels common to an agricultural setting. Individually, fish taxonomic completeness decreased with increasing streambed sedimentation, whereas fish community tolerance to total phosphorus increased with increasing streambed sedimentation, riparian disturbance, and total nitrogen. These findings underscore the importance of multiple biological response metrics to better understand the effects of water‐quality stressors on fish communities and highlight the complex relations between total phosphorus and fish communities.
Underactuated Surface Vessels Trajectory Tracking Control Based on Concise Finite-Time Disturbance Observer Under False Data Injection Attacks

This paper studies the trajectory tracking control problem of underactuated surface vessels (USVs) under false data injection attacks (FDIAs). We propose a concise nonlinear finite-time disturbance observer (FTDO) to compensate composite disturbances caused by FDIAs and external disturbances. An online approximator is designed using adaptive techniques to compensate for the dynamic uncertainties of the system. In addition, we consider the limit of input saturation at the control input. Under the framework of the backstepping method, this paper designs a practical finite-time control (FTC) method and performs a rigorous theoretical analysis of it through the Lyapunov stability theory. Finally, the effectiveness of the proposed control scheme was verified through simulation. This research provides a feasible solution to the trajectory tracking problem of underactuated vessels in FDIAs environment and provides theoretical support for further research in related fields.
Integrating Puzzles and STAD Model in Teaching Angle Measurement to Elementary School Third Graders: An Action Research Study

The purpose of this study was to improve the process (student activity and response) and learning outcomes of mathematics in the application of the STAD type cooperative learning model with puzzle media on the material for measuring angles for grade 3 students. Puzzle learning media applied in grade 3 about angles included sorting large sizes. angles, recognize and make the types of angles. This study uses classroom action research through at least two cycles to determine the improvement. If there is no improvement, then proceed to the third cycle and so on, until there is an increase. For data analysis using the percentage of classical completeness of the average percentage of observations. The results showed that the percentage of classical student activity in the first cycle was 63.13% with moderate success and 85.27% in the second cycle classical student activity with high success. The results of student responses using interviews in cycle I and cycle II both received 9 positive comments and 1 negative comment. This is a lot of students who give positive comments so that the response is said to be good. Meanwhile, the learning outcomes test in the first cycle with the classical percentage of 57.1% and in the second cycle as many as 29 students who completed the classical percentage 82.8% with an increase of 25.7% from the previous cycle. The conclusion that can be drawn from the research results is an increase in the percentage of student activity, student responses and learning outcomes.
Fe-Ti-V-(P) resources in the Upper Zone of the Bushveld Complex, South Africa

The Bushveld Complex in South Africa is the largest layered intrusion on Earth. Its upper part is known for huge resources of iron, titanium, vanadium and phosphorus. Associated with the layered character of the rocks, these resources are enriched at certain levels of the intrusion, which makes it important to understand the formation processes of those layers. In this paper we give an introduction and overview of recent debates and challenges.
Ovarian Cancer Detection Based on Elman Recurrent Neural Network

The early detection of cancers increases the possibility of health recovery and prevents the disease from becoming a silent killer. This study introduces an effective method for identifying ovarian cancer (OC) using Elman Recurrent Neural Network (ERNN), which can recognize cancer via mass spectrometry data. The network has a topology of 100 input neurons for receiving data, five neurons for hidden and context layers, and two output nodes to indicate the status. The proposed method uses reduced-size features, including ion concentration levels at specific mass/charge values, which are trained using various learning algorithms to determine the suitable one that achieves the best results. The experimental results show that all the training algorithms achieve about 100% performance rate, with the Levenberg Marquardt (LM) being the most accurate and fastest algorithm, which converges after six epochs and achieves 0.0035, 0.0045 and 0.0045 mean square errors for training, validation, and test performances, respectively. Based on comparative results, the proposed LM-ERNN method outperforms other OC detection methods and holds promise for detecting other types of cancer.
The effectiveness of canal blocking for hydrological restoration in tropical peatland

The Peatland Restoration Agency of the Republic of Indonesia (BRG-RI), an agency that is mandated to restore 2 million hectares of degraded peatland by 2020, has developed a 3-R approach towards tackling the problem based on the program of rewetting, revegetation, and revitalization of livelihood for the peatlands restoration in Indonesia. The Rewetting program that aims to rehabilitate hydrologically a peatland to a near natural state is carried out by canal blocking, canal backfilling, and construction of deep wells. To know the progress of the restoration activities by BRG, it is very important to understand the effectiveness of canal blocking on rewetting of the tropical peatland. The effectiveness of canal blocking was investigated through the monitoring of groundwater level (GWL) fluctuation around the canal block. This study was carried out at a canal block that is located at the peatland of Sungai Tohor Village, Kepulauan Meranti Regency, Riau Province. For monitoring of GWL fluctuation as the impact of canal blocking, five dipwells were set at the peatland that are perpendicular to the canal with the distance of 20 m, 70 m, 120 m, 170 m, and 220 m respectively. The results of this study show that the impact of canal blocking could raise the water table in the peatland at the radius of about 170 m from the canal. The radius impact of the re-wetting might be bigger or smaller, that strongly depends on the hydrotopography situation of the area.
Non-Suicidal Self-Injury (NSSI) Patterns in Adolescents from a Romanian Child Psychiatry Inpatient Clinic

Non-Suicidal Self-Injury (NSSI) involves deliberately causing harm to one’s body without the intention of suicide. As the numbers of adolescents presenting NSSI have been steadily increasing during the last years, we intended to investigate adolescent patients exhibiting NSSI, admitted to our clinic—a Romanian child psychiatry inpatient clinic, over the course of five years. A total of 100 adolescents (80 females, 20 males, mean age: 14.9 years) hospitalized for various neuropsychiatric disorders and engaging in self-harm were studied. The self-harm methods most frequently used in our sample were, for the female group: cutting (all), skin tearing (76%), scratching/pinching (72%), and for males: cutting (all), wound-healing hindrance (85%), striking objects (80%). The initial motivations for NSSI were represented by distress (females 89%, males 90%) and seeking pleasure (females 84%). In terms of the roles of NSSI, it was primarily used for emotional regulation (females 89%) and anger management (males 90%). This study highlights the prevalence of self-harm in hospitalized adolescents, differences in methods and motivations between genders, and the need for more targeted therapy interventions. By documenting trends, investigating underlying motivations and functions, and proposing hypotheses for further research, our findings offer valuable insights on adolescent NSSI and have the potential to increase awareness among various clinicians and specialists who interact with adolescents, thus addressing the escalating prevalence of self-harm behaviours among teenagers.
Revisiting the Northern Mode of East Asian Winter Monsoon Variation and Its Response to Global Warming

This study revisits the northern mode of East Asian winter monsoon (EAWM) variation and investigates its response to global warming based on the ERA dataset and outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. Results show that the observed variation in East Asian surface air temperature (EAT) is tightly coupled with sea level pressure variation in the expanded Siberian high (SH) region during boreal winter. The first singular value decomposition (SVD) mode of the EAT and SH explains 95% of the squared covariance in observations from 1961 to 2005, which actually represents the northern mode of EAWM variation. Meanwhile, the first SVD mode of the EAT and SH is verified to be equivalent to the first empirical orthogonal function mode (EOF1) of the EAT and SH, respectively. Since the leading mode of the temperature variation is significantly influenced by radiative forcing in a rapidly warming climate, for reliable projection of long-term changes in the northern mode of the EAWM, we further employ the EOF1 mode of the SH to represent the northern mode of EAWM variation. The models can well reproduce this coupling between the EAT and SH in historical simulations. Meanwhile, a robust weakening of the northern mode of the EAWM is found in the RCP4.5 scenario, and with stronger warming in the RCP8.5 scenario, the weakening of the EAWM is more pronounced. It is found that the weakening of the northern mode of the EAWM can contribute 6.7% and 9.4% of the warming trend in northern East Asian temperature under the RCP4.5 and RCP8.5 scenarios, respectively.
Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases

It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.
Enzalutamide: an evidence-based review of its use in the treatment of prostate cancer

demonstrated impressive efficacy in men with metastatic CRPC, moving swiftly from a phase I/II study to two pivotal phase III trials testing this agent in both chemotherapy-pretreated as well as chemotherapy-naïve CRPC patients. Ongoing studies are aiming to explore the utility of enzalutamide in earlier stages of the disease, and to investigate the optimal sequencing and combination of enzalutamide with other standard and novel therapies for prostate cancer. Disease-oriented evidence Preclinical studies in castration-resistant prostate cancer cell lines and mouse xenograft models, including preclinical models harboring androgen receptor amplification. Enzalutamide mediates its activity by potently antagonizing the androgen receptor, inhibits nuclear translocation of the androgen receptor, and impairs binding of the androgen receptor to promoter areas of androgen-regulated genes. of Alpharadin in Patients with Symptomatic Hormone Prostate Cancer with Skeletal Metastases; iMPACT, COU-AA-301, Abiraterone Acetate in Castration-Resistant Prostate Cancer Treated with Docetaxel-Based Chemotherapy; COU-AA-302, Abiraterone Acetate in Asymptomatic or Mildly Symptomatic Patients with Metastatic Castration-Resistant Prostate Cancer; mCRPC, metastatic castration-resistant prostate cancer; TAX 327, Docetaxel plus Prednisone or Mitoxantrone plus Prednisone for Advanced Prostate Cancer; TROPiC, Treatment of Hormone-Refractory Metastatic Prostate Cancer Previously Treated with a Taxotere-Containing Regimen.
MMI partial extraction geochemistry for the resolution of anthropogenic activities across the archaeological Roman town of Calleva Atrebatum

Sixty three soils samples, fourteen samples of previously excavated archaeological material, and five background soil samples taken at the Silchester Roman Town of Calleva Atrebatum in the County of Hampshire, United Kingdom were analysed by the Mobile Metal Ion (MMI) method for a total of fifty three elements. Samples from within the town walls showed considerably higher concentrations than samples outside for many elements; Au, Ag, Cu and Sn were in extremely anomalous concentrations, Bi, Cd, Hg, Mo, P and Pb were anomalous and Sb and Zn in elevated concentrations. The overall pattern of element distribution is one of an annulus of higher elemental concentrations surrounding a centre of generally lower values centred on the previously excavated Forum basilica. The elements Zr, Ti, Th, Ti, Tl, Nb, Sn, Sc, Cr, Co, Sb, Bi, Ce, Nd (and all other REEs), show similar distributions to one another, and their distribution and that of the noble and base metals, as highlighted by various additive indices, is considered to be the result of metallurgical processing on site. The low values for most elements around the Forum basilica are the result of disturbance of the soil geochemical profile in this area by previous archaeological excavation.