Miracle-dz/UniFAR-FTUs huggingface dataset:
Antineutrophil Cytoplasmic Antibody-Associated Vasculitis Update: Genetic Pathogenesis

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is characterized by the inflammation of small and medium vessels and presence of proteinase 3-ANCA or myeloperoxidase-ANCA in the circulation. AAV comprises three clinical subtypes: granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic GPA (EGPA). Although the pathogenesis of AAV is still unclear, genetic and environmental factors and the immune system are thought to be involved. Genetic factors have been confirmed to play an important role in AAV. Genome-wide association studies have identified numerous genetic variants in MHC and non-MHC regions associated with AAV. The strongest evidence of MHC association in AAV is human leukocyte antigen (HLA)-DP. A significant association between AAV and genetic variations in non-MHC regions, such as CTLA-4, FCGR2A, PTPN22, SERPINA1, and TLR9 has also been found. Moreover, different clinical subtypes of AAV have distinct genetic backgrounds. GPA is associated with HLA-DP1, MPA with HLA-DQ, and EGPA with HLA-DRB4. These findings could help elucidate the etiology of AAV and develop new biomarkers for diagnosis and targeted therapy. Herein, we briefly summarize the updates on the genetic pathogenesis and biomarkers of AAV.
Advances in Rodent Models for Breast Cancer Formation, Progression, and Therapeutic Testing

Breast cancer is a highly complicated disease. Advancement in the treatment and prevention of breast cancer lies in elucidation of the mechanism of carcinogenesis and progression. Rodent models of breast cancer have developed into premier tools for investigating the mechanisms and genetic pathways in breast cancer progression and metastasis and for developing and evaluating clinical therapeutics. Every rodent model has advantages and disadvantages, and the selection of appropriate rodent models with which to investigate breast cancer is a key decision in research. Design of a suitable rodent model for a specific research purpose is based on the integration of the advantages and disadvantages of different models. Our purpose in writing this review is to elaborate on various rodent models for breast cancer formation, progression, and therapeutic testing.
Development of a Novel Prognostic Model for Predicting Lymph Node Metastasis in Early Colorectal Cancer: Analysis Based on the Surveillance, Epidemiology, and End Results Database

Background Identification of a simplified prediction model for lymph node metastasis (LNM) for patients with early colorectal cancer (CRC) is urgently needed to determine treatment and follow-up strategies. Therefore, in this study, we aimed to develop an accurate predictive model for LNM in early CRC. Methods We analyzed data from the 2004-2016 Surveillance Epidemiology and End Results database to develop and validate prediction models for LNM. Seven models, namely, logistic regression, XGBoost, k-nearest neighbors, classification and regression trees model, support vector machines, neural network, and random forest (RF) models, were used. Results A total of 26,733 patients with a diagnosis of early CRC (T1) were analyzed. The models included 8 independent prognostic variables; age at diagnosis, sex, race, primary site, histologic type, tumor grade, and, tumor size. LNM was significantly more frequent in patients with larger tumors, women, younger patients, and patients with more poorly differentiated tumor. The RF model showed the best predictive performance in comparison to the other method, achieving an accuracy of 96.0%, a sensitivity of 99.7%, a specificity of 92.9%, and an area under the curve of 0.991. Tumor size is the most important features in predicting LNM in early CRC. Conclusion We established a simplified reproducible predictive model for LNM in early CRC that could be used to guide treatment decisions. These findings warrant further confirmation in large prospective clinical trials.
Kinetics, Moderators and Reference Limits of Exercise-Induced Elevation of Cardiac Troponin T in Athletes: A Systematic Review and Meta-Analysis

Background: Kinetics, moderators and reference limits for exercise-induced cardiac troponin T (cTnT) elevations are still unclear. Methods: A systematic review of published literature was conducted adhering to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Studies reporting high-sensitivity cardiac troponin T (hs-cTnT) concentrations before and after a bout of exercise in athletes were included and analyzed. The final dataset consisted of 62 estimates from 16 bouts in 13 studies of 5–1,002 athletes (1,421 in total). Meta-analysis was performed using general linear mixed modeling and Bayesian inferences about effect magnitudes. Modifying fixed-effect moderators of gender, age, baseline level, exercise duration, intensity and modalities were investigated. Simulation was used to derive 99th percentile with 95% limits of upper reference ranges for hs-cTnT of athletic populations. Results: The mean and upper reference limits of hs-cTnT before exercise were 4.4 and 19 ng.L−1. Clear increases in hs-cTnT ranging from large to very large (factor changes of 2.1–7.5, 90% compatibility limits, ×/÷1.3) were evident from 0.7 through 25 h, peaking at 2.9 h after the midpoint of a 2.5-h bout of running, when the mean and upper reference limit for hs-cTnT were 33 and 390 ng L−1. A four-fold increase in exercise duration produced a large clear increase (2.4, ×/÷1.7) in post-exercise hs-cTnT. Rowing exercise demonstrated an extremely large clear reduction (0.1 ×/÷2.4). Conclusions: The kinetics of cTnT elevation following exercise, the positive effect of exercise duration, the impact of exercise modality and 99th upper reference limits for athletic populations were reasonably well defined by this meta-analysis.
Partition satellite derived bathymetry for coral reefs based on spatial residual information

ABSTRACT Satellite imagery with high spatial and temporal resolutions provides strong support for remote sensing bathymetry. Heterogeneous underwater environments such as complex bottom types represent the main factor affecting accuracy of satellite derived bathymetry. Segmenting imagery can enhance geospatial adaptability and improve the accuracy. Conventionally, satellite imagery can be subdivided based on bottom types confirmed by a priori knowledge gained from in situ observations (e.g. video and photos); however, this process poses challenges concerning some politically sensitive or unreachable areas. Besides, water depth in coral reef areas is generally shallow (depth range: 0 to 5 m) and complex, and it has proved difficult to improve bathymetric mapping accuracy with passive multispectral imagery in such areas using traditional models. With the spatial distribution of the residuals of discrete points to realize regional segmentation, this study developed an effective partition model for satellite derived bathymetry without the need for a priori knowledge of bottom types. WorldView-2 high resolution (2 m) imagery of Dongdao Island (China) was used in the study. The results of a practical application of the model revealed the following. (1) The developed partition bathymetry model reflected underwater topographical changes and features more clearly than a comparative global model, especially in certain areas of complex terrain. (2) The accuracy of the residual partition model (Mean Absolute Error (MAE): 0.96 m, Mean Relative Error (MRE): 11.04%, and Root Mean Square Error (RMSE): 1.36 m) is greater than the global model and the conventional partition model. (3) In different water depth intervals, the residual partition model has always the best accuracy. Subregion models corresponding to the proposed models are more stable and more accurate than those to the conventional partition model. In areas of shallow water (depth: 0 to 5 m), the MRE of a subregion-A model using the residual partition method is 14.09%, i.e. better than the global model (19.45%). The findings demonstrate that the developed model can largely overcome the problem of poor precision in shallow water associated with conventional models.
A new checklist of the bryophytes of Britain and Ireland, 2020

ABSTRACT Introduction. Many taxa have been added to the bryophyte flora of Britain and Ireland since the publication of the previous checklist in 2008. This has coincided with numerous taxonomic and nomenclatural changes in the European flora, especially as a consequence of molecular sequencing. With the publication of a new European checklist in 2020 it is now appropriate to provide an updated and annotated list for Britain and Ireland. Methods. A working group was convened to consider and reach a consensus on taxonomic and nomenclatural matters arising from the European checklist. Special consideration was given to the citation of the authors of names. Taxa reported as new to Britain and Ireland since the previous checklist in 2008 were incorporated. Key results. A new checklist of bryophytes (hornworts, liverworts and mosses) is presented. Based on the taxonomy adopted in the checklist, the bryophyte flora of Britain and Ireland comprises four hornworts (Anthocerotophyta), 299 liverworts (Marchantiophyta), and 795 mosses (Bryophyta) at species rank, with 42 additional infraspecific taxa.
Tracking historical chlorophyll-a change in the guanting reservoir, Northern China, based on landsat series inter-sensor normalization

ABSTRACT The Guanting Reservoir supplied drinking water to Beijing until 1997, following which the water quality of the reservoir deteriorated. The chlorophyll-a concentration (C chl-a ) of water is an important indicator of eutrophication. Therefore, changes in the C chl-a of the Guanting Reservoir should be monitored and analysed. For more than 30 years, the monitoring of C chl-a in inland waterbodies has only been possible using the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI). However, there are data consistency problems in monitoring C chl-a using these sensors. To address this issue, this study utilized inter-sensor normalization models of the different sensors and a unified C chl-a estimation model for Landsat series data with a long time span. After inter-sensor normalization, the mean relative error (MRE) of remote sensing reflectance (R rs) between OLI and TM/ETM+ was corrected to 1–5%. The unified model of C chl-a estimation employed the normalized ratio index of blue R B and near-infrared R NIR remote sensing reflectance: (R NIR – R B): (R NIR + R B). The MRE for estimating C chl-a was 25.7% and the root-mean-square error (RMSE) was 5.65 mg m–3. The C chl-a of the Guanting Reservoir was then estimated for years between 1985 and 2019. During this time, C chl-a had a distinct seasonal distribution trend; the annual changes go through four stages, and each period shows different change characteristics. This study is the first to systematically understand the history of chlorophyll-a changes in the Guanting Reservoir over the past 30 years and provides references data for its eutrophication process and management.
Antecedents and Consequences of Perceived Fit of an Interactive Digital Textbook

Recent research suggests that the fit between a digital textbook and the learning tasks students perform with the textbook has a significant role in determining the outcomes of usage. An empirical assessment was performed on the impacts of six antecedents on perceived fit and impacts of perceived fit on utilization, satisfaction, and perceived learning performance for an interactive digital textbook used in an introductory information systems course. Analysis of survey data collected from 253 university students indicates that interactivity, reading comfort, ease of use, and searchability contribute to the perceived fit of the digital textbook. The results also confirmed perceived fit to be a significant predictor of utilization, satisfaction, and perceived learning performance. These findings suggest that students value the digital textbook as an interactive, online learning tool. To achieve better learning outcomes, university instructors should select digital textbooks that are interactive, comfortable on the eyes, easy to learn and use, and easy to search.
Effects of Structure, Temperature, and Strain Rate on Mechanical Properties of SiGe Nanotubes

The effects of structure, temperature, and strain rate on mechanical properties of all the SiGe nanotubes in armchair and zigzag structures (n = 4−13) in two atomic arrangement types are investigated by classical molecular dynamics simulation. During the extending tests, we observe three structural transformations from initial structure, tensile structure, to critical structure deformation. The simulation results indicate that the Young’s modulus of nanotubes is closely dependent on their diameter, chirality, and arrangement structure. The type 1 (alternating atom arrangement type) armchair SiGe nanotube exhibits the largest Young’s modulus, compared with other nanotubes with the same index n. By exploring the effects of temperature and strain rate on mechanical properties of SiGe nanotubes, it is found that the higher temperature and lower strain rate lead to the lower critical strain and tensile strength. Furthermore, it is also found that the critical strains for both armchair and zigzag nanotubes in t.
Density functional theory for nonuniform polymers: Accurate treatment of the effect of attractive interactions

A density functional theory is presented for the effect of fluid–fluid and fluid–surface attractive interactions on the structure of polymers at surfaces. The theory treats the ideal gas free-energy functional exactly and uses a weighted density approximation for the hard chain contribution to the excess free-energy functional. The attractive interactions are calculated using the bulk fluid direct correlation function obtained from the polymer reference interaction site model theory. The predictions of the theory are in good agreement with computer simulation results for the density profiles of freely rotating fused-sphere chains at surfaces for a wide range of densities and temperatures. The results emphasize the importance of using different approximations for the hard sphere and attractive interactions in density functional theories for polymers.
Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.
Trajectory Data Restoring: A Way of Visual Analysis of Vessel Identity Base on OPTICS

Automatic identification system (AIS) data is a significant analysis and decision-making basis for maritime situational awareness. Because of particular navigation environment and the vulnerability of AIS equipment onboard, results in the phenomenon that numerous vessels share the same Maritime Mobile Service Identity (MMSI) in the AIS data collected in ocean and inland waterway. This kind of mixed trajectory information dramatically affects the judgement of the maritime manager and supervisors. In this paper, the visual analytics combined with the algorithm named Ordering Points to Identify the Clustering Structure (OPTICS) is adopted to realize the separation of vessels sharing same MMSI, which can help analysts to recognize the vessel trajectory information and assess the risk of marine traffic correctly. Firstly, this paper illustrates the application of OPTICS clustering method based Journal of Web Engineering, Vol. 20 2, 413–430. doi: 10.13052/jwe1540-9589.2028 © 2021 River Publishers
Assessment Measures in Game-based Learning Research

The use of games in educational contexts is well documented in GBL research. Nevertheless, effectiveness evidence needs to be more extensively analyzed. An effective GBL strategy should address the learning aspects and promote players' engagement in an easy-to-use system. To gather the information already present in literature, we sought to answer how learning, engagement, and usability of games are evaluated in GBL research. We conducted a systematic review of empirical studies in ERIC, IEEE, Springer, and Web of Science databases. We included 91 studies for the final analysis and categorized their measures and instruments. We find a prevalence of learning assessments over engagement and usability assessments. Learning is mainly evaluated by direct measures, while indirect measures mostly assess engagement and usability. The use of indirect measures and instruments without psychometric qualities compromises the strength of the evidence for the effectiveness of game-based learning. Future studies should add direct assessments and indirect measures with psychometric qualities to assess engagement and usability. The study’s limitations are discussed.
p-Hydroxyphenyl:Guaiacyl:Syringyl Ratio of Lignin in Some Austral Hardwoods Estimated by CuO-Oxidation and Solid-State NMR

The white-rot fungus Canoderma australe causes extensive decay of hardwood in Chilean rain forest, producing a delignified material used äs cattle feed. Since lignin composition can influence the delignification efficiency, it was investigated by CuO alkaline degradation and solid-state NMR of wood. The lignin syringyl/guaiacyl ratio estimated by both techniques prescnted high values in the two woods, i.e., Eucryphia cordifolia and Nothofagus dombeyi, which showed the most selective delignification and the highest digestibility after fungal decay. On the other hand, a reduced S/G ratio was found in the Aextoxicon punctatum wood, which presented a low biodegradability. An additional peculiarity of the two former hardwoods is the relatively high yield of non-cinnamic p-hydroxyphenyl compounds after CuO alkaline degradation.
Controlled wettability of quartz surfaces

The wettability of flat quartz-crystal surfaces has been assessed by measurement at 25 °C of contact angle at the water-vapour/water-drop/quartz-plate three-phase line. Plates pretreated by heating in vacuum gave angles, measured through the drop 1 min after removal from vacuum, of 0–44 ° as pretreatment temperature was increased from 200 to 1000 °C. Fully hydroxylated, therefore hydrophilic, quartz surfaces are progressively rendered hydrophobic by mutual condensation of surface hydroxyls to form siloxane bridges. Hysteresis was at a maximum after heating at 700–800 °C, indicating that maximum surface chemical heterogeneity was produced by heating in this temperature range. Plates methylated subsequent to heat treatment gave angles that were constant at ca. 80° up to treatment at 600 °C and that decreased from 80 to ca. 47 ° as the pretreatment temperature was further increased to 1000 °C. This variation with temperature is consistent with a mechanism for methylation in which only non-hydrogen-bonded surface hydroxyl groups on quartz are reactive towards the methylating reagent. Contact angles on both heat-treated and methylated plates were observed to decrease following extended exposure to water vapour.
Redox processes in silicon dioxide thin films using copper microelectrodes

Although SiO2 is a typical insulator, we demonstrate an electrochemical characteristic of the Cu/Cu+ oxidation at the interface with 30 nm thick silicon dioxide thin films studied by cyclic voltammetry. This study reveals the process of anodic oxidation and subsequent reduction of oxidized Cu ions injected in the SiO2 layer with special attention to the kinetics of the redox process. We estimated the diffusion coefficient and the mobility of Cu ions in SiO2. The results gain deeper insight in the processes involved during resistive switching of Cu/SiO2 based nonvolatile memory devices.
Spray Combustion Modeling in Lean Direct Injection Combustors, Part II: Multi-Point LDI

The multi-point lean direct injection (MPLDI) strategy, which incorporates several lean direct injection (LDI) modules, has been developed for low-emission aero-propulsion systems. There is a lack of information on MPLDI flow physics and spray characteristics. In extending previous work, this article investigates the reacting spray characteristics of an MPLDI system and makes an effort to highlight the salient features of this system, which enable low NOx combustion. The simulation approach and the numerical models applied and validated for the single-element LDI are used to compute the combusting spray in MPLDI. Simulations are performed under different inlet flow conditions to study the spray properties and combustion due to multi-swirler injection. The results illustrate small-sized drops and uniform distribution of drop diameter with increasing swirl, resulting in rapid fuel vaporization and mixing. In comparison with the single-element LDI, MPLDI creates smaller reaction zones and accomplishes a more uniform drop diameter distribution and a thorough mixing of fully vaporized fuel and air at a shorter distance from the fuel nozzles. Overall, the numerical results are able to explain the underlying features of the MPLDI technique, which primarily influence its NOx reduction performance.
Direct growth of monodisperse SnO2 nanorods on graphene as high capacity anode materials for lithium ion batteries

We developed a facile one-step hydrothermal procedure to prepare hybrid materials of SnO2 nanorods on graphene sheets (GS). Composites with individual SnO2 nanorods of 10–20 nm in diameter and 100–200 nm in length show a high reversible specific capacity and outstanding cycling stability (710 mAh g−1) as anode materials for lithium ion batteries. Owing to the enhanced lithium storage properties, the SnO2/GS hybrid could be a promising candidate material for a high-capacity, low cost and environmentally friendly anode for lithium ion batteries (LIBs). This one-step hydrothermal procedure provides a facile technique for the design and morphology control of nanocrystals on GS and will be a versatile route in producing metal oxide/GS composites.