Density Patterns Research Articles

TetraDENSITY 2.0—A Database of Population Density Estimates in Tetrapods

ABSTRACTMotivationPopulation density is a fundamental parameter in ecology and conservation, and taxonomic and geographic patterns of population density have been an important focus of macroecological research. However, population density data are time‐consuming and costly to collect, so their availability is limited. Leveraging decades of research, TetraDENSITY 1.0 was developed as a global repository containing over 18,000 population density estimates for > 2100 terrestrial vertebrate species, aiding researchers in investigating patterns of population density, its intrinsic and extrinsic drivers, and for developing predictive models. Here we present a substantially expanded version of the database, which now includes marine tetrapods and encompasses over 54,300 estimates for 3717 species associated with error estimates and geographical coordinates when available, hence enabling meta‐analytical approaches and better spatial matching of estimates with environmental conditions.Main Types of Variables ContainedPopulation density estimates and associated errors, time and location of data collection, taxonomic information, estimation method.Spatial LocationGlobal.Time Period and Grain1925–2024.Major Taxa and Level of MeasurementAmphibia, Reptilia, Aves and Mammalia. Estimates reported at the population level.Software Format.csv.

Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning vs. deep learning.

Virtual reality (VR) simulates real-life events and scenarios, widely used in education, entertainment, and medicine. VR can be presented in two or three dimensions (2D or 3D), and 3D VR produces a more realistic and immersive experience. Previous research has revealed that the electroencephalogram (EEG) induced by 3D VR has a different profile from that of 2D VR, manifesting in many aspects, such as the power of brain rhythm, brain activation, and brain functional connectivity. However, studies on how to classify EEG in 2D and 3D VR were limited. 64-channel EEG was recorded, while visual stimuli were given in 2D and 3D VR. The classification of these recorded EEG signals was done using two machine learning methods: the traditional method and the deep learning method. In the traditional machine learning classification, EEG features of power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classification algorithms, support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF), were used. A specialized convolutional neural network, EEGNet, was used in the deep learning classification. These classification approaches were compared with respect to their classification performance. In aspects of four performance evaluations for classification, accuracy, precision, recall, and F1-score, respectively, classification using the deep learning method is better than the traditional machine learning approaches. Classification accuracy with deep learning with EEGNet could reach up to 97.86%. The classification performance of 2D and 3D VR-induced EEG can be achieved with EEGNet-based deep learning, outperforming conventional machine learning approaches. Given the role of EEGNet, which is designed for EEG-based brain-computer interfaces (BCI), better performance classification of EEG in 2D and 3D VR environments might be predicted to be helpful for the application of 3D VR in BCI.

Clinical relevance and frequency of cytoplasmic patterns observed in ANA-Hep-2: experience of Cairo University Hospitals.

Antinuclear antibodies (ANA) are the most common biomarkers observed in autoimmune diseases. Cytoplasmic staining patterns on ANA-Hep-2 are gaining recognition but with scanty information about their clinical and diagnostic role. The aim is to assess the frequency of cytoplasmic ANA patterns in autoimmune diseases, and to evaluate their possible associations with clinical diagnoses and autoantibodies. This observational cross-sectional study was conducted by examining and/or reviewing ANA by indirect immunofluorescence assay during a 13-month period. This was followed by testing the group of patients with a positive cytoplasmic staining pattern (n = 92) using the Microblot-Array ANA plus for the presence of 44 specific autoantibodies. Out of 2741 samples, 1791 (65.3%) tested negative, 845 (30.9%) tested positive nuclear staining patterns, 56 (2.0%) positive solitary cytoplasmic staining patterns, and 49 (1.8%) positive mixed nuclear and cytoplasmic patterns. Ninety-two cases (3.4% of the total cases) were analyzed using Microblot-Array ANA plus, with reticular as the most frequent cytoplasmic pattern, followed by dense fine speckled. The most frequently associated disease with reticular pattern was primary biliary cholangitis (28.9%), and the most frequently detected autoantibodies were against M2 (66.7%). The most frequently associated disease with dense fine speckled pattern was systemic lupus erythematosus (69.4%), and the most frequently detected autoantibodies were against nucleosome (57.7%) and ribosomal P0 (53.8%). This study highlights the significance of reporting cytoplasmic staining patterns and their importance in assessment of autoimmune diseases. Larger cohort studies on treatment naïve patients are recommended.

Gastric Epithelial Neoplasm of Fundic-Gland Mucosa Lineage: Showed the Same Genetic Lineage and Ki67 May Help in Their Identification

Abstract Introduction/Objective Gastric epithelial neoplasm of the fundic-gland mucosa lineages (GEN-FGMLs) are rare form of gastric tumors that encompasses oxyntic gland adenoma (OGA), gastric adenocarcinoma of the fundic-gland type (GA-FG), and gastric adenocarcinoma of the fundic-gland mucosa type (GA-FGM). There is no consensus on the clinicopathologica and molecular characteristics of GEN-FGMLs, and misdiagnosis is common because of similarities in symptoms. Methods/Case Report The NGS and immunohistochemical staining were performed on 37 cases diagnosed with GEN-FGMLs with a thorough histological evaluation. Gene alternations were analysed and compared across histological subtypes. we used an artificial intelligence approach for the recognition of the Ki67 immunohistochemistry stained. Results (if a Case Study enter NA) The patient’s ages ranged from 42 to 79 years, with a median age of 60. 17 were male and 20 were female. Morphologically, 19 OGAs, 16 GA-FGs, and two GA-FGMs were identified. Histopathological similarities exist between GEN-FGMLs. The tumors demonstrated well-formed glands, expanding with dense growth patterns comprising pale, blue-grey columnar cells with mild nuclear atypia. The normal gastric pit epithelium covered the entire surface of the OGA and GA-FG, but the dysplasia pit epithelium covered the GA-FGM. Compared with OGA, GA-FG and GA-FGM were more prominent in the macroscopic view (p<0.05) and had larger sizes (p<0.0001). Additionally, GA-FG and GA-FGM exhibited higher Ki67 indices than OGA (p<0.0001). The heterogeneity of mutational signatures, somatic mutations, and copy number variations was revealed across histological subtypes. The top five mutational signatures in OGA were ROS1, CTNNA1, GNAS, BRCA2, and ZFHX3. The top five mutational signatures in GA-FG and GA-FGM were GNAS, MLL3, BRAF, MSH3, and EGFR. GNAS were both found in OGA, GA-FG and GA-FGM. Compared with well-differencent gastric adenocarcinoma, The top five mutational signatures in GEN-FGMLs were GNAS, BRAF, ROS1, MLL3, and MSH3. Recurrently mutated key signaling pathways across tumor types in GEN-FGML were Wnt signaling pathways. Conclusion GEN-FGMLs are a group of well-differentiated gastric tumors with favourable biological behaviours. Compared with OGA, GA-FG and GA-FGM have larger sizes and higher Ki67 proliferation indices, indicating that they play a critical role in the identification of GEN-FGML. GEN-FGMLs showed the same genetic lineage, the Wnt signalling pathway plays a role in the development and progression of GEN-FGMLs.

Generation of patterns and higher harmonics in 1D quantum droplets in tilted and driven quasi-periodic confinements

The generation of patterns by breaking the spatial symmetry in external confinement is a captivating area of physics. The emergence of patterns is a fundamental inquiry spanning various disciplines such as nonlinear optics, condensed matter physics, and fluid dynamics. The article investigates the generation of a variety of patterns in a one-dimensional binary mixture of Bose–Einstein condensate forming quantum droplets. By solving the extended Gross–Pitaevskii equation in the presence of tilted and driven engineered bi-chromatic optical lattices (BOL), the out-of-equilibrium dynamics of droplets under strong dc and ac fields are illustrated. Under the influence of a dc field, a stripe-like pattern emerges in the temporal domain, while the scenario with ac fields demonstrates temporal periodic and bi-periodic oscillations of density waves. The width and period of formed patterns are directly correlated with the strength of ac and dc fields. Moreover, temporal modulation of the BOL potential depth yields various harmonics in the oscillations of the condensate density pattern. Through Fast Fourier Transform (FFT) analysis, it is confirmed that these harmonics encompass multiple and combinational frequencies, suggesting potential applications in generating desired frequency combs within quantum droplets. We have also carried out a thorough numerical stability check of the obtained solutions and found them sufficiently stable.

Seasonal Distribution of Key Small-Sized Fish in the South Inshore of Zhejiang, China

Small-sized fish are a vital food source for large predatory commercial fish and play a key role in marine food webs, bridging lower and higher trophic levels. They are indispensable in maintaining the energy flow and material cycling within aquatic ecosystems. This study utilized bottom-trawl survey data from 2017 to 2020 along the south inshore of Zhejiang, China, complemented by concurrent environmental data, to examine the influence of environmental factors on the resource density and seasonal distribution patterns of four dominant small-sized fish species. The research findings indicated that SSH (sea surface height) and Chl (chlorophyll-a concentration) emerged as the key environmental factors influencing resource densities, with all four species exhibiting similar preferences toward these variables. However, significant disparities were observed in their preferences for SST (sea surface temperature), SSS (sea surface salinity), and DO (dissolved oxygen). The various species’ resource density and distribution patterns underwent significant seasonal variations. Additionally, the seasons and regions with the highest resource densities consistently aligned, occurring predominantly in autumn within the northern waters of the study area. This research further elucidated the environmental predilections and seasonal spatial distribution traits of small-sized fish in the south inshore of Zhejiang, an important feeding ground for economic fish species in the East China Sea. This provides scientific backing for forecasting alterations in coastal fishery resources under environmental and climate change scenarios and supports ecosystem-based fisheries management strategies.

Stress Distribution Patterns of the Femorotibial Joint After Medial Closing-Wedge Distal Femoral Varus Osteotomy: An Evaluation Using Computed Tomography Osteoabsorptiometry.

Distal femoral varus osteotomy (DFVO) is an established surgical procedure for addressing valgus malalignment across various knee conditions. However, the effect of DFVO on stress distribution within the femorotibial joint has not been explored through in vivo studies. To (1) explore the distribution pattern of subchondral bone density across the proximal tibia in nonarthritic knees without arthritis and in those of patients with valgus knees, (2) assess changes in the pattern of bone density distribution in patients with valgus knees before and after medial closing-wedge (MCW) DFVO, and (3) determine the correlation between leg alignment and changes in bone density distribution. Cohort study; Level of evidence, 3. The authors retrospectively analyzed clinical and radiographic data from 14 patients (14 knees; mean age, 44 years; 3 men, 11 women) treated with MCW-DFVO for lateral compartment osteoarthritis (OA) due to valgus malalignment, alongside a control group of 18 patients (18 knees; mean age, 21 years; 4 men, 14 women) without OA. The distribution patterns of subchondral bone density distribution on the femorotibial articular surface of the tibia were examined both preoperatively and >1 year postoperatively using computed tomography osteoabsorptiometry. Quantitative analyses were conducted on the locations and percentages of the high-density areas (HDAs) on the articular surface. The mean time between surgery and the postoperative radiograph and computed tomography absorptiometry imaging was 13.6 months (range, 11-19 months). The mean length of clinical follow-up was 28.7 months (range, 14-62 months) after surgery. The mean proportion of HDA in the lateral compartment relative to the total HDA (lateral ratio) was significantly greater in the preoperative OA group (58.8%) compared with the control group (41.1%) (P < .001). After MCW-DFVO, the mean lateral ratio in the OA group notably declined to 45.3% (P < .001). The lateral ratio exhibited a significant correlation with the hip-knee-ankle angle in both the control (r = 0.630; P = .011) and OA (r = 0.537; P = .047) groups. Moreover, the alteration in the lateral ratio after MCW-DFVO showed a significant relationship with changes in the hip-knee-ankle angle (r = 0.742; P = .002) and the mechanical lateral distal femoral angle (r = -0.752; P = .002). Within the lateral compartment, HDAs in the 3 lateral regions of the 4 lateral subregions diminished after MCW-DFVO, whereas in the medial compartment, HDAs in the 3 lateral subregions saw an increase. The mean lateral ratio was significantly greater in the preoperative OA group compared with the control group. MCW-DFVO resulted in a redistribution of HDA from the lateral to the medial compartment of the proximal tibial articular surface. The extent of alignment correction after MCW-DFVO was closely linked to the shifts in HDA distribution, reflecting changes in stress distribution.

Habitat area more consistently affects seagrass faunal communities than fragmentation per se

AbstractSeminal ecological theories, island biogeography and the single large or several small (SLOSS) reserve debate, examine whether large contiguous habitats conserve biodiversity better than multiple smaller patches. Today, delineating the ecological effects of habitat area versus configuration in a fragmentation context remains difficult, and often confounds efforts to understand proximate and ultimate drivers of community change in response to habitat alteration. We examined how the major components of fragmentation, habitat division versus area loss, independently influence faunal communities using landscapes constructed from artificial seagrass at scales relevant for juvenile estuarine nekton. We deployed 25 unique, 234‐m2 landscapes designed along orthogonal axes: habitat percent cover (i.e., area) and fragmentation per se (i.e., patchiness) to examine their effects on faunal density, community composition, and probability of bait‐assay consumption. Faunal sampling occurred in both artificial seagrass and interspaced sandflat matrix. We also examined whether larval‐settler density drove faunal density patterns across landscapes. Further, we assessed the relative importance of landscape‐scale parameters versus fine‐scale complexity–canopy height and epiphyte biomass–in determining faunal densities. We most consistently observed increasing epibenthic fish and macroinvertebrate density with increasing seagrass percent cover. Fragmentation per se only negatively affected epibenthic faunal density within the matrix at low seagrass coverage. Bait consumption increased with seagrass cover, suggesting larger habitats are relative foraging hotspots. Alternatively, benthopelagic fish density was unaffected by habitat parameters, reflecting lower seagrass reliance, or increased matrix tolerance. Community compositions did not vary across landscapes, suggesting that abundant species used landscapes indiscriminately. Finally, the relative importance of habitat parameters shifted across faunal guilds and life stages. Landscape percent cover most affected epibenthic faunal density, but not benthopelagic fish density, and neither pattern was related to settler density. Further, only fine‐scale complexity influenced settler densities. Collectively, our results indicate habitat area is a primary, positive driver of faunal densities and generalist consumption, and therefore should be prioritized in seagrass conservation. However, sampling across spatial scales and habitat types revealed nuances in habitat use patterns among faunal guilds and life stages that were not solely area‐dependent, illustrating that a variety of landscape configurations support essential nursery functions.

Can We Protect China's Northern Coast Wetlands From Tidal Dynamics by Restoring Native Species After Spartina alterniflora Is Eradicated? A Model‐Based Analysis

ABSTRACTIn recent years, the Chinese government has implemented a series of initiatives with the objective of eradicating the invasive Spartina alterniflora along the entire coastline. This has given rise to concerns about the potential for subsequent coastal erosion and salt marsh shrinkage. This study introduces a novel contribution in the form of a model‐based analysis, grounded in fieldwork from the Yellow River Delta, which assesses the potential of native Suaeda salsa as an alternative for coastal protection. Our approach is distinctive in that it not only compares the flow velocity reduction effects of S. alterniflora and S. salsa, but also evaluates the efficacy of three distinct planting patterns in managing tidal dynamics. In this study, we present a significant finding: while both species can mitigate tidal flow velocity, S. alterniflora shows superior performance, which represents a novel point of discussion in the field of coastal wetland management. Furthermore, our research indicates that dense aggregated planting patterns are particularly effective during storm surges, offering a strategic insight for future ecological engineering. However, we recognize the potential limitations of our approach, including the need for continuous management to control the spread of S. alterniflora and the necessity for further research to optimize the restoration potential of S. salsa under varying tidal conditions. Our findings highlight the importance of a balanced approach, suggesting the retention of a portion of S. alterniflora while also emphasizing the strategic planting of S. salsa in high‐density configurations to enhance coastal resilience.

Investigating the distribution of a unique crustacean microendemic to tree hollows.

Water-filled tree hollows constitute phytotelmata that harbor specialized organisms. One striking example of extreme adaptation to these microhabitats is the case of the microcrustacean Micromoina arboricola, which has been found inhabiting the hollow of a single tree individual in the Atlantic Forest. We investigated the spatial distribution and influence of microhabitat characteristics such as hollow volume and height from the ground in the occurrence of M. arboricola. We surveyed all the trunks present in ca. 5000 m2 of an Atlantic old-growth forest area. We found M. arboricola individuals in 75% of the hollows, thus expanding the known distribution of the species. Spatial analysis suggested a clustered pattern of population densities across hollows, indicating that the dispersal capacity to new microhabitat patches may vary in space. Although we did not find an effect of hollow volume, population density was negatively related to hollow height. This suggests that more restrictive abiotic conditions at greater heights limit the occurrence of the species, emphasizing its vulnerability to environmental changes. Hollow-mediated ecosystem engineering depends on the occasional formation of cavities that require time and ancient trees. Preserving old forests with hollows under varying conditions tends to maintain ecosystem functionality and the conservation of this unique microendemic species.

Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m2, of which the 22-year average AGB was 48.73 gC/m2 and 22-year average belowground biomass was 988.37 gC/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p &gt; 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.

SPH modelling of AGB wind morphology in hierarchical triple systems and a comparison to observation of R Aql

Complex asymmetric 3D structures are observed in the outflows of evolved low- and intermediate-mass stars, and are believed to be shaped through the interaction of companions that remain hidden within the dense wind. One example is the AGB star R Aql, for which ALMA (Atacama Large Millimeter Array) observations have revealed complex wind structures that might originate from a higher-order stellar system. We investigate how triple systems can shape the outflow of asymptotic giant branch (AGB) stars and characterise the different wind structures that form. For simplicity, we solely focus on co-planar systems in a hierarchical, stable orbit, consisting of an AGB star with one relatively close companion, and another one at a large orbital separation. We modelled a grid of hierarchical triple systems including a wind-launching AGB star, with the smoothed-particle-hydrodynamic Phantom code. We varied the outer companion mass, the AGB wind velocity, and the orbital eccentricities to study the impact of these parameters on the wind morphology. To study the impact of adding a triple companion, we additionally modelled and analysed a small grid of binary sub-systems, for comparison. To investigate if R Aql could be shaped by a triple system, we post-processed one of our triple models with a radiative transfer routine, and compared this to data of the ALMA ATOMIUM programme. The characteristic wind structures resulting from a hierarchical triple system are the following. A large two-edged spiral wake results behind the outer companion star. This structure lies on top of the spiral structure formed by the close binary, which is itself affected by the orbital motion around the system's centre of mass, such that it resembles a snail-shell pattern. This dense inner spiral pattern interacts with, and strongly impacts, the spiral wake of the outer companion, resulting in a wave pattern on the outer edge of this spiral wake. The higher the mass of the outer companion, the larger the density enhancement and the more radially compressed the outer spiral. Lowering the wind velocity has a similar effect, and additionally results in an elongation of the global wind morphology. Introducing eccentricity in the inner and outer orbit of the hierarchical system results in complex phase-dependent wind-companion interactions, and consequently in asymmetries in the inner part of the wind and the global morphology, respectively. From the comparison of our models to the observations of R Aql, we conclude that this circumstellar environment might be shaped by a similar system to the ones modelled in this work, but an elaborate study of the observational data is needed to better determine the orbital parameters and characteristics of the central system. The modelled outflow of an AGB star in a co-planar hierarchical systems is characterised by a large-scale spiral wake with a wavey outer edge, attached to the outer companion, on top of a compact inner spiral pattern that resembles a snail-shell pattern.

Development of ANN model for predicting mechanical properties of 3D printed PEEK polymer using FDM and optimization of process parameters for better mechanical properties

Additive manufacturing (AM) has evolved from a proven technology to a rapid prototyping tool with great potential. This technology is widely used in many industries such as automotive, aerospace, and medical; fused deposition modeling (FDM) is a popular 3D printing technique for producing PEEK (polyether ether ketone) parts, including implant prosthetic teeth. In this study, artificial neural network (ANN) modeling, parametric optimization, and experimental examination of PEEK 3D printing were conducted to enhance 3D printing processes. In this study, four critical process factors (infill density, layer height, printing speed, and infill pattern) influence the surface roughness, mechanical strength, and elastic modulus of the printed samples. Utilizing a 4–12–3 network design, this study demonstrates that an ANN model with an average error of less than 5% is optimal for all three responses. Furthermore, the study employed a teaching and learning based optimization algorithm (TLBO) and a non-dominated sorting genetic algorithm (NSGA) to optimize the printing process to obtain improved mechanical properties. The findings highlight TLBO’s ability to minimize surface roughness to 6.01 μm and NSGA’s capability to maximize the elastic modulus to 1253.35 MPa and ultimate tensile strength to 65.55 MPa. Microstructural studies supported the results obtained through parametric analysis and optimization.

A Nomogram Using Imaging Features to Predict Ipsilateral Breast Tumor Recurrence After Breast-Conserving Surgery for Ductal Carcinoma In Situ.

To develop a nomogram that integrates clinical-pathologic and imaging variables to predict ipsilateral breast tumor recurrence (IBTR) in women with ductal carcinoma in situ (DCIS) treated with breast-conserving surgery (BCS). This retrospective study included consecutive women with DCIS who underwent BCS at two hospitals. Patients who underwent BCS between 2003 and 2016 in one hospital and between 2005 and 2013 in another were classified into development and validation cohorts, respectively. Twelve clinical-pathologic variables (age, family history, initial presentation, nuclear grade, necrosis, margin width, number of excisions, DCIS size, estrogen receptor, progesterone receptor, radiation therapy, and endocrine therapy) and six mammography and ultrasound variables (breast density, detection modality, mammography and ultrasound patterns, morphology and distribution of calcifications) were analyzed. A nomogram for predicting 10-year IBTR probabilities was constructed using the variables associated with IBTR identified from the Cox proportional hazard regression analysis in the development cohort. The performance of the developed nomogram was evaluated in the external validation cohort using a calibration plot and 10-year area under the receiver operating characteristic curve (AUROC) and compared with the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram. The development cohort included 702 women (median age [interquartile range], 50 [44-56] years), of whom 30 (4%) women experienced IBTR. The validation cohort included 182 women (48 [43-54] years), 18 (10%) of whom developed IBTR. A nomogram was constructed using three clinical-pathologic variables (age, margin, and use of adjuvant radiation therapy) and two mammographic variables (breast density and calcification morphology). The nomogram was appropriately calibrated and demonstrated a comparable 10-year AUROC to the MSKCC nomogram (0.73 vs. 0.66, P = 0.534) in the validation cohort. Our nomogram provided individualized risk estimates for women with DCIS treated with BCS, demonstrating a discriminative ability comparable to that of the MSKCC nomogram.

Integrating granular computing with density estimation for anomaly detection in high-dimensional heterogeneous data

Detecting anomalies in complex data is crucial for knowledge discovery and data mining across a wide range of applications. While density-based methods are effective for handling varying data densities and diverse distributions, they often struggle with accurately estimating densities in heterogeneous, uncertain data and capturing interdependencies among features in high-dimensional spaces. This paper proposes a fuzzy granule density-based anomaly detection algorithm (GDAD) for heterogeneous data. Specifically, GDAD first partitions high-dimensional attributes into subspaces based on their interdependencies and employs fuzzy information granules to represent data. The core of the method is the definition of fuzzy granule density, which leverages local neighborhood information alongside global density patterns and effectively characterizes anomalies in data. Each object is then assigned a fuzzy granule density-based anomaly factor, reflecting its likelihood of being anomalous. Through extensive experimentation on various real-world datasets, GDAD has demonstrated superior performance, matching or surpassing existing state-of-the-art methods. GDAD's integration of granular computing with density estimation provides a practical framework for anomaly detection in high-dimensional heterogeneous data.

Baseline computed tomography imaging findings could assist in early diagnosis of visceral pleural invasion for newly discovered early subpleural non-small cell lung cancer: T1 or T2.

Preoperative accurate visceral pleural infiltration (VPI) diagnosis for T1-size non-small cell lung cancer (NSCLC) is significant for clinical decision-making. The study aimed to explore the diagnostic efficacy of computed tomography (CT) imaging features and serum biomarkers in diagnosing VPI in newly discovered subpleural NSCLC ≤3 cm. There were 447 patients with NSCLC ≤3 cm retrospectively enrolled and assigned to the VPI group (n=81) and the non-VPI group (n=366) based on elastic fiber staining results. The serum biomarkers and CT imaging features were obtained for each subject. Univariate and multivariate analyses were used to identify the independent predictors for VPI. Area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic performance of each independent predictor and combined predictors in predicting VPI, with performance compared using the DeLong test. For tumor biomarkers, the VPI group had a significantly higher percentage of cases with abnormal carcino-embryonic antigen (CEA) level, cytokeratin 19 fragment (CYFRA21-1) level, and pro-gastrin-releasing peptide (ProGRP) level than that of the non-VPI group (P<0.001, P=0.003, P=0.004). However, in multivariate analysis, only the lesion-pleura relationship patterns type Ia [odds ratio (OR) =20.689; 95% confidence interval (CI): 5.058-84.622; P<0.001], type Ib (OR =5.155; 95% CI: 1.178-22.552; P=0.03), type II (OR =7.154; 95% CI: 1.733-29.53; P=0.007) with type III as reference, solid lesion density (OR =9.954; 95% CI: 4.976-19.911; P<0.001) with part-solid density as reference were identified as the independent predictors for VPI. In predicting VPI, the combined model (AUC =0.885) significantly outperformed models based on lesion density (AUC =0.833) and lesion-pleura relationship patterns (AUC =0.655) (all P<0.001). The CT predictors for VPI in patients with subpleural NSCLC (≤3 cm) were lesion density and lesion-pleura relationship patterns (pleural attachment and indentation), but not serum tumor biomarkers.

Allometry in limb regeneration and scale-invariant patterning as the basis of normal morphogenesis from different sizes of blastemas.

Axolotl (Ambystoma mexicanum) limb regeneration begins with blastemas of various sizes, in contrast to the limb developmental process. Despite this size variation, normal limb morphology, consistent with a limb stump size, is regenerated. This outcome suggests the existence of underlying scale-invariant mechanisms. To identify such mechanisms, we examined the allometric relationships between blastema size, and Sonic Hedgehog (Shh) and Fibroblast Growth Factor 8 (Fgf8) expression patterns against limb stump size. We found that all factors showed allometric rather than isometric scaling; specifically, their relative sizes decrease with an increase in limb stump size. However, the ratio of Shh/Fgf8 signaling dominant region was nearly constant, independent of blastema/body size. Furthermore, the relative spatial patterns of cell density and proliferation activity and the relative position of first digit formation were scale-invariant in the summed Shh/Fgf8 crosstalk region. This scale-invariant nature may underlie the morphogenesis of normal limbs from different sizes of blastemas.

Electric Field Features and Charge Behavior in Oil-Pressboard Composite Insulation under Impulse Voltage

Oil-pressboard/paper insulation materials are essential in transformers for ensuring their safe and stable operation, primarily due to their roles in spatial electric field distribution and charge migration mechanisms. Current spatial distribution analyses rely on computational methods that lack empirical validation, particularly for oil-pressboard/paper composites. This study leverages the principles of the Kerr electro-optic effect to develop a rapid measurement platform for electric fields within oil-pressboard/paper insulation under impulse voltage conditions, which measures the spatial electric field characteristics using Cu-Cu and Al-Al electrodes under various scenarios: with asymmetric and symmetric pressboard coverage and different numbers of insulating paper layers. Findings indicated: (1) In asymmetric pressboard models, Cu-Cu electrodes exhibit a consistent peak electric field of approximately 16 kV/mm, while Al-Al electrodes show peak values of 18.13 kV/mm and −14.98 kV/mm. Charge density patterns are similar, with Cu-Cu at about 68 μC/m2 and Al-Al at 11.2 μC/m2 and −124.8 μC/m2. (2) Symmetric models present consistent peak electric fields and charge densities for both polarities. (3) Increasing insulating paper layers elevates electric field strengths. Both electrodes show the similar peak field of about 17 kV/mm with differing paper layers due to higher charge injection from the Al electrode. (4) Utilizing the Schottky effect and field emission principles, the study clarifies charge generation and migration mechanisms. These insights could provide a theoretical foundation for designing and verifying oil-pressboard/paper insulation structures in transformers.

Settlement Patterns of the Population Use the Average Nearest Neighbor Method

This study utilizes the Average Nearest Neighbor (ANN) method to analyze settlement patterns based on population density data across 15 regencies and cities in Lampung Province, Indonesia. ANN is a spatial statistical technique used to determine spatial distribution patterns by calculating the average distance between points, the number of points, and the area of the region. The analysis involved computing the ANN index and z-scores for each regency and city. The results, as shown in the analysis, indicate that the ANN index values are below one, ranging from 0.114 to 0.769, and the z-scores are negative, ranging from -146.719 to -13.235. These findings suggest that the settlement patterns across all studied areas in Lampung Province exhibit a clustered pattern. This implies that densely populated areas do not significantly influence the settlement patterns of less densely populated regions. The study concludes that the population density pattern in Lampung Province is predominantly clustered, reflecting a tendency for settlements to be concentrated in specific areas rather than dispersed.

Perspective: imaging atomic step geometry to determine surface terminations of kagome materials and beyond

Here we review scanning tunneling microscopy research on the surface determination for various types of kagome materials, including 11-type (CoSn, FeSn, FeGe), 32-type (Fe3Sn2), 13-type (Mn3Sn), 135-type (AV3Sb5, A = K, Rb, Cs), 166-type (TbMn6Sn6, YMn6Sn6 and ScV6Sn6), and 322-type (Co3Sn2S2 and Ni3In2Se2). We first demonstrate that the measured step height between different surfaces typically deviates from the expected value of ±0.4 ∼0.8Å, which is owing to the tunneling convolution effect with electronic states and becomes a serious issue for Co3Sn2S2 where the expected Sn-S interlayer distance is 0.6Å. Hence, we put forward a general methodology for surface determination as atomic step geometry imaging, which is fundamental but also experimentally challenging to locate the step and to image with atomic precision. We discuss how this method can be used to resolve the surface termination puzzle in Co3Sn2S2. This method provides a natural explanation for the existence of adatoms and vacancies, and beyond using unknown impurity states, we propose and use designer layer-selective substitutional chemical markers to confirm the validity of this method. Finally, we apply this method to determine the surface of a new kagome material Ni3In2Se2, as a cousin of Co3Sn2S2, and we image the underlying kagome geometry on the determined Se surface above the kagome layer, which directly visualizes the p-d hybridization physics. We emphasize that this general method does not rely on theory, but the determined surface identity can provide guidelines for first-principles calculations with adjustable parameters on the surface-dependent local density of states and quasi-particle interference patterns.