Spectroscopic Evidence of Type II Weyl Semimetal State in WTe2

Quantum topological materials, exemplified by topological insulators, three-dimensional Dirac semimetals and Weyl semimetals, have attracted much attention recently because of their unique electronic structure and physical properties. Very lately it is proposed that the three-dimensional Weyl semimetals can be further classified into two types. In the type I Weyl semimetals, a topologically protected linear crossing of two bands, i.e., a Weyl point, occurs at the Fermi level resulting in a point-like Fermi surface. In the type II Weyl semimetals, the Weyl point emerges from a contact of an electron and a hole pocket at the boundary resulting in a highly tilted Weyl cone. In type II Weyl semimetals, the Lorentz invariance is violated and a fundamentally new kind of Weyl Fermions is produced that leads to new physical properties. WTe2 is interesting because it exhibits anomalously large magnetoresistance. It has ignited a new excitement because it is proposed to be the first candidate of realizing type II Weyl Fermions. Here we report our angle-resolved photoemission (ARPES) evidence on identifying the type II Weyl Fermion state in WTe2. By utilizing our latest generation laser-based ARPES system with superior energy and momentum resolutions, we have revealed a full picture on the electronic structure of WTe2. Clear surface state has been identified and its connection with the bulk electronic states in the momentum and energy space shows a good agreement with the calculated band structures with the type II Weyl states. Our results provide spectroscopic evidence on the observation of type II Weyl states in WTe2. It has laid a foundation for further exploration of novel phenomena and physical properties in the type II Weyl semimetals.Comment: 16 Pages, 4 Figure

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra). METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine. RESULTS: Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra\u27s estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p \u3c 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram). CONCLUSION: We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field

The Relationship Between Educational Achievement and Oral Health Status: A Systematic Review of Cross-Sectional Studies

Background: Oral health status significantly affects general health and quality of life, with mounting evidence suggesting a positive correlation between educational level and oral health outcomes. However, comprehensive synthesis of this relationship across diverse populations and healthcare systems remains limited. Objective: To systematically review and analyze the relationship between educational level and oral health status across global populations, providing evidence-based insights for health policy development and oral health improvement strategies. Methods: A systematic literature search was conducted using PubMed and The Cochrane Library databases from January 2007 to January 2025. Search terms included "oral health," "education level," "caries," "periodontosis," and "tooth loss." Cross-sectional studies examining the relationship between educational attainment and oral health outcomes were included. Study quality was assessed using the Agency for Healthcare Research and Quality 11-item checklist. Data extraction focused on correlations between educational level and dental caries, periodontal disease, and tooth loss across different populations and healthcare systems. Results: A total of 236 articles were identified, with 32 cross-sectional studies meeting inclusion criteria after systematic screening. The studies encompassed populations from both developed countries (Britain, United States, Germany, Denmark, Belgium, Finland) and developing nations (Chile, Egypt, India, Thailand, Colombia, Nigeria, China). Consistent evidence demonstrated that educational level was negatively correlated with the prevalence of dental caries, periodontal disease, and tooth loss across all examined populations. This inverse relationship persisted even in developed countries with established national public health insurance systems, indicating that educational gradients in oral health transcend healthcare access barriers. Conclusions: Educational level demonstrates a robust and consistent association with oral health outcomes across diverse global populations and healthcare systems. The universality of this relationship suggests that expanding educational opportunities represents a promising upstream intervention strategy for improving population oral health. These findings support the integration of educational advancement into comprehensive oral health promotion policies and highlight the potential for educational interventions to address oral health disparities at the population level

Association and prediction utilizing craniocaudal and mediolateral oblique view digital mammography and long-term breast cancer risk

UNLABELLED: Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval. PREVENTION RELEVANCE: Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care

Automated breast density assessment for full-field digital mammography and digital breast tomosynthesis

Mammographic density is a strong risk factor for breast cancer and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for both full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) as both types of exams are acquired in standard clinical practice. We trained a deep learning model to automate the estimation of BI-RADS density from a prospective Washington University clinic-based cohort of 9,714 women, entering into the cohort in 2013 with follow-up through October 31, 2020. The cohort included 27% non-Hispanic Black women. The trained algorithm was assessed in an external validation cohort that included 18,360 women screened at Emory from January 1, 2013, and followed up through December 31, 2020, that included 42% non-Hispanic Black women. Our model-estimated BI-RADS density demonstrated substantial agreement with the density as assessed by radiologists. In the external validation, the agreement with radiologists for category B 81% and C 77% for FFDM and B 83% and C 74% for DBT shows important distinction for separation of women with dense breast. We obtained a Cohen\u27s κ of 0.72 (95% confidence interval, 0.71-0.73) in FFDM and 0.71 (95% confidence interval, 0.69-0.73) in DBT. We provided a consistent and fully automated BI-RADS estimation for both FFDM and DBT using a deep learning model. The software can be easily implemented anywhere for clinical use and risk prediction. Prevention Relevance: The proposed model can reduce interobserver variability in BI-RADS density assessment, thereby providing more standard and consistent density assessment for use in decisions about supplemental screening and risk assessment

Responses of macroinvertebrate functional trait structure to river damming : From within-river to basin-scale patterns

AbstractRevealing how aquatic organisms respond to dam impacts is essential for river biomonitoring and management. Traditional examinations of dam impacts on macroinvertebrate assemblages were frequently conducted within single rivers (i.e., between upstream vs. downstream locations) and based on taxonomic identities but have rarely been expanded to level of entire basins (i.e., between dammed vs. undammed rivers) and from a functional trait perspective. Here, we evaluated the effects of dams on macroinvertebrate assemblages at both the within-river and basin scales using functional traits in two comparable tropical tributaries of the Lancang-Mekong River. At different scales, maximum body size, functional feeding groups (FFG), voltinism and occurrence in drift respond significantly to dam impact. Armoring categories varied significantly between downstream sites and upstream sites, and oviposition behavior, habits and adult life span significantly differed between rivers. The key traits at the within-river scale resembled to those at the between-river scale, suggesting that within-river trait variation could further shape functional trait structure at the basin scale in dammed rivers. Furthermore, water nutrients and habitat quality induced by dams showed the most important role in shaping trait structure, although trait-environment relationships varied between the two different scales. In addition, the trait-environment relationships were stronger in the dry season than in the wet season, suggesting a more important role of environmental filtering processes in the dry season compared with the wet season. This study highlights the utility of the trait-based approach to diagnose the effects of damming and emphasizes the importance of spatial scale to examine dam impacts in riverine systems.Abstract Revealing how aquatic organisms respond to dam impacts is essential for river biomonitoring and management. Traditional examinations of dam impacts on macroinvertebrate assemblages were frequently conducted within single rivers (i.e., between upstream vs. downstream locations) and based on taxonomic identities but have rarely been expanded to level of entire basins (i.e., between dammed vs. undammed rivers) and from a functional trait perspective. Here, we evaluated the effects of dams on macroinvertebrate assemblages at both the within-river and basin scales using functional traits in two comparable tropical tributaries of the Lancang-Mekong River. At different scales, maximum body size, functional feeding groups (FFG), voltinism and occurrence in drift respond significantly to dam impact. Armoring categories varied significantly between downstream sites and upstream sites, and oviposition behavior, habits and adult life span significantly differed between rivers. The key traits at the within-river scale resembled to those at the between-river scale, suggesting that within-river trait variation could further shape functional trait structure at the basin scale in dammed rivers. Furthermore, water nutrients and habitat quality induced by dams showed the most important role in shaping trait structure, although trait-environment relationships varied between the two different scales. In addition, the trait-environment relationships were stronger in the dry season than in the wet season, suggesting a more important role of environmental filtering processes in the dry season compared with the wet season. This study highlights the utility of the trait-based approach to diagnose the effects of damming and emphasizes the importance of spatial scale to examine dam impacts in riverine systems

Improved Inactivation Effect of Bacteria Fabrication of Mesoporous Anatase Films with Fine Ag Nanoparticles Prepared by Coaxial Vacuum Arc Deposition

We realize ultrarapid inactivation of bacteria by modifying fine Ag nanoparticles with uniform size on mesoporous anatase films with high surface areas

Combining Karhunen–Loève expansion and stochastic modeling for probabilistic delineation of well capture zones in heterogeneous aquifers

The delineation of well capture zones (WCZs), particularly for water supply wells, is of utmost importance to ensure water quality. This task requires a comprehensive understanding of the aquifer’s hydrogeological parameters for precise delineation. However, the inherent uncertainty associated with these parameters poses a significant challenge. Traditional deterministic methods bear inherent risks, emphasizing the demand for more resilient and probabilistic techniques. This study introduces a novel approach that combines the Karhunen–Loève expansion (KLE) technique with stochastic modeling to probabilistically delineate well capture zones in heterogeneous aquifers. Through numerical examples involving moderate and strong heterogeneity, the effectiveness of KLE dimension reduction and the reliability of stochastic simulations are explored. The results show that increasing the number of KL-terms significantly improves the statistical attributes of the samples. When employing more KL-terms, the statistical properties of the hydraulic conductivity field outperform those of cases with fewer KL-terms. Notably, particularly in scenarios of strong heterogeneity, achieving a convergent probabilistic WCZs map requires a greater number of KL-terms and stochastic simulations compared to cases with moderate heterogeneity

Network-based prediction of anti-cancer drug combinations

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes