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Abstract Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 106 atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.

In this computational study, we explore a viable route to access the Kitaev-quantum spin liquid (QSL) state in a recently synthesized monolayer of a so-called spin-orbit assisted Mott insulator OsCl_{3}. In addition to other magnetic ground states in different regions, a substantial region of small J_{H}/U in our Hubbard U–Hund's J_{H} quantum phase diagram, obtained by combining second-order perturbation and pseudo-fermion functional renormalization group calculations, hosts Kitaev-QSL phase. Only the first neighbor Kitaev interaction of a smaller magnitude, nearly independent of the choice of U, is obtained in this region. Experimental tuning of the single parameter J_{H} to access the QSL state can purportedly be achieved with epitaxial strain in advanced crystal growth techniques. Furthermore, negligibly small farther neighbor interactions appear as a distinct feature of monolayer OsCl_{3}, suggesting this material to be a better candidate for the exploration of the possible Kitaev-QSL state than previously proposed materials.

Objective: To create a rapid, cloud-based, and deployable machine learning (ML) method named hemorrhage evaluation and detector system for underserved populations, potentially across the Mayo Clinic enterprise, then expand to involve underserved areas and detect the 5 subtypes of intracranial hemorrhage (IH). Methods: We used Radiological Society of North America dataset for IH detection. We made 4 total iterations using Google Cloud Vertex AutoML. We trained an AutoML model with 2000 images, followed by 6000 images from both IH positive and negative classes. Pixel values were measured by the Hounsfield units, presenting a width of 80 Hounsfield and a level of 40 Hounsfield as the bone window. This was followed by a more detailed image preprocessing approach by combining the pixel values from each of the brain, subdural, and soft tissue window-based gray-scale images into R(red)-channel, G(green)-channel, and B(blue)-channel images to boost the binary IH classification performance. Four experiments with AutoML were applied to study the effects of training sample size and image preprocessing on model performance. Results: Out of the 4 AutoML experiments, the best-performing model was the fourth experiment, where 95.80% average precision, 91.40% precision, and 91.40% recall were achieved. On the basis of this analysis, our binary IH classifier hemorrhage evaluation and detector system for underserved populations appeared both accurate and performed well. Conclusion: Hemorrhage evaluation and detector system for underserved populations is a rapid, cloud-based, deployable ML method to detect IH. This tool can help expedite the care of patients with IH in resource-limited hospitals.

The combination of Mobile Edge Computing (MEC) and Digital Twin (DT) is anticipated to enhance the quality of mobile application services in the 6G era. However, current research often overlooks service caching and task dependency, which may deteriorate system performance. Moreover, Edge Servers (ESs) have limited computing resources and caching capacities, which require collaboration to meet user demands. To address these challenges, we propose a DT-empowered MEC architecture that supports Mobile Users (MUs) offloading dependency-aware tasks, while considering service caching and edge collaboration. The objective is to jointly optimize computation offloading and resource allocation to minimize the system’s energy consumption. Hence, this problem can be formulated as a Mixed Integer Non-linear Programming (MINLP) problem and addressed by utilizing the Asynchronous Advantage Actor-Critic (A3C)-based method. Extensive simulation results demonstrate that our approach outperforms other benchmark algorithms under various scenarios, significantly reducing energy consumption.

Colorectal cancer (CRC) is the second most commonly diagnosed cancer in the United States. Genetic testing is critical in assisting in the early detection of CRC and selection of individualized treatment plans, which have shown to improve the survival rate of CRC patients. The tissue slide review (TSR), a tumor tissue macro-dissection procedure, is a required pre-analytical step to perform genetic testing. Due to the subjective nature of the process, major discrepancies in CRC diagnostics by pathologists are reported, and metrics for quality are often only qualitative. Progressive context encoder anomaly detection (P-CEAD) is an anomaly detection approach to detect tumor tissue from whole slide images (WSIs), since tumor tissue is by its nature, an anomaly. P-CEAD-based CRC tumor segmentation achieves a 71% 26% sensitivity, 92% 7% specificity, and 63% 23% F1 score. The proposed approach provides an automated CRC tumor segmentation pipeline with a quantitatively reproducible quality compared with the conventional manual tumor segmentation procedure.

The recent observation of superconductivity in nickelate thin films has attracted a lot of attentions. Here, authors report single particle tunneling spectra on the superconducting nickelate thin films revealing two types of gap feature with one V-shape and the other a full gap.

Superconductivity has been discovered recently in infinite-layer nickel-based 112 thin films R1−xAxNiO2 (R = La, Nd, Pr and A = Sr, Ca). They are isostructural to the infinite-layer cuprate (Ca,Sr)CuO2 and are supposed to have a formal Ni 3d9 valence, thus providing a new platform to study the unconventional pairing mechanism of high-temperature superconductors. This important discovery immediately triggers a huge amount of innovative scientific curiosity in the field. In this paper, we try to give an overview of the recent research progress on the newly found superconducting nickelate systems, both from experimental and theoretical aspects. We mainly focus on the electronic structures, magnetic excitations, phase diagrams and superconducting gaps, and finally make some open discussions for possible pairing symmetries in Ni-based 112 systems. Public summary: • The infinite-layer nickel-based 112 thin films R1−xAxNiO2 can host superconductivity up to 15 K • R1−xAxNiO2 is a multiband system, in which the short-range antiferromagnetic fluctuations can be detected • R1−xAxNiO2 has an unconventional superconducting pairing sate with a robust d-wave gap and a full gap without unified understanding • The nickelate system provides a new platform for researching unconventional superconductivity

The superconducting gap structure contains important information to understand the pairing mechanism of unconventional superconductivity. Here, by using a newly established phase-referenced quasiparticle interference technique, the authors visualize the sign change of the d-wave superconducting gap directly in Bi2Sr2CaCu2O8+δ.

To experimentally confirm the predicted type-II Weyl fermions in transition metal dichalcogenide, the evidence of inversion symmetry breaking is required. Here, Zhanget al. report Raman spectroscopic evidence for the inversion symmetry breaking in MoTe2.

Background: Adoption of the Digital Imaging and Communications in Medicine (DICOM) standard for whole slide images (WSIs) has been slow, despite significant time and effort by standards curators. One reason for the lack of adoption is that there are few tools which exist that can meet the requirements of WSIs, given an evolving ecosystem of best practices for implementation. Eventually, vendors will conform to the specification to ensure enterprise interoperability, but what about archived slides? Millions of slides have been scanned in various proprietary formats, many with examples of rare histologies. Our hypothesis is that if users and developers had access to easy to use tools for migrating proprietary formats to the open DICOM standard, then more tools would be developed as DICOM first implementations. Methods: The technology we present here is dicom_wsi, a Python based toolkit for converting any slide capable of being read by the OpenSlide library into DICOM conformant and validated implementations. Moreover, additional postprocessing such as background removal, digital transformations (e.g., ink removal), and annotation storage are also described. dicom_wsi is a free and open source implementation that anyone can use or modify to meet their specific purposes. Results: We compare the output of dicom_wsi to two other existing implementations of WSI to DICOM converters and also validate the images using DICOM capable image viewers. Conclusion: dicom_wsi represents the first step in a long process of DICOM adoption for WSI. It is the first open source implementation released in the developer friendly Python programming language and can be freely downloaded at https:// github.com/Steven N Hart/dicom_wsi.

Electronic systems with quasi-one-dimensional (Q1D) Fermi surface tend to form either a charge-density-wave (CDW) or a spin-density-wave ground state at low temperatures due to one-dimensional instabilities. Among various CDW states, surface CDWs are different from that within the bulk due to the reduced dimensionality. Here we report the systematic investigation of charge density modulation on the surface of in situ cleaved TaTe _4 crystal by means of low temperature scanning tunneling microscopy/spectroscopy, corroborated with density functional theory calculation. Well-defined Q1D modulation (4 a , 6 c ) accompanied with a periodic lattice distortion is clearly observed on the (010) cleaved surface, distinct from that of its bulk CDW (2 a × 2 a × 3 c ). Tunneling spectroscopic measurements reveal a partially-opened energy gap about 23 meV around Fermi level. Such gap shows similar spatial variation with the periodicity of surface modulation and diminishes subsequently as temperature rises, which indicates a novel surface-related CDW gap states. The surface modulation vectors fit well with the Fermi surface nesting vectors, derived from the calculated Fermi surfaces. Surprisingly, such surface modulation can be suppressed greatly by applying vertical magnetic field and a critical field about 9.05 T can be estimated from field-dependent data. Our results demonstrate that this unique CDW modulation is strongly related to Fermi surface nesting mediated electron–electron coupling due to the reduced dimensionality of the surface, and can be readily tuned by relatively small magnetic field.

Colorectal cancer (CRC) is the second most commonly diagnosed cancer in the United States. Genetic testing is critical in assisting in the early detection of CRC and selection of individualized treatment plans, which have shown to improve the survival rate of CRC patients. The tissue slide review (TSR), a tumor tissue macro-dissection procedure, is a required pre-analytical step to perform genetic testing. Due to the subjective nature of the process, major discrepancies in CRC diagnostics by pathologists are reported, and metrics for quality are often only qualitative. Progressive context encoder anomaly detection (P-CEAD) is an anomaly detection approach to detect tumor tissue from whole slide images (WSIs), since tumor tissue is by its nature, an anomaly. P-CEAD-based CRC tumor segmentation achieves a 71% 26% sensitivity, 92% 7% specificity, and 63% 23% F1 score. The proposed approach provides an automated CRC tumor segmentation pipeline with a quantitatively reproducible quality compared with the conventional manual tumor segmentation procedure. [ABSTRACT FROM AUTHOR]

In cuprate superconductors, due to strong electronic correlations, there are multiple intertwined orders which either coexist or compete with superconductivity. Among them, the antiferromagnetic (AF) order is the most prominent one. In the region where superconductivity sets in, the long-range AF order is destroyed. Yet the residual short-range AF spin fluctuations are present up to a much higher doping, and their role in the emergence of the superconducting phase is still highly debated. Here, by using a spin-polarized scanning tunneling microscope, we directly visualize an emergent incommensurate AF order in the nearby region of Fe impurities embedded in the optimally doped Bi2Sr2CaCu2O8+δ (Bi2212). Remarkably, the Fe impurities suppress the superconducting coherence peaks with the gapped feature intact, but pin down the ubiquitous short-range incommensurate AF order. Our work shows an intimate relation between antiferromagnetism and superconductivity. [ABSTRACT FROM AUTHOR]

Abstract Background As systematically developed statements regarding possible courses of action, health system guidance (HSG) can assist with making decisions about addressing problems or achieving goals in health systems. However, there are conceptual and methodological challenges in HSG implementation due to the complexity of health-system policy-making, the diversity of available evidence and vast differences in contexts. To address these gaps, we aim to develop a theoretical framework for supporting HSG implementation as part of a broader effort to promote evidence-informed policy-making in health systems. Methods To develop a theoretical framework about facilitators, barriers and strategies for HSG implementation, we will apply a critical interpretive synthesis (CIS) approach to synthesize the findings from a range of relevant literature. We will search 11 electronic databases and seven organizational websites to identify relevant published and grey literature. We will check the references of included studies and contact experts to identify additional eligible papers. Finally, we will conduct purposively sampling of the literature to fill any identified conceptual gaps. We will use relevance and five quality criteria to assess included papers. A standardized form will be developed for extracting information. We will use an interpretive analytic approach to synthesize the findings, including a constant comparative method throughout the analysis. Two independent reviewers will conduct the literature screening and relevance assessment, and disagreements will be resolved through discussion. The principal investigator will conduct data extraction and synthesis, and a second reviewer will check the sample of extracted data for consistency and accuracy. Discussion A new theoretical framework about facilitators, barriers and strategies for HSG implementation will be developed using a CIS approach. The HSG implementation framework could be widely used for supporting the implementation of HSG covering varied topics and in different contexts (including low-, middle- and high-income countries). In later work, we will develop a tool for supporting HSG implementation based on the theoretical framework. Registration PROSPERO CRD42020214072. Date of Registration: 14 December 2020.

IntroductionHealth information literacy is critical for individuals to obtain, understand, screen, and apply health information. However, there is currently no specific tool available to evaluate all four dimensions of health information literacy in China. Public health emergencies can present an opportunity to evaluate and monitor the health information literacy level of residents. Therefore, this study aimed to develop a questionnaire to evaluate the level of health information literacy and to measure the reliability and validity.MethodsThe development process of the questionnaire consisted of the determination of questionnaire items, expert consultation, and validation. Based on the National Residents Health Literacy Monitoring Questionnaire (2020) and the 2019 Informed Health Choices key concepts, the researchers drafted the questionnaire, including all four dimensions of health information literacy. Experts in relevant fields were invited to evaluate the draft questionnaire, and revisions were made accordingly. Finally, the reliability and validity of the finalized version were examined in Gansu Province, China.ResultsThe research team preliminarily formulated 14 items encompassing the four dimensions of health information literacy. After consulting with 28 experts, modifications were made. A convenience sample of 185 Chinese residents was invited to participate. Cronbach's alpha coefficient was 0.715 and McDonald's omega was 0.739 for internal consistency, and the test-retest intra-class correlation coefficient after 4 weeks was 0.906, indicating that the questionnaire content and measurement structure was relatively stable.ConclusionThis questionnaire is the first evidence-based assessment tool developed for monitoring health information literacy in China, and it has shown good reliability and validity. It can help to monitor the health information literacy levels of Chinese residents, promote evidence-based decision-making, and guide interventions to improve health information literacy.

Objectives: This study aims to investigate the risk of COVID-19 transmission on aircraft. Methods: We obtained data on all international flights to Lanzhou, China, from June 1, 2020, to August 1, 2020, through the Gansu Province National Health Information Platform and the official website of the Gansu Provincial Center for Disease Control and Prevention. We then performed the statistical analysis. Results: Three international flights arrived in Lanzhou. The flights had a total of 700 passengers, of whom 405 (57.9%) were male, and 80 (11.4%) were children under the age of 14 years. Twenty-seven (3.9%) passengers were confirmed to have COVID-19. Confirmed patients were primarily male (17, 65.4%) with a median age of 27.0 years. Most confirmed cases were seated in the middle rows of economy class or near public facility areas such as restrooms and galleys. The prevalence of COVID-19 did not differ between passengers sitting in the window, aisle, or middle seats. However, compared with passengers sitting in the same row up to 2 rows behind a confirmed case, passengers seated in the 2 rows in front of a confirmed case were at a slightly higher risk of being infected. Conclusions: COVID-19 may be transmitted during a passenger flight, although there is still no direct evidence.

Objective: This study aimed to summarize the characteristics and methodological quality of systematic reviews on the application of artificial intelligence (AI) in clinical diagnosis and treatment. Methods: We systematically searched seven English- and Chinese-language literature databases to identify systematic reviews on the application of AI, deep learning, or machine learning in the diagnosis and treatment of any disease published in 2020. We evaluated the methodological quality of the included systematic reviews using “A Measurement tool for the assessment of multiple systematic reviews” (AMSTAR). We also conducted meta-analyses on the diagnostic accuracy of AI on selected disease categories with a large number of included studies and low clinical heterogeneity. Results: A total of 40 systematic reviews reporting 1,083 original studies were included, covering 31 diseases from 11 groups of diseases. Eleven systematic reviews were related to neoplasms and nine were systematic reviews related to diseases of the digestive system. We selected digestive system diseases for the meta-analysis. The pooled sensitivities (with 95% confidence interval (CI)) of AI to assist the diagnosis of helicobacter pylori, gastrointestinal ulcers, hemorrhage, esophageal tumors, gastric tumors, and intestinal tumors (with 95% CI) were 0.91 (0.83–0.95), 0.99 (0.76–1.00), 0.95 (0.83–0.99), 0.90 (0.85–0.93), 0.90 (0.82–0.95), and 0.93 (0.88–0.96), respectively, and the pooled specificities were 0.82 (0.77–0.87), 0.97 (0.86–1.00), 1.00 (0.99–1.00), 0.80 (0.71–0.87), 0.93 (0.87–0.97), and 0.89 (0.85–0.92), respectively. The AMSTAR items “the list of included studies” (n = 39, 97.5%) and “the characteristics of the included studies” (n = 39, 97.5%) had the highest compliance among the reviews; the compliance was relatively low to the items “the consideration of publication status” (n = 1, 2.5%), “the consideration of scientific quality” (n = 19, 47.5%), “data synthesis methods” (n = 18, 45.0%), and “ the evaluation of publication bias” (n = 13, 32.5%). Conclusions: The main subjects of systematic reviews on AI applications in clinical diagnosis and treatment published in 2020 were diseases of the digestive system and neoplasms. The methodological quality of the systematic reviews on AI needs to be improved, paying particular attention to publication bias and the rigorous evaluation of the quality of the included studies.

ObjectivesTo systematically explore how the sources of evidence, types of primary studies, and tools used to assess the quality of the primary studies vary across systematic reviews (SRs) in public health.MethodsWe conducted a methodological survey of SRs in public health by searching the of literature in selected journals from electronic bibliographic databases. We selected a 10% random sample of the SRs that met the explicit inclusion criteria. Two researchers independently extracted data for analysis.ResultsWe selected 301 SRs for analysis: 94 (31.2%) of these were pre-registered, and 211 (70.1%) declared to have followed published reporting standard. All SRs searched for evidence in electronic bibliographic databases, and more than half (n = 180, 60.0%) searched also the references of the included studies. The common types of primary studies included in the SRs were primarily cross-sectional studies (n = 132, 43.8%), cohort studies (n = 126, 41.9%), randomized controlled trials (RCTs, n = 89, 29.6%), quasi-experimental studies (n = 83, 27.6%), case-control studies (n = 58, 19.3%) qualitative studies (n = 38, 12.6%) and mixed-methods studies (n = 32, 10.6%). The most frequently used quality assessment tools were the Newcastle-Ottawa Scale (used for 50.0% of cohort studies and 55.6% of case-control studies), Cochrane Collaboration's Risk of Bias tool (50.7% of RCTs) and Critical Appraisal Skills Program (38.5% of qualitative studies). Only 20 (6.6%) of the SRs assessed the certainty of the body of evidence, of which 19 (95.0%) used the GRADE approach. More than 65% of the evidence in the SRs using GRADE was of low or very low certainty.ConclusionsSRs should always assess the quality both at the individual study level and the body of evidence for outcomes, which will benefit patients, health care practitioners, and policymakers.

Topological Weyl semimetals (TWSs) with pairs of Weyl points and topologically protected Fermi arc states have broadened the classification of topological phases and provide superior platform for study of topological superconductivity. Here we report the nontrivial superconductivity and topological features of sulfurdoped Td-phase MoTe2 with enhanced Tc compared with type-II TWS MoTe2. It is found that Td-phase S-doped MoTe2 (MoTe2-xSx, x~0.2) is a two-band s-wave bulk superconductor (~0.13 meV and 0.26 meV), where the superconducting behavior can be explained by the s+- pairing model. Further, measurements of the quasi-particle interference (QPI) patterns and a comparison with band-structure calculations reveal the existence of Fermi arcs in MoTe2-xSx . More interestingly, a relatively large superconducting gap (~1.7 meV) is detected by scanning tunneling spectroscopy on the sample surface, showing a hint of topological nontrivial superconductivity based on the pairing of Fermi arc surface states. Our work demonstrates that the Td-phase MoTe2-xSx is not only a promising topological superconductor candidate but also a unique material for study of s+- superconductivity. [ABSTRACT FROM AUTHOR]

BackgroundArtificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.MethodsAll literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.ResultsBeginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.ConclusionResearches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.