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The lack of a simple design strategy to obtain ideal conjugated polymers (CPs) with high absorbance and fluorescence (FL) in the near-infrared-II (NIR-II; 1000–1700 nm) region still hampers the success of NIR-II light-triggered phototheranostics. Herein, novel phototheranostic nanoparticles (PPN-NO NPs) were successfully prepared by coloading a cationic NIR-II CPs (PBC-co-PBF-NMe3) and a NO donor (S-nitroso-N-acetylpenicillamine, SNAP) onto a 1:1 mixture of DSPE-PEG5000 and dimyristoylphosphatidylcholine (DMPC) for NIR-II FL and NIR-II photoacoustic (PA) imaging-guided low-temperature NIR-II photothermal therapy (PTT) and gas combination therapy for cancer treatment. A precise NIR-II FL dually enhanced design tactic was proposed herein by integrating flexible nonconjugated segments (C6) into the CPs backbone and incorporating quaternary ammonium salt cationic units into the CPs side chain, which considerably increased the radiative decay pathway, resulting in desirable NIR-II FL intensity and balanced NIR-II absorption and NIR PTT properties. The phototheranostic PPN-NO NPs exhibited distinguished NIR-II FL and PA imaging performance in tumor-bearing mice models. Furthermore, the low-temperature photothermal effect of PPN-NO NPs could initiate NO release upon 980 nm laser irradiation, efficiently suppressing tumor growth owing to the combination of low-temperature NIR-II PTT and NO gas therapy in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Photothermal immunotherapy is regarded as the ideal cancer therapeutic modality to against malignant solid tumors; however, its therapeutic benefits are often modest and require improvement. In this study, a thermoresponsive nanoparticle (BTN@LND) composed of a photothermal agent (PTA) and pyroptosis inducer (lonidamine) were developed to enhance immunotherapy applications. Specifically, our "two-step" donor engineering strategy produced the strong NIR-II-absorbing organic small-molecule PTA (BTN) that exhibited high NIR-II photothermal performance (ε1064 = 1.51 × 104 M−1 cm−1, η = 75.8%), and this facilitates the diagnosis and treatment of deep tumor tissue. Moreover, the fabricated thermally responsive lipid nanoplatform based on BTN efficiently delivered lonidamine to the tumor site and achieved spatiotemporal release triggered by the NIR-II photothermal effect. In vitro and in vivo experiments demonstrated that the NIR-II photothermal therapy (PTT)-mediated on-demand release of cargo effectively faciliated tumor cell pyroptosis, thereby intensifying the immunogenic cell death (ICD) process to promote antitumor immunotherapy. As a result, this intelligent component bearing photothermal and chemotherapy can maximally suppress the growth of tumors, thus providing a promising approach for pyroptosis/NIR-II PTT synergistic therapy against tumors. [ABSTRACT FROM AUTHOR]

Pre-trained language model-based neural rankers have been widely applied in information retrieval (IR). However, the robustness issue of current IR models has not received sufficient attention, which could significantly impact the user experience in practical applications. In this study, we focus on the defensive ability of IR models against query attacks while guaranteeing their retrieval performance. We discover that improving the robustness of IR models not only requires a focus on model architecture and training methods but is also closely related to the quality of data. Different from previous research, we use large language models (LLMs) to generate query variations with the same intent, which exhibit richer and more realistic expressions while maintaining consistent query intent. Based on LLM-generated query variations, we propose a novel contrastive training framework that substantially enhances the robustness of IR models to query perturbations. Specifically, we combine the contrastive loss in the representation space of query variations with the ranking loss in the retrieval training stage to improve the model's ability to understand the underlying semantic information of queries. Experimental results on two public datasets, WikiQA and ANTIQUE, demonstrate that the proposed contrastive training approach effectively improves the robustness of models facing query attack scenarios while outperforming baselines in retrieval performance. Compared with the best baseline approach, the improvements in average robustness performance of Reranker IR models are 24.9%, 26.5%, 27.0%, and 75.0% on WikiQA and 8.7%, 1.9%, 6.3%, and 13.6% on ANTIQUE, in terms of the MAP (Mean Average Precision), MRR (Mean Reciprocal Rank), nDCG@10 (Normalized Discounted Cumulative Gain) and P@10 (Precision), respectively. [ABSTRACT FROM AUTHOR]

The visualization of bone imaging in vivo is of great significance for the understanding of some bone-related diseases or physiological processes. Herein, a bone-targeted NIR-II small molecule (TTQF-SO3), which was modified with multiple sulfonate groups, was successfully fabricated for the second near-infrared (NIR-II) bone imaging. In vitro studies revealed that TTQF-SO3 showed high affinity for hydroxyapatite and excellent macrophage accumulation ability. In in vivo assays, TTQF-SO3 displayed high bone uptake ability and high NIR-II bone imaging quality, demonstrating the specific bone-targeting ability of the sulfonate-containing probe. In addition, the noninvasive NIR-II imaging detection in bone calcium loss was successfully verified in osteoporosis mice models. Moreover, the negative charge characteristic of TTQF-SO3 showed efficient lymphoid enrichment in living mice by intravenous injection. Overall, these warrant that our TTQF-SO3 is an optimal bone-targeted diagnostic agent for high-quality NIR-II imaging, highlighting its potential promise for clinical translation. [ABSTRACT FROM AUTHOR]

Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error). [ABSTRACT FROM AUTHOR]

Gene therapy holds great promise as a biomedicine for treating cancer, viral infection, and recessive gene disorders, while genetic detection has been used for disease diagnosis and prevention. The appropriate carrier is crucial to improve the delivery efficiency of nucleic acids. Although viral and non-viral delivery vectors have been developed, their poor nucleic acid loading efficiency, off-target toxicity, and immunogenicity significantly hamper their use. Metal-organic frameworks (MOFs) have attracted a lot of attention owing to their high surface areas and tunable structures. Recently, nano-metric MOFs are shown to be promising nonviral vectors in gene delivery and detection although at an early stage of development. This review focuses on highlighting the current status and promising breakthroughs of MOF use in nucleic acid delivery, including siRNA, miRNA and mRNA delivery, DNA gene delivery and transfection, as well as probes for detection of gene expression. This study will help promote the clinical transformation and application of gene modulation, gene delivery, and detection that use of MOFs can offer. [ABSTRACT FROM AUTHOR]

To improve bone metastases treatment efficacy, current strategies are focused on the integration of chemotherapy with phototheranostic. However, the success of phototheranostic approaches is hampered by the limited tissue penetration depth of near‐infrared‐I (NIR‐I) light (700–900 nm). In this study, a NIR‐II (1000–1700 nm) excitation phototheranostic (BTZ/Fe2+@BTF/ALD) is presented for NIR‐II fluorescence imaging and NIR‐II photoacoustic imaging‐guided NIR‐II photothermal therapy (PTT), chemotherapy, and chemodynamic therapy (CDT) of breast cancer bone metastases. This phototheranostic is developed by integrating a dopamine‐modified NIR‐II absorbing donor–acceptor–donor small molecule (BBT‐FT‐DA), the boronate anticancer drug bortezomib (BTZ), and Fe2+ ions, as CDT catalysts, into an amphiphilic PEGylated phospholipid modified with the bone‐targeting ligand alendronate. In acidic and hydrogen peroxide (H2O2) over expression tumor microenvironment, the boronate–catechol linkage is cleaved and BTZ and Fe2+ ions are released to initiate the Fenton reaction, that is, chemotherapy and CDT, respectively, are initialized. It is confirmed using the murine 4T1 bone metastasis model that BTZ/Fe2+@BTF/ALD significantly suppresses the progression of tumor cells in the bone tissue via a synergistic NIR‐II PTT/chemotherapy/CDT effect. Overall, this work provides fresh insights to guide the development of NIR‐II phototheranostics for breast cancer bone metastases. [ABSTRACT FROM AUTHOR]

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First‐time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first‐time collaboration increased after the outbreak of COVID‐19, and international collaboration witnessed a sudden decrease. During COVID‐19, papers with more first‐time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic. [ABSTRACT FROM AUTHOR]

Previous studies have showed promising but short-lived activity of sorafenib in osteosarcoma treatments. Researches have suggested ameliorated sensitivity to standard dose of conventional cancer therapies in combination with histone deacetylase inhibitors (HDACis) through various mechanisms. Herein, for the first time, we exploited the synergism of combination therapies with sorafenib and chidamide, a member of HDACis, in the control of OS using human OS cell lines and OS xenograft mouse model and discussed interactive mechanisms between the two drugs. The combination therapy exerted a strong synergism in the inhibition of OS cell proliferation, meanwhile prominently induced cell apoptosis and cell cycle arrest in G0/G1 phase in OS cells with increased expression of MCL-1, decreased expression of caspase-3 and P21, along with diminished level of the overlapped protein P-ERK1/2. Furthermore, oral administration of the combined treatment led to a more optical therapeutic outcome, including lower degrees of tumoral cell proliferation, greater extent of apoptosis, along with induction of cell cycle arrest in tumor tissues, while exhibiting minimal toxicity. This study shows that the combination of sorafenib and chidamide can combat OS in a synergistic fashion and prompts the promising development of innovative combined therapeutic strategies for OS. [ABSTRACT FROM AUTHOR]

Background: The overall survival rate of osteosarcoma (OS) patients has not been improved for 30 years, and the diagnosis and treatment of OS is still a critical issue. To improve OS treatment and prognosis, novel kinds of theranostic modalities are required. Molecular optical imaging and phototherapy, including photothermal therapy (PTT) and photodynamic therapy (PDT), are promising strategies for cancer theranostics that exhibit high imaging sensitivity as well as favorable therapeutic efficacy with minimal side effect. In this study, semiconducting polymer nanoparticles (SPN-PT) for OS-targeted PTT/PDT are designed and prepared, using a semiconducting polymer (PCPDTBT), providing fluorescent emission in the second near-infrared window (NIR-II, 1000 - 1700 nm) and photoacoustic (PA) signal in the first near-infrared window (NIR-I, 650 - 900 nm), served as the photosensitizer, and a polyethylene glycolylated (PEGylated) peptide PT, providing targeting ability to OS. Results: The results showed that SPN-PT nanoparticles significantly accelerated OS-specific cellular uptake and enhanced therapeutic efficiency of PTT and PDT effects in OS cell lines and xenograft mouse models. SPN-PT carried out significant anti-tumor activities against OS both in vitro and in vivo. Conclusions: Peptide-based semiconducting polymer nanoparticles permit efficient NIR-II fluorescence/NIR-I PA dual-modal imaging and targeted PTT/PDT for OS. [ABSTRACT FROM AUTHOR]

COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking. [ABSTRACT FROM AUTHOR]

We would like to extend the ideas of entity paper networks to a full-fledged scholarly communication knowledge graph and apply graph deep learning methods to detect hidden patterns and relationships. After that, the authors proposed new ways to form an entity citation network by using citation contexts and believed that paper-entity bipartite citation networks might be a more accurate way to represent entity networks. By using genes extracted from titles and abstracts, Song et al. ([4]) formed a gene-gene citation network (GCG) that if gene A in the citing article cites gene B in the cited article, then A and B form an edge between them. [Extracted from the article]

Purpose: The small peptide TMTP1 (NVVRQ) has been proved to target a series of highly metastatic tumor cells. The aim of this study was to develop a new agent based on TMTP1 conjugated with Evans blue (EB), to increase tumor uptake and modify the pharmacokinetic characteristics of the resulting radiolabeled agent.Procedures: DOTA-EB-TMTP1 was prepared through conventional solid-phase peptide synthesis chemistry. Then, it was successfully labeled with Cu-64 to obtain [64Cu]DOTA-EB-TMTP1. The tumor targeting properties were evaluated in vivo using 143B xenografts.Results: DOTA-EB-TMTP1 was successfully labeled with Cu-64 in a yield of 87.3 ± 5.2 %. In a small animal positron emission tomography/X-ray computed tomography (PET/CT) study in osteosarcoma 143B xenograft mice, [64Cu]DOTA-EB-TMTP1 was found to rapidly accumulate in the tumor tissue. The tumor uptake increased over time and reached a plateau of 6.50 ± 0.88 % ID/g 8 h after tail vein injection. The radioactivity remained in the tumor tissue 48 h postinjection with a negligible decrease.Conclusions: Overall, the introduction of the EB motif to TMTP1 significantly changed its pharmacokinetics in vivo, and this strategy fulfills the purpose of prolonging the blood circulation and enhancing the tumor uptake. [64Cu]DOTA-EB-TMTP1 is a promising agent for osteosarcoma targeting. Moreover, our study highlights that DOTA-EB-TMTP1 is a good candidate for labeling with different radionuclides for potential theranostic applications. [ABSTRACT FROM AUTHOR]

PubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities. Measurement(s) textual entity • author information textual entity • funding source declaration textual entity • abstract • Biologic Entity Classification Technology Type(s) machine learning • computational modeling technique Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12452597 [ABSTRACT FROM AUTHOR]

The dysfunction of the circulatory system leads to various pathological processes with high morbidity. Recently, fluorescence imaging in the near‐infrared II (NIR‐II) window (1000–1700 nm) has attracted immense attention in many biological processes. The rapid metabolism and low toxicity of some NIR‐II organic small molecules indicate their feasibility for use in visualizing the circulatory system. However, most of the reported NIR‐II organic small molecules presently encounter such dilemmas as complicated synthetic procedures and low quantum yields (QY). To address this challenge, a series of facilely prepared NIR‐II organic small molecule CQ‐T (CQ‐1‐4T) are designed and these compounds are loaded with biocompatible human serum albumin (HSA) to improve QY. Among them, CQL (CQ‐4T/HSA) demonstrates superior optical properties and a 6.65‐fold increase in fluorescence compared to the small molecule alone. Further work validates the efficacy and accuracy of CQL in monitoring the real‐time circulatory system‐related physiological and pathological processes in vivo, including thrombosis, peripheral arterial disease, tumor angiogenesis, and lymphatic drainage. Moreover, the excellent optical properties of CQL enable precise tumor resection and sentinel lymph node biopsy under NIR‐II navigation. In conclusion, CQL is a novel and promising NIR‐II organic probe with multifunctional imaging capability. It is highly desirable to accelerate its future translations into the clinic. [ABSTRACT FROM AUTHOR]

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3‐month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)‐based Tencent finance‐related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods‐based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen‐300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short‐term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model. [ABSTRACT FROM AUTHOR]

Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding to represent biochemical entities and their relations as dense and low-dimensional vectors. We build a cascade learning framework which incorporates semantic features from the knowledge embedding model, and graph features from the graph embedding model, to score the probability of linking. The proposed method performs noticeably better than the models with which it is compared. It predicted links and entities with an accuracy of 93%, and its average hits@10 score has an average of 8.6% absolute improvement compared with original knowledge embedding model, 1.1% to 9.7% absolute improvement compared with other knowledge and graph embedding algorithm. In addition, we designed a meta-path algorithm to detect path relations in the biomedical network. Case studies further verify the value of the proposed model in finding potential relationships between diseases, drugs, genes, treatments, etc. Amongst the findings of the proposed model are the suggestion that VDR (vitamin D receptor) may be linked to prostate cancer. This is backed by evidence from medical databases and published research, supporting the suggestion that our proposed model could be of value to biomedical researchers. [ABSTRACT FROM AUTHOR]

Purpose Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.Design/methodology/approach To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.Findings Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.Practical implications The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.Originality/value The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links. [ABSTRACT FROM AUTHOR]

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods. [ABSTRACT FROM AUTHOR]

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo. [ABSTRACT FROM AUTHOR]

This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them. [ABSTRACT FROM AUTHOR]

This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1007/s11307-021-01598-w [ABSTRACT FROM AUTHOR]

Recently, various second near‐infrared window (NIR‐II, 1000–1700 nm) fluorophores have been synthesized for in vivo imaging with nonradiation, high resolution, and low autofluorescence. However, most of the NIR‐II fluorophores, especially inorganic nanoprobes, are mainly retained in the reticuloendothelial system (RES) such as the liver and spleen, leading to long‐term safety concerns. Herein, a type of lanthanide‐based excretable NIR‐II nanoparticle, RENPs@Lips, which can be quickly cleared out of body after intravenous administration with half‐lives of 23.0 h for the liver and 14.9 h for the spleen, is reported. Interestingly, over 90% of RENPs@Lips can be excreted through a hepatobiliary system within 72 h postinjection. The moderate blood half‐time (T1/2 = 17.96 min) allows for multifunctional applications in delineating the hemodynamics of vascular disorders (artery thrombosis, ischemia, and tumor angiogenesis) and monitoring blood perfusion in response to acute ischemia. In addition, RENPs@Lips exhibit high performance in identifying orthotopic tumor vessels intraoperatively and embolization surgery under NIR‐II imaging navigation. Moreover, excellent signal‐to‐background ratio (SBR) is successfully achieved to facilitate sentinel lymph nodes biopsy (SLNB) with tumor‐bearing mice. The high biocompatibility, favorable excretability, and outstanding optical properties warrant RENPs@Lips as novel promising NIR‐II nanoparticles for future applications and translation into an interdisciplinary amalgamation of research in diverse fields. [ABSTRACT FROM AUTHOR]