Unveiling the Potential of Highly Porous Covalent Organic Frameworks for Water-Jet Rewritable Papers.

The massive use of paper has resulted in significant negative impacts on the environment. Fortunately, recent progress has been made in the field of rewritable paper, which has great potential in solving the increasing demand for paper while minimizing its environmental footprint. In this work, we report a green and economic strategy to develop ink-free rewritable paper by introducing hydrochromic covalent organic frameworks (COFs) in paper and using water as the sole trigger. When exposed to water or acidic solvents, two kinds of imino COFs change their colors reversibly from red to black. Additionally, a new visible absorption band appears, indicating that it can be transformed into another structure reversibly. This reversibility may be due to the isomerization from the diiminol to an iminol/cisketoenamine and its inability to doubly tautomerize to a diketoenamine. Specifically, we prepared the rewritable paper by loading these two COFs onto filter paper by using the decompression filtration method. When exposed to water, the paper undergoes a color change from red to black, which shows promising potential for applications in water-jet printing. Additionally, there is no significant performance degradation after 20 uses and 10 days between, further highlighting their potential as rewritable papers. To further improve its uniformity, we take the interface polymerization strategy to yield highly crystalline and more compact membranes, which are then transferred to paper to prepare writable papers. Our research has opened up a way for the application of COFs as a water-based printing material.

Dynamic Fluorescence Materials Based on Naphthalimide-Functionalized Silica Aerogels and Applications in Advanced Information Encryption.

With the progress of forgery and decryption, the traditional encryption technology is apparent not enough, which strongly requires the development of advanced multidimensional encryption strategies and technologies. Photo-stimuli responsive fluorescent materials are promising as candidate materials for advanced information encryption. Here, we have reported new photo-stimuli responsive materials by encapsulating photochromic molecules spiropyrans (SPs) into naphthalimide-functionalized silica aerogels. By introducing different modification groups (dimethylamino) into 1,8-naphthalimide, we obtained two kinds of silica aerogels that emit blue and green colors. The naphthalimide-functionalized silica aerogels/dye composite exhibits a blue (dimethylamino-modified naphthalimide-functionalized silica aerogel showing green) emission from naphthalimide of silica aerogels at 450 nm (520 nm) and a red emission around 650 nm of SP. Under exposure to ultraviolet light, SP gradually transformed into the merocyanine (MC) form, and a strong absorption band appeared near 540 nm. At that time, the fluorescence resonance energy-transfer (FRET) process occurred between naphthalimide and the MC isomer. As the irradiation time is extended, the fluorescence color changes continuously from blue (green) to red through the FRET process. Using the time dependence of fluorescence, dynamic encryption patterns and multiple codes were successfully developed based on these functionalized silica aerogels. This work has provided important guidance for designing advanced information encryption materials.

A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK.

Objective: Given the uncertainty about the trends and extent of the rapidly evolving COVID-19 outbreak, and the lack of extensive testing in the United Kingdom, our understanding of COVID-19 transmission is limited. We proposed to use Twitter to identify personal reports of COVID-19 to assess whether this data can help inform as a source of data to help us understand and model the transmission and trajectory of COVID-19. Methods: We used natural language processing and machine learning framework. We collected tweets (excluding retweets) from the Twitter Streaming API that indicate that the user or a member of the user's household had been exposed to COVID-19. The tweets were required to be geo-tagged or have profile location metadata in the UK. Results: We identified a high level of agreement between personal reports from Twitter and lab-confirmed cases by geographical region in the UK. Temporal analysis indicated that personal reports from Twitter appear up to 2 weeks before UK government lab-confirmed cases are recorded. Conclusions: Analysis of tweets may indicate trends in COVID-19 in the UK and provide signals of geographical locations where resources may need to be targeted or where regional policies may need to be put in place to further limit the spread of COVID-19. It may also help inform policy makers of the restrictions in lockdown that are most effective or ineffective.

Fabrication of Ag3PO4/polyaniline-activated biochar photocatalyst for efficient triclosan degradation process and toxicity assessment.

Triclosan (TCS) is a typical environmental pollutant, which seriously threatens the health of humans and organisms. A novel strategy of biochar/Ag3PO4/polyaniline (PANI) composite photocatalyst was synthesized by a facile chemical precipitation method to efficiently degrade TCS. XRD, Raman, ESR, etc. were used to reveal the effective associations among physiochemistry, photochemistry and photocatalytic properties of the composite. It was proved the synergistic effects of biochar (T-Bio) and PANI resulted in the decrease of Ag3PO4 particle size, the enhancement of adsorption, the improvement of light utilization, the increase of photogenerated carrier separation and the promotion of reactive species. The photocatalytic mechanism showed h+ was the main active species, O2- and OH played minor roles. Under the irradiation of visible light, the optimal photocatalyst (1.0% T-Bio/AP/1.0% PANI) displayed excellent photocatalytic activity with the removal rate of 85.21% for TCS within 10 min, and the apparent rate constant K' was 2.38 times of Ag3PO4. 11 main intermediates for TCS degradation were identified, and their toxicity was significantly reduced. The possible degradation pathways were proposed. This work is the first systematic study on the degradation behavior of TCS by Ag3PO4-based photocatalyst, and it provides a new approach to fabricate photocatalysts with synergistic effects and amazing photocatalytic activity by biochar.

The enhancement mechanism of ultra-active Ag3PO4 modified by tungsten and the effective degradation towards phenolic pollutants.

A novel strategy of W modification was applied to overcome the disadvantages of Ag3PO4. Ultra-active Ag3PO4 with different W doping ratios were successfully synthesized by facile chemical precipitation method, among which 0.5%W-AP showed the best results. Meanwhile, the stability and yield were enhanced. XRD, Raman and ESR etc. were employed to investigate the morphology, structure and optical properties of samples. It was proved W6+ entered into the Ag3PO4 lattice, occupied the position of P5+ and doped in the form of WO42-. The significant improvement of photocatalytic performance of W doped Ag3PO4 was attributed to the change of morphology, the decrease of particle size, the increase of crystallinity, the shrink of band gap energy and the reduction of photo-induced carriers recombination rate with W doping. The photocatalytic mechanism analysis showed h+ was the main oxidative species in the photocatalytic process, •O2- and •OH played minor roles. Under visible light irradiation, the impacts of the important operating parameters on the typical phenolic pollutants, phenol and bisphenol A, were evaluated with 0.5%W-AP. It was confirmed that 68% and 82% of phenol and bisphenol A were respectively degraded within 15 min and 40 min under optimized photocatalytic parameters: 0.4 g/L catalyst dosage, 20 mg/L pollutant concentration, pH 5.7 and 125 mW/cm2 irradiation intensity, and the corresponding K' were 2.14 and 5.50 times of undoped samples. This work provides a new approach for effective degradation towards phenolic pollutants by Ag3PO4 with ultra-high photocatalytic activity, high applicability and enhanced stability and yield.

Text classification models for the automatic detection of nonmedical prescription medication use from social media.

BACKGROUND: Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. METHODS: We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class. RESULTS: Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. CONCLUSIONS: BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK.

The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language processing and machine learning framework could help provide an early indication of the chronological and geographical distribution of COVID-19 in England.

A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter.

The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.

Estradiol regulates the expression of CD45 splicing isoforms in lymphocytes.

CD45, a common leukocyte antigen expressed on the surface of all nucleated hematopoietic cells, indicates the developmental stage and functional status of lymphocytes by its alternative splicing isoforms. Estrogen is correlated with the immune activity of lymphocytes and is involved in the sex bias of several human autoimmune diseases, but the effect of estrogen on the expression of the CD45 splicing isoforms remains unknown. In the present study, a potential estrogen response element was identified on the opposite strand of the CD45 gene by bioinformatics software prediction. The results from RT-qPCR results showed that the expression levels of CD45RO isoform and CD45 antisense RNA were increased after the lymphocytes were treated with 10 nM 17beta-estradiol, and this effect of 17beta-estradiol was reversed when the lymphocytes were cotreated with an estrogen receptor antagonist. Moreover, bisulfite sequencing PCR showed that CD45 DNA methylation in lymphocytes was increased after the treatment with 10 nM 17beta-estradiol. In conclusion, estradiol regulated the expression of CD45 in an estrogen receptor-dependent manner and was associated with CD45 antisense RNA and DNA methylation. The results helped elucidate the regulatory mechanism of the expression of CD45 isoforms and the correlation between estrogen levels and immune activity in females.

A natural language processing pipeline to advance the use of Twitter data for digital epidemiology of adverse pregnancy outcomes.

BACKGROUND: In the United States, 17% of pregnancies end in fetal loss: miscarriage or stillbirth. Preterm birth affects 10% of live births in the United States and is the leading cause of neonatal death globally. Preterm births with low birthweight are the second leading cause of infant mortality in the United States. Despite their prevalence, the causes of miscarriage, stillbirth, and preterm birth are largely unknown. OBJECTIVE: The primary objectives of this study are to (1) assess whether women report miscarriage, stillbirth, and preterm birth, among others, on Twitter, and (2) develop natural language processing (NLP) methods to automatically identify users from which to select cases for large-scale observational studies. METHODS: We handcrafted regular expressions to retrieve tweets that mention an adverse pregnancy outcome, from a database containing more than 400 million publicly available tweets posted by more than 100,000 users who have announced their pregnancy on Twitter. Two annotators independently annotated 8109 (one random tweet per user) of the 22,912 retrieved tweets, distinguishing those reporting that the user has personally experienced the outcome ("outcome" tweets) from those that merely mention the outcome ("non-outcome" tweets). Inter-annotator agreement was κ = 0.90 (Cohen's kappa). We used the annotated tweets to train and evaluate feature-engineered and deep learning-based classifiers. We further annotated 7512 (of the 8109) tweets to develop a generalizable, rule-based module designed to filter out reported speech-that is, posts containing what was said by others-prior to automatic classification. We performed an extrinsic evaluation assessing whether the reported speech filter could improve the detection of women reporting adverse pregnancy outcomes on Twitter. RESULTS: The tweets annotated as "outcome" include 1632 women reporting miscarriage, 119 stillbirth, 749 preterm birth or premature labor, 217 low birthweight, 558 NICU admission, and 458 fetal/infant loss in general. A deep neural network, BERT-based classifier achieved the highest overall F1-score (0.88) for automatically detecting "outcome" tweets (precision = 0.87, recall = 0.89), with an F1-score of at least 0.82 and a precision of at least 0.84 for each of the adverse pregnancy outcomes. Our reported speech filter significantly (P < 0.05) improved the accuracy of Logistic Regression (from 78.0% to 80.8%) and majority voting-based ensemble (from 81.1% to 82.9%) classifiers. Although the filter did not improve the F1-score of the BERT-based classifier, it did improve precision-a trade-off of recall that may be acceptable for automated case selection of more prevalent outcomes. Without the filter, reported speech is one of the main sources of errors for the BERT-based classifier. CONCLUSION: This study demonstrates that (1) women do report their adverse pregnancy outcomes on Twitter, (2) our NLP pipeline can automatically identify users from which to select cases for large-scale observational studies, and (3) our reported speech filter would reduce the cost of annotating health-related social media data and can significantly improve the overall performance of feature-based classifiers.

Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter.

BACKGROUND: Although birth defects are the leading cause of infant mortality in the United States, methods for observing human pregnancies with birth defect outcomes are limited. OBJECTIVE: The primary objectives of this study were (i) to assess whether rare health-related events-in this case, birth defects-are reported on social media, (ii) to design and deploy a natural language processing (NLP) approach for collecting such sparse data from social media, and (iii) to utilize the collected data to discover a cohort of women whose pregnancies with birth defect outcomes could be observed on social media for epidemiological analysis. METHODS: To assess whether birth defects are mentioned on social media, we mined 432 million tweets posted by 112,647 users who were automatically identified via their public announcements of pregnancy on Twitter. To retrieve tweets that mention birth defects, we developed a rule-based, bootstrapping approach, which relies on a lexicon, lexical variants generated from the lexicon entries, regular expressions, post-processing, and manual analysis guided by distributional properties. To identify users whose pregnancies with birth defect outcomes could be observed for epidemiological analysis, inclusion criteria were (i) tweets indicating that the user's child has a birth defect, and (ii) accessibility to the user's tweets during pregnancy. We conducted a semi-automatic evaluation to estimate the recall of the tweet-collection approach, and performed a preliminary assessment of the prevalence of selected birth defects among the pregnancy cohort derived from Twitter. RESULTS: We manually annotated 16,822 retrieved tweets, distinguishing tweets indicating that the user's child has a birth defect (true positives) from tweets that merely mention birth defects (false positives). Inter-annotator agreement was substantial: κ = 0.79 (Cohen's kappa). Analyzing the timelines of the 646 users whose tweets were true positives resulted in the discovery of 195 users that met the inclusion criteria. Congenital heart defects are the most common type of birth defect reported on Twitter, consistent with findings in the general population. Based on an evaluation of 4169 tweets retrieved using alternative text mining methods, the recall of the tweet-collection approach was 0.95. CONCLUSIONS: Our contributions include (i) evidence that rare health-related events are indeed reported on Twitter, (ii) a generalizable, systematic NLP approach for collecting sparse tweets, (iii) a semi-automatic method to identify undetected tweets (false negatives), and (iv) a collection of publicly available tweets by pregnant users with birth defect outcomes, which could be used for future epidemiological analysis. In future work, the annotated tweets could be used to train machine learning algorithms to automatically identify users reporting birth defect outcomes, enabling the large-scale use of social media mining as a complementary method for such epidemiological research.

Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis.

BACKGROUND: Pregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias. OBJECTIVE: The primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information. METHODS: Our discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined. RESULTS: Our rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa (κ)=.79. On a blind test set, the implemented classifier obtained an overall F1 score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them. CONCLUSIONS: Social media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them.

Histone acetylation regulates orphan nuclear receptor NR4A1 expression in hypercholesterolaemia.

Hypercholesterolaemia and inflammation are correlated with atherogenesis. Orphan nuclear receptor NR4A1, as a key regulator of inflammation, is closely associated with lipid levels in vivo. However, the mechanism by which lipids regulate NR4A1 expression remains unknown. We aimed to elucidate the underlying mechanism of NR4A1 expression in monocytes during hypercholesterolaemia, and reveal the potential role of NR4A1 in hypercholesterolaemia-induced circulating inflammation. Circulating leucocytes were collected from blood samples of 139 patients with hypercholesterolaemia and 139 sex- and age-matched healthy subjects. We found that there was a low-grade inflammatory state and higher expression of NR4A1 in patients. Both total cholesterol and low-density lipoprotein cholesterol levels in plasma were positively correlated with NR4A1 mRNA level. ChIP revealed that acetylation of histone H3 was enriched in the NR4A1 promoter region in patients. Human mononuclear cell lines THP-1 and U937 were treated with cholesterol. Supporting our clinical observations, cholesterol enhanced p300 acetyltransferase and decreased HDAC7 (histone deacetylase 7) recruitment to the NR4A1 promoter region, resulting in histone H3 hyperacetylation and further contributing to NR4A1 up-regulation in monocytes. Moreover, cytosporone B, an NR4A1 agonist, completely reversed cholesterol-induced IL-6 (interleukin 6) and MCP-1 (monocyte chemoattractant protein 1) expression to below basal levels, and knockdown of NR4A1 expression by siRNA not only mimicked, but also exaggerated the effects of cholesterol on inflammatory biomarker up-regulation. Thus we conclude that histone acetylation contributes to the regulation of NR4A1 expression in hypercholesterolaemia, and that NR4A1 expression reduces hypercholesterolaemia-induced inflammation.

Capacitive Behavior of Single Gallium Oxide Nanobelt.

In this research, monocrystalline gallium oxide (Ga2O3) nanobelts were synthesized through oxidation of metal gallium at high temperature. An electronic device, based on an individual Ga2O3 nanobelt on Pt interdigital electrodes (IDEs), was fabricated to investigate the electrical characteristics of the Ga2O3 nanobelt in a dry atmosphere at room temperature. The current-voltage (I-V) and I/V-t characteristics show the capacitive behavior of the Ga2O3 nanobelt, indicating the existence of capacitive elements in the Pt/Ga2O3/Pt structure.