Genome-Wide Identification and Characterization of WRKY Transcription Factors in Betula platyphylla Suk. and Their Responses to Abiotic Stresses.

The WRKY transcription factor (TF) family is one the largest plant-specific transcription factor families. It has been proven to play significant roles in multiple plant biological processes, especially stress response. Although many WRKY TFs have been identified in various plant species, WRKYs in white birch (Betula platyphylla Suk.) remain to be studied. Here, we identified a total of 68 BpWRKYs, which could be classified into four main groups. The basic physiochemical properties of these TFs were analyzed using bioinformatics tools, including molecular weight, isoelectric point, chromosome location, and predicted subcellular localization. Most BpWRKYs were predicted to be located in the nucleus. Synteny analysis found 17 syntenic gene pairs among BpWRKYs and 52 syntenic gene pairs between BpWRKYs and AtWRKYs. The cis-acting elements in the promoters of BpWRKYs could be enriched in multiple plant biological processes, including stress response, hormone response, growth and development, and binding sites. Tissue-specific expression analysis using qRT-PCR showed that most BpWRKYs exhibited highest expression levels in the root. After ABA, salt (NaCl), or cold treatment, different BpWRKYs showed different expression patterns at different treatment times. Furthermore, the results of the Y2H assay proved the interaction between BpWRKY17 and a cold-responsive TF, BpCBF7. By transient expression assay, BpWRKY17 and BpWRKY67 were localized in the nucleus, consistent with the previous prediction. Our study hopes to shed light for research on WRKY TFs and plant stress response.

Assessment of health benefit of PM2.5 reduction during COVID-19 lockdown in China and separating contributions from anthropogenic emissions and meteorology.

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015-2019, and find that the national average of PM2.5 decreases by 18 µg/m3, and mean PM2.5 for most sites (about 75%) decrease by 30%-60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov-Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing-Tianjin-Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 µg/m3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively.

Population-Wide Peer Comparison Audit and Feedback to Reduce Antibiotic Initiation and Duration in Long-Term Care Facilities with Embedded Randomized Controlled Trial.

BACKGROUND: Antibiotic overprescribing in long-term care settings is driven by prescriber preferences and is associated with preventable harms for residents. We aimed to determine whether peer comparison audit and feedback reporting for physicians reduces antibiotic overprescribing among residents. METHODS: We employed a province wide, difference-in-differences study of antibiotic prescribing audit and feedback, with an embedded pragmatic randomized controlled trial (RCT) across all long-term care facilities in Ontario, Canada, in 2019. The study year included 1238 physicians caring for 96 185 residents. In total, 895 (72%) physicians received no feedback; 343 (28%) were enrolled to receive audit and feedback and randomized 1:1 to static or dynamic reports. The primary outcomes were proportion of residents initiated on an antibiotic and proportion of antibiotics prolonged beyond 7 days per quarter. RESULTS: Among all residents, between the first quarter of 2018 and last quarter of 2019, there were temporal declines in antibiotic initiation (28.4% to 21.3%) and prolonged duration (34.4% to 29.0%). Difference-in-differences analysis confirmed that feedback was associated with a greater decline in prolonged antibiotics (adjusted difference -2.65%, 95% confidence interval [CI]: -4.93 to -.28%, P = .026), but there was no significant difference in antibiotic initiation. The reduction in antibiotic durations was associated with 335 912 fewer days of treatment. The embedded RCT detected no differences in outcomes between the dynamic and static reports. CONCLUSIONS: Peer comparison audit and feedback is a pragmatic intervention that can generate small relative reductions in the use of antibiotics for prolonged durations that translate to large reductions in antibiotic days of treatment across populations. Clinical Trials Registration. NCT03807466.

Acute effects of air pollution on lupus nephritis in patients with systemic lupus erythematosus: A multicenter panel study in China.

Air pollution may trigger systemic lupus erythematosus (SLE). However, few studies have investigated the associations between air pollution and complications of SLE, such as lupus nephritis (LN). In this study, multicenter longitudinal data from 13 hospitals in China, including 8552 SLE patients with 24,762 visits, were used. Based on the generalized estimating equation (GEE) model, we assessed the associations of LN occurrence with short-term exposures to different air pollutants including particulate matter with an aerodynamic diameter < 2.5 µm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). We identified 2672 LN patients, and about half of them were from east China. Our results based on the entire data set showed that PM2.5 and NO2 were risk factors for LN within one month after exposure, with odds ratio of 1.16 (95% confidence interval (CI), 1.08-1.19) at lag 18 day and 1.19 (95% CI, 1.12-1.26) at lag 16 day relative to an interquartile range (IQR) increase in PM2.5 and NO2, respectively. This positive association between LN and NO2 was also observed for south, west, and east China. In addition, we found that the short term exposure to CO and O3 was not generally associated with LN. Finally, the negative associations of LN with SO2 were found for the entire region and east China. Our results implied that SLE patients may gain the health benefits of air quality improvement in China. Our work also provided evidence that short-term variations in air pollution may trigger LN, and further studies are needed to confirm these findings and the potential pathogenic mechanisms should be explored.

Influence of spatial resolution of PM2.5 concentrations and population on health impact assessment from 2010 to 2020 in China.

Ambient PM2.5 pollution is a leading environmental health risk factor worldwide. The spatial resolution of PM2.5 concentrations and population strongly impacts PM2.5-related health impact estimates. However, long-term variations and regional differences in this impact have rarely been explored, particularly in China. Here, by aggregating satellite-derived PM2.5 concentration and population datasets at 1-km resolution in China to coarser resolutions (10, 50, and 100 km), we evaluated decadal changes in the impact of resolution on health assessments at national and local scales. For the sensitivity of population-weighted mean (PWM) PM2.5 concentrations to resolution, we found that the national PWM PM2.5 concentration decreased with coarser resolutions; this pattern was widely observed and was more obvious in southern and central China and the Sichuan Basin. The results showed that the sensitivity of national PWM PM2.5 concentrations to resolution continuously weakened from 2010 to 2020, likely due to a reduction in the spatial heterogeneity of PM2.5 concentrations in regions with high sensitivity. This weakness caused a large underestimation of the long-term trend of national PWM PM2.5 using a 100-km resolution, which was 7% lower than the trend at 1 km. Regarding the sensitivity of PM2.5-attributable mortality to resolution, most of China exhibited a pattern in which attributable mortality decreased with coarser resolution. The sensitivity of the estimated PM2.5-attributable mortality to resolution also weakened over time on a national scale and in most parts of China. Nevertheless, the weakness for mortality sensitivity was not as apparent as for PWM PM2.5 sensitivity. This was likely because different drivers played distinct roles in the temporal variation of the mortality sensitivity: population aging enhanced the sensitivity, and variations in PM2.5 concentrations and population distribution both weakened the sensitivity. However, the national attributable mortality trend at a 100-km resolution was still underestimated by 1.75% relative to the 1-km resolution.

Behavioral Nudges to Improve Audit and Feedback Report Opening Among Antibiotic Prescribers: A Randomized Controlled Trial.

Background: Peer comparison audit and feedback has demonstrated effectiveness in improving antibiotic prescribing practices, but only a minority of prescribers view their reports. We rigorously tested 3 behavioral nudging techniques delivered by email to improve report opening. Methods: We conducted a pragmatic randomized controlled trial among Ontario long-term care prescribers enrolled in an ongoing peer comparison audit and feedback program which includes data on their antibiotic prescribing patterns. Physicians were randomized to 1 of 8 possible sequences of intervention/control allocation to 3 different behavioral email nudges: a social peer comparison nudge (January 2020), a maintenance of professional certification incentive nudge (October 2020), and a prior participation nudge (January 2021). The primary outcome was feedback report opening; the primary analysis pooled the effects of all 3 nudging interventions. Results: The trial included 421 physicians caring for >28 000 residents at 450 facilities. In the pooled analysis, physicians opened only 29.6% of intervention and 23.9% of control reports (odds ratio [OR], 1.51 [95% confidence interval {CI}, 1.10-2.07], P = .011); this difference remained significant after accounting for physician characteristics and clustering (adjusted OR [aOR], 1.74 [95% CI, 1.24-2.45], P = .0014). Of individual nudging techniques, the prior participation nudge was associated with a significant increase in report opening (OR, 1.62 [95% CI, 1.06-2.47], P = .026; aOR, 2.16 [95% CI, 1.33-3.50], P = .0018). In the pooled analysis, nudges were also associated with accessing more report pages (aOR, 1.28 [95% CI, 1.14-1.43], P < .001). Conclusions: Enhanced nudging strategies modestly improved report opening, but more work is needed to optimize physician engagement with audit and feedback. Clinical Trials Registration: NCT04187742.

Epilepsy Signal Recognition Using Online Transfer TSK Fuzzy Classifier Underlying Classification Error and Joint Distribution Consensus Regularization.

In this study, an online transfer TSK fuzzy classifier O-T-TSK-FC is proposed for recognizing epilepsy signals. Compared with most of the existing transfer learning models, O-T-TSK-FC enjoys its merits from the following three aspects: 1) Since different patients often response to the same neuronal firing stimulation in different neural manners, the labeled data in the source domain cannot accurately represent the primary EEG data in the target domain. Therefore, we design an objective function which can integrate with subject-specific data in the target domain to induce the target predictive function. 2) A new regularization used for knowledge transfer is proposed from the perspective of error consensus, and its rationality is explained from the perspective of probability density estimation. 3) Clustering is used to partition source domains so as to reduce the computation of O-T-TSK-FC without affecting its performance. Based on the EEG signals collected from Bonn University, six different online scenarios for transfer learning are constructed. Experimental results on them show that O-T-TSK-FC performs better than benchmarking algorithms and robustly.

Seizure Classification From EEG Signals Using an Online Selective Transfer TSK Fuzzy Classifier With Joint Distribution Adaption and Manifold Regularization.

To recognize abnormal electroencephalogram (EEG) signals for epileptics, in this study, we proposed an online selective transfer TSK fuzzy classifier underlying joint distribution adaption and manifold regularization. Compared with most of the existing transfer classifiers, our classifier has its own characteristics: (1) the labeled EEG epochs from the source domain cannot accurately represent the primary EEG epochs in the target domain. Our classifier can make use of very few calibration data in the target domain to induce the target predictive function. (2) A joint distribution adaption is used to minimize the marginal distribution distance and the conditional distribution distance between the source domain and the target domain. (3) Clustering techniques are used to select source domains so that the computational complexity of our classifier is reduced. We construct six transfer scenarios based on the original EEG signals provided by the Bonn University to verify the performance of our classifier and introduce four baselines and a transfer support vector machine (SVM) for benchmarking studies. Experimental results indicate that our classifier wins the best performance and is not very sensitive to its parameters.

A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports.

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

EHR2Vec: Representation Learning of Medical Concepts From Temporal Patterns of Clinical Notes Based on Self-Attention Mechanism.

Efficiently learning representations of clinical concepts (i. e., symptoms, lab test, etc.) from unstructured clinical notes of electronic health record (EHR) data remain significant challenges, since each patient may have multiple visits at different times and each visit may contain different sequential concepts. Therefore, learning distributed representations from temporal patterns of clinical notes is an essential step for downstream applications on EHR data. However, existing methods for EHR representation learning can not adequately capture either contextual information per-visit or temporal information at multiple visits. In this study, we developed a new vector embedding method called EHR2Vec that can learn semantically-meaningful representations of clinical concepts. EHR2Vec incorporated the self-attention structure and showed its utility in accurately identifying relevant clinical concept entities considering time sequence information from multiple visits. Using EHR data from systemic lupus erythematosus (SLE) patients as a case study, we showed EHR2Vec outperforms in identifying interpretable representations compared to other well-known methods including Word2Vec and Med2Vec, according to clinical experts' evaluations.

Environmental epidemiology of Kawasaki disease: Linking disease etiology, pathogenesis and global distribution.

BACKGROUND: The pathogenesis of Kawasaki disease (KD) is commonly ascribed to an exaggerated immunologic response to an unidentified environmental or infectious trigger in susceptible children. A comprehensive framework linking epidemiological data and global distribution of KD has not yet been proposed. METHODS AND FINDINGS: Patients with KD (n = 81) were enrolled within 6 weeks of diagnosis along with control subjects (n = 87). All completed an extensive epidemiological questionnaire. Geographic localization software characterized the subjects' neighborhood. KD incidence was compared to atmospheric biological particles counts and winds patterns. These data were used to create a comprehensive risk framework for KD, which we tested against published data on the global distribution. Compared to controls, patients with KD were more likely to be of Asian ancestry and were more likely to live in an environment with low exposure to environmental allergens. Higher atmospheric counts of biological particles other than fungus/spores were associated with a temporal reduction in incidence of KD. Finally, westerly winds were associated with increased fungal particles in the atmosphere and increased incidence of KD over the Greater Toronto Area. Our proposed framework was able to explain approximately 80% of the variation in the global distribution of KD. The main limitations of the study are that the majority of data used in this study are limited to the Canadian context and our proposed disease framework is theoretical and circumstantial rather than the result of a single simulation. CONCLUSIONS: Our proposed etiologic framework incorporates the 1) proportion of population that are genetically susceptible; 2) modulation of risk, determined by habitual exposure to environmental allergens, seasonal variations of atmospheric biological particles and contact with infectious diseases; and 3) exposure to the putative trigger. Future modelling of individual risk and global distribution will be strengthened by taking into consideration all of these non-traditional elements.

[Study on Bartonella species in rodents in western Yunnan, China].

OBJECTIVE: To study the infection status of Bartonella spp. in rodents in western part of Yunnan province. METHODS: Blood samples were collected from four species of rodents captured in four counties in western Yunnan in 2004. Bartomella was isolated through being cultured in brain and heart infusion agar media containing 5% rabbit blood. Suspective Bartomella strains isolates were confirmed by amplification of 379 bp of citrate synthase (gltA) gene with specific primer by polymerase chin reaction (PCR). RESULTS: Fifty-four strains of Bartomella isolates were obtained from 397 samples including four rodent species captured in the fields with an overall isolation-rate of 13.6% (54/397). The rates of isolation among different species were: 22.0% (22/100) in Rattus nitidus, 14.8% (31/210) in Rattus flavipectus and 1.2%(1/87) in Rattus norvegicus while in R. t. yunnanensis it was negative. CONCLUSION: These findings demonstrated that the local rodents in western Yunnan were widely infected by Bartomella spp. It is indispensable to study the vector and the route of transmission to discover the relations between Bartomella and human diseases.