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A previous 1H-NMR method allowed the quantification of ephedrine alkaloids; however, there were some disadvantages. The cyclized derivatives resulted from the impurities of diethyl ether were identified and benzene was selected as the better extraction solvent. The locations of ephedrine alkaloids were confirmed with 2D NMR. Therefore, a specific 1H-NMR method has been modified for the quantification of ephedrine alkaloids. Accordingly, twenty Ephedrae Herba samples could be classified into three classes: (I) E. sinica-like species; (II) E. intermedia-like species; (III) others (lower alkaloid contents). The results indicated that ephedrine and pseudoephedrine are the major alkaloids in Ephedra plants, but the concentrations vary greatly determined by the plant species and the collection locations.

The Hutoubi Reservoir and its mainstream, Huyuan Stream, in the southern mountainous region of Taiwan, have experienced riverbed sedimentation and flood disasters for the past 150 years. In addition to climate change, it is necessary to scientifically consider its regulation for the next hundred years. This study adopted a collaborative approach, involving industry, government, and academia, using Nature-based Solutions (NbS) to enhance ecosystem services. The solution layout is constructed by widening the channel and constructing additional farm ponds and wetlands. An hydraulic simulation indicated that flood control was addressed. The restoration project would create diverse aquatic habitats by simulating and evaluating the distribution of ecological biotopes, using porous materials as revetments. It provided urban residents with forest leisure and recreational sites and supported the local agricultural and forestry products. The restoration has propagated local culture and created environmental and professional education. Therefore, ecological services are enhanced regarding regulation, support, provision, and culture. This pilot study, led by researchers, aimed to promote comprehensive management concepts considering all stakeholders and their active participation. We integrated NbS into the watershed and its river system as a pathway for facing the challenges of rapid urbanization and climate change and improving ecosystem services.

Inflammation is a cancer hallmark. NSAIDs improve overall survival (OS) in certain cancers. Real-world studies explored here whether NSAIDs improve non–small cell lung cancer (NSCLC) OS. Analyses independently interrogated clinical databases from The University of Texas MD Anderson Cancer Center (MDACC cohort, 1987 to 2015; 33,162 NSCLCs and 3,033 NSAID users) and Georgetown-MedStar health system (Georgetown cohort, 2000 to 2019; 4,497 NSCLCs and 1,993 NSAID users). Structured and unstructured clinical data were extracted from electronic health records using natural language processing (NLP). Associations were made between NSAID use and NSCLC prognostic features (tobacco use, gender, race, and body mass index, BMI). NSAIDs were statistically significantly (P < 0.0001) associated with increased NSCLC survival (5-year OS 29.7% for NSAID users vs. 13.1% for nonusers) in the MDACC cohort. NSAID users gained 11.6 months over nonusers in 5-year restricted mean survival time. Stratified analysis by stage, histopathology, and multicovariable assessment substantiated benefits. NSAID users were pooled independent of NSAID type and by NSAID type. Landmark analysis excluded immortal time bias. Survival improvements (P < 0.0001) were confirmed in the Georgetown cohort. Thus, real-world NSAID usage was independently associated with increased NSCLC survival in the MDACC and Georgetown cohorts. Findings were confirmed by landmark analyses and NSAID type. The OS benefits persisted despite tobacco use and did not depend on gender, race, or BMI (MDACC cohort, P < 0.0001). These real-world findings could guide future NSAID lung cancer randomized trials. Significance: NLP and real-world studies conducted in large cohorts explored whether NSAIDs improved survival across NSCLC stages, histopathology, gender, smoking history, or demographic groups. A statistically significant association between NSAID use and NSCLC survival was found. This provides a rationale for future NSAID randomized NSCLC trials.

Supplementary Table 6: The MD Anderson cohort 5-year survival rate and difference of 5-year restricted mean survival time in months between NSAID users and non-users by gender, race, and smoking status corresponding to Figure 2. Comparisons are made to the Georgetown cohort.

Supplemental Figure 2. The Kaplan-Meier analysis of overall survival and NSAID use in lung cancer cases within the MedStar-Georgetown University database (Georgetown cohort).

Inflammation is a cancer hallmark. NSAIDs improve overall survival (OS) in certain cancers. Real-world studies explored here whether NSAIDs improve non–small cell lung cancer (NSCLC) OS. Analyses independently interrogated clinical databases from The University of Texas MD Anderson Cancer Center (MDACC cohort, 1987 to 2015; 33,162 NSCLCs and 3,033 NSAID users) and Georgetown-MedStar health system (Georgetown cohort, 2000 to 2019; 4,497 NSCLCs and 1,993 NSAID users). Structured and unstructured clinical data were extracted from electronic health records using natural language processing (NLP). Associations were made between NSAID use and NSCLC prognostic features (tobacco use, gender, race, and body mass index, BMI). NSAIDs were statistically significantly (P < 0.0001) associated with increased NSCLC survival (5-year OS 29.7% for NSAID users vs. 13.1% for nonusers) in the MDACC cohort. NSAID users gained 11.6 months over nonusers in 5-year restricted mean survival time. Stratified analysis by stage, histopathology, and multicovariable assessment substantiated benefits. NSAID users were pooled independent of NSAID type and by NSAID type. Landmark analysis excluded immortal time bias. Survival improvements (P < 0.0001) were confirmed in the Georgetown cohort. Thus, real-world NSAID usage was independently associated with increased NSCLC survival in the MDACC and Georgetown cohorts. Findings were confirmed by landmark analyses and NSAID type. The OS benefits persisted despite tobacco use and did not depend on gender, race, or BMI (MDACC cohort, P < 0.0001). These real-world findings could guide future NSAID lung cancer randomized trials.Significance:NLP and real-world studies conducted in large cohorts explored whether NSAIDs improved survival across NSCLC stages, histopathology, gender, smoking history, or demographic groups. A statistically significant association between NSAID use and NSCLC survival was found. This provides a rationale for future NSAID randomized NSCLC trials.

The online question answering (QA) community has been popular in recent years. In this paper, we focus on the online health question answering (HQA) community. The HQA community provides a platform for health consumers to inquire about health information. There are two ways to use this platform. One is to post a question and wait for answers to be provided by authenticated doctors. The other is to search for relevant questions with answers. For the latter, health consumers may prefer an accepted answer marked by the previous health consumer. However, there is a large proportion of questions without an accepted answer and it is inconvenient for people who want to search for relevant questions. To address this issue, we aim to select high-quality answers from the answers without marked accepted answers. We propose a deep learning approach to achieve this goal. To train the model for the prediction of answer quality, we first view the accepted answer as the positive answer and propose a method to label the negative answer. Next, we capture the semantic information on the question and the answer by the deep learning structure. We then combine the information to predict the quality score of the answer. We collect data from one of the biggest Chinese HQA community and divide them into groups by the medical departments for detailed analysis. Finally, we conduct experiments to show the effectiveness of categorization and the labeling method. The results show that our approach outperforms other studies and we further research into the differences among the results of different categories.

OBJECTIVE: Conventional ultrafiltration (CUF) during cardiopulmonary bypass (CPB) serves to hemoconcentrate blood volume to avoid allogeneic blood transfusions. Previous studies have determined weight-indexed CUF volumes as a continuous variable are associated with postoperative AKI after cardiac surgery but optimal weight-indexed volumes that predict AKI have not been described. DESIGN: Retrospective cohort SETTING: Single-center university hospital PARTICIPANTS: 1,641 consecutive patients that underwent elective cardiac surgery between June 2013-December 2015. INTERVENTIONS: CUF volume was removed during CPB in all participants as part of our routine practice. We investigated the association of dichotomized weight indexed CUF volume removal with postoperative AKI development to provide pragmatic guidance for clinical practice at our institution. MEASUREMENTS AND MAIN RESULTS: Primary outcomes of postoperative AKI was defined by the KDIGO staging criteria and dichotomized, weight indexed CUF volumes (ml/kg) were defined by (i) extreme quartiles (Q3) and by (ii) Youden’s criterion that best predicted AKI development. Multivariable logistic regression models were developed to test the association of these dichotomized indices with AKI status. Postoperative AKI occurred in 827 patients (50.4%). Higher CUF volumes were associated with AKI development by quartiles (CUF >Q(3)=32.6 vs. CUF 32 mL/kg increased the risk for postoperative AKI development. Importantly, CUF volume removal of any amount did not mitigate allogeneic blood transfusion during elective cardiac surgery. Prospective studies are needed to validate these findings.

Pines evolve with fire and have diverged into fire-avoiders and fire-adapters. Pine needles are resistant to microbial decomposition, and their accumulation serves as a major fuel source for forest fires. Facilitated needle decomposition reduces forest fuel loads and probably interacts with the adaptive strategies of pines. However, there is little information available regarding differences in microbiomes associated with needle decomposition of the two types of pine. In this study, we monitored the microbial communities in three compartments associated with needle decomposition, namely, fresh needles, needle litter, and humus, along altitudinal gradients at three sites of the fire-tolerator Pinus taiwanensis and one site of the fire-avoider Pinus morrisonicola in Taiwan. The humus microbiome was dominated by an overwhelming proportion of bacteria. As a reservoir of decomposers, humus bacteria accounted for > 70% of the litter communities and showed marked differences in composition between the two Pinus species, as determined by non-metric multidimensional scaling. Such differences were associated with the abundance of bacterial genes encoding lignin-degrading enzymes, which were highest in the P. morrisonicola stand. Additionally, the humus accumulating under this fire-avoider was characterized by low ammonium concentrations and lower pH compared with that under the fire-tolerator, which is indicative of conditions conducive to litter decomposition. Conclusively, our findings suggested that P. morrisonicola grows in more favorable conditions for needle decomposition compared with the habitats where the P. taiwanensis lives.

Objectives To determine whether precardiopulmonary bypass (CPB) normalization of antithrombin levels in infants to 100% improves heparin sensitivity and anticoagulation during CPB and has beneficial effects into the postoperative period. Design Randomized, double-blinded, placebo-controlled prospective study. Setting Multicenter study performed in 2 academic hospitals. Participants The study comprised 40 infants younger than 7 months with preoperative antithrombin levels Interventions Antithrombin levels were increased with exogenous antithrombin to 100% functional level intraoperatively before surgical incision. Measurements and Main Results Demographics, clinical variables, and blood samples were collected up to postoperative day 4. Higher first post-heparin activated clotting times (sec) were observed in the antithrombin group despite similar initial heparin dosing. There was an increase in heparin sensitivity in the antithrombin group. There was significantly lower 24-hour chest tube output (mL/kg) in the antithrombin group and lower overall blood product unit exposures in the antithrombin group as a whole. Functional antithrombin levels (%) were significantly higher in the treatment group versus placebo group until postoperative day 2. D-dimer was significantly lower in the antithrombin group than in the placebo group on postoperative day 4. Conclusion Supplementation of antithrombin in infants with low antithrombin levels improves heparin sensitivity and anticoagulation during CPB without increased rates of bleeding or adverse events. Beneficial effects may be seen into the postoperative period, reflected by significantly less postoperative bleeding and exposure to blood products and reduced generation of D-dimers.

Introduction: Postoperative acute kidney injury (AKI) confers poor outcomes after cardiac surgery. Conventional ultrafiltration (CUF) is performed on cardiopulmonary bypass (CPB) to raise hematocrit, avoid intraoperative blood transfusion, and provide renal protection. These protective effects remain unclear with high CUF volumes, which may cause hypovolemia and renal hypoperfusion. Hypothesis: Higher CUF volumes will be associated with increased AKI and poor outcomes. Methods: Retrospective analysis of 1,791 consecutive patients undergoing cardiac surgery (CABG, CABG/Valve, Valve only) was performed between June 2013 and December 2015. Institutional CPB protocol with CUF was used during all cases. Uni- and multivariable logistic regression analyses determined predictors for AKI. CUF volume analyses were performed, defined by two extreme quartiles or by Youden index derived from ROC analysis, and compared for their effect on AKI rate. Mortality data was acquired from the Social Security Administration’s Death Master File through 1 year after index surgery. Results: CUF volume independently predicted AKI (prd quartile, was > 2,900 ml (OR=1.72, 95% CI 1.25, 2.38) and defined by Youden index, was > 2,239 ml, (OR=1.52, 95% CI 1.22, 1.89). While associated with increased intraoperative blood transfusion volume (p=0.002), CUF volume in AKI patients (N=903) predicted increased ICU (p Conclusions: CUF volume removal during CPB predicted blood transfusion requirements, postoperative AKI, and ICU/Hospital LOS, while AKI predicted 30-day mortality.

In this paper, we propose a novel algorithm for mining frequent sequences from transaction databases. The transactions of the same customers form a set of customer sequences. A sequence (an ordered list of itemsets) is frequent if the number of customer sequences containing it satisfies the user-specified threshold. The 1-sequence is a special type of sequences because it consists of only a single itemset instead of an ordered list, while the k-sequence is a sequence composed of k itemsets. Compared with the cost of mining frequent k-sequences (k???2), the cost of mining frequent 1-sequences is negligible. We adopt a two-phase architecture to find the two types of frequent sequences separately in order that the discovery of frequent k-sequences can be well designed and optimized. For efficient frequent k-sequence mining, every frequent 1-sequence is encoded as a unique symbol and the database is transformed into one constituted by the symbols. We find that it is unnecessary to encode all the frequent 1-seqences, and make full use of the discovered frequent 1-sequences to transform the database into one with a smaller size. For every k???2, the customer sequences in the transformed database are scanned to find all the frequent k-sequences. We devise the compact representation for a customer sequence and elaborate the method to enumerate all distinct subsequences from a customer sequence without redundant scans. The soundness of the proposed approach is verified and a number of experiments are performed. The results show that our approach outperforms the previous works in both scalability and execution time.

Querying polyphonic music from a large data collection is an interesting topic. Recently, researchers have attempted to provide efficient methods for content-based retrieval in polyphonic music databases where queries are polyphonic. However, most of them do not work well for similarity search, which is important to many applications. In this paper, we propose three polyphonic representations with the associated similarity measures and a novel method to retrieve k music works that contain segments most similar to the query. In general, most of the index-based methods for similarity search generate all the possible answers to the query and then perform exact matching on the index for each possible answer. Based on the edit distance, our method generates only a few possible answers by performing the deletion and/or replacement operations on the query. Each possible answer is then used to perform exact matching on a list-based index, which allows the insertion operations to be performed. For each possible answer, its edit distance to the query is regarded as a lower bound of the edit distances between the matched results and the query. Based on the kNN results that match a possible answer, the possible answers that cannot provide better results are skipped. By using this mechanism, we design a method for efficient kNN search in polyphonic music databases. The experimental results show that our method outperforms the previous methods in efficiency. We also evaluate the effectiveness of our method by showing the search results to the musician and nonmusician user groups. The experimental results provide useful guidelines on the design of a polyphonic music database.

Since tree-structured data such as XML files are widely used for data representation and exchange on the Internet, discovering frequent tree patterns over tree-structured data streams becomes an interesting issue. In this paper, we propose an online algorithm to continuously discover the current set of frequent tree patterns from the data stream. A novel and efficient technique is introduced to incrementally generate all candidate tree patterns without duplicates. Moreover, a framework for counting the approximate frequencies of the candidate tree patterns is presented. Combining these techniques, the proposed approach is able to compute frequent tree patterns with guarantees of completeness and accuracy.

Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. In this paper, we develop a novel approach for mining frequent itemsets from data streams based on a time-sensitive sliding window model. Our approach consists of a storage structure that captures all possible frequent itemsets and a table providing approximate counts of the expired data items, whose size can be adjusted by the available storage space. Experiment results show that in our approach both the execution time and the storage space remain small under various parameter settings. In addition, our approach guarantees no false alarm or no false dismissal to the results yielded.

Querying polyphonic music from a large data collection is an interesting and challenging topic. Recently, researchers attempt to provide efficient techniques for content-based retrieval in polyphonic music databases where queries can also be polyphonic. However, most of the techniques do not perform the approximate matching well. In this paper, we present a novel method to efficiently retrieve k music works that contain segments most similar to the user query based on the edit distance. A list-based index structure is first constructed using the feature of the polyphony. A set of candidate approximate answers is then generated for the user query. A lower bounding mechanism is proposed to prune these candidates such that the k answers can be obtained efficiently. The efficiency of the proposed method is evaluated by real data set and synthetic data set, reporting significant improvement over existing approaches in the response time yielded.