Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion.Methods: A total of 100 subjects aged 18–30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland–Altman analysis.Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland–Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed.Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA

Impact of meteorological factors on the COVID-19 transmission: A multicity study in China

The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID- 19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below0 °C, 12 cities had average AHbelow4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition,we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission

Association of CYP2C19 Loss-of-Function Metabolizer Status With Stroke Risk Among Chinese Patients Treated With Ticagrelor-Aspirin vs Clopidogrel-Aspirin: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

Importance: The Clopidogrel With Aspirin in High-Risk Patients With Acute Nondisabling Cerebrovascular Events II (CHANCE-2) trial showed that ticagrelor-aspirin combination therapy reduced the risk of stroke compared with a clopidogrel-aspirin combination among carriers of CYP2C19 loss-of-function (LOF) alleles after a transient ischemic attack (TIA) or minor ischemic stroke. However, the association between the degree of CYP2C19 LOF and ideal treatment allocation remains unknown.Objective: To investigate whether the efficacy and safety of ticagrelor-aspirin vs clopidogrel-aspirin are consistent with the expected degree of CYP2C19 LOF after TIA or minor stroke.Design, Setting, and Participants: CHANCE-2 was a multicenter, double-blind, double-dummy, placebo-controlled randomized clinical trial. Patients were enrolled at 202 centers in China from September 23, 2019, through March 22, 2021. Patients with at least two *2 or *3 alleles (*2/*2, *2/*3, or *3/*3) according to point-of-care genotyping were classified as “poor metabolizers,” and those with one *2 or *3 allele (*1/*2 or *1/*3) were classified as “intermediate metabolizers.”Interventions: Patients were randomly assigned in a 1:1 ratio to receive ticagrelor (180-mg loading dose on day 1 followed by 90 mg twice daily for days 2-90) or clopidogrel (300-mg loading dose on day 1 followed by 75 mg/d for days 2-90). All patients received aspirin (75- to 300-mg loading dose followed by 75 mg/d for 21 days).Main Outcomes and Measures: The primary efficacy outcome was a new ischemic or hemorrhagic stroke. The secondary efficacy outcome was a composite of new clinical vascular events and individual ischemic stroke events within 3 months. The primary safety outcome was severe or moderate bleeding. Analyses were performed according to the intention-to-treat principle.Results: Of the 6412 patients enrolled, the median age was 64.8 years (IQR, 57.0-71.4 years), and 4242 patients (66.2%) were men. Of the 6412 patients, 5001 (78.0%) were intermediate metabolizers, and 1411 (22.0%) were poor metabolizers. The primary outcome occurred less often with ticagrelor-aspirin vs clopidogrel-aspirin, irrespective of metabolizer status (6.0% [150 of 2486] vs 7.6% [191 of 2515]; hazard ratio [HR], 0.78 [95% CI, 0.63-0.97] among intermediate metabolizers and 5.7% [41 of 719] vs 7.5% [52 of 692]; HR, 0.77 [95% CI, 0.50-1.18] among poor metabolizers; P = .88 for interaction). Patients taking ticagrelor-aspirin had a higher risk of any bleeding event compared with those taking clopidogrel-aspirin, irrespective of metabolizer status: 5.4% (134 of 2486) vs 2.6% (66 of 2512) (HR, 2.14 [95% CI, 1.59-2.89]) among intermediate metabolizers and 5.0% (36 of 719) vs 2.0% (14 of 692) (HR, 2.99 [95% CI, 1.51-5.93]) among poor metabolizers (P = .66 for interaction).Conclusions and Relevance: This prespecified analysis of a randomized clinical trial found no difference in treatment effect between poor and intermediate CYP2C19 metabolizers. The relative clinical efficacy and safety of ticagrelor-aspirin vs clopidogrel-aspirin were consistent across CYP2C19 genotypes.Trial Registration: ClinicalTrials.gov Identifier: NCT0407873

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

Genome-wide association studies and CRISPR/Cas9-mediated gene editing identify regulatory variants influencing eyebrow thickness in humans

Hair plays an important role in primates and is clearly subject to adaptive selection. While humans have lost most facial hair, eyebrows are a notable exception. Eyebrow thickness is heritable and widely believed to be subject to sexual selection. Nevertheless, few genomic studies have explored its genetic basis. Here, we performed a genome-wide scan for eyebrow thickness in 2961 Han Chinese. We identified two new loci of genome-wide significance, at 3q26.33 near SOX2 (rs1345417: P = 6.51×10−10) and at 5q13.2 near FOXD1 (rs12651896: P = 1.73×10−8). We further replicated our findings in the Uyghurs, a population from China characterized by East Asian-European admixture (N = 721), the CANDELA cohort from five Latin American countries (N = 2301), and the Rotterdam Study cohort of Dutch Europeans (N = 4411). A meta-analysis combining the full GWAS results from the three cohorts of full or partial Asian descent (Han Chinese, Uyghur and Latin Americans, N = 5983) highlighted a third signal of genome-wide significance at 2q12.3 (rs1866188: P = 5.81×10−11) near EDAR. We performed fine-mapping and prioritized four variants for further experimental verification. CRISPR/Cas9-mediated gene editing provided evidence that rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes, which are both involved in hair development. Finally, suitable statistical analyses revealed that none of the associated variants showed clear signals of selection in any of the populations tested. Contrary to popular speculation, we found no evidence that eyebrow thickness is subject to strong selective pressure

Limb development genes underlie variation in human fingerprint patterns

Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized “pattern-block” correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning

Ticagrelor vs Clopidogrel in CYP2C19 loss-of-function carriers with Stroke or TIA

BACKGROUNDComparisons between ticagrelor- aspirin and clopidogrel-aspirin in CYP2C19 loss-of-function carriers have not been well studied for secondary stroke prevention.METHODSWe conducted a randomized, double-blind, placebo-controlled trial of 6,412 patients with a minor ischemic stroke or TIA who carried CYP2C19 LOF alleles determined by point-of-care testing. Patients were randomly assigned within 24 hours after symptom onset, in a 1:1 ratio to receive ticagrelor (180 mg loading dose on day 1 followed by 90 mg twice daily for days 2 through 90) or clopidogrel (300 mg loading dose on day 1 followed by 75 mg per day for days 2 through 90), plus aspirin (75-300 mg loading dose followed by 75 mg daily for 21 days). The primary efficacy outcome was stroke and the primary safety outcome was severe or moderate bleeding, both within 90 days. RESULTSStroke occurred within 90 days in 191 (6.0%) versus 243 (7.6%), respectively (hazard ratio, 0.77; 95% confidence interval, 0.64 to 0.94; P=0.008). Moderate or severe bleeding occurred in 9 patients (0.3%) in the ticagrelor-aspirin group and in 11 patients (0.3%) in the clopidogrel-aspirin group; any bleeding event occurred in 170 patients (5.3%) vs 80 (2.5%), respectively. CONCLUSIONSAmong Chinese patients with minor ischemic stroke or TIA within 24 hours after symptoms onset who were carriers of CYP2C19 loss-of-function alleles, ticagrelor- aspirin was modestly better than clopidogrel-aspirin for reducing the risk of stroke but was associated with more total bleeding events at 90 days. (CHANCE-2 ClinicalTrials.gov number, NCT04078737.