Twitter sentiment analysis offers organizations ability to monitor public feeling towards the products and events related to them in real time. The first step of the sentiment analysis is the text ...pre-processing of Twitter data. Most existing researches about Twitter sentiment analysis are focused on the extraction of new sentiment features. However, to select the pre-processing method is ignored. This paper discussed the effects of text pre-processing method on sentiment classification performance in two types of classification tasks, and summed up the classification performances of six pre-processing methods using two feature models and four classifiers on five Twitter datasets. The experiments show that the accuracy and F1-measure of Twitter sentiment classification classifier are improved when using the pre-processing methods of expanding acronyms and replacing negation, but barely changes when removing URLs, removing numbers or stop words. The Naive Bayes and Random Forest classifiers are more sensitive than Logistic Regression and support vector machine classifiers when various pre-processing methods were applied.
Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment ...features by analyzing lexical and syntactic features. These features are expressed explicitly through sentiment words, emoticons, exclamation marks, and so on. In this paper, we introduce a word embeddings method obtained by unsupervised learning based on large twitter corpora, this method using latent contextual semantic relationships and co-occurrence statistical characteristics between words in tweets. These word embeddings are combined with n-grams features and word sentiment polarity score features to form a sentiment feature set of tweets. The feature set is integrated into a deep convolution neural network for training and predicting sentiment classification labels. We experimentally compare the performance of our model with the baseline model that is a word n-grams model on five Twitter data sets, the results indicate that our model performs better on the accuracy and F1-measure for twitter sentiment classification.
In this paper, we will study finite multiple T-values (MTVs) and their alternating versions, which are level two and level four variations of finite multiple zeta values, respectively. We will first ...provide some structural results for level two finite multiple zeta values (i.e., finite Euler sums) for small weights, guided by the author’s previous conjecture that the finite Euler sum space of weight, w, is isomorphic to a quotient Euler sum space of weight, w. Then, by utilizing some well-known properties of the classical alternating MTVs, we will derive a few important Q-linear relations among the finite alternating MTVs, including the reversal, linear shuffle, and sum relations. We then compute the upper bound for the dimension of the Q-span of finite (alternating) MTVs for some small weights by rigorously using the newly discovered relations, numerically aided by computers.
Inflammation and oxidative stress are associated with the pathogenesis of cerebral ischemia-reperfusion (I/R) injury. Fraxin, one of the primary active ingredients of Cortex Fraxini, may have potent ...anti-inflammatory activity. This study intended to investigate the function and mechanism of fraxin in a middle cerebral artery occlusion (MCAO) model.
A middle cerebral artery occlusion (MCAO) rat model was engineered. Both in-vivo and in-vitro models were dealt with Fraxin. The profiles of inflammation-concerned cytokines, proteins and oxidative stress factors were determined by RT-PCR, western blot, and enzyme-linked immunosorbent assay (ELISA), and neuronal apoptosis and reactive oxygen species (ROS) levels were measured. The neurological functions of rats were evaluated by Morris water maze and modified neurological severity scores (mNSS).
The data revealed that fraxin abated the OGD/R-mediated release of inflammatory and oxidative stress mediators, enhanced “M2″-like BV2 microglia polarization, and mitigated HT22 cell apoptosis. Mechanistically, fraxin boosted PPAR-γ expression, activated the Nrf2/HO-1 pathway, and suppressed NF-κB, IKK-β，p38 MAPK, ERK1/2 and Keap1 in a dose-dependent manner. Furthermore, attenuating PPAR-γ through pharmacological treatment with GW9662 (a PPAR-γ antagonist) mainly weakened the neuroprotective and anti-inflammatory functions of fraxin.
Fraxin could considerably ameliorate cerebral I/R damage by repressing oxidative stress, inflammatory response, and cell apoptosis through abrogating the PPARγ/ NF-κB pathway.
•Fraxin attenuated OGD-mediated microglia activation and neuron apoptosis.•Fraxin exerted neuroprotective effects in I/R rats.•Fraxin attenuated inflammation and oxidative stress in I/R rats.•PPARγ pathway is activated after fraxin treatment.
Atrial fibrillation (AF) is the most common cardiac arrhythmia; it has been known to increase the risk of stroke and heart failure. The association between air pollutants and AF has remained to be ...controversial. Thus, in this study, we sought to undertake a systematic review and meta-analysis in order to assess the short- and long-term effects of ambient air pollution on AF.We searched PubMed, Web of Science, Embase, and Ovid for all related studies up to October 2019. We used the random-effects model to estimate the excess risk percentage (ER%) and confidence intervals (CI) for particulate matter with diameter ≤ 2.5 (PM2.5) and ≤ 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Results were further analyzed by subgroups according to location, age, outcome, and gender.In total, 18 studies were included in our meta-analysis: 5 evaluated for long-term effects, 12 for short-term effects, and 1 for both long- and short-term effects. For the short term, ER per 10 μg/m3 increase of pollutants was 1.8% (0%-3.7%) for PM2.5 and 1.1% (−0.2%-2.4%) for PM10; per 10 parts per billion (ppb) increment of gaseous pollutions was 3.2% (0.6%-5.8%) for NO2, 2.9% (0.3%-5.7%) for SO2, 0.5% (−3.4%-4.7%) for O3, and 2.0% (−1.3%-5.4%) for CO per 1000 ppb change. The subgroup analysis showed the short-term effect was significantly different by region, gender, outcome, and age. Meanwhile, in the long term, except for O3, a statistically significant association was noted between AF incidence and all pollutants.Our meta-analysis suggests that short-term exposure to part of pollutants (PM2.5, SO2, and NO2) increases AF attack. Further, long-term exposure to air pollution can significantly contribute to the incidence of AF in a healthy population.
Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have ...been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke.
Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin.
After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10
) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10
), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10
), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10
), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes.
The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.
Micro-blog services have become popular tools in the social networks. Online users discuss various topics in the micro-blog and some influential users can affect the opinions, attitudes, behaviors, ...or emotions of others. This paper proposes a user influence rank (UIRank) algorithm to identify the influential users through interaction information flow and interaction relationships among users in the micro-blog. The UIRank algorithm considers the contribution of user's tweet and the characteristics of information dissemination in the micro-blog networks and calculates user influence score iteratively by user follower graph. Experimental results show that the UIRank algorithm outperforms other existing related algorithms in the precision, recall, and F1-Measure value.
Microbial fuel cells (MFCs) have gained increasing attention as an effective pathway for wastewater treatment and electrical energy recovery. In this study, the pollutant removal, N
2
O production, ...and electricity generation at different temperatures (25–45 °C) in a dual-chamber MFC were investigated. The optimal temperature for pollutant removal and electricity generation was 35 °C. The results showed that maximal removal efficiencies of chemical oxygen demand (COD), ammonium (NH
4
+
), and total nitrogen (TN) were 73.98 ± 0.92%, 99.24 ± 0.85%, and 8.28 ± 0.45%, respectively. The maximum of nitrous oxide (N
2
O) was 4.27 ± 0.11 mg L
−1
at 35 °C. Meanwhile, the maximum of power density, current density, and coulombic efficiency (CE) were 0.54 W m
−3
, 6.51 A m
−3
, and 8.12 ± 0.04%, respectively. The results indicate that MFC is a promising technology for simultaneous pollutant removal and electricity generation in temperate regions. Moreover, microbial community analysis suggested that the dominant reaction in the cathode may include aerobic nitrification, autotrophic denitrification, heterotrophic denitrification (aerobic and anoxic), reduction of oxygen, and nitrogen fixation.
This study was to determine the racial disparities in incidence, clinicopathological features and prognosis of hypopharyngeal squamous cell carcinoma (HPSCC) in the US.
The National Program of Cancer ...Registries and Surveillance, Epidemiology, and End Results (SEER) database was used to determine racial disparity in age adjusted incidence rate (AAIR) of HPSCC and its temporal trend during 2004-2019. Using the separate SEER 17 database, we further evaluated racial disparity in clinicopathological features, and in prognosis using Kaplan-Meier curves and Cox proportional hazard models.
HPSCC accounted for 95.8% of all hypopharyngeal cancers and occurred much more frequently in males. Its incidence decreased in both male and females, in male non-Hispanic white (NHW), non-Hispanic black (NHB) and Hispanic as well as female NHW and NHB during the study period. NHB had the highest, whereas non-Hispanic Asian or Pacific Islanders (API) had comparable and the lowest incidence in both males and females. Among 6,172 HPSCC patients obtained from SEER 17 database, 80.6% were males and 83.9% were at the advanced stages III/IV. Five-year cancer specific and overall survival rates were 41.2% and 28.9%, respectively. NHB patients were more likely to be younger, unmarried, from the Southern region, larger sized tumor, and at the stage IV, but less likely to receive surgery. They also had higher proportions of dying from HPSCC and all causes. Multivariate analyses revealed that NHB with HPSCC at the locally advanced stage had both significantly worse cancer specific and overall survival compared with NHW, but not at early stage (I/II) or distant metastatic stage. Hispanic patients had significantly better prognosis than NHW at locally advanced and metastatic stages. NHW and API had comparable prognoses.
HPSCC displays continuously decreased incidence and racial disparity. The majority of the disease is diagnosed at the advanced stage. NHB have the highest burden of HPSCC and a worse prognosis. More studies are needed to curtail racial disparity and improve early detection.
PDF
