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In recent years, a variety of multiple zeta values (MZVs) variants have been defined and studied. One way to produce these variants is to restrict the indices in the definition of MZVs to some fixed parity pattern, which include Hoffman’s multiple t-values, Kaneko and Tsumura’s multiple T-values, and Xu and this paper’s author’s multiple S-values. Xu and this paper’s author have also considered the so-called multiple mixed values by allowing all possible parity patterns and have studied a few important relations among these values. In this paper, we turn to the finite analogs and the symmetric forms of the multiple mixed values, motivated by a deep conjecture of Kaneko and Zagier, which relates the finite MZVs and symmetric MZVs, and a generalized version of this conjecture by the author to the Euler sum (i.e., level two) setting. We present a few important relations among these values such as the stuffle, reversal, and linear shuffle relations. We also compute explicitly the (conjecturally smallest) generating set in weight one and two cases. In the appendix, we tabulate some dimension computations for various subspaces of the finite multiple mixed values and propose a conjecture.

BackgroundCurrently, Sodium-glucose cotransporter 2 (SGLT2) inhibitors demonstrate additional effects beyond glucose control on the gut microbiota and circulating metabolites. The gut microbiota and metabolites have been found to be useful in elucidating potential biological mechanisms of pulmonary diseases. Therefore, our study aims to investigate the effects of gut microbiota and metabolites mediating SGLT2 inhibition in 10 pulmonary diseases through Mendelian randomization (MR) research.MethodsWe conducted a two-sample, two-step MR study to assess the association between SGLT2 inhibition and 10 pulmonary diseases and to investigate the mediating effects of gut microbiota and metabolite. Gene-fine mapping and annotation of mediators by FUMA and Magma analyses were performed, and causal associations of mapped genes with diseases were assessed by muti-omics MR analyses. Possible side effects of SGLT2 inhibition were assessed by PheWAS analysis.ResultsSGLT2 inhibition was linked to a reduced risk of T2DM, Interstitial lung disease (ILD), Pneumoconiosis, Pulmonary tuberculosis, and Asthma(OR=0.457, 0.054, 0.002, 0.280, 0.706). The family Enterobacteriaceae and order Enterobacteriales were associated with SGLT2 inhibition and ILD(95% CI:0.079–0.138). The family Alcaligenaceae and X-12719 were linked to pneumoconiosis (95% CI: 0.042–0.120, 0.050–0.099). The genus Phascolarctobacterium was connected to pulmonary tuberculosis (95% CI: 0.236–0.703).The degree of unsaturation (Fatty Acids), ratio of docosahexaenoic acid to total fatty acids, and 4-androsten-3beta,17beta-diol disulfate 2, were associated with asthma(95% CI: 0.042–0.119, 0.039–0.101, 0.181–0.473). Furthermore, Fuma and Magma analyses identified target genes for the four diseases, and proteomic MR analysis revealed six overlapping target genes in asthma. PheWAS analysis also highlighted potential side effects of SGLT2 inhibition.ConclusionsThis comprehensive study strongly supports a multi-omics association between SGLT2 inhibition and reduced risk of interstitial lung disease, tuberculosis, pneumoconiosis, and asthma. Four identified gut microbiota, four metabolites, sixteen metabolic pathways, and six target genes appear to play a potential role in this association. The results of the comprehensive phenome-wide association analysis also identified the full effect of SGLT2 inhibitors.

Abstract Background This study investigates the relationship between triglyceride-glucose (TyG) index trajectories and the results of ablation in patients with stage 3D atrial fibrillation (AF). Methods A retrospective cohort study was carried out on patients who underwent AF Radiofrequency Catheter Ablation (RFCA) at the Cardiology Department of the Fourth Affiliated Hospital of Zhejiang University and Taizhou Hospital of Zhejiang Province from January 2016 to December 2022. The main clinical endpoint was determined as the occurrence of atrial arrhythmia for at least 30 s following a 3-month period after ablation. Using a latent class trajectory model, different trajectory groups were identified based on TyG levels. The relationship between TyG trajectory and the outcome of AF recurrence in patients was assessed through Kaplan-Meier survival curve analysis and multivariable Cox proportional hazards regression model. Results The study included 997 participants, with an average age of 63.21 ± 9.84 years, of whom 630 were males (63.19%). The mean follow-up period for the participants was 30.43 ± 17.75 months, during which 200 individuals experienced AF recurrence. Utilizing the minimum Bayesian Information Criterion (BIC) and the maximum Entropy principle, TyG levels post-AF RFCA were divided into three groups: Locus 1 low-low group (n = 791), Locus 2 low-high-low group (n = 14), and Locus 3 high-high group (n = 192). Significant differences in survival rates among the different trajectories were observed through the Kaplan-Meier curve (P

This paper presents an approach to summing a few families of infinite series involving hyperbolic functions, some of which were first studied by Ramanujan. The key idea is based on their contour integral representations and residue computations with the help of some well-known results of Eisenstein series given by Ramanujan, Berndt, et al. As our main results, several series involving hyperbolic functions are evaluated and expressed in terms of z=F12(1/2,1/2;1;x) and z′=dz/dx. When a certain parameter in these series is equal to π, the series are expressed in closed forms in terms of some special values of the Gamma function. Moreover, many new illustrative examples are presented.

Summary: This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.

Abstract Background 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. Methods 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. Results 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 × 10−5) and AIS (OR: 1.776, 95% CI 1.380–2.285, p = 8.05 × 10−6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091–0.428, p = 3.92 × 10−5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876–5.635, p = 2.66 × 10−5), 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. Conclusions 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.

Abstract To explore the development of global tumor immunotherapy in the past 20 years, and analyze the research status, hotspots and trends to provide theoretical support for subsequent research. CiteSpace 5.6.R2 software were used to analyze 290 articles related to tumor immunotherapy research from the Web of Science core data set from 2000 to 2019. CiteSpace was used to draw related visual maps and tables of coauthors, cooperating countries, cooperating institutions, literature and journal co‐citation, keyword co‐occurrence, and cluster analysis. The total amount of papers published in the field of tumor immunotherapy research has gradually increased. After receiving the Nobel Prize in Physiology or Medicine in 2018, the number of papers published in the field of research in tumor research reached 20. We found that the United States has the largest number of papers in all countries and China ranks 6. “Proceedings of the National Academy of Sciences of the United States of America,” “Journal of Immunology,” “Cancer Research,” “New England Journal of Medicine,” “Blood” is the top five core academic journals in this field. Cancer, disease, and tumor necrosis factor are the three keywords with the highest highlighting intensity. Comprehensive analysis shows that the coauthors, co‐institutions, and co‐cited journals of the literature are mostly concentrated in the United States. The rise of immunotherapy provides a new direction for tumor treatment. From 2018 to 2019, the number of literatures on tumor immunotherapy worldwide increased sharply. Immunotherapy combined with specific diseases is the current research frontier and hotspot.

BackgroundRecent research linked changes in the gut microbiota and serum metabolite concentrations to intracerebral hemorrhage (ICH). However, the potential causal relationship remained unclear. Therefore, the current study aims to estimate the effects of genetically predicted causality between gut microbiota, serum metabolites, and ICH.MethodsSummary data from genome-wide association studies (GWAS) of gut microbiota, serum metabolites, and ICH were obtained separately. Gut microbiota GWAS (N = 18,340) were acquired from the MiBioGen study, serum metabolites GWAS (N = 7,824) from the TwinsUK and KORA studies, and GWAS summary-level data for ICH from the FinnGen R9 (ICH, 3,749 cases; 339,914 controls). A two-sample Mendelian randomization (MR) study was conducted to explore the causal effects between gut microbiota, serum metabolites, and ICH. The random-effects inverse variance-weighted (IVW) MR analyses were performed as the primary results, together with a series of sensitivity analyses to assess the robustness of the results. Besides, a reverse MR was conducted to evaluate the possibility of reverse causation. To validate the relevant findings, we further selected data from the UK Biobank for analysis.ResultsMR analysis results revealed a nominal association (p

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.

Designing low-cost, high-performing electrocatalysts is key to green energy development, yet relying solely on the ''synthesis-characterization'' catalyst screening model is time-consuming and costly. There are two main applications for Molecular dynamics (MD) simulations in electrochemical reactions: explaining mechanisms and predicting performance, which play important roles in fabricating robust electrocatalysts. MD simulations of electrocatalysis include the adsorption and desorption of reactants, intermediates, and products in this review. The structural changes in active centers under various electric field states, the effects of alkali metal cations, common anions, and pH effects in the electrolyte on the electrocatalytic process are also discussed to reveal the reaction mechanism. Then the prediction of the catalysts performance in specific reaction using MD simulations are introduced. Finally, the optimization and challenges of MD techniques are discussed.

Background: 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. Methods: 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). Results: 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. Conclusion: Fraxin could considerably ameliorate cerebral I/R damage by repressing oxidative stress, inflammatory response, and cell apoptosis through abrogating the PPARγ/ NF-κB pathway.

BackgroundPostoperative outcomes for patients suffering from resectable esophageal squamous cell carcinoma (ESCC) are related to sarcopenia. In patients with resectable ESCC, this study investigated the link between sarcopenia and postoperative pneumonia.MethodsThe McKewon procedure was the only one used to treat resectable ESCC patients from January 2018 to December 2021 in this retrospective analysis. Sarcopenia was assessed using skeletal muscles at L3 and planning CT scans. It was defined when PMI was below 6.36 cm2/m2 and 3.92 cm2/m2 for men and women, separately. Analyses of multivariate and univariate logistic regression were applied for identifying the risk factors for postoperative pneumonia.ResultsThe study included 773 patients with resectable ESCC in total. Sarcopenia was an independent risk factor for postoperative pneumonia in individuals with resectable ESCC based on univariate and multivariate analysis (P < 0.05). The stratified analysis indicated that neither of the clinical outcomes in the logistic regression model were affected by gender, age, BMI, smoking, or pre-albumin (P for interaction > 0.006).ConclusionFollowing the McKewon procedure, patients with resectable ESCC who were sarcopenic had a higher postoperative pneumonia rate. To prevent the development of postoperative pneumonia during the perioperative period, it may be important to control the incidence of sarcopenia.

ObjectiveThis study was to determine the racial disparities in incidence, clinicopathological features and prognosis of hypopharyngeal squamous cell carcinoma (HPSCC) in the US.MethodsThe 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.ResultsHPSCC 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.ConclusionsHPSCC 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.

BackgroundSeveral observational studies have identified that handgrip strength was inversely associated with cardiovascular diseases (CVDs). Nevertheless, causality remains controversial. We conducted Mendelian randomization (MR) analysis to examine whether handgrip strength and risk of CVDs are causally associated.MethodsWe identified 160 independent single nucleotide polymorphisms (SNPs) for right-hand grip strength and 136 independent SNPs for left-hand grip strength at the genome-wide significant threshold (P < 5 × 10−8) from UK Biobank participants and evaluated these in relation to risk of CVDs. MR estimates was calculated using the inverse-variance weighted (IVW) method and multiple sensitivity analysis was further conducted.ResultsGenetical liability to handgrip strength was significantly associated with coronary artery disease (CAD) and myocardial infarction (MI), but not stroke, hypertension, or heart failure. Additionally, there was significant association between right-hand grip strength and atrial fibrillation (OR, 0.967; 95% CI, 0.950–0.984; p = 0.000222), however, suggestive significance was found between left-hand grip strength and atrial fibrillation (OR, 0.977; 95% CI, 0.957–0.998; p = 0.033). Results were similar in several sensitivity analysis.ConclusionOur study provides support at the genetic level that handgrip strength is negatively associated with the risk of CAD, MI, and atrial fibrillation. Specific handgrip strength interventions on CVDs warrant exploration as potential CVDs prevention measures.

Korotkoff sounds (K-sounds) have been around for over 100 years and are considered the gold standard for blood pressure (BP) measurement. K-sounds are also unique for the diagnosis and treatment of cardiovascular diseases; however, their efficacy is limited. The incidences of heart failure (HF) are increasing, which necessitate the development of a rapid and convenient pre-hospital screening method. In this review, we propose a deep learning (DL) method and the possibility of using K-methods to predict cardiac function changes for the detection of cardiac dysfunctions.

According to the principles of bionics and predatory behavior of dolphins, Leader Dolphin Herd Algorithm (LDHA) was designed, whose performance is also tested on the basis of heuristic algorithm, showing that LDHA is obviously superior to the other algorithms in calculation accuracy and efficiency. The minimum safety factor of sliding surface was taken as the objective function, the recurrence formula of safety factor based on simplified Bishop Method was regarded as constraint condition, a non-linear multi-objective optimization mathematical model used for analyzing slope stability was established. the process of searching slope sliding surface and solving safety factor were designed on account of LDHA. By taking homogeneous slope and layered slope as calculation examples compared to other intelligent algorithms, the results show that the calculation result of LDHA is close to that of Genetic Algorithm and Simulated Annealing Algorithm and improved Genetic Algorithm. Moreover, the LDHA has less iterations and faster convergence rate, and the result is closer to the optimal solution, which indicates the feasibility and practicability of LDHA in solving the minimum stability safety factor.

Recently, gait has been gathering extensive interest for the non-fungible position in applications. Although various methods have been proposed for gait recognition, most of them can only attain an excellent recognition performance when the probe and gallery gaits are in a similar condition. Once external factors (e.g., clothing variations) influence people’s gaits and changes happen in human appearances, a significant performance degradation occurs. Hence, in our article, a robust hybrid part-based spatio-temporal feature learning method is proposed for gait recognition to handle this cloth-changing problem. First, human bodies are segmented into the affected and non/less unaffected parts based on the anatomical studies. Then, a well-designed network is proposed in our method to formulate our required hybrid features from the non/less unaffected body parts. This network contains three sub-networks, aiming to generate features independently. Each sub-network emphasizes individual aspects of gait, hence an effective hybrid gait feature can be created through their concatenation. In addition, temporal information can be used as complement to enhance the recognition performance, a sub-network is specifically proposed to establish the temporal relationship between consecutive short-range frames. Also, since local features are more discriminative than global features in gait recognition, in this network a sub-network is specifically proposed to generate features of local refined differences. The effectiveness of our proposed method has been evaluated by experiments on the CASIA Gait Dataset B and OU-ISIR Treadmill Gait Dataset B. Related experiments illustrate that compared with other gait recognition methods, our proposed method can achieve a prominent result when handling this cloth-changing gait recognition problem.

Background: Although some observational studies have shown that physical activity may have a positive relationship with cardiovascular diseases, the causal effect remains uncertain. We conducted a Mendelian randomization (MR) study to identify the potential causal effect between physical activity and cardiovascular diseases.Methods: Summary statistics of genome-wide association studies on four physical activity phenotypes and cardiovascular diseases were utilized. MR analysis was performed using inverse-variance weighted (IVW) and multivariable MR. Multiple sensitivity analysis was further conducted to identify the robustness of our results.Results: Genetically predicted self-reported vigorous physical activity (VPA) was significantly associated with lower risk of myocardial infarction (IVW OR: 0.24, 95% CI: 0.08–0.68, p-value: 0.007). Additionally, the causal effect of VPA with myocardial infarction was robust after adjusting for several cardiovascular risk factors through using the multivariable MR. There were no apparent causal associations between physical activity with other cardiovascular diseases. Results were consistent with the sensitivity analysis.Conclusion: The present study supports a protective role of self-reported vigorous physical activity in the initiation of myocardial infarction and highlights the importance of activity levels of physical activity. Further studies are required to elucidate the potential biological pathways of physical activity with cardiovascular diseases.

Several observational studies have shown that cannabis use has negative effects on the cardiovascular system, but the causality of this relationship has not been confirmed. The aim of the current study was to estimate the effects of genetically determined cannabis use on risk of cardiovascular diseases. Ten single-nucleotide polymorphisms related to cannabis use were employed as instruments to estimate the association between genetically determined cannabis use and risk of cardiovascular diseases using a two-sample Mendelian randomization (MR) method. Summary statistics data on exposure and outcomes were obtained from different genome-wide association meta-analysis studies. The results of this MR analysis showed no causal effects of cannabis use on the risk of several common cardiovascular diseases, including coronary artery disease, myocardial infarction, stroke and ischemic stroke subtypes, atrial fibrillation (AF), and heart failure. Various sensitivity analyses yielded similar results, and no heterogeneity and directional pleiotropy were observed. After adjusting for tobacco use and body mass index, multivariable MR analysis suggested a causal effect of cannabis use on small vessel stroke (SVS) [odds ratio (OR) 1.17; 95% CI 1.02–1.35; p = 0.03] and AF (OR 1.06; 95% CI 1.01–1.10; p = 0.01), respectively. This two-sample MR study did not demonstrate a causal effect of genetic predisposition to cannabis use on several common cardiovascular outcomes. After adjusting for tobacco use and body mass index, the multivariable MR analysis suggested a detrimental effect of cannabis use on the risk of SVS and AF, respectively.

Subjectivity classification aims to discriminate whether the given text presents opinions or states facts, which is an important foundation and component of sentiment analysis task. The characteristics of microblog (e.g., short text, full noise, casual expression, and incomplete grammatical structure) bring new challenges to microblog text subjectivity classification task. The existing subjectivity classification approaches of microblog text mainly depend on the techniques of artificially constructing emotional features that are commonly based on the bag-of-words model, which will lead to the curse of dimensionality and data sparsity problem. Furthermore, microblog text usually contains the left-handed compliment or implicit representation. It is difficult to find and describe this implicit semantic representation by artificially constructing emotional features, which seriously affects the subjectivity classification accuracy. To address these problems, we introduce a deep recurrent convolution neural network model for subjectivity classification of microblog text with the multi-features combination. In our model, we apply the convolution neural networks to automatically learn classification features and use the recurrent neural networks to abstract and memorize the sequence semantic relations of microblog text. We also employ pre-trained word embedding to reduce the representation dimension of the text, which avoids dimension disasters and data sparsity issues. We perform experiments on six commonly used Twitter datasets. The experimental results demonstrate that our proposed model outperforms the state-of-the-art approaches on several datasets. Compared with the baseline methods, the accuracy and the average F1-measure of our proposed model for subjectivity classification are improved by a minimum of 1.21% and 2.82%, respectively.

Both short (