Six new species of Margattea Shelford, 1911 (Blaberoidea, Pseudophyllodromiidae, Neoblattellini) from China.

Six Margattea species are established and described: three are cryptic species, namely, M.parabisignata Li & Che, sp. nov., M.semicircularis Li & Che, sp. nov., and M.forcipata Li & Che, sp. nov. They are distinguished from known species M.bisignata, M.spinifera, and M.paratransversa by their male genitalia with the aid of molecular species delimitation method (ABGD) using COI as the molecular marker. The other three new species are M.pedata Li & Che, sp. nov., M.undulata Li & Che, sp. nov., and M.bisphaerica Li & Che, sp. nov. Morphological and genitalia photographs of these new species of Margattea, as well as a key to the species of Margattea from China, are provided.

Discovery of five new species of Allacta from Yunnan and Hainan, China (Blattodea, Pseudophyllodromiidae).

We examined new Allacta materials from Yunnan and Hainan Province, China, and discovered new species using both morphological and molecular species delimitation (ABGD) methods. Five new species are described: A.bifolium Li & Wang, sp. nov., A.hemiptera Li & Wang, sp. nov., A.lunulara Li & Wang, sp. nov., A.redacta Li & Wang, sp. nov., and A.unicaudata Li & Wang, sp. nov. All five species are placed under the hamifera species group. An updated key and checklist of Allacta species from China are provided.

FCER1G as a novel immune-associated blood biomarker in cardiogenic stroke.

Background: Cardioembolic stroke (CE) exhibits the highest recurrence rate and mortality rate among all subtypes of cerebral ischemic stroke (CIS), yet its pathogenesis remains uncertain. The immune system plays a pivotal role in the progression of CE. Growing evidence indicates that several immune-associated blood biomarkers may inform the causes of stroke. The study aimed to identify new immune-associated blood biomarkers in patients with CE and create an online predictive tool in distinguishing CE from noncardioembolic stroke (non-CE) in CIS. Methods: Gene expression profiles that were publicly available were obtained from the Gene Expression Omnibus (GEO). The identification of differentially expressed genes (DEGs) was conducted using the Limma package. The hub module and hub genes were identified through the application of weighted gene coexpression network analysis (WGCNA). In order to identify potential diagnostic biomarkers for CE, both the random forest (RF) model and least absolute shrinkage and selection operator (LASSO) regression analysis were employed. Concurrently, the CIBERSORT algorithm was employed to evaluate the infiltration of immune cells in CE samples and examine the correlation between the biomarkers and the infiltrating immune cells. The diagnostic gene expression in blood samples was confirmed using qRT-PCR in a self-constructed dataset. Univariate and multiple logistic regression analyses were used to identify the risk factors for CE. Subsequently, the mathematical model of the nomogram was employed via Java's "Spring Boot" framework to develop the corresponding online tool, which was then deployed on a cloud server utilizing "nginx". Results: Eleven differentially expressed genes (DEGs) that were upregulated and seven DEGs that were downregulated were identified. Through bioinformatics analysis and clinical sample verification, it was discovered that Fc Fragment of IgE Receptor Ig (FCER1G) could serve as a novel potential blood biomarker for CE. FCER1G, along with other risk factors associated with CE, were utilized to develop a nomogram. The training and validation sets, which consisted of 65 CIS patients, yielded areas under the curve (AUCs) of 0.9722 and 0.9689, respectively. These results indicate a high level of precision in risk delineation by the nomogram. Furthermore, the associated online predictive platform has the potential to serve as a more efficacious and appropriate predictive instrument (https://www.origingenetic.com/CardiogenicStroke-FCER1G) for distinguishing between CE and non-CE. Conclusion: Blood biomarker FCER1G has the potential to identify patients who are at a higher risk of cardioembolism and direct the search for occult AF.The utilization of this online tool is anticipated to yield significant implications in terms of distinguishing between CE and non-CE, as well as enhancing the optimization of treatment decision support.

Two new genera (Vittiblatta gen. nov. and Planiblatta gen. nov.) of Blattinae (Blattodea, Blattidae) from Southwest China and the discovery of chirally dimorphic male genitalia in Vittiblattapunctata sp. nov.

This study examines Blattinae samples from Southwest China collected in recent years. Based on morphological characters, we establish two genera, Vittiblattagen. nov. and Planiblattagen. nov., and describe four new species, Vittiblattapunctata Luo & Wang, sp. nov., Vittiblattaferruginea Luo & Wang, sp. nov., Vittiblattaundulata Luo & Wang, sp. nov., and Planiblattacrassispina Luo & Wang, sp. nov. These two new genera resemble Periplaneta s.s., but are easily distinguished from it and other genera of Blattinae by morphological characters (genital sclerite L4C). Our results indicate that sclerites L4C and R1G of male genitalia might be important in species delimitation of Blattinae. In addition, chiral dimorphism is found in male genitalia of Vittiblattapunctata sp. nov.

Factors driving FIFA world cup 2022 viewership ratings in mainland China: marketing outlooks for FIFA world cup 2026.

Introduction: The FIFA World Cup is not only the most lucrative athletic event globally, but it also functions as a platform for promoting peace owing to FIFA's new vision. Nevertheless, the determinants of TV viewership ratings, especially in the Chinese market, which is a critical revenue stream for FIFA TV broadcasting, are still unsolved. Using a distributional regression, this study aimed to quantify the dynamics of viewership ratings for the FIFA World Cup 2022 in mainland China. Methods: The CCTV viewership ratings were modeled using 12 covariables related to Chinese TV consumer behavior. Given the data structure, a Logit Normal regression model was chosen to fit the location and dispersion parameters of viewership ratings to explanatory variables. Results: In the fitted heteroscedastic model, the viewership ratings dynamics in mainland China were driven by the match kick-off time: µ^=logistic[-4.874+0.043×matchkick-offtime]. In addition, the model captures the factors that influence the variations in viewership ratings: σ^=exp[-14.26-1.346 (if, FIFA World Cup champion = "Yes") + 0.004 × FIFA world ranking]. Thus, it shows that the FIFA World Cup champions tamp down such variations, leading to a more stable viewing behavior among Chinese consumers. Conclusions: Time- and team-sensitive strategies are proposed to aid in crafting uncertainty-suppressing business decisions for the FIFA World Cup 2026. Ultimately, in the more insecure 2020s, a broader live coverage of the FIFA World Cup would be invaluable for promoting peace.