Contribution of coherent electron production to measurements of heavy-flavor decayed electrons in heavy-ion collisions

Heavy quarks, produced at early stages of heavy-ion collisions, are an excellent probe of the Quark-Gluon Plasma (QGP) also created in these collisions. Electrons from open heavy-flavor hadron decays (HFE) are good proxies for heavy quarks, and have been measured extensively in the last two decades to study QGP properties. These measurements are traditionally carried out by subtracting all known background sources from the inclusive electron sample. More recently, a significant enhancement of e+e- pair production at very low transverse momenta was observed in peripheral heavy-ion collisions. The production characteristics is consistent with coherent photon–photon interactions, which should also constitute a background source to the HFE measurements. In this article, we provide theoretical predictions for the contribution of coherent electron production to HFEs as a function of transverse momentum, centrality and collision energy in Au+Au and Pb+Pb collisions

Genetic-Based Hypertension Subtype Identification Using Informative SNPs

In this work, we proposed a process to select informative genetic variants for identifying clinically meaningful subtypes of hypertensive patients. We studied 575 African American (AA) and 612 Caucasian hypertensive participants enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) study and analyzed each race-based group separately. All study participants underwent GWAS (Genome-Wide Association Studies) and echocardiography. We applied a variety of statistical methods and filtering criteria, including generalized linear models, F statistics, burden tests, deleterious variant filtering, and others to select the most informative hypertension-related genetic variants. We performed an unsupervised learning algorithm non-negative matrix factorization (NMF) to identify hypertension subtypes with similar genetic characteristics. Kruskal–Wallis tests were used to demonstrate the clinical meaningfulness of genetic-based hypertension subtypes. Two subgroups were identified for both African American and Caucasian HyperGEN participants. In both AAs and Caucasians, indices of cardiac mechanics differed significantly by hypertension subtypes. African Americans tend to have more genetic variants compared to Caucasians; therefore, using genetic information to distinguish the disease subtypes for this group of people is relatively challenging, but we were able to identify two subtypes whose cardiac mechanics have statistically different distributions using the proposed process. The research gives a promising direction in using statistical methods to select genetic information and identify subgroups of diseases, which may inform the development and trial of novel targeted therapies

Recent advances in ion‐sensitive field‐effect transistors for biosensing applications

Abstract Over the past decades, considerable development and improvement can be observed in the area of the ion‐sensitive field‐effect transistor (ISFET) for biosensing applications. The mature semiconductor industry provides a solid foundation for the commercialization of the ISFET‐based sensors and extensive research has been conducted to improve the performance of ISFET, with a special research focus on the materials, device structures, and readout topologies. In this review, the basic theories and mechanisms of ISFET are first introduced. Research on ISFET gate materials is reviewed, followed by a summary of typical gate structures and signal readout methods for the ISFET sensing system. After that, a variety of biosensing applications including ions, deoxyribonucleic acid, proteins, and microbes are presented. Finally, the prospects and challenges of the ISFET‐based biosensors are discussed

The Recovery of China’s Industrial Parks in the First Wave of COVID-19

Industrial parks are functional urban areas that carry the capacity to support highly concentrated production activities. The robustness and anti-interference ability of these areas are of great importance to maintaining economic vitality of a country. Focusing on the rate of production recovery (RPR), this paper examines the recovery of 436 major industrial parks in mainland China during the first wave of COVID-19. Leveraging spatio-temporal big data, we measured 14 attributes pertaining to industrial parks, covering four categories, namely spatial location, central city, park development, and public service. We focused on the spatial association and heterogeneity of the recovery patterns and identified the factors that truly affected the recovery of industrial parks with quantitative evaluation of their effects. The results reveal that: (1) RPR of industrial parks are significantly spatially clustered, with an obvious “cold spot” in the early outbreak area of Hubei Province and a prominent “center-periphery” pattern in developed areas, which is highly correlated with the spread of the epidemic. (2) The mechanisms driving the resumption of industrial parks are complex and versatile. All four categories in the variable matrix are related to RPR, including up to eight effective influencing factors. The effect of influencing factors is spatially heterogeneous, and its intensity varies significantly across regions. What is more interesting is that some impact factors show positive effects in some industrial parks while inhibiting the recovery in others. On the basis of the discussion of those findings with practical experiences, the planning and construction strategies of industrial park are suggested to mitigate the impact of similar external shocks