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Spinal cord injury (SCI) triggers microglial/monocytes activation with distinct pro-inflammatory or inflammation-resolving phenotypes, which potentiate tissue damage or facilitate functional repair, respectively. The major integrin Mac-1 (CD11b/CD18), a heterodimer consisting of CD11b and CD18 chains, is expressed in multiple immune cells of the myeloid lineage. Here, we examined the effects of CD11b gene ablation in neuroinflammation and functional outcomes after SCI. qPCR analysis of C57BL/6 female mice showed upregulation of CD11b mRNA starting from 1 d after injury, which persisted up to 28 d. CD11b knockout (KO) mice and their wildtype littermates were subjected to moderate SCI. At 1 d post-injury, qPCR showed increased expression of genes involved with inflammation-resolving processes in CD11b KO mice. Flow cytometry analysis of CD45intLy6C−CX3CR1+ microglia, CD45hiLy6C+Ly6G− monocytes, and CD45hiLy6C+Ly6G+ neutrophils revealed significantly reduced cell counts as well as reactive oxygen species (ROS) production in CD11b KO mice at d3 post-injury. Further examination with NanoString and RNA-seq showed upregulation of pro-inflammatory genes, but downregulation of the ROS pathway. Importantly, CD11b KO mice exhibited significantly improved locomotor function, reduced cutaneous mechanical/thermal hypersensitivity, and limited tissue damage at 8 weeks post-injury. Collectively, our data suggest an important role for CD11b in regulating tissue inflammation and functional outcome following SCI. [ABSTRACT FROM AUTHOR]

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing.sup.1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study.sup.2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P [less than or equal to] 2.18 × 10.sup.-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale. Whole-exome sequencing analysis of 454,787 individuals in the UK Biobank is used to examine the association of protein-coding variants with nearly 4,000 health-related traits, identifying 564 distinct genes with significant trait associations., Author(s): Joshua D. Backman [sup.1] , Alexander H. Li [sup.1] , Anthony Marcketta [sup.1] , Dylan Sun [sup.1] , Joelle Mbatchou [sup.1] , Michael D. Kessler [sup.1] , Christian Benner [...]

Background: Skincare and makeup "pilling" is an unsightly and undesirable phenomenon whereby skincare such as moisturizers or foundation ball up to form flakes on the skin. To date, the causes of skincare product pilling have not been studied. This study aimed to examine the relationship between skin physiology and pilling potential of sunscreen and foundation (the two products most reported by consumers to cause pilling). This study also examined the effects of product application methods on pilling. Materials and methods: 528 female volunteers from Guangzhou, China, aged between 20 and 49 years, underwent various clinical skin assessments, followed by three steps of product layering. Pilling was assessed after each product application step. Results: 217 volunteers (41%) experienced pilling. The majority of pilling (n = 655 events) occurred following sunscreen application, while only a few pilling events (n = 35) occurred with foundation. Foundation improved pilling caused by sunscreen in 98.9% of cases. Volunteers experiencing pilling with both sunscreen and foundation had significantly lower facial skin hydration and oiliness, higher pH, and smoother skin texture (P < 0.05). Two application methods, rubbing of products in circular and linear motions, yielded the highest numbers of pilling events. Conclusion: This study has provided the first insights into the causes of pilling. Sunscreen is a promoter of pilling, while foundation may resolve sunscreen‐induced pilling in many cases. Skin physiology, particularly drier, smoother skin with higher pH, and product application methods are likely contributing factors to this undesirable phenomenon. [ABSTRACT FROM AUTHOR]

Aims: Intracerebral hemorrhage (ICH) is a condition that arises due to the rupture of cerebral blood vessels, leading to the flow of blood into the brain tissue. One of the pathological alterations that occurs during an acute ICH is an impairment of the blood–brain barrier (BBB), which leads to severe perihematomal edema and an immune response. Discussion: A complex interplay between the cells of the BBB, for example, pericytes, astrocytes, and brain endothelial cells, with resident and infiltrating immune cells, such as microglia, monocytes, neutrophils, T lymphocytes, and others accounts for both damaging and protective mechanisms at the BBB following ICH. However, the precise immunological influence of BBB disruption has yet to be richly ascertained, especially at various stages of ICH. Conclusion: This review summarizes the changes in different cell types and molecular components of the BBB associated with immune‐inflammatory responses during ICH. Furthermore, it highlights promising immunoregulatory therapies to protect the integrity of the BBB after ICH. By offering a comprehensive understanding of the mechanisms behind BBB damage linked to cellular and molecular immunoinflammatory responses after ICH, this article aimed to accelerate the identification of potential therapeutic targets and expedite further translational research. [ABSTRACT FROM AUTHOR]

Time and angle-resolved two photon photoemission spectroscopy is used to investigate the electron dynamics at the polar adsorbate/Ag(111) interfaces.

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations. Author summary: In this paper we develop a deep learning method to predict condition specific gene expression in various kinds of fungi ranging from baker's yeast to red bread mold. Predicting condition-specific gene expression is useful because it measures the response of organisms to different environmental conditions. Among other uses, our model would allow us to predict the effects of TF knockout experiments and discover novel genes that play an important role in a given environment. In addition, our framework allows us to predict how an organism will express genes in a novel environment. Being able to make these predictions is an important part of the metabolic engineering of fungi to optimize their use in the production of desired chemicals in industrially scalable conditions. [ABSTRACT FROM AUTHOR]

This study employed a quantitative research design to examine the predictive relationships between: (a) learner autonomy and course success; and (b) learner autonomy and final grades in community college online courses. Learner autonomy was defined as the characteristic of an individual who exhibited intentional behavior in learning activities. The results of the binary and ordinal logistic regression analyses suggested that learner autonomy was a valid predictor of course success and final grades in community college online courses. The implications of the results are discussed in the context of early identification and effective intervention. Specifically, two interventions are recommended: (a) blended learning programs, and (b) advisory and counseling services. (Contains 3 tables.)

This study employed a quantitative research design to examine the predictive relationships between social presence and course retention as well as final grade in community college online courses. Social presence is defined as the degree of one's feeling, perception and reaction to another intellectual entity in the online environment. Course final grades included A, B. C, D, F, I, or W. Course retention was defined as successfully completed a course with an A to C grade. The results of the binary and ordinal logistic regression analyses suggest that social presence is a significant predictor of course retention and final grade in the community college online environment. Two effective interventions are recommended: establishing integrated social and learning communities; and building effective blended learning programs. (Contains 8 tables.)

Community colleges serve diverse segments of society through a flexible and open access. By their very nature, community colleges provide a good fit with online learning. Unfortunately, online course dropout rates are high. The costs of course dropout are borne by students, community colleges, and the society. There is, therefore, a vital need to better understand factors influencing community college online course dropout. A review of studies on conventional and online learning dropout models and a survey of 30 community colleges on online self-assessment practices conclude that there is no single indicator that can effectively predict online course dropout. On the other hand, there are several learner controllable indicators that, taken collectively, are potential predictors of online course dropout. Among them, psychological, technological, and social factors have emerged as common and widely-used predictive concepts. This article proposes a framework along these three dimensions to describe, organize, and explain learner-oriented factors influencing community college online course dropout. This proposed framework, when applied as part of a comprehensive retention strategy, has the potential for contributing to the reduction of the dropout rate in community college online learning programs. (Contains 1 figure and 4 tables.)

The degradation of individual subcell J-V parameters, such as short circuit current, open circuit voltage, fill factor, and power of a GaInP/GaInAs/Ge triple junction solar cell by 1 MeV electrons were derived utilizing the spectral reciprocity relation between electroluminescence and external quantum efficiency. After exposure to a fluence of 1 × 1015 1 MeV electrons, it was observed that up to 67% of the voltage loss is from the middle, GaInAs subcell. Also, the dark saturation current of the Ge and GaInAs subcells increased but a simultaneous decrease in ideality factor caused a reduction of the open circuit voltage. The reduced ideality factor further indicates a change in the primary recombination mechanism.

The cover image is based on the article Understanding the causes of skincare product pilling by Bee Leng Lua et al., https://doi.org/10.1111/srt.13828..By Bee Leng LUA; Linda RUAN; Ying LYU and Simon LIUReported by Author; Author; Author; Author [Extracted from the article]