Combined contribution of biochar and introduced AM fungi on lead stability and microbial community in polluted agricultural soil.

Introduction: Lead (Pb) pollution in agricultural soil has been accelerated by industrial development and human activities, and poses a major threat to agricultural ecosystems. Both biochar and arbuscular mycorrhiza (AM) fungi are considered to play an important role in remediation of Pb contaminated soil. Methods: The combined remediation effects of introduced AM fungi and biochar on soil properties, Pb availability, microbial community and functional profiles were systematically investigated in unsterilized Pb-polluted agricultural soil. Results: Results indicated that soil nutrients were significantly improved through the combined application of biochar and introduced AM fungi. The introduced AM fungi combined with biochar prepared at 400°C and 500°C promoted the transformation of Pb to a more stable state with low bioavailability. Moreover, the addition of AM fungi and biochar affected the relative abundances of dominant bacteria and fungi at the phylum and genus levels. Biochar mainly affected soil bacterial community and obviously increased the relative abundance of Actinobacteria and Blastococcus. The interactions between biochar and introduced AM fungi mainly affected fungal community, and increased the abundance of Ascomycota and Botryotrichum. Further, PICRUSt analysis indicated biochar amendment supported stronger bacterial metabolic functional potentials. Discussion: Therefore, the combined application of biochar and Therefore, the combined application of biochar and introduced AM fungi could improve soil nutrients, reduce Pb introduced AM fungi could improve soil nutrients, reduce Pb availability, availability, and show and show a positive effect on a positive effect on indigenous microbial communities and indigenous microbial communities and metabolic functions in metabolic functions in farmland soil.

Defective neurite elongation and branching in Nibp/Trappc9 deficient zebrafish and mice.

Loss of function in transport protein particles (TRAPP) links a new set of emerging genetic disorders called "TRAPPopathies". One such disorder is NIBP syndrome, characterized by microcephaly and intellectual disability, and caused by mutations of NIBP/TRAPPC9, a crucial and unique member of TRAPPII. To investigate the neural cellular/molecular mechanisms underlying microcephaly, we developed Nibp/Trappc9-deficient animal models using different techniques, including morpholino knockdown and CRISPR/Cas mutation in zebrafish and Cre/LoxP-mediated gene targeting in mice. Nibp/Trappc9 deficiency impaired the stability of the TRAPPII complex at actin filaments and microtubules of neurites and growth cones. This deficiency also impaired elongation and branching of neuronal dendrites and axons, without significant effects on neurite initiation or neural cell number/types in embryonic and adult brains. The positive correlation of TRAPPII stability and neurite elongation/branching suggests a potential role for TRAPPII in regulating neurite morphology. These results provide novel genetic/molecular evidence to define patients with a type of non-syndromic autosomal recessive intellectual disability and highlight the importance of developing therapeutic approaches targeting the TRAPPII complex to cure TRAPPopathies.

Exploring the Role of Visual Design in Digital Public Health Safety Education.

In this research, the positive role of interface visual design in digital safety education was verified taking COVID-19 prevention and control knowledge as the content of public health safety education, where interface emotion (positive, negative, and neutral) and interface layout (waterfall typed and juxtaposition typed) were regarded as independent variables, and readers' understanding, course evaluation and system usability score were dependent variables. As revealed in the results of a 3 × 2 two-factor experiment in which 252 college students participated: first, different interface emotion can cause significantly different understanding, where negative emotion has the best learning transfer effect; second, due to the difference in interface emotion, participants may give certain courses significantly different evaluation scores, while positive emotional interface contributes to the obviously high scores of three course-evaluation items, "appeal of the lesson", "enjoyment of the lesson" and "interface quality"; third, significantly different system usability can be caused by different interface layout, where waterfall-type layout enjoys higher appraisal from users; fourth, interface emotion and interface layout have a similar interactive effects in terms of "effort of the lesson" and "interface quality", where waterfall-type layout is favored in terms of positive emotional interface, and juxtaposition-type layout is more advantageous in terms of negative emotional interface. These results are of vital significance for interface design and safety education. Further, the visual design method for interface emotion and interface layout were analyzed to determine the most suitable design principles so as to improve the effect of digital public health safety education and provide constructive ideas for fighting against COVID-19 at the educational level.

Health-Related Quality of Life With Ulipristal Acetate for Treatment of Uterine Leiomyomas: A Randomized Controlled Trial.

OBJECTIVE: To investigate effects of ulipristal acetate on health-related quality of life (QOL) and symptom severity in women with symptomatic uterine leiomyomas and abnormal uterine bleeding. METHODS: Women were randomized to ulipristal (5 mg, 10 mg) or placebo in two phase 3, multicenter, double-blind, placebo-controlled trials (VENUS I and II). Health-related QOL and symptom severity were assessed at baseline, and over one (VENUS I and II) and two (VENUS II) 12-week treatment courses using the Uterine Fibroid Symptom Health-Related Quality of Life questionnaire. In pooled VENUS I and II data, change from baseline to the end of the first course for each Uterine Fibroid Symptom Health-Related Quality of Life scale was analyzed, including a Revised Activities subscale that measured physical and social activities. The proportion of women achieving meaningful change in the Symptom Severity (20 or more points), Health-Related QOL Total (20 or more points), and Revised Activities (30 or more points) scales was calculated. In VENUS II data, change from baseline to the end of each course in each scale was analyzed for each treatment arm. RESULTS: In pooled analyses, the intent-to-treat population included 589 patients (placebo, n=169; ulipristal 5 mg, n=215; ulipristal 10 mg, n=205). Significantly greater improvements from baseline in all Uterine Fibroid Symptom Health-Related Quality of Life scales were observed with both ulipristal doses compared with placebo (P<.001). A meaningful change in Revised Activities was achieved by 51 patients receiving placebo (34.9%), compared with 144 (73.5%; OR 5.0 [97.5% CI 2.9-8.6]) and 141 (80.6%; OR 7.9 [97.5% CI 4.3-14.6]) patients receiving ulipristal 5 mg, and 10 mg, respectively. In VENUS II, at end of courses 1 and 2, both ulipristal doses demonstrated significant improvements from baseline compared with placebo for all Uterine Fibroid Symptom Health-Related Quality of Life scales (P<.01). Mean Revised Activities scores showed that beneficial ulipristal effects were maintained in course 2, and improvements occurred on switching to ulipristal; results for other scales were similar. CONCLUSION: Ulipristal was associated with significant improvements in health-related QOL and symptom severity compared with placebo for women with symptomatic uterine leiomyomas. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02147197 and NCT02147158. FUNDING SOURCE: Allergan plc, Dublin, Ireland.

Impact of Outpatient vs Inpatient ABSSSI Treatment on Outcomes: A Retrospective Observational Analysis of Medical Charts Across US Emergency Departments.

BACKGROUND: The objective of this study was to characterize treatment of patients with acute bacterial skin and skin structure infections (ABSSSIs) and describe the association between hospital admission and emergency department (ED) visits or readmissions within 30 days after initial episode of care (IEC). METHODS: This was a retrospective, observational, cohort study of adults with ABSSSI who presented to an ED between July 1, 2012, and June 30, 2013. Patient, health care facility, and treatment characteristics, including unplanned ED visits or readmissions, were obtained through manual chart review and abstraction. Adjusted logistic regression analysis examined likelihood of all-cause unplanned ED visits or readmissions between admitted and nonadmitted patients. RESULTS: Records from 1527 ED visits for ABSSSI from 40 centers were reviewed (admitted, n = 578 [38%]; nonadmitted, n = 949 [62%]). Admitted patients were typically older (mean age, 52.2 years vs 43.0 years), more likely to be morbidly obese (body mass index > 40 kg/m2; 17.3% vs 9.1%), and had more comorbidities (Charlson Comorbidity Index ≥ 4; 24.4% vs 6.8%) compared with those not admitted. In the primary analysis, adjusted logistic regression, controlling for comorbidities and severity of illness, demonstrated that there was a similar likelihood of all-cause unplanned ED visits or readmissions between admitted and nonadmitted patients (odds ratio, 1.03; 95% confidence interval, 0.74-1.43; P = .87). CONCLUSIONS: ABSSSI treatment pathways leveraging outpatient treatment vs hospital admission support similar likelihood of unplanned 30-day ED visits or readmissions, an important clinical outcome and quality metric at US hospitals. Further research regarding the decision criteria around hospital admission to avoid potentially unnecessary hospitalizations is warranted.

SCT Promoter Methylation is a Highly Discriminative Biomarker for Lung and Many Other Cancers.

Aberrant DNA methylation has long been implicated in cancers. In this work we present a highly discriminative DNA methylation biomarker for non-small cell lung cancers and fourteen other cancers. Based on 69 NSCLC cell lines and 257 cancer-free lung tissues we identified a CpG island in SCT gene promoter which was verified by qMSP experiment in 15 NSCLC cell lines and 3 immortalized human respiratory epithelium cells. In addition, we found that SCT promoter was methylated in 23 cancer cell lines involving >10 cancer types profiled by ENCODE. We found that SCT promoter is hyper-methylated in primary tumors from TCGA lung cancer cohort. Additionally, we found that SCT promoter is methylated at high frequencies in fifteen malignancies and is not methylated in~1000 non-cancerous tissues across >30 organ types. Our study indicates that SCT promoter methylation is a highly discriminative biomarker for lung and many other cancers.

A novel Bayesian change-point algorithm for genome-wide analysis of diverse ChIPseq data types.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein(1). For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment(2). Reliably identifying these regions was the focus of our work. Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics(3-5) to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)(6-8). We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized. Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types. To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs(9), which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor(10,11) and epigenetic data(12) to illustrate its usefulness.

Novel Foxo1-dependent transcriptional programs control T(reg) cell function.

Regulatory T (T(reg)) cells, characterized by expression of the transcription factor forkhead box P3 (Foxp3), maintain immune homeostasis by suppressing self-destructive immune responses. Foxp3 operates as a late-acting differentiation factor controlling T(reg) cell homeostasis and function, whereas the early T(reg)-cell-lineage commitment is regulated by the Akt kinase and the forkhead box O (Foxo) family of transcription factors. However, whether Foxo proteins act beyond the T(reg)-cell-commitment stage to control T(reg) cell homeostasis and function remains largely unexplored. Here we show that Foxo1 is a pivotal regulator of T(reg )cell function. T(reg) cells express high amounts of Foxo1 and display reduced T-cell-receptor-induced Akt activation, Foxo1 phosphorylation and Foxo1 nuclear exclusion. Mice with T(reg)-cell-specific deletion of Foxo1 develop a fatal inflammatory disorder similar in severity to that seen in Foxp3-deficient mice, but without the loss of T(reg) cells. Genome-wide analysis of Foxo1 binding sites reveals ~300 Foxo1-bound target genes, including the pro-inflammatory cytokine Ifng, that do not seem to be directly regulated by Foxp3. These findings show that the evolutionarily ancient Akt-Foxo1 signalling module controls a novel genetic program indispensable for T(reg) cell function.

Genome-wide localization of protein-DNA binding and histone modification by a Bayesian change-point method with ChIP-seq data.

Next-generation sequencing (NGS) technologies have matured considerably since their introduction and a focus has been placed on developing sophisticated analytical tools to deal with the amassing volumes of data. Chromatin immunoprecipitation sequencing (ChIP-seq), a major application of NGS, is a widely adopted technique for examining protein-DNA interactions and is commonly used to investigate epigenetic signatures of diffuse histone marks. These datasets have notoriously high variance and subtle levels of enrichment across large expanses, making them exceedingly difficult to define. Windows-based, heuristic models and finite-state hidden Markov models (HMMs) have been used with some success in analyzing ChIP-seq data but with lingering limitations. To improve the ability to detect broad regions of enrichment, we developed a stochastic Bayesian Change-Point (BCP) method, which addresses some of these unresolved issues. BCP makes use of recent advances in infinite-state HMMs by obtaining explicit formulas for posterior means of read densities. These posterior means can be used to categorize the genome into enriched and unenriched segments, as is customarily done, or examined for more detailed relationships since the underlying subpeaks are preserved rather than simplified into a binary classification. BCP performs a near exhaustive search of all possible change points between different posterior means at high-resolution to minimize the subjectivity of window sizes and is computationally efficient, due to a speed-up algorithm and the explicit formulas it employs. In the absence of a well-established "gold standard" for diffuse histone mark enrichment, we corroborated BCP's island detection accuracy and reproducibility using various forms of empirical evidence. We show that BCP is especially suited for analysis of diffuse histone ChIP-seq data but also effective in analyzing punctate transcription factor ChIP datasets, making it widely applicable for numerous experiment types.