A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.</p

A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks

Breast-feeding Protects against Arsenic Exposure in Bangladeshi Infants

BACKGROUND: Chronic arsenic exposure causes a wide range of health effects, but little is known about critical windows of exposure. Arsenic readily crosses the placenta, but the few available data on postnatal exposure to arsenic via breast milk are not conclusive. AIM: Our goal was to assess the arsenic exposure through breast milk in Bangladeshi infants, living in an area with high prevalence of arsenic-rich tube-well water. METHODS: We analyzed metabolites of inorganic arsenic in breast milk and infant urine at 3 months of age and compared them with detailed information on breast-feeding practices and maternal arsenic exposure, as measured by concentrations in blood, urine, and saliva. RESULTS: Arsenic concentrations in breast-milk samples were low (median, 1 microg/kg; range, 0.25-19 microg/kg), despite high arsenic exposures via drinking water (10-1,100 microg/L in urine and 2-40 microg/L in red blood cells). Accordingly, the arsenic concentrations in urine of infants whose mothers reported exclusive breast-feeding were low (median, 1.1 microg/L; range, 0.3-29 microg/L), whereas concentrations for those whose mothers reported partial breast-feeding ranged from 0.4 to 1,520 microg/L (median 1.9 microg/L). The major part of arsenic in milk was inorganic. Still, the infants had a high fraction (median, 87%) of the dimethylated arsenic metabolite in urine. Arsenic in breast milk was associated with arsenic in maternal blood, urine, and saliva. CONCLUSION: Very little arsenic is excreted in breast milk, even in women with high exposure from drinking water. Thus, exclusive breast-feeding protects the infant from exposure to arsenic

Beneficial autoimmunity at body surfaces – immune surveillance and rapid type 2 immunity regulate tissue homeostasis and cancer

Epithelial cells line body surface tissues and provide a physicochemical barrier to the external environment. Frequent microbial and non-microbial challenges such as those imposed by mechanical disruption, injury or exposure to noxious environmental substances including chemicals, carcinogens, ultraviolet-irradiation or toxins cause activation of epithelial cells with release of cytokines and chemokines as well as alterations in the expression of cell surface ligands. Such display of epithelial stress is rapidly sensed by tissue resident immunocytes, which can directly interact with self-moieties on epithelial cells and initiate both local and systemic immune responses. Epithelial cells are thus key drivers of immune surveillance at body surface tissues. However, epithelial cells have a propensity to drive type 2 immunity (rather than type 1) upon non-invasive challenge or stress – a type of immunity whose regulation and function still remain enigmatic. Here we review the induction and possible role of type 2 immunity in epithelial tissues and propose that rapid immune surveillance and type 2 immunity are key regulators of tissue homeostasis and carcinogenesis

The Candida albicans Ku70 Modulates Telomere Length and Structure by Regulating Both Telomerase and Recombination

The heterodimeric Ku complex has been shown to participate in DNA repair and telomere regulation in a variety of organisms. Here we report a detailed characterization of the function of Ku70 in the diploid fungal pathogen Candida albicans. Both ku70 heterozygous and homozygous deletion mutants have a wild-type colony and cellular morphology, and are not sensitive to MMS or UV light. Interestingly, we observed complex effects of KU70 gene dosage on telomere lengths, with the KU70/ku70 heterozygotes exhibiting slightly shorter telomeres, and the ku70 null strain exhibiting long and heterogeneous telomeres. Analysis of combination mutants suggests that the telomere elongation in the ku70 null mutant is due mostly to unregulated telomerase action. In addition, elevated levels of extrachromosomal telomeric circles were detected in the null mutant, consistent with activation of aberrant telomeric recombination. Altogether, our observations point to multiple mechanisms of the Ku complex in telomerase regulation and telomere protection in C. albicans, and reveal interesting similarities and differences in the mechanisms of the Ku complex in disparate systems

Comment letters to the National Commission on Commission on Fraudulent Financial Reporting, 1987 (Treadway Commission) Vol. 1

https://egrove.olemiss.edu/aicpa_sop/1661/thumbnail.jp

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Associations between depressive symptoms and disease progression in older patients with chronic kidney disease: results of the EQUAL study

Background Depressive symptoms are associated with adverse clinical outcomes in patients with end-stage kidney disease; however, few small studies have examined this association in patients with earlier phases of chronic kidney disease (CKD). We studied associations between baseline depressive symptoms and clinical outcomes in older patients with advanced CKD and examined whether these associations differed depending on sex. Methods CKD patients (&gt;= 65 years; estimated glomerular filtration rate &lt;= 20 mL/min/1.73 m(2)) were included from a European multicentre prospective cohort between 2012 and 2019. Depressive symptoms were measured by the five-item Mental Health Inventory (cut-off &lt;= 70; 0-100 scale). Cox proportional hazard analysis was used to study associations between depressive symptoms and time to dialysis initiation, all-cause mortality and these outcomes combined. A joint model was used to study the association between depressive symptoms and kidney function over time. Analyses were adjusted for potential baseline confounders. Results Overall kidney function decline in 1326 patients was -0.12 mL/min/1.73 m(2)/month. A total of 515 patients showed depressive symptoms. No significant association was found between depressive symptoms and kidney function over time (P = 0.08). Unlike women, men with depressive symptoms had an increased mortality rate compared with those without symptoms [adjusted hazard ratio 1.41 (95% confidence interval 1.03-1.93)]. Depressive symptoms were not significantly associated with a higher hazard of dialysis initiation, or with the combined outcome (i.e. dialysis initiation and all-cause mortality). Conclusions There was no significant association between depressive symptoms at baseline and decline in kidney function over time in older patients with advanced CKD. Depressive symptoms at baseline were associated with a higher mortality rate in men