What can you do with 0.1× genome coverage? A case study based on a genome survey of the scuttle fly Megaselia scalaris (Phoridae)

<p>Abstract</p> <p>Background</p> <p>The declining cost of DNA sequencing is making genome sequencing a feasible option for more organisms, including many of interest to ecologists and evolutionary biologists. While obtaining high-depth, completely assembled genome sequences for most non-model organisms remains challenging, low-coverage genome survey sequences (GSS) can provide a wealth of biologically useful information at low cost. Here, using a random pyrosequencing approach, we sequence the genome of the scuttle fly <it>Megaselia scalaris </it>and evaluate the utility of our low-coverage GSS approach.</p> <p>Results</p> <p>Random pyrosequencing of the <it>M. scalaris </it>genome provided a depth of coverage (0.05x0.1x) much lower than typical GSS studies. We demonstrate that, even with extremely low-coverage sequencing, bioinformatics approaches can yield extensive information about functional and repetitive elements. We also use our GSS data to develop genomic resources such as a nearly complete mitochondrial genome sequence and microsatellite markers for <it>M. scalaris</it>.</p> <p>Conclusion</p> <p>We conclude that low-coverage genome surveys are effective at generating useful information about organisms currently lacking genomic sequence data.</p

Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium

BACKGROUND: Tools for the prediction of atrial fibrillation (AF) may identify high-risk individuals more likely to benefit from preventive interventions and serve as a benchmark to test novel putative risk factors. METHODS AND RESULTS: Individual-level data from 3 large cohorts in the United States (Atherosclerosis Risk in Communities [ARIC] study, the Cardiovascular Health Study [CHS], and the Framingham Heart Study [FHS]), including 18 556 men and women aged 46 to 94 years (19% African Americans, 81% whites) were pooled to derive predictive models for AF using clinical variables. Validation of the derived models was performed in 7672 participants from the Age, Gene and Environment-Reykjavik study (AGES) and the Rotterdam Study (RS). The analysis included 1186 incident AF cases in the derivation cohorts and 585 in the validation cohorts. A simple 5-year predictive model including the variables age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and history of myocardial infarction and heart failure had good discrimination (C-statistic, 0.765; 95% CI, 0.748 to 0.781). Addition of variables from the electrocardiogram did not improve the overall model discrimination (C-statistic, 0.767; 95% CI, 0.750 to 0.783; categorical net reclassification improvement, -0.0032; 95% CI, -0.0178 to 0.0113). In the validation cohorts, discrimination was acceptable (AGES C-statistic, 0.664; 95% CI, 0.632 to 0.697 and RS C-statistic, 0.705; 95% CI, 0.664 to 0.747) and calibration was adequate. CONCLUSION: A risk model including variables readily available in primary care settings adequately predicted AF in diverse populations from the United States and Europe

Point process models for novelty detection on spatial point patterns and their extremes

Novelty detection is a particular example of pattern recognition identifying patterns that departure from some model of "normal behaviour". The classification of point patterns is considered that are defined as sets of N observations of a multivariate random variable X and where the value N follows a discrete stochastic distribution. The use of point process models is introduced that allow us to describe the length N as well as the geometrical configuration in data space of such patterns. It is shown that such infinite dimensional study can be translated into a one-dimensional study that is analytically tractable for a multivariate Gaussian distribution. Moreover, for other multivariate distributions, an analytic approximation is obtained, by the use of extreme value theory, to model point patterns that occur in low-density regions as defined by X. The proposed models are demonstrated on synthetic and real-world data sets

Fixed lateral unicompartmental knee replacement is a reliable treatment for lateral compartment osteoarthritis after mobile-bearing medial unicompartmental replacement

Purpose: Lateral osteoarthritis following medial unicompartmental knee replacement (UKR) is usually treated with total knee replacement, however, lateral UKR is a less invasive option that preserves a well-functioning medial UKR. This study aimed to determine the 5-year outcome of the cemented Fixed Lateral Oxford UKR (FLO) when used for the treatment of severe lateral disease after medial Oxford unicompartmental knee replacement. Methods: Forty-four knees with lateral bone-on-bone osteoarthritis (n = 43) and avascular necrosis (n = 1) treated with the FLO following medial Oxford UKR were followed up prospectively. The Oxford Knee Score (OKS) and Tegner Activity Score (TAS) were collected pre- and post-operatively. Life-table analysis was used to determine survival rates. Results: The mean patient age at the time of FLO surgery was 74.4 years with a mean time of 12.1 years between the primary medial UKR and the conversion to a bi-UKR with a FLO. Mean follow-up of the FLO was 3.5 years. After FLO no intra-operative or medical complications, re-admissions, or mortality occurred. There was one reoperation in which a bearing was exchanged for a medial bearing dislocation. There were no revisions of the FLO, so the FLO survival rate at 5 years was 100% (24 at risk). The mean pre-operative OKS was 22, which significantly (p < 0.0001) improved to a mean of 42, 42, and 40 at 1, 2, and 5 years, respectively. The median TAS had a non-significant improvement from 2.5 (Range 0–8) pre-operatively to 2 (Range 1–6) at 5 years postoperatively. Conclusion: The FLO is a reliable treatment for lateral osteoarthritis following medial UKR. At 5 years there was a 100% survival of the FLO with a mean OKS of 40. Level of evidence: IV, Prospective Case Series

Development of an enhanced scoring system to predict ICU readmission or in-hospital death within 24 hours using routine patient data from two NHS Foundation Trusts

Rationale: Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration. Objectives: We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU. Design: A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a ‘static’ score of the patient’s condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient’s risk of deterioration throughout their hospital stay. Setting: Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model. Participants: A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort. Results: Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653). Conclusions: We showed that a scoring system incorporating data from a patient’s stay in the ICU has better performance than commonly used EWS systems based on vital signs alone. Trial registration number: ISRCTN32008295

Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters.

GOAL: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. METHODS: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. RESULTS: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25[Formula: see text]-75[Formula: see text] percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). CONCLUSION: Increased robustness of RR estimation by the proposed method was demonstrated. SIGNIFICANCE: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice

Copper-catalyzed synthesis of masked (hetero)aryl sulfinates

Catalysis using substoichiometric copper facilitates the synthesis of masked (hetero)aryl sulfinates under mild, base-free conditions from aryl iodides and the commercial sulfonylation reagent sodium 1-methyl 3-sulfinopropanoate (SMOPS). The development of a tert-butyl ester variant of the SMOPS reagent allowed the use of aryl bromide substrates. The sulfones thus generated can be unmasked and functionalized in situ to form a variety of sulfonyl-containing functional groups

Eltoprazine counteracts l-DOPA-induced dyskinesias in Parkinson's disease:A dose-finding study

In advanced stages of Parkinson's disease, serotonergic terminals take up l-DOPA and convert it to dopamine. Abnormally released dopamine may participate in the development of l-DOPA-induced dyskinesias. Simultaneous activation of 5-HT1A and 5-HT1B receptors effectively blocks l-DOPA-induced dyskinesias in animal models of dopamine depletion, justifying a clinical study with eltoprazine, a 5-HT1A/B receptor agonist, against l-DOPA-induced dyskinesias in patients with Parkinson's disease. A double-blind, randomized, placebo-controlled and dose-finding phase I/IIa study was conducted. Single oral treatment with placebo or eltoprazine, at 2.5, 5 and 7.5 mg, was tested in combination with a suprathreshold dose of l-DOPA (Sinemet®) in 22 patients with Parkinson's disease (16 male/six female; 66.6 ± 8.8 years old) with l-DOPA-induced dyskinesias. A Wilcoxon Signed Ranked Test was used to compare each eltoprazine dose level to paired randomized placebo on the prespecified primary efficacy variables; area under the curve scores on Clinical Dyskinesia Rating Scale for 3 h post-dose and maximum change of Unified Parkinson's Disease Rating Scale part III for 3 h post-dose. Secondary objectives included effects on maximum Clinical Dyskinesia Rating Scale score, area under the curve of Rush Dyskinesia Rating Scale score for 3 h post-dose, mood parameters measured by Hospital Anxiety Depression Scale and Montgomery Asberg Depression Rating Scale along with the pharmacokinetics, safety and tolerability profile of eltoprazine. A mixed model repeated measures was used for post hoc analyses of the area under the curve and peak Clinical Dyskinesia Rating Scale scores. It was found that serum concentrations of eltoprazine increased in a dose-proportional manner. Following levodopa challenge, 5 mg eltoprazine caused a significant reduction of l-DOPA-induced dyskinesias on area under the curves of Clinical Dyskinesia Rating Scale [-1.02(1.49); P = 0.004] and Rush Dyskinesia Rating Scale [-0.15(0.23); P = 0.003]; and maximum Clinical Dyskinesia Rating Scale score [-1.14(1.59); P = 0.005]. The post hoc analysis confirmed these results and also showed an antidyskinetic effect of 7.5 mg eltoprazine. Unified Parkinson's Disease Rating Scale part III scores did not differ between the placebo and eltoprazine treatments. The most frequent adverse effects after eltoprazine were nausea and dizziness. It can be concluded that a single dose, oral treatment with eltoprazine has beneficial antidyskinetic effects without altering normal motor responses to l-DOPA. All doses of eltoprazine were well tolerated, with no major adverse effects. Eltoprazine has a favourable risk-benefit and pharmacokinetic profile in patients with Parkinson's disease. The data support further clinical studies with chronic oral eltoprazine to treat l-DOPA-induced-dyskinesias

Breast milk nutrient content and infancy growth.

AIM: Benefits of human breast milk (HM) in avoiding rapid infancy weight gain and later obesity could relate to its nutrient content. We tested the hypothesis that differential HM total calorie content (TCC) or macronutrient contents may be associated with infancy growth. METHODS: HM hindmilk samples were collected at ages 4-8 weeks from 614 mothers participating in a representative birth cohort, with repeated infancy anthropometry. HM triglyceride (fat), lipid analytes and lactose (carbohydrate) were measured by (1) H-NMR, and protein content by the Dumas method. TCC and %macronutrients were determined. RESULTS: In 614 HM samples, fat content was as follows: [median(IQR)]: 2.6 (1.7-3.6) g/100 mL, carbohydrate: 8.6 (8.2-8.8) g/100 mL, protein: 1.2 (1.1-1.2) g/100 mL; TCC: 61.8 (53.7-71.3) kcal/100 mL. HM of mothers exclusively breast feeding vs. mixed feeding was more calorific with higher %fat, lower %carbohydrate and lower %protein. Higher HM TCC was associated with lower 12-months body mass index (BMI)/adiposity, and lower 3-12 months gains in weight/BMI. HM %fat was inversely related to 3-12 months gains in weight, BMI and adiposity, whereas %carbohydrate was positively related to these measures. HM %protein was positively related to 12-months BMI. CONCLUSION: HM analysis showed wide variation in %macronutrients. Although data on milk intakes were unavailable, our findings suggest functional relevance of HM milk composition to infant growth.PP was supported by a MRC Clinical Training Fellowship (G1001995). The Cambridge Baby Growth Study has been supported by the European Union, the World Cancer Research Foundation International, the Medical Research Council, the NIHR Cambridge Comprehensive Biomedical Research Centre, the Newlife Foundation for disabled children, the Mothercare Group Foundation, and Mead Johnson Nutrition.This is the final version of the article. It first appeared from Wiley via https://doi.org/ 10.1111/apa.1336