Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations

We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25∘ × 0.3125∘ (≈ 25 km × 25 km) resolution by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of period-average emissions on the 0.25∘ × 0.3125∘ grid including error statistics, information content, and visualization code for inspection of results. The inversion uses an advanced research-grade algorithm fully documented in the literature. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI's capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.</p

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Dissection of Pol II Trigger Loop Function and Pol II Activity–Dependent Control of Start Site Selection In Vivo

Structural and biochemical studies have revealed the importance of a conserved, mobile domain of RNA Polymerase II (Pol II), the Trigger Loop (TL), in substrate selection and catalysis. The relative contributions of different residues within the TL to Pol II function and how Pol II activity defects correlate with gene expression alteration in vivo are unknown. Using Saccharomyces cerevisiae Pol II as a model, we uncover complex genetic relationships between mutated TL residues by combinatorial analysis of multiply substituted TL variants. We show that in vitro biochemical activity is highly predictive of in vivo transcription phenotypes, suggesting direct relationships between phenotypes and Pol II activity. Interestingly, while multiple TL residues function together to promote proper transcription, individual residues can be separated into distinct functional classes likely relevant to the TL mechanism. In vivo, Pol II activity defects disrupt regulation of the GTP-sensitive IMD2 gene, explaining sensitivities to GTP-production inhibitors, but contrasting with commonly cited models for this sensitivity in the literature. Our data provide support for an existing model whereby Pol II transcriptional activity provides a proxy for direct sensing of NTP levels in vivo leading to IMD2 activation. Finally, we connect Pol II activity to transcription start site selection in vivo, implicating the Pol II active site and transcription itself as a driver for start site scanning, contravening current models for this process

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Reduced costs with bisoprolol treatment for heart failure - An economic analysis of the second Cardiac Insufficiency Bisoprolol Study (CIBIS-II)

Background Beta-blockers, used as an adjunctive to diuretics, digoxin and angiotensin converting enzyme inhibitors, improve survival in chronic heart failure. We report a prospectively planned economic analysis of the cost of adjunctive beta-blocker therapy in the second Cardiac Insufficiency BIsoprolol Study (CIBIS II). Methods Resource utilization data (drug therapy, number of hospital admissions, length of hospital stay, ward type) were collected prospectively in all patients in CIBIS . These data were used to determine the additional direct costs incurred, and savings made, with bisoprolol therapy. As well as the cost of the drug, additional costs related to bisoprolol therapy were added to cover the supervision of treatment initiation and titration (four outpatient clinic/office visits). Per them (hospital bed day) costings were carried out for France, Germany and the U.K. Diagnosis related group costings were performed for France and the U.K. Our analyses took the perspective of a third party payer in France and Germany and the National Health Service in the U.K. Results Overall, fewer patients were hospitalized in the bisoprolol group, there were fewer hospital admissions perpatient hospitalized, fewer hospital admissions overall, fewer days spent in hospital and fewer days spent in the most expensive type of ward. As a consequence the cost of care in the bisoprolol group was 5-10% less in all three countries, in the per them analysis, even taking into account the cost of bisoprolol and the extra initiation/up-titration visits. The cost per patient treated in the placebo and bisoprolol groups was FF35 009 vs FF31 762 in France, DM11 563 vs DM10 784 in Germany and pound 4987 vs pound 4722 in the U.K. The diagnosis related group analysis gave similar results. Interpretation Not only did bisoprolol increase survival and reduce hospital admissions in CIBIS II, it also cut the cost of care in so doing. This `win-win' situation of positive health benefits associated with cost savings is Favourable from the point of view of both the patient and health care systems. These findings add further support for the use of beta-blockers in chronic heart failure

Activation of Selected Core Muscles during Pressing

Introduction: Unstable surface training is often used to activate core musculature during resistance training. Unfortunately, unstable surface training is risky and leads to detraining. Purpose: The purpose of this study was to determine core muscle activation during stable surface ground-based lifts. Methods: Fourteen recreational trained and former NCAA DI athletes (weight 84.2 ± 13.3 kg; height 176.0 ± 9.5 cm; age 20.9 ± 2.0 years) volunteered for participation. Subjects completed two ground-based lifts: overhead press and push-press. Surface EMG was recorded from 4 muscles on the right side of the body (Rectus Abdominus (RA), External Oblique (EO), Transverse Abdominus (TA), and Erector Spinae (ES). Results: Paired sample T-tests identified significant muscle activation differences between the overhead press and the push-press included ES and EO. Average and peak EMG for ES was significantly greater in push-press (P<0.01). Anterior displacement of COP was significantly greater in push-press compared to overhead press during the eccentric phase. Conclusion: The push-press was identified as superior in core muscle activation when compared to the overhead pressing exercise. Keywords: torso, stability, weight lifting, resistance trainin

Activation of Selected Core Muscles during Pressing

Introduction: Unstable surface training is often used to activate core musculature during resistance training. Unfortunately, unstable surface training is risky and leads to detraining. Purpose: The purpose of this study was to determine core muscle activation during stable surface ground-based lifts. Methods: Fourteen recreational trained and former NCAA DI athletes (weight 84.2 ± 13.3 kg; height 176.0 ± 9.5 cm; age 20.9 ± 2.0 years) volunteered for participation. Subjects completed two ground-based lifts: overhead press and push-press. Surface EMG was recorded from 4 muscles on the right side of the body (Rectus Abdominus (RA), External Oblique (EO), Transverse Abdominus (TA), and Erector Spinae (ES). Results: Paired sample T-tests identified significant muscle activation differences between the overhead press and the push-press included ES and EO. Average and peak EMG for ES was significantly greater in push-press (P0.01). Anterior displacement of COP was significantly greater in push-press compared to overhead press during the eccentric phase. Conclusion: The push-press was identified as superior in core muscle activation when compared to the overhead pressing exercise.Keywords: torso, stability, weight lifting, resistance trainin