Introducing pour points: Characteristics and hydrological significance of a rainfall-concentrating mechanism in a water-limited woodland ecosystem

The interception of rainfall by plant canopies alters the depth and spatial distribution of water arriving at the soil surface, and thus the location, volume, and depth of infiltration. Mechanisms like stemflow are known to concentrate rainfall and route it deep into the soil, yet other mechanisms of flow concentration are poorly understood. This study characterizes pour points, formed by the detachment of water flowing under a branch, using a combination of field observations in Western Australian banksia woodlands and rainfall simulation experiments on Banksia menziesii branches. We aim to establish the hydrological significance of pour points in a water-limited woodland ecosystem, along with the features of the canopy structure and rainfall that influence pour point formation and fluxes. Pour points were common in the woodland and could be identified by visually inspecting trees. Throughfall depths at pour points were up to 15 times greater than rainfall and generally comparable to or greater than stemflow. Soil water content beneath pour points was greater than in adjacent controls, with 20%–30% of the seasonal rainfall volume infiltrated into the top 1 m of soil beneath pour points, compared to 5% in controls. Rainfall simulations showed that pour points amplified the spatial heterogeneity of throughfall, violating assumptions used to close the water balance. The simulation experiments demonstrated that pour point fluxes depend on the interaction of branch angle and foliation for a given branch architecture. Pour points can play a significant part in the water balance, depending on their density and rainfall concentration ability

Examining Muscle Activity Differences During Single and Dual Vector Elastic Resistance Exercises

# Background Elastic resistance exercise is a common part of rehabilitation programs. While these exercises are highly prevalent, little information exists on how adding an additional resistance vector with a different direction from the primary vector alters muscle activity of the upper extremity. # Purpose The purpose of this study was to examine the effects of dual vector exercises on torso and upper extremity muscle activity in comparison to traditional single vector techniques. # Study Design Repeated measures design. # Methods Sixteen healthy university-aged males completed four common shoulder exercises against elastic resistance (abduction, flexion, internal rotation, external rotation) while using a single or dual elastic vector at a fixed cadence and standardized elastic elongation. Surface electromyography was collected from 16 muscles of the right upper extremity. Mean, peak and integrated activity were extracted from linear enveloped and normalized data and a 2-way repeated measures ANOVA examined differences between conditions. # Results All independent variables differentially influenced activation. Interactions between single/dual vectors and exercise type affected mean activation in 11/16 muscles, while interactions in peak activation existed in 7/16 muscles. Adding a secondary vector increased activation predominantly in flexion or abduction exercises; little changes existed when adding a second vector in internal and external rotation exercises. The dual vector exercise in abduction significantly increased mean activation in lower trapezius by 25.6 ± 8.11 %MVC and peak activation in supraspinatus by 29.4 ± 5.94 %MVC (p<0.01). Interactions between single/dual vectors and exercise type affected integrated electromyography for most muscles; the majority of these muscles had the highest integrated electromyography in the dual vector abduction condition. # Conclusion Muscle activity often increased with a second resistance vector added; however, the magnitude was exercise-dependent. The majority of these changes existed in the flexion and abduction exercises, with little differences in the internal or external rotation exercises. # Level of Evidence 3

The Threshold Bias Model: A Mathematical Model for the Nomothetic Approach of Suicide

Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters.A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka.The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health

Elevated maternal lipids in early pregnancy are not associated with risk of intrapartum caesarean in overweight and obese nulliparous women

Background: Maternal overweight and obesity are associated with slower labour progress and increased caesarean delivery for failure to progress. Obesity is also associated with hyperlipidaemia and cholesterol inhibits myometrial contractility in vitro. Our aim was, among overweight and obese nulliparous women, to investigate 1. the role of early pregnancy serum cholesterol and 2. clinical risk factors associated with first stage caesarean for failure to progress at term. Methods: Secondary data analysis from a prospective cohort of overweight/obese New Zealand and Australian nullipara recruited to the SCOPE study. Women who laboured at term and delivered vaginally (n=840) or required first stage caesarean for failure to progress (n=196) were included. Maternal characteristics and serum cholesterol at 14–16 weeks’ of gestation were compared according to delivery mode in univariable and multivariable analyses (adjusted for BMI, maternal age and height, obstetric care type, induction of labour and gestation at delivery ≥41 weeks). Results: Total cholesterol at 14–16 weeks was not higher among women requiring first stage caesarean for failure to progress compared to those with vaginal delivery (5.55 ± 0.92 versus 5.67 ± 0.85 mmol/L, p= 0.10 respectively). Antenatal risk factors for first stage caesarean for failure to progress in overweight and obese women were BMI (adjusted odds ratio [aOR (95% CI)] 1.15 (1.07-1.22) per 5 unit increase, maternal age 1.37 (1.17-1.61) per 5 year increase, height 1.09 (1.06-1.12) per 1cm reduction), induction of labour 1.94 (1.38-2.73) and prolonged pregnancy ≥41 weeks 1.64 (1.14-2.35). Conclusions: Elevated maternal cholesterol in early pregnancy is not a risk factor for first stage caesarean for failure to progress in overweight/obese women. Other clinically relevant risk factors identified are: increasing maternal BMI, increasing maternal age, induction of labour and prolonged pregnancy ≥41 weeks’ of gestation.Elaine M Fyfe, Karen S Rivers, John MD Thompson, Kamala PL Thiyagarajan, Katie M Groom, Gustaaf A Dekker, Lesley ME McCowan and On behalf of the SCOPE consortiu

Mechanisms of Resistance to Noncovalent Bruton's Tyrosine Kinase Inhibitors

BackgroundCovalent (irreversible) Bruton's tyrosine kinase (BTK) inhibitors have transformed the treatment of multiple B-cell cancers, especially chronic lymphocytic leukemia (CLL). However, resistance can arise through multiple mechanisms, including acquired mutations in BTK at residue C481, the binding site of covalent BTK inhibitors. Noncovalent (reversible) BTK inhibitors overcome this mechanism and other sources of resistance, but the mechanisms of resistance to these therapies are currently not well understood.MethodsWe performed genomic analyses of pretreatment specimens as well as specimens obtained at the time of disease progression from patients with CLL who had been treated with the noncovalent BTK inhibitor pirtobrutinib. Structural modeling, BTK-binding assays, and cell-based assays were conducted to study mutations that confer resistance to noncovalent BTK inhibitors.ResultsAmong 55 treated patients, we identified 9 patients with relapsed or refractory CLL and acquired mechanisms of genetic resistance to pirtobrutinib. We found mutations (V416L, A428D, M437R, T474I, and L528W) that were clustered in the kinase domain of BTK and that conferred resistance to both noncovalent BTK inhibitors and certain covalent BTK inhibitors. Mutations in BTK or phospholipase C gamma 2 (PLCγ2), a signaling molecule and downstream substrate of BTK, were found in all 9 patients. Transcriptional activation reflecting B-cell-receptor signaling persisted despite continued therapy with noncovalent BTK inhibitors.ConclusionsResistance to noncovalent BTK inhibitors arose through on-target BTK mutations and downstream PLCγ2 mutations that allowed escape from BTK inhibition. A proportion of these mutations also conferred resistance across clinically approved covalent BTK inhibitors. These data suggested new mechanisms of genomic escape from established covalent and novel noncovalent BTK inhibitors. (Funded by the American Society of Hematology and others.)

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

A structural equation modeling approach to understanding pathways that connect socioeconomic status and smoking.

The inverse association between socioeconomic status and smoking is well established, yet the mechanisms that drive this relationship are unclear. We developed and tested four theoretical models of the pathways that link socioeconomic status to current smoking prevalence using a structural equation modeling (SEM) approach. Using data from the 2013 National Health Interview Survey, we selected four indicator variables (poverty ratio, personal earnings, educational attainment, and employment status) that we hypothesize underlie a latent variable, socioeconomic status. We measured direct, indirect, and total effects of socioeconomic status on smoking on four pathways through four latent variables representing social cohesion, financial strain, sleep disturbance, and psychological distress. Results of the model indicated that the probability of being a smoker decreased by 26% of a standard deviation for every one standard deviation increase in socioeconomic status. The direct effects of socioeconomic status on smoking accounted for the majority of the total effects, but the overall model also included significant indirect effects. Of the four mediators, sleep disturbance and psychological distress had the largest total effects on current smoking. We explored the use of structural equation modeling in epidemiology to quantify effects of socioeconomic status on smoking through four social and psychological factors to identify potential targets for interventions. A better understanding of the complex relationship between socioeconomic status and smoking is critical as we continue to reduce the burden of tobacco and eliminate health disparities related to smoking