On the structure of finitely generated subgroups of branch groups

We describe the block structure of finitely generated subgroups of branch groups with the so-called subgroup induction property, including the first Grigorchuk group $\mathcal{G}$ and the torsion GGS groups.Comment: 31 pages, 3 figure

Urbanization changes the composition and bioavailability of dissolved organic matter in headwater streams

Population growth in cities has resulted in the rapid expansion of urbanized land. Most research and management of stream ecosystems affected by urban expansion has focused on the maintenance and restoration of biotic communities rather than their basal resources. We examined the potential for urbanization to induce bottom-up ecosystem effects by looking at its influence on dissolved organic matter (DOM) composition and bioavailability and microbial enzyme activity. We selected 113 headwater streams across a gradient of urbanization in central and southern Maine and used elemental and optical analyses, including parallel factor analysis of excitation-emission matrices, to characterize DOM composition. Results show that fluorescent and stoichiometric DOM composition changed significantly across the rural to urban gradient. Specifically, the proportion of humic-like allochthonous DOM decreased while that of more bioavailable autochthonous DOM increased in the more urbanized streams. In laboratory incubations, increased autochthonous DOM was associated with a doubling in the decay rate of dissolved organic carbon as well as increased activity of C-acquiring enzymes. These results suggest that urbanization replaces upstream humic material with more local sources of DOM that turnover more rapidly and may drive bottom-up changes in microbial communities and affect the quality and quantity of downstream DOM delivery

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

Les 25 ans de SDM : une histoire à suivre

L’auteur tente de dégager certaines caractéristiques des Services documentaires multimedia (SDM), organisme unique dans le monde francophone. Des débuts quelque peu incertains jusqu’à la reconnaissance finale de la qualité de sa gestion administrative et professionnelle par tous les milieux bibliothéconomiques tant québécois, canadiens qu’étrangers, SDM a diversifié ses services et ses marchés pour assurer son avenir : choix et traitement des livres, des documents audiovisuels, des périodiques, des jeux et jouets éducatifs, des logiciels.The author identifies the characteristics of the Services documentaires multimedia (SDM), a unique organisation in the francophone world. From its shaky beginning to the recognition of its administrative and professional management by libraries in Québec, in Canada and elsewhere, SDM has developed its services and its markets in order to insure its future: selection and cataloguing of books, audiovisual material, periodicals, games and educational toys, and software.El autor trata de destacar ciertas características de los Servicios Documentales Multimedia (SDM), organismo único en el mundo de habla francesa. Desde sus comienzos inciertos hasta el reconocimiento final de la calidad de su gestión administrativa y profesional en todos los medios biblioteconómicos tanto quebequenses como canadienses o extranjeros, SDM ha diversificado sus servicios y sus mercados para asegurar su porvenir: selección y tratamiento de libros, de documentos audiovisuales, de periódicos, de juegos y juguetes educativos y también de programas y sistemas de programación

Advancing Machine Learning for Small Molecule Property Prediction

Recently, machine learning (ML) models have rapidly become the state of the art at various molecular property prediction tasks. The speed of ML models, without sacrificing accuracy, makes them especially attractive in screening contexts, where a large number of potential molecules need to reduced to a number feasible for experimental testing. However, the black box nature and rapid advancement of ML models has resulted in a proliferation of input representations and model architectures. This makes selection of the ``best'' model architecture and input representation for a given task difficult. Additionally, while ML models thrive on having large datasets for training, the amount of labeled structures for properties like receptor-ligand binding affinity is small. This work sets out to help address these two problems with ML models for molecular property prediction. First, a wide variety of molecular input representations and ML model architectures were trained to predict calculated molecular properties. The characterization of both the performance of these models, and how well they utilize the training data, yields suggestions on how to best select a ML approach for more realistic property prediction tasks, given the amount of compute resources and training data available. Next, in order to address the lack of labeled structural data, a new dataset, CrossDocked2020, was created to expand the PDBbind dataset to expand the available binding pose classification data. By docking ligands into non-cognate, but similar, receptors we were able to expand the ~200,000 poses available from the PDBbind General set into ~22.5 million poses in CrossDocked2020. Various data imputation techniques were then explored to see if they could improve the binding affinity regression of a convolutional neural network (CNN) on CrossDocked2020. The utilization of an ensemble of CNN models to impute the missing binding affinity labels of complexes in CrossDocked2020 had a small, but significant improvement on model performance. Lastly, in order to give further support that the knowledge from this work is applicable in the real world, the CNN developed in this work was utilized to identify a small molecule to disrupt the actin-profilin1 protein-protein binding complex

Subgroup induction property for branch groups

22 pagesRecently, the so-called subgroup induction property attracted the attention of mathematicians working with branch groups. It was for example used to prove that groups with this property are subgroup separable (locally extensively residually finite) or to describe their finitely generated subgroups as well as their weakly maximal subgroups. Alas, until now, there were only two know examples of groups with this property: the first Grigorchuk group and the Gupta-Sidki $3$-group. The aim of this article is twofold. First, we investigate various consequences of the subgroup induction property, such as being just infinite or having all maximal subgroups of finite index. Then, we show that every torsion GGS group has the subgroup induction property, hence providing infinitely many new examples

Active Learning for Small Molecule pKa Regression; a Long Way To Go

The immense size of chemical space, the relative scarcity of high quality data, and the cost of running experiments to accurately measure molecular properties makes active learning (AL) an attractive approach to efficiently explore the space and train high-quality models for molecular property prediction. While AL is traditionally successful at classification, there have been recent advances in using AL for regression tasks. Recently, regressing to a normal inverse gamma distribution has been shown to be effective at predicting molecular properties in the QM9 dataset. However, we present a series of experiments demonstrating that various state of the art AL regression techniques are indistinguishable from random selection for small molecule pKa prediction. Source code for this paper is available at https://github.com/francoep/pKa_activelearning

3D Convolutional Neural Networks and a CrossDocked Dataset for Structure-Based Drug Design

One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measuring generalization to new targets, and there does not exist a standard dataset of sufficient size to compare performance between models. We present a new dataset for structure-based machine learning, the CrossDocked2020 set, with 22.5 million poses of ligands docked into multiple similar binding pockets across the Protein Data Bank and perform a comprehensive evaluation of grid-based convolutional neural network models on this dataset. We also demonstrate how the partitioning of the training data and test data can impact the results of models trained with the PDBbind dataset, how performance improves by adding more, lower-quality training data, and how training with docked poses imparts pose sensitivity to the predicted affinity of a complex. Our best performing model, an ensemble of 5 densely connected convolutional newtworks, achieves a root mean squared error of 1.42 and Pearson R of 0.612 on the affinity prediction task, an AUC of 0.956 at binding pose classification, and a 68.4% accuracy at pose selection on the CrossDocked2020 set. By providing data splits for clustered cross-validation and the raw data for the CrossDocked2020 set, we establish the first standardized dataset for training machine learning models to recognize ligands in non-cognate target structures while also greatly expanding the number of poses available for training. In order to facilitate community adoption of this dataset for benchmarking protein-ligand binding affinity prediction, we provide our models, weights, and the CrossDocked2020 set at https://github.com/gnina/models