Noninvasive measures of brain edema predict outcome in pediatric cerebral malaria.

BackgroundIncreased brain volume (BV) and subsequent herniation are strongly associated with death in pediatric cerebral malaria (PCM), a leading killer of children in developing countries. Accurate noninvasive measures of BV are needed for optimal clinical trial design. Our objectives were to examine the performance of six different magnetic resonance imaging (MRI) BV quantification measures for predicting mortality in PCM and to review the advantages and disadvantages of each method.MethodsReceiver operator characteristics were generated from BV measures of MRIs of children admitted to an ongoing research project with PCM between 2009 and 2014. Fatal cases were matched to the next available survivor. A total of 78 MRIs of children aged 5 months to 13 years (mean 4.0 years), of which 45% were males, were included.ResultsAreas under the curve (AUC) with 95% confidence interval on measures from the initial MRIs were: Radiologist-derived score = 0.69 (0.58-0.79; P = 0.0037); prepontine cistern anteroposterior (AP) dimension = 0.70 (0.56-0.78; P = 0.0133); SamKam ratio [Rt. parietal lobe height/(prepontine AP dimension + fourth ventricle AP dimension)] = 0.74 (0.63-0.83; P = 0.0002); and global cerebrospinal fluid (CSF) space ascertained by ClearCanvas = 0.67 (0.55-0.77; P = 0.0137). For patients with serial MRIs (n = 37), the day 2 global CSF space AUC was 0.87 (0.71-0.96; P P ConclusionAll noninvasive measures of BV performed well in predicting death and providing a proxy measure for brain volume. Initial MRI assessment may inform future clinical trials for subject selection, risk adjustment, or stratification. Measures of temporal change may be used to stage PCM

Predominant and novel de novo variants in 29 individuals with ALG13 deficiency: Clinical description, biomarker status, biochemical analysis, and treatment suggestions

Asparagine-linked glycosylation 13 homolog (ALG13) encodes a nonredundant, highly conserved, X-linked uridine diphosphate (UDP)-N-acetylglucosaminyltransferase required for the synthesis of lipid linked oligosaccharide precursor and proper N-linked glycosylation. De novo variants in ALG13 underlie a form of early infantile epileptic encephalopathy known as EIEE36, but given its essential role in glycosylation, it is also considered a congenital disorder of glycosylation (CDG), ALG13-CDG. Twenty-four previously reported ALG13-CDG cases had de novo variants, but surprisingly, unlike most forms of CDG, ALG13-CDG did not show the anticipated glycosylation defects, typically detected by altered transferrin glycosylation. Structural homology modeling of two recurrent de novo variants, p.A81T and p.N107S, suggests both are likely to impact the function of ALG13. Using a corresponding ALG13-deficient yeast strain, we show that expressing yeast ALG13 with either of the highly conserved hotspot variants rescues the observed growth defect, but not its glycosylation abnormality. We present molecular and clinical data on 29 previously unreported individuals with de novo variants in ALG13. This more than doubles the number of known cases. A key finding is that a vast majority of the individuals presents with West syndrome, a feature shared with other CDG types. Among these, the initial epileptic spasms best responded to adrenocorticotropic hormone or prednisolone, while clobazam and felbamate showed promise for continued epilepsy treatment. A ketogenic diet seems to play an important role in the treatment of these individuals.Fil: Ng, Bobby G.. Sanford Burnham Prebys Medical Discovery Institute; Estados UnidosFil: Eklund, Erik A.. Sanford Burnham Prebys Medical Discovery Institute; Estados Unidos. Lund University; SueciaFil: Shiryaev, Sergey A.. Sanford Burnham Prebys Medical Discovery Institute; Estados UnidosFil: Dong, Yin Y.. University of Oxford; Reino UnidoFil: Abbott, Mary Alice. University of Massachusetts Medical School; Estados UnidosFil: Asteggiano, Carla Gabriela. Universidad Católica de Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Medicina. Centro de Estudios de las Metabolopatías Congénitas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bamshad, Michael J.. University of Washington; Estados UnidosFil: Barr, Eileen. University of Emory; Estados UnidosFil: Bernstein, Jonathan A.. University of Stanford; Estados UnidosFil: Chelakkadan, Shabeed. Monash Children's Hospital; AustraliaFil: Christodoulou, John. Sydney Medical School; Australia. University of Melbourne; AustraliaFil: Chung, Wendy K.. Columbia University; Estados UnidosFil: Ciliberto, Michael A.. University of Iowa; Estados UnidosFil: Cousin, Janice. National Human Genome Research Institute ; Estados UnidosFil: Gardiner, Fiona. University of Melbourne; AustraliaFil: Ghosh, Suman. University of Florida; Estados UnidosFil: Graf, William D.. University of Connecticut; Estados UnidosFil: Grunewald, Stephanie. University College London; Estados UnidosFil: Hammond, Katherine. University of Alabama at Birmingahm; Estados UnidosFil: Hauser, Natalie S.. Inova, Fairfax Hospital Falls Church; Estados UnidosFil: Hoganson, George E.. University Of Illinois At Chicago; Estados UnidosFil: Houck, Kimberly M.. Baylor College of Medicine; Estados UnidosFil: Kohler, Jennefer N.. University of Stanford; Estados UnidosFil: Morava, Eva. Mayo Clinic; Estados UnidosFil: Larson, Austin A.. University Of Colorado Anschutz Medical Campus.; Estados UnidosFil: Liu, Pengfei. Baylor Genetics; Estados Unidos. Baylor College Of Medicine; Estados UnidosFil: Madathil, Sujana. University of Iowa; Estados UnidosFil: McCormack, Colleen. University of Stanford; Estados UnidosFil: Meeks, Naomi J.L.. University Of Colorado Anschutz Medical Campus.; Estados UnidosFil: Papazoglu, Gabriela Magali. Universidad Nacional de Córdoba. Facultad de Medicina. Centro de Estudios de las Metabolopatías Congénitas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

Case-based learning: Predictive features in indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This “predictive features” hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46928/1/10994_2004_Article_BF00993173.pd

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Annual (2023) taxonomic update of RNA-directed RNA polymerase-encoding negative-sense RNA viruses (realm Riboviria: kingdom Orthornavirae: phylum Negarnaviricota)

55 Pág.In April 2023, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by one new family, 14 new genera, and 140 new species. Two genera and 538 species were renamed. One species was moved, and four were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.This work was supported in part through the Laulima Government Solutions, LLC, prime contract with the U.S. National Institute of Allergy and Infec tious Diseases (NIAID) under Contract No. HHSN272201800013C. J.H.K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC, under Contract No. HHSN272201800013C. U.J.B. was supported by the Division of Intramural Resarch, NIAID. This work was also funded in part by Contract No. HSHQDC15-C-00064 awarded by DHS S and T for the management and operation of The National Biodefense Analysis and Countermeasures Centre, a federally funded research and development centre operated by the Battelle National Biodefense Institute (V.W.); and NIH contract HHSN272201000040I/HHSN27200004/D04 and grant R24AI120942 (N.V., R.B.T.). S.S. acknowl edges support from the Mississippi Agricultural and Forestry Experiment Station (MAFES), USDA-ARS project 58-6066-9-033 and the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch Project, under Accession Number 1021494. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of the Army, the U.S. Department of Defence, the U.S. Department of Health and Human Services, including the Centres for Disease Control and Prevention, the U.S. Department of Homeland Security (DHS) Science and Technology Directorate (S and T), or of the institutions and companies affiliated with the authors. In no event shall any of these entities have any responsibility or liability for any use, misuse, inability to use, or reliance upon the information contained herein. The U.S. departments do not endorse any products or commercial services mentioned in this publication. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S.Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes.Peer reviewe

In vitro analysis of riboswitch-Spinach aptamer fusions as metabolite-sensing fluorescent biosensors.

The development of fluorescent biosensors has been motivated by the interest to monitor and measure the levels of specific metabolites in live cells in real time. Common approaches include fusing a protein-based receptor to fluorescent proteins or synthesizing a small molecule reactive probe. Natural metabolite-sensing riboswitches also have been used in reporter-based systems that take advantage of ligand-dependent regulation of downstream gene expression. More recently, it has been shown that RNA-based fluorescent biosensors can be generated by fusing a riboswitch aptamer to the in vitro selected Spinach aptamer, which binds a cell-permeable and conditionally fluorescent molecule. Here, we describe methods to design, prepare, and analyze riboswitch-Spinach aptamer fusion RNAs for ligand-dependent activation of fluorescence in vitro. Examples of procedures to measure fluorescence activation, ligand binding selectivity and affinity, and binding kinetics are given for a cyclic di-GMP-responsive biosensor. The relative ease of in vitro RNA synthesis and purification should make this method accessible to other researchers interested in developing riboswitch-based fluorescent biosensors

Live Cell Imaging Using Riboswitch-Spinach tRNA Fusions as Metabolite-Sensing Fluorescent Biosensors.

The development of fluorescent biosensors is motivated by the desire to monitor cellular metabolite levels in real time. Most genetically encodable fluorescent biosensors are based on receptor proteins fused to fluorescent protein domains. More recently, small molecule-binding riboswitches have been adapted for use as fluorescent biosensors through fusion to the in vitro selected Spinach aptamer, which binds a pro-fluorescent, cell-permeable small molecule mimic of the GFP chromophore, DFHBI. Here we describe methods to prepare and analyze riboswitch-Spinach tRNA fusions for ligand-dependent activation of fluorescence in vivo. Example procedures describe the use of the Vc2-Spinach tRNA biosensor to monitor perturbations in cellular levels of cyclic di-GMP using either fluorescence microscopy or flow cytometry. The relative ease of cloning and imaging of these biosensors, as well as their modular nature, should make this method appealing to other researchers interested in utilizing riboswitch-based biosensors for metabolite sensing