fMRI biomarkers of social cognitive skills training in psychosis: Extrinsic and intrinsic functional connectivity.

Social cognitive skills training interventions for psychotic disorders have shown improvement in social cognitive performance tasks, but little was known about brain-based biomarkers linked to treatment effects. In this pilot study, we examined whether social cognitive skills training could modulate extrinsic and intrinsic functional connectivity in psychosis using functional magnetic resonance imaging (fMRI). Twenty-six chronic outpatients with psychotic disorders were recruited from either a Social Cognitive Skills Training (SCST) or an activity- and time-matched control intervention. At baseline and the end of intervention (12 weeks), participants completed two social cognitive tasks: a Facial Affect Matching task and a Mental State Attribution Task, as well as resting-state fMRI (rs-fMRI). Extrinsic functional connectivity was assessed using psychophysiological interaction (PPI) with amygdala and temporo-parietal junction as a seed region for the Facial Affect Matching Task and the Mental State Attribution task, respectively. Intrinsic functional connectivity was assessed with independent component analysis on rs-fMRI, with a focus on the default mode network (DMN). During the Facial Affect Matching task, we observed stronger PPI connectivity in the SCST group after intervention (compared to baseline), but no treatment-related change in the Control group. Neither group showed treatment-related changes in PPI connectivity during the Mental State Attribution task. During rs-fMRI, we found treatment-related changes in the DMN in the SCST group, but not in Control group. This study found that social cognitive skills training modulated both extrinsic and intrinsic functional connectivity in individuals with psychotic disorders after a 12-week intervention. These findings suggest treatment-related changes in functional connectivity as a potential brain-based biomarker of social cognitive skills training

Using stable isotopes of hydrogen to quantify biogenic and thermogenic atmospheric methane sources: A case study from the Colorado Front Range

Global atmospheric concentrations of methane (CH4), a powerful greenhouse gas, are increasing, but because there are many natural and anthropogenic sources of CH4, it is difficult to assess which sources may be increasing in magnitude. Here we present a data set of δ2H-CH4 measurements of individual sources and air in the Colorado Front Range, USA. We show that δ2H-CH4, but not δ13C, signatures are consistent in air sampled downwind of landfills, cattle feedlots, and oil and gas wells in the region. Applying these source signatures to air in ground and aircraft samples indicates that at least 50% of CH4 emitted in the region is biogenic, perhaps because regulatory restrictions on leaking oil and natural gas wells are helping to reduce this source of CH4. Source apportionment tracers such as δ2H may help close the gap between CH4 observations and inventories, which may underestimate biogenic as well as thermogenic sources

Using Medical Students to Enhance Curricular Integration of Cross-Cultural Content

We hypothesized that an interested medical student group would be helpful in reviewing tutorial cases and giving relevant feedback on the curricular integration of cross-cultural content using case triggers in a preclinical gastrointestinal pathophysiology course. Self-selected student leaders (n = 9) reviewed pre-existing problem-based learning tutorial cases (n = 3) with cross-cultural triggers, and provided narrative feedback to course faculty. The cases were modified and used for the entire class in the following 2 years. Participating course students' comments and teaching faculty feedback were also noted. Outcomes were a change in case content, student global evaluations of the course, and self-reported faculty comfort with teaching the cases. All three tutorial cases were reviewed by a separate group of 2–3 students. Major and minor revisions were made to each case based on the student feedback. These cases were used in 2007 and 2008 and were the major change to the course during that time. Overall course evaluation scores improved significantly from 2006 to 2008 (p = 0.000). Tutors (n = 22 in 2007; n = 23 in 2008) expressed relief during tutor meetings that students had reviewed the cases. A general framework for eliciting student feedback on problem-based cases was developed. Student feedback, consisting of self-selected students' case reviews and solicited course and tutor comments, added value to a curricular reform to improve the integration of cross-cultural content into a problem-based learning curriculum. Our study underscores the fundamental link between teachers and students as partners in curricular development

Long-Term Functionality of Rural Water Services in Developing Countries: A System Dynamics Approach to Understanding the Dynamic Interaction of Causal Factors

Research has shown that sustainability of rural water infrastructure in developing countries is largely affected by the dynamic and systemic interactions of technical, social, financial, institutional, and environmental factors that can lead to premature water system failure. This research employs systems dynamic modeling, which uses feedback mechanisms to understand how these factors interact dynamically to influence long-term rural water system functionality. To do this, the research first identified and aggregated key factors from literature, then asked water sector experts to indicate the polarity and strength between factors through Delphi and cross impact survey questionnaires, and finally used system dynamics modeling to identify and prioritize feedback mechanisms. The resulting model identified 101 feedback mechanisms that were dominated primarily by three and four-factor loops that contained some combination of the factors: Water System Functionality, Community, Financial, Government, Management, and Technology. These feedback mechanisms were then scored and prioritized, with the most dominant feedback mechanism identified as Water System Functionality – Community – Finance – Management. This research offers insight into the dynamic interaction of factors impacting sustainability of rural water infrastructure through the identification of these feedback mechanisms and makes a compelling case for future research to longitudinally investigate the interaction of these factors in various contexts

Predicting sepsis severity at first clinical presentation:The role of endotypes and mechanistic signatures

BACKGROUND: Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use. METHODS: Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms. FINDINGS: Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77–80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89–97%) accurately predicted endotype status in both ER and ICU validation cohorts. INTERPRETATION: The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients

A systems approach delivers a functional microRNA catalog and expanded targets for seizure suppression in temporal lobe epilepsy

Temporal lobe epilepsy is the most common drug-resistant form of epilepsy in adults. The reorganization of neural networks and the gene expression landscape underlying pathophysiologic network behavior in brain structures such as the hippocampus has been suggested to be controlled, in part, by microRNAs. To systematically assess their significance, we sequenced Argonaute-loaded microRNAs to define functionally engaged microRNAs in the hippocampus of three different animal models in two species and at six time points between the initial precipitating insult through to the establishment of chronic epilepsy. We then selected commonly up-regulated microRNAs for a functional in vivo therapeutic screen using oligonucleotide inhibitors. Argonaute sequencing generated 1.44 billion small RNA reads of which up to 82% were microRNAs, with over 400 unique microRNAs detected per model. Approximately half of the detected microRNAs were dysregulated in each epilepsy model. We prioritized commonly up-regulated microRNAs that were fully conserved in humans and designed custom antisense oligonucleotides for these candidate targets. Antiseizure phenotypes were observed upon knockdown of miR-10a-5p, miR-21a-5p, and miR-142a-5p and electrophysiological analyses indicated broad safety of this approach. Combined inhibition of these three microRNAs reduced spontaneous seizures in epileptic mice. Proteomic data, RNA sequencing, and pathway analysis on predicted and validated targets of these microRNAs implicated derepressed TGF-\u3b2 signaling as a shared seizure-modifying mechanism. Correspondingly, inhibition of TGF-\u3b2 signaling occluded the antiseizure effects of the antagomirs. Together, these results identify shared, dysregulated, and functionally active microRNAs during the pathogenesis of epilepsy which represent therapeutic antiseizure targets

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected