Volatile methyl jasmonate from roots triggers host-beneficial soil microbiome biofilms

The rhizosphere is a niche surrounding plant roots, where soluble and volatile molecules mediate signaling between plants and the associated microbiota. The preferred lifestyle of soil microorganisms is in the form of biofilms. However, less is known about whether root volatile organic compounds (rVOCs) can influence soil biofilms beyond the 2-10 mm rhizosphere zone influenced by root exudates. We report that rVOCs shift the microbiome composition and growth dynamics of complex soil biofilms. This signaling is evolutionarily conserved from ferns to higher plants. Methyl jasmonate (MeJA) is a bioactive signal of rVOCs that rapidly triggers both biofilm and microbiome changes. In contrast to the planktonic community, the resulting biofilm community provides ecological benefits to the host from a distance via growth enhancement. Thus, a volatile host defense signal, MeJA, is co-opted for assembling host-beneficial biofilms in the soil microbiota and extending the sphere of host influence in the rhizosphere.

An essential protease, FtsH, influences daptomycin resistance acquisition in Enterococcus faecalis

Daptomycin is a last-line antibiotic commonly used to treat vancomycin-resistant Enterococci, but resistance evolves rapidly and further restricts already limited treatment options. While genetic determinants associated with clinical daptomycin resistance (DAPR) have been described, information on factors affecting the speed of DAPR acquisition is limited. The multiple peptide resistance factor (MprF), a phosphatidylglycerol-modifying enzyme involved in cationic antimicrobial resistance, is linked to DAPR in pathogens such as methicillin-resistant Staphylococcus aureus. Since Enterococcus faecalis encodes two paralogs of mprF and clinical DAPR mutations do not map to mprF, we hypothesized that functional redundancy between the paralogs prevents mprF-mediated resistance and masks other evolutionary pathways to DAPR. Here, we performed in vitro evolution to DAPR in mprF mutant background. We discovered that the absence of mprF results in slowed DAPR evolution and is associated with inactivating mutations in ftsH, resulting in the depletion of the chaperone repressor HrcA. We also report that ftsH is essential in the parental, but not in the ΔmprF, strain where FtsH depletion results in growth impairment in the parental strain, a phenotype associated with reduced extracellular acidification and reduced ability for metabolic reduction. This presents FtsH and HrcA as enticing targets for developing anti-resistance strategies.

Using administrative data to identify U.S. Army soldiers at high-risk of perpetrating minor violent crimes

Growing concerns exist about violent crimes perpetrated by U.S. military personnel. Although interventions exist to reduce violent crimes in high-risk populations, optimal implementation requires evidence-based targeting. The goal of the current study was to use machine learning methods (stepwise and penalized regression; random forests) to develop models to predict minor violent crime perpetration among U.S. Army soldiers. Predictors were abstracted from administrative data available for all 975,057 soldiers in the U.S. Army 2004-2009, among whom 25,966 men and 2728 women committed a first founded minor violent crime (simple assault, blackmail-extortion-intimidation, rioting, harassment). Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build separate male and female prediction models that were then tested in an independent 2011-2013 sample. Final model predictors included young age, low education, early career stage, prior crime involvement, and outpatient treatment for diverse emotional and substance use problems. Area under the receiver operating characteristic curve was 0.79 (for men and women) in the 2004-2009 training sample and 0.74-0.82 (men-women) in the 2011-2013 test sample. 30.5-28.9% (men-women) of all administratively-recorded crimes in 2004-2009 were committed by the 5% of soldiers having highest predicted risk, with similar proportions (28.5-29.0%) when the 2004-2009 coefficients were applied to the 2011-2013 test sample. These results suggest that it may be possible to target soldiers at high-risk of violence perpetration for preventive interventions, although final decisions about such interventions would require weighing predicted effectiveness against intervention costs and competing risks.

Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the Receiver Operating Characteristic curve was .83-.88. 33.7-63.2% of victimizations occurred among soldiers in the highest-risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks.

Post-traumatic stress disorder associated with life-threatening motor vehicle collisions in the WHO World Mental Health Surveys

BACKGROUND

Motor vehicle collisions (MVCs) are a substantial contributor to the global burden of disease and lead to subsequent post-traumatic stress disorder (PTSD). However, the relevant literature originates in only a few countries, and much remains unknown about MVC-related PTSD prevalence and predictors.

METHODS

Data come from the World Mental Health Survey Initiative, a coordinated series of community epidemiological surveys of mental disorders throughout the world. The subset of 13 surveys (5 in high income countries, 8 in middle or low income countries) with respondents reporting PTSD after life-threatening MVCs are considered here. Six classes of predictors were assessed: socio-demographics, characteristics of the MVC, childhood family adversities, MVCs, other traumatic experiences, and respondent history of prior mental disorders. Logistic regression was used to examine predictors of PTSD. Mental disorders were assessed with the fully-structured Composite International Diagnostic Interview using DSM-IV criteria.

RESULTS

Prevalence of PTSD associated with MVCs perceived to be life-threatening was 2.5 % overall and did not vary significantly across countries. PTSD was significantly associated with low respondent education, someone dying in the MVC, the respondent or someone else being seriously injured, childhood family adversities, prior MVCs (but not other traumatic experiences), and number of prior anxiety disorders. The final model was significantly predictive of PTSD, with 32 % of all PTSD occurring among the 5 % of respondents classified by the model as having highest PTSD risk.

CONCLUSION

Although PTSD is a relatively rare outcome of life-threatening MVCs, a substantial minority of PTSD cases occur among the relatively small proportion of people with highest predicted risk. This raises the question whether MVC-related PTSD could be reduced with preventive interventions targeted to high-risk survivors using models based on predictors assessed in the immediate aftermath of the MVCs.

Predicting non-familial major physical violent crime perpetration in the US Army from administrative data

BACKGROUND

Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers.

METHOD

A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample.

RESULTS

Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample.

CONCLUSIONS

Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.

Subthreshold posttraumatic stress disorder in the world health organization world mental health surveys

BACKGROUND

Although only a few people exposed to a traumatic event (TE) develop posttraumatic stress disorder (PTSD), symptoms that do not meet full PTSD criteria are common and often clinically significant. Individuals with these symptoms sometimes have been characterized as having subthreshold PTSD, but no consensus exists on the optimal definition of this term. Data from a large cross-national epidemiologic survey are used in this study to provide a principled basis for such a definition.

METHODS

The World Health Organization World Mental Health Surveys administered fully structured psychiatric diagnostic interviews to community samples in 13 countries containing assessments of PTSD associated with randomly selected TEs. Focusing on the 23,936 respondents reporting lifetime TE exposure, associations of approximated DSM-5 PTSD symptom profiles with six outcomes (distress-impairment, suicidality, comorbid fear-distress disorders, PTSD symptom duration) were examined to investigate implications of different subthreshold definitions.

RESULTS

Although consistently highest outcomes for distress-impairment, suicidality, comorbidity, and PTSD symptom duration were observed among the 3.0% of respondents with DSM-5 PTSD rather than other symptom profiles, the additional 3.6% of respondents meeting two or three of DSM-5 criteria B-E also had significantly elevated scores for most outcomes. The proportion of cases with threshold versus subthreshold PTSD varied depending on TE type, with threshold PTSD more common following interpersonal violence and subthreshold PTSD more common following events happening to loved ones.

CONCLUSIONS

Subthreshold DSM-5 PTSD is most usefully defined as meeting two or three of DSM-5 criteria B-E. Use of a consistent definition is critical to advance understanding of the prevalence, predictors, and clinical significance of subthreshold PTSD.

How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys

Post-traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE-exposed people who develop PTSD is small. To be cost-effective, risk prediction rules are needed to target high-risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, socio-demographics, and prior histories of cumulative TE exposure and DSM-IV disorders. DSM-IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest-risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0-1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic-geographic sub-samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice.

DSM-5 and ICD-11 definitions of posttraumatic stress disorder: investigating "narrow" and "broad" approaches

BACKGROUND

The development of the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5) and ICD-11 has led to reconsideration of diagnostic criteria for posttraumatic stress disorder (PTSD). The World Mental Health (WMH) Surveys allow investigation of the implications of the changing criteria compared to DSM-IV and ICD-10.

METHODS

WMH Surveys in 13 countries asked respondents to enumerate all their lifetime traumatic events (TEs) and randomly selected one TE per respondent for PTSD assessment. DSM-IV and ICD-10 PTSD were assessed for the 23,936 respondents who reported lifetime TEs in these surveys with the fully structured Composite International Diagnostic Interview (CIDI). DSM-5 and proposed ICD-11 criteria were approximated. Associations of the different criteria sets with indicators of clinical severity (distress-impairment, suicidality, comorbid fear-distress disorders, PTSD symptom duration) were examined to investigate the implications of using the different systems.

RESULTS

A total of 5.6% of respondents met criteria for "broadly defined" PTSD (i.e., full criteria in at least one diagnostic system), with prevalence ranging from 3.0% with DSM-5 to 4.4% with ICD-10. Only one-third of broadly defined cases met criteria in all four systems and another one third in only one system (narrowly defined cases). Between-system differences in indicators of clinical severity suggest that ICD-10 criteria are least strict and DSM-IV criteria most strict. The more striking result, though, is that significantly elevated indicators of clinical significance were found even for narrowly defined cases for each of the four diagnostic systems.

CONCLUSIONS

These results argue for a broad definition of PTSD defined by any one of the different systems to capture all clinically significant cases of PTSD in future studies.

Cumulative traumas and risk thresholds: 12-month PTSD in the World Mental Health (WMH) surveys

BACKGROUND

Clinical research suggests that posttraumatic stress disorder (PTSD) patients exposed to multiple traumatic events (TEs) rather than a single TE have increased morbidity and dysfunction. Although epidemiological surveys in the United States and Europe also document high rates of multiple TE exposure, no population-based cross-national data have examined this issue.

METHODS

Data were analyzed from 20 population surveys in the World Health Organization World Mental Health Survey Initiative (n = 51,295 aged 18+). The Composite International Diagnostic Interview (3.0) assessed 12-month PTSD and other common DSM-IV disorders. Respondents with 12-month PTSD were assessed for single versus multiple TEs implicated in their symptoms. Associations were examined with age of onset (AOO), functional impairment, comorbidity, and PTSD symptom counts.

RESULTS

19.8% of respondents with 12-month PTSD reported that their symptoms were associated with multiple TEs. Cases who associated their PTSD with four or more TEs had greater functional impairment, an earlier AOO, longer duration, higher comorbidity with mood and anxiety disorders, elevated hyperarousal symptoms, higher proportional exposures to partner physical abuse and other types of physical assault, and lower proportional exposure to unexpected death of a loved one than cases with fewer associated TEs.

CONCLUSIONS

A risk threshold was observed in this large-scale cross-national database wherein cases who associated their PTSD with four or more TEs presented a more "complex" clinical picture with substantially greater functional impairment and greater morbidity than other cases of PTSD. PTSD cases associated with four or more TEs may merit specific and targeted intervention strategies.

Religion and Intergroup Conflict

How might religion shape intergroup conflict? We tested whether religious infusion—the extent to which religious rituals and discourse permeate the everyday activities of groups and their members—moderated the effects of two factors known to increase intergroup conflict: competition for limited resources and incompatibility of values held by potentially conflicting groups. We used data from the Global Group Relations Project to investigate 194 groups (e.g., ethnic, religious, national) at 97 sites around the world. When religion was infused in group life, groups were especially prejudiced against those groups that held incompatible values, and they were likely to discriminate against such groups. Moreover, whereas disadvantaged groups with low levels of religious infusion typically avoided directing aggression against their resource-rich and powerful counterparts, disadvantaged groups with high levels of religious infusion directed significant aggression against them—despite the significant tangible costs to the disadvantaged groups potentially posed by enacting such aggression. This research suggests mechanisms through which religion may increase intergroup conflict and introduces an innovative method for performing nuanced, cross-societal research.

Trauma exposure and posttraumatic stress disorder in a national sample of adolescents

OBJECTIVE

Although exposure to potentially traumatic experiences (PTEs) is common among youths in the United States, information on posttraumatic stress disorder (PTSD) risk associated with PTEs is limited. We estimate lifetime prevalence of exposure to PTEs and PTSD, PTE-specific risk of PTSD, and associations of sociodemographics and temporally prior DSM-IV disorders with PTE exposure, PTSD given exposure, and PTSD recovery among U.S. adolescents.

METHOD

Data were drawn from 6,483 adolescent-parent pairs in the National Comorbidity Survey Replication Adolescent Supplement (NCS-A), a national survey of adolescents aged 13 through 17 years. Lifetime exposure to interpersonal violence, accidents/injuries, network/witnessing, and other PTEs was assessed along with DSM-IV PTSD and other distress, fear, behavior, and substance disorders.

RESULTS

A majority (61.8%) of adolescents experienced a lifetime PTE. Lifetime prevalence of DSM-IV PTSD was 4.7% and was significantly higher among females (7.3%) than among males (2.2%). Exposure to PTEs, particularly interpersonal violence, was highest among adolescents not living with both biological parents and with pre-existing behavior disorders. Conditional probability of PTSD was highest for PTEs involving interpersonal violence. Predictors of PTSD among PTE-exposed adolescents included female gender, prior PTE exposure, and pre-existing fear and distress disorders. One-third (33.0%) of adolescents with lifetime PTSD continued to meet criteria within 30 days of interview. Poverty, U.S. nativity, bipolar disorder, and PTE exposure occurring after the focal trauma predicted nonrecovery.

CONCLUSIONS

Interventions designed to prevent PTSD in PTE-exposed youths should be targeted at victims of interpersonal violence with pre-existing fear and distress disorders, whereas interventions designed to reduce PTSD chronicity should attempt to prevent secondary PTE exposure.

Validation of the Narrative Emplotment Scale and its correlations with well-being and psychological adjustment

Two studies examined correlates of the Narrative Emplotment Scale (NES), which measures the extent to which individuals perceive chance events and unchosen experiences as meaningfully connected. In Study 1 (N=99), the NES demonstrated adequate test-retest stability and good internal reliability. The scale was positively related to paranormal beliefs, mystical experiences, and absorption. In Study 2 (N=342), personality measures indicative of external locus of control, intrinsic religiosity, well-being, satisfaction with life, and a measure of frequency of coincidence experience were all positively correlated with narrative emplotment, providing further support for the construct validity of the scale. In terms of the question of whether meaning making is predictive of better or worse psychological adjustment, analyses indicated that the relationship between narrative emplotment and psychological adjustment was moderated by individual differences in coping strategies. Path analysis indicated that emplotment was a mediator of the pathway between religiosity and well-being. Emplotment had a negative effect on well-being through chance locus of control. These analyses suggest that this type of meaning-making is an important variable for understanding religious/spiritual beliefs and their influence on psychological adjustment.

Race as a Moderator of the Relationship Between Religiosity and Political Alignment

Religiosity, especially religious fundamentalism, is often assumed to have an inherent connection with conservative politics. This article proposes that the relationship varies by race in the United States. In Study 1, race moderated the relationships between religiosity indicators and political alignment in a nationally representative sample. In Study 2, the effect replicated in a student sample with more reliable measures. Among both Black and Latino Americans, the relationship between religiosity and conservative politics is far weaker than it is among White Americans, and it is sometimes altogether absent. In Study 3, a tradition-focused view of religion was found to more strongly mediate the link between religiosity and political attitudes among Whites than it did among Blacks and Latinos. It is argued that the relationship between religiosity and political alignment is best understood as a product of cultural—historical conditions associated with group memberships.

Planning for emergency ambulance service systems

The application of a computer model to evaluate ambulance deployment configurations in an urban ambulance service is described. A planned expansion of the Boston Emergency Ambulance Service was accomplished using computer projections. The use of analytic models in the planning and implementation of an Emergency Medical Services (EMS) system allows for greater understanding of the interactions between various performance measures, facilitating the effective and cost-efficient allocation of resources.