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According to word and paradigm morphology (Matthews 1974, Blevins 2016), the word is the basic cognitive unit over which paradigmatic analogy operates to predict form and meaning of novel forms. Baayen et al. (2019b, 2018) introduced a computational formalization of word and paradigm morphology which makes it possible to model the production and comprehension of complex words without requiring exponents, morphemes, inflectional classes, and separate treatment of regular and irregular morphology. This computational model, Linear Discriminative Learning (LDL), makes use of simple matrix algebra to move from words’ forms to meanings (comprehension) and from words’ meanings to their forms (production). In Baayen et al. (2018), we showed that LDL makes accurate predictions for Latin verb conjugations. The present study reports results for noun declension in Estonian. Consistent with previous findings, the model’s predictions for comprehension and production are highly accurate. Importantly, the model achieves this high accuracy without being informed about stems, exponents, and inflectional classes. The speech errors produced by the model look like errors that native speakers might make. When the model is trained on incomplete paradigms, comprehension accuracy for unseen forms is hardly affected, but production accuracy decreases, reflecting the well-known asymmetry between comprehension and production.
High rates of psychiatric comorbidity are subject of debate: to what extent do they depend on classification choices such as diagnostic thresholds?
To investigate the influence of different thresholds on rates of comorbidity between major depressive disorder (MDD) and generalized anxiety disorder (GAD).
Point prevalence of comorbidity between MDD and GAD was measured in 74,092 subjects from the general population according to DSM-IV-TR criteria. Comorbidity rates were compared for different thresholds by varying the number of necessary criteria from ≥ 1 to all 9 symptoms for MDD, and from ≥ 1 to all 7 symptoms for GAD.
According to DSM-thresholds, 0.86% had MDD only, 2.96% GAD only and 1.14% both MDD and GAD (Odds Ratio [OR] 42.6). Lower thresholds for MDD led to higher rates of comorbidity (1.44% for ≥ 4 of 9 MDD-symptoms, OR 34.4), whereas lower thresholds for GAD hardly influenced comorbidity (1.16% for ≥ 3 of 7 GAD-symptoms, OR 38.8). Specific patterns in the distribution of symptoms within the population explained this finding: 37.3% of subjects with core criteria of MDD and GAD reported subthreshold MDD symptoms, whereas only 7.6% reported subthreshold GAD symptoms.
Lower thresholds for MDD increased comorbidity with GAD, but not vice versa, owing to specific symptom patterns in the population. Generally, comorbidity rates result from both empirical symptom distributions and classification choices and cannot be reduced to either of these exclusively. This insight invites further research into the formation of disease concepts that allow for reliable predictions and targeted therapeutic interventions.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Data-driven techniques are frequently applied to identify subtypes of depression and anxiety. Although they are highly comorbid and often grouped under a single internalizing banner, most subtyping studies have focused on either depression or anxiety. Furthermore, most previous subtyping studies have not taken into account experienced disability.
To incorporate disability into a data-driven cross-diagnostic subtyping model.
To capture heterogeneity of depression and anxiety symptomatology and investigate the importance of domain-specific disability-levels to distinguish between homogeneous subtypes.
Sixteen symptoms were assessed without skips using the MINI-interview in a population sample (LifeLines; n = 73403). Disability was measured with the RAND-36. To identify the best-fitting subtyping model, different nested latent variable models (latent class analysis, factor analysis and mixed-measurement item response theory [MM-IRT]) with and without disability covariates were compared. External variables were compared between the best model's classes.
A five-class MM-IRT model incorporating disability showed the best fit (Fig. 1). Accounting for disability improved the differentiation between classes reporting isolated non-specific symptoms (“Somatic” [13.0%], and “Worried” [14.0%]) and those reporting more psychopathological symptoms (“Subclinical” [8.8%], and “Clinical” [3.3%]). A “Subclinical” class reported symptomatology at subthreshold levels. No pure depression or anxiety, but only mixed classes were observed.
An overarching subtyping model incorporating both symptoms and disability identified distinct cross-diagnostic subtypes. Diagnostic nets should be cast wider than current phenomenology-based categorical systems.
Figure not available.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
As uncertainty remains about whether clinical response influences cognitive function after electroconvulsive therapy (ECT) for depression, we examined the effect of remission status on cognitive function in depressed patients 4 months after a course of ECT.
A secondary analysis was undertaken on participants completing a randomised controlled trial of ketamine augmentation of ECT for depression who were categorised by remission status (MADRS ⩽10 v. >10) 4 months after ECT. Cognition was assessed with self-rated memory and neuropsychological tests of anterograde verbal and visual memory, autobiographical memory, verbal fluency and working memory. Patients were assessed through the study, healthy controls on a single occasion, and compared using analysis of variance.
At 4-month follow-up, remitted patients (N = 18) had a mean MADRS depression score of 3.8 (95% CI 2.2–5.4) compared with 27.2 (23.0–31.5) in non-remitted patients (N = 19), with no significant baseline differences between the two groups. Patients were impaired on all cognitive measures at baseline. There was no deterioration, with some measures improving, 4-months after ECT, at which time remitted patients had significantly improved self-rated memory, anterograde verbal memory and category verbal fluency compared with those remaining depressed. Self-rated memory correlated with category fluency and autobiographical memory at follow-up.
We found no evidence of persistent impairment of cognition after ECT. Achieving remission improved subjective memory and verbal memory recall, but other aspects of cognitive function were not influenced by remission status. Self-rated memory may be useful to monitor the effects of ECT on longer-term memory.
The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m2) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural–geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status.
Whether monozygotic (MZ) and dizygotic (DZ) twins differ from each other in a variety of phenotypes is important for genetic twin modeling and for inferences made from twin studies in general. We analyzed whether there were differences in individual, maternal and paternal education between MZ and DZ twins in a large pooled dataset. Information was gathered on individual education for 218,362 adult twins from 27 twin cohorts (53% females; 39% MZ twins), and on maternal and paternal education for 147,315 and 143,056 twins respectively, from 28 twin cohorts (52% females; 38% MZ twins). Together, we had information on individual or parental education from 42 twin cohorts representing 19 countries. The original education classifications were transformed to education years and analyzed using linear regression models. Overall, MZ males had 0.26 (95% CI [0.21, 0.31]) years and MZ females 0.17 (95% CI [0.12, 0.21]) years longer education than DZ twins. The zygosity difference became smaller in more recent birth cohorts for both males and females. Parental education was somewhat longer for fathers of DZ twins in cohorts born in 1990–1999 (0.16 years, 95% CI [0.08, 0.25]) and 2000 or later (0.11 years, 95% CI [0.00, 0.22]), compared with fathers of MZ twins. The results show that the years of both individual and parental education are largely similar in MZ and DZ twins. We suggest that the socio-economic differences between MZ and DZ twins are so small that inferences based upon genetic modeling of twin data are not affected.
In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures.
A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables.
The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms [‘Somatic’ (13.0%), and ‘Worried’ (14.0%)] and psychopathological symptoms [‘Subclinical’ (8.8%), and ‘Clinical’ (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1–12.3, and chronic stress: OR 3.7–4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found.
An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.
Transcranial direct current stimulation (tDCS) is a non-pharmacological intervention for depression. It has mixed results, possibly caused by study heterogeneity.
To assess tDCS efficacy and to explore individual response predictors.
Systematic review and individual patient data meta-analysis.
Data were gathered from six randomised sham-controlled trials, enrolling 289 patients. Active tDCS was significantly superior to sham for response (34% v. 19% respectively, odds ratio (OR) = 2.44, 95% CI 1.38–4.32, number needed to treat (NNT) = 7), remission (23.1% v. 12.7% respectively, OR = 2.38, 95% CI 1.22–4.64, NNT = 9) and depression improvement (B coefficient 0.35, 95% CI 0.12–0.57). Mixed-effects models showed that, after adjustment for other predictors and confounders, treatment-resistant depression and higher tDCS ‘doses' were, respectively, negatively and positively associated with tDCS efficacy.
The effect size of tDCS treatment was comparable with those reported for repetitive transcranial magnetic stimulation and antidepressant drug treatment in primary care. The most important parameters for optimisation in future trials are depression refractoriness and tDCS dose.
Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.
We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.
Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.
Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.
Our knowledge about the impact of coping behavior styles in people exposed to stressful disaster events is limited. Effective coping behavior has been shown to be a psychosocial stress modifier in both occupational and nonoccupational settings.
Data were collected by using a web-based survey that administered the Post-Traumatic Stress Disorder (PTSD) Checklist–Civilian, General Coping Questionnaire-30, and a supplementary questionnaire assessing various risk factors. Logistic regression models were used to test for the association of the 3 coping styles with probable PTSD following disaster exposure among federal disaster responders.
In this sample of 549 study subjects, avoidant coping behavior was most associated with probable PTSD. In tested regression models, the odds ratios ranged from 1.19 to 1.26 and 95% confidence intervals ranged from 1.08 to 1.35. With control for various predictors, emotion-based coping behavior was also found to be associated with probable PTSD (odds ratio=1.11; 95% confidence interval: 1.01-1.22).
This study found that in disaster responders exposed to traumatic disaster events, the likelihood of probable PTSD can be influenced by individual coping behavior style and other covariates. The continued probability of disasters underscores the critical importance of these findings both in terms of guiding mental health practitioners in treating exposed disaster responders and in stimulating future research. (Disaster Med Public Health Preparedness. 2016;10:108–117)
A trend toward greater body size in dizygotic (DZ) than in monozygotic (MZ) twins has been suggested by some but not all studies, and this difference may also vary by age. We analyzed zygosity differences in mean values and variances of height and body mass index (BMI) among male and female twins from infancy to old age. Data were derived from an international database of 54 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins), and included 842,951 height and BMI measurements from twins aged 1 to 102 years. The results showed that DZ twins were consistently taller than MZ twins, with differences of up to 2.0 cm in childhood and adolescence and up to 0.9 cm in adulthood. Similarly, a greater mean BMI of up to 0.3 kg/m2 in childhood and adolescence and up to 0.2 kg/m2 in adulthood was observed in DZ twins, although the pattern was less consistent. DZ twins presented up to 1.7% greater height and 1.9% greater BMI than MZ twins; these percentage differences were largest in middle and late childhood and decreased with age in both sexes. The variance of height was similar in MZ and DZ twins at most ages. In contrast, the variance of BMI was significantly higher in DZ than in MZ twins, particularly in childhood. In conclusion, DZ twins were generally taller and had greater BMI than MZ twins, but the differences decreased with age in both sexes.
For over 100 years, the genetics of human anthropometric traits has attracted scientific interest. In particular, height and body mass index (BMI, calculated as kg/m2) have been under intensive genetic research. However, it is still largely unknown whether and how heritability estimates vary between human populations. Opportunities to address this question have increased recently because of the establishment of many new twin cohorts and the increasing accumulation of data in established twin cohorts. We started a new research project to analyze systematically (1) the variation of heritability estimates of height, BMI and their trajectories over the life course between birth cohorts, ethnicities and countries, and (2) to study the effects of birth-related factors, education and smoking on these anthropometric traits and whether these effects vary between twin cohorts. We identified 67 twin projects, including both monozygotic (MZ) and dizygotic (DZ) twins, using various sources. We asked for individual level data on height and weight including repeated measurements, birth related traits, background variables, education and smoking. By the end of 2014, 48 projects participated. Together, we have 893,458 height and weight measures (52% females) from 434,723 twin individuals, including 201,192 complete twin pairs (40% monozygotic, 40% same-sex dizygotic and 20% opposite-sex dizygotic) representing 22 countries. This project demonstrates that large-scale international twin studies are feasible and can promote the use of existing data for novel research purposes.
The thermal conductivities of epoxy composites of mixtures of graphite and graphene in varying ratios were measured. Thermal characterization results showed unexpectedly high conductivities at a certain ratio filler ratio. This phenomenon was exhibited by samples with three different overall filler concentrations (graphene + graphite) of 7, 14, and 35 wt%. The highest thermal conductivity of 42.4 ± 4.8 W/m K (nearly 250 times the thermal conductivity of pristine epoxy) was seen for a sample with 30 wt% graphite and 5 wt% graphene when characterized using the dual-mode heat flow meter technique. This significant improvement in thermal conductivity can be attributed to the lowering of overall thermal interface resistance due to small amounts of nanofillers (graphene) improving the thermal contact between the primary microfillers (graphite). The synergistic effect of this hybrid filler system is lost at higher loadings of the graphene relative to graphite. Graphite and graphene mixed in the ratio of 6:1 yielded the highest thermal conductivities at three different filler loadings.
Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose–response curve between fruit and vegetable consumption and carotenoid (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258·0 g, the correlation between observed and predicted intake was 0·78 and the mean difference between observed and predicted intake was − 1·7 g (limits of agreement: − 466·3, 462·8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201·1 g, the correlation was 0·65 and the mean bias was 2·4 g (limits of agreement: − 368·2, 373·0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.
Previously published guidelines are available that provide comprehensive recommendations for detecting and preventing healthcare-associated infections (HAIs). The intent of this document is to highlight practical recommendations in a concise format designed to assist acute care hospitals in implementing and prioritizing their Clostridium difficile infection (CDI) prevention efforts. This document updates “Strategies to Prevent Clostridium difficile Infections in Acute Care Hospitals,” published in 2008. This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA) and is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the American Hospital Association (AHA), the Association for Professionals in Infection Control and Epidemiology (APIC), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise. The list of endorsing and supporting organizations is presented in the introduction to the 2014 updates.
Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.
Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.
Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.
Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.
Limitations with current chemotherapeutic and vaccinal control of coccidiosis caused by Eimeria species continue to prompt development of novel controls, including the identification of new drug targets. Glucose-6-phosphate isomerase (G6-PI) has been proposed as a valid drug target for many protozoa, although polymorphism revealed by electrophoretic enzyme mobility has raised doubts for Eimeria. In this study we identified and sequenced the Eimeria tenella G6-PI orthologue (EtG6-PI) from the reference Houghton strain and confirmed its position within the prevailing taxonomic hierarchy, branching with the Apicomplexa and Plantae, distinct from the Animalia including the host, Gallus gallus. Comparison of the deduced 1647 bp EtG6-PI coding sequence with the 9016 bp genomic locus revealed 15 exons, all of which obey the intron-AG-/exon/-GT-intron splicing rule. Comparison with the Weybridge and Wisconsin strains revealed the presence of 33 single nucleotide polymorphisms (SNPs) and 14 insertion/deletion sites. Three SNPs were exonic and all yielded non-synonymous substitutions. Preliminary structural predictions suggest little association between the coding SNPs and key G6-PI catalytic residues or residues thought to be involved in the coordination of the G6-PI's substrate phosphate group. Thus, the significant polymorphism from its host orthologue and minimal intra-specific polymorphism suggest G6-PI remains a valid anti-coccidial drug target.
Inflammation is a stereotypical physiological response to infections and tissue injury; it initiates pathogen killing as well as tissue repair processes and helps to restore homeostasis at infected or damaged sites. Acute inflammatory reactions are usually self-limiting and resolve rapidly, due to the involvement of negative feedback mechanisms. Thus, regulated inflammatory responses are essential to remain healthy and maintain homeostasis. However, inflammatory responses that fail to regulate themselves can become chronic and contribute to the perpetuation and progression of disease. Characteristics typical of chronic inflammatory responses underlying the pathophysiology of several disorders include loss of barrier function, responsiveness to a normally benign stimulus, infiltration of inflammatory cells into compartments where they are not normally found in such high numbers, and overproduction of oxidants, cytokines, chemokines, eicosanoids and matrix metalloproteinases. The levels of these mediators amplify the inflammatory response, are destructive and contribute to the clinical symptoms. Various dietary components including long chain ω-3 fatty acids, antioxidant vitamins, plant flavonoids, prebiotics and probiotics have the potential to modulate predisposition to chronic inflammatory conditions and may have a role in their therapy. These components act through a variety of mechanisms including decreasing inflammatory mediator production through effects on cell signaling and gene expression (ω-3 fatty acids, vitamin E, plant flavonoids), reducing the production of damaging oxidants (vitamin E and other antioxidants), and promoting gut barrier function and anti-inflammatory responses (prebiotics and probiotics). However, in general really strong evidence of benefit to human health through anti-inflammatory actions is lacking for most of these dietary components. Thus, further studies addressing efficacy in humans linked to studies providing greater understanding of the mechanisms of action involved are required.