The symptom‐specific efficacy of antidepressant medication vs. cognitive behavioral therapy in the treatment of depression: results from an individual patient data meta‐analysis

A recent individual patient data meta‐analysis showed that antidepressant medication is slightly more efficacious than cognitive behavioral therapy (CBT) in reducing overall depression severity in patients with a DSM‐defined depressive disorder. We used an update of that dataset, based on seventeen randomized clinical trials, to examine the comparative efficacy of antidepressant medication vs. CBT in more detail by focusing on individual depressive symptoms as assessed with the 17‐item Hamilton Rating Scale for Depression. Five symptoms (i.e., “depressed mood” , “feelings of guilt” , “suicidal thoughts” , “psychic anxiety” and “general somatic symptoms”) showed larger improvements in the medication compared to the CBT condition (effect sizes ranging from .13 to .16), whereas no differences were found for the twelve other symptoms. In addition, network estimation techniques revealed that all effects, except that on “depressed mood” , were direct and could not be explained by any of the other direct or indirect treatment effects. Exploratory analyses showed that information about the symptom‐specific efficacy could help in identifying those patients who, based on their pre‐treatment symptomatology, are likely to benefit more from antidepressant medication than from CBT (effect size of .30) versus those for whom both treatments are likely to be equally efficacious. Overall, our symptom‐oriented approach results in a more thorough evaluation of the efficacy of antidepressant medication over CBT and shows potential in “precision psychiatry” .     

Asthma Trajectories in Early Childhood: Identifying Modifiable Factors

Background

There are conflicting views as to whether childhood wheezing represents several discreet entities or a single but variable disease. Classification has centered on phenotypes often derived using subjective criteria, small samples, and/or with little data for young children. This is particularly problematic as asthmatic features appear to be entrenched by age 6/7. In this paper we aim to: identify longitudinal trajectories of wheeze and other atopic symptoms in early childhood; characterize the resulting trajectories by the socio-economic background of children; and identify potentially modifiable processes in infancy correlated with these trajectories.

Data and Methods

The Millennium Cohort Study is a large, representative birth cohort of British children born in 2000–2002. Our analytical sample includes 11,632 children with data on key variables (wheeze in the last year; ever hay-fever and/or eczema) reported by the main carers at age 3, 5 and 7 using a validated tool, the International Study of Asthma and Allergies in Childhood module. We employ longitudinal Latent Class Analysis, a clustering methodology which identifies classes underlying the observed population heterogeneity.

Results

Our model distinguished four latent trajectories: a trajectory with both low levels of wheeze and other atopic symptoms (54% of the sample); a trajectory with low levels of wheeze but high prevalence of other atopic symptoms (29%); a trajectory with high prevalence of both wheeze and other atopic symptoms (9%); and a trajectory with high levels of wheeze but low levels of other atopic symptoms (8%). These groups differed in terms of socio-economic markers and potential intervenable factors, including household damp and breastfeeding initiation.

Conclusion

Using data-driven techniques, we derived four trajectories of asthmatic symptoms in early childhood in a large, population based sample. These groups differ in terms of their socio-economic profiles. We identified correlated intervenable pathways in infancy, including household damp and breastfeeding initiation.     

Finite mixture modeling with mixture outcomes using the EM algorithm

This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random-coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.     

Sleep Trajectories of Women Undergoing Elective Cesarean Section: Effects on Body Weight and Psychological Well-Being

Background

After cesarean section (CS), women may be at great risk for sleep disturbance, but little is known about temporal changes in their sleep patterns and characteristics. We had two aims: 1) to identify distinct classes of sleep-disturbance trajectories in women considering elective CS from third-trimester pregnancy to 6 months post-CS and 2) to examine associations of sleep trajectories with body mass index (BMI), depressive symptoms, and fatigue scores.

Methods

We analyzed data from a prospective cohort study of 139 Taiwanese pregnant women who elected CS. Sleep components were assessed using the Pittsburgh Sleep Quality Index in third-trimester pregnancy, 1 day, 1 week, 1 month, and 6 months post-CS. Data were collected on depressive symptoms, fatigue symptoms, and BMI. Sleep-quality trajectories were identified by group-based trajectory modeling.

Results

We identified three distinct trajectories: stable poor sleep (50 women, 36.0%), progressively worse sleep (67 women, 48.2%), and persistently poor sleep (22 women, 15.8%). Poor sleep was significantly associated with pre-pregnancy BMI and more baseline (third-trimester pregnancy) depressive and fatigue symptoms. At 6 months post-CS, women classified as progressively worse or persistently poor sleepers showed a trend toward higher BMI (p<0.03), more depressive symptoms (p<0.001), and higher fatigue scores (p<0.001) than those with stable poor sleep.

Conclusions

Women had three distinct sleep-disturbance trajectories before and after elective CS. These poor-sleep courses were associated with BMI and psychological well-being. Our findings suggest a need to continuously assess sleep quality among women considering elective CS and up to 6 months post-CS.     

Evidence of Associations between Cytokine Genes and Subjective Reports of Sleep Disturbance in Oncology Patients and Their Family Caregivers

The purposes of this study were to identify distinct latent classes of individuals based on subjective reports of sleep disturbance; to examine differences in demographic, clinical, and symptom characteristics between the latent classes; and to evaluate for variations in pro- and anti-inflammatory cytokine genes between the latent classes. Among 167 oncology outpatients with breast, prostate, lung, or brain cancer and 85 of their FCs, growth mixture modeling (GMM) was used to identify latent classes of individuals based on General Sleep Disturbance Scale (GSDS) obtained prior to, during, and for four months following completion of radiation therapy. Single nucleotide polymorphisms (SNPs) and haplotypes in candidate cytokine genes were interrogated for differences between the two latent classes. Multiple logistic regression was used to assess the effect of phenotypic and genotypic characteristics on GSDS group membership. Two latent classes were identified: lower sleep disturbance (88.5%) and higher sleep disturbance (11.5%). Participants who were younger and had a lower Karnofsky Performance status score were more likely to be in the higher sleep disturbance class. Variation in two cytokine genes (i.e., IL6, NFKB) predicted latent class membership. Evidence was found for latent classes with distinct sleep disturbance trajectories. Unique genetic markers in cytokine genes may partially explain the interindividual heterogeneity characterizing these trajectories.     

The Relationship between Internalising Symptom Development and Academic Attainment in Early Adolescence

Evidence for the longitudinal associations between internalising symptom development and academic attainment is sparse and results from existing studies are largely inconclusive. The approaches that have been used in existing studies examining this relationship have in common the limitation of grouping together all individuals in the sample which makes the assumption that the relationship between time, symptoms and attainment across all individuals is the same. The current study aimed to use heterogeneous trajectories of symptom development to examine the longitudinal associations between internalising symptom development and change in academic attainment over a three years period in early adolescence, a key period for internalising symptom development. Internalising symptoms were assessed for 3 consecutive years in a cohort from age 11–14 years (n = 2647, mean age at T1 = 11.7 years). National standardised test scores prior to the first wave and subsequent to the last wave were used as measures of academic attainment. Heterogeneous symptom development trajectories were identified using latent class growth analysis and socio-demographic correlates, such as gender, SES and ethnicity, of the different trajectory groupings were investigated. Derived trajectory groupings were examined as predictors of subsequent academic attainment, controlling for prior attainment. Results demonstrate that symptom trajectories differentially predicted change in academic attainment with increasing trajectories associated with significantly worse academic outcomes when compared to pupils with low levels of symptoms in all waves. Hence, a trajectory based approach provides a more nuanced breakdown of complexities in symptom development and their differential relationships with academic outcomes and in doing so helps clarify the longitudinal relationship between these two key domains of functioning in early adolescence.     

Trajectories of ADHD severity over 10 years from childhood into adulthood

The study examined unique trajectories of ADHD severity from childhood (7–16 yo at baseline) through adulthood in a sample of ADHD, bipolar and healthy subjects. Comorbid disorders and temperament were examined as correlates of course of ADHD. N = 81 participants with an ADHD diagnosis, ascertained as a comparison group in a study of bipolar disorder (BP-I), were followed over a 10-year period. Growth mixture modeling (GMM) of ADHD severity was used to investigate trajectories of ADHD severity over 10 years. GMM revealed four trajectories in the N = 251 participants included in these analyses. A persisting high ADHD trajectory had the highest rates of comorbid major depressive disorder and oppositional defiant disorder. This persisting high ADHD group also had higher fantasy and lower persistence and self-directedness compared with those who displayed a pattern of decreasing ADHD symptoms over time. Psychopathologic features that characterize divergent trajectories of ADHD into adulthood are elucidated, and additional, larger studies are warranted.     

Lack of exposure to natural light in the workspace is associated with physiological,sleep and depressive symptoms

The diurnal light cycle has a crucial influence on all life on earth. Unfortunately, modern society has modified this life-governing cycle by stressing maximum production and by giving insufficient attention to the ecological balance and homeostasis of the human metabolism. The aim of this study is to evaluate the effects of exposure or lack of exposure to natural light in a rest/activity rhythm on cortisol and melatonin levels, as well as on psychological variables in humans under natural conditions. This is a cross-sectional study. The subjects were allocated split into two groups according to their workspace (10 employees in the “with window” group and 10 in the “without window” group). All participants were women and wore anactigraph (Actiwatch 2, Philips Respironics), which measures activity and ambient light exposure, for seven days. Concentrations of melatonin and cortisol were measured from the saliva samples. Participants were instructed to collect saliva during the last day of use of the actigraph at 08:00 am, 4:00 pm and 10:00 pm. The subjects answered the Self-Reporting Questionnaire-20 (SRQ-20) to measure the presence of minor psychiatric disorders; the Montgomery-Asberg (MA) scale was used to measure depression symptoms, and the Pittsburgh Sleep Quality Index questionnaire (PSQI) was used to evaluate the quality of sleep. The Rayleigh analysis indicates that the two groups, “with window” an d “without window”, exhibited similar activities and light acrophases. In relation to light exposure, the mesor was significantly higher (t?=??2.651, p?=?0.023) in t he “with window” group (191.04?±?133.36) than in the “without window” group (73.8?±?42.05). Additionally, the “with window” group presented the highest amplitude of light exposure (298.07?±?222.97). Cortisol levels were significantly different between the groups at 10:00 pm (t?=?3.009, p?=?0.008; “without window” (4.01?±?0.91) “with window” (3.10?±?0.30)). In terms of the melatonin levels, the groups differed at two different times of day: 08:00 am (t?=?2.593, p?=?0.018) and 10:00 pm (t?=??2.939, p?=?0.009). The “with window” group had a lower melatonin level at 08:00 am (3.54?±?0.60) but a higher level at 10:00 pm (24.74?±?4.22) than the “without window” group. Higher cortisol levels were positively correlated with minor psychiatric disorders and depressive symptoms (MA) at 10:00 pm. Lower melatonin levels at 10:00 pm were correlated with depressive symptoms and poor quality of sleep (PSQI). Our study demonstrated that not only may light pollution affect human physiology but also lack of exposure to natural light is related to high levels of cortisol and lower levels of melatonin at night, and these, in turn, are related to depressive symptoms and poor quality of sleep.     

20-year trajectories of positive and negative symptoms after the first psychotic episode in patients with schizophrenia spectrum disorder: results from the OPUS study

This study aimed to identify the 20-year trajectories of positive and negative symptoms after the first psychotic episode in a sample of patients with an ICD-10 diagnosis of schizophrenia spectrum disorder, and to investigate the baseline characteristics and long-term outcomes associated with these trajectories. A total of 373 participants in the OPUS trial were included in the study. Symptoms were assessed at baseline and after 1, 2, 5, 10 and 20 years using the Scales for the Assessment of Positive and Negative Symptoms. We used latent class growth mixture modelling to identify trajectories, and multinominal regression analyses to investigate predictors of membership to identified trajectories. Five trajectories of positive symptoms were identified: early continuous remission (50.9% of the sample), stable improvement (18.0%), intermittent symptoms (10.2%), relapse with moderate symptoms (11.9%), and continuous severe symptoms (9.1%). Substance use disorder (odds ratio, OR: 2.83, 95% CI: 1.09-7.38, p=0.033), longer duration of untreated psychosis (OR: 1.02, 95% CI: 1.00-1.03, p=0.007) and higher level of negative symptoms (OR: 1.60, 95% CI: 1.07-2.39, p=0.021) were predictors of the relapse with moderate symptoms trajectory, while only longer duration of untreated psychosis (OR: 1.01, 95% CI: 1.00-1.02, p=0.030) predicted membership to the continuous severe symptoms trajectory. Two trajectories of negative symptoms were identified: symptom remission (51.0%) and continuous symptoms (49.0%). Predictors of the continuous symptoms trajectory were male sex (OR: 3.03, 95% CI: 1.48-6.02, p=0.002) and longer duration of untreated psychosis (OR: 1.01, 95% CI: 1.00-1.02, p=0.034). Trajectories displaying continuous positive and negative symptoms were linked to lower neurocognition, as measured by the Brief Assessment of Cognition in Schizophrenia (BACS) (z-score: –0.78, CI: –1.39 to –0.17, for continuous positive symptoms; z-score: –0.33, CI: –0.53 to –0.13, for continuous negative symptoms). The same trajectories were also linked to higher use of antipsychotic medication at 20-year follow-up (continuous positive symptoms: 78%; continuous negative symptoms: 67%). These findings suggest that the majority of patients with first-episode schizophrenia spectrum disorder have a trajectory with early stable remission of positive symptoms. Long duration of untreated psychosis and comorbid substance abuse are modifiable predictors of poor trajectories for positive symptoms in these patients. In about half of patients, negative symptoms do not improve over time. These symptoms, in addition to being associated with poor social and neurocognitive functioning, may prevent patients from seeking help.     

Trajectory Pathways for Depressive Symptoms and Their Associated Factors in a Chinese Primary Care Cohort by Growth Mixture Modelling

Background

The naturalistic course for patients suffering from depressive disorders can be quite varied. Whilst some remit with little or no intervention, others may suffer a more prolonged course of symptoms. The aim of this study was to identify trajectory patterns for depressive symptoms in a Chinese primary care cohort and their associated factors.

Methods and Results

A 12-month cohort study was conducted. Patients recruited from 59 primary care clinics across Hong Kong were screened for depressive symptoms using the Centre for Epidemiologic Studies Depression Scale (CES-D) and monitored over 12 months using the Patient Health Questionnaire-9 items (PHQ-9) administered at 12, 26 and 52 weeks. 721 subjects were included for growth mixture modelling analysis. Using Akaike Information Criterion, Bayesian Information Criterion, Entropy and Lo-Mendell-Rubin adjusted likelihood ratio test, a seven-class trajectory path model was identified. Over 12 months, three trajectory groups showed improvement in depressive symptoms, three remained static, whilst one deteriorated. A mild severity of depressive symptoms with gradual improvement was the most prevalent trajectory identified. Multivariate, multinomial regression analysis was used to identify factors associated with each trajectory. Risk factors associated with chronicity included: female gender; not married; not in active employment; presence of multiple chronic disease co-morbidities; poor self-rated general health; and infrequent health service use.

Conclusions

Whilst many primary care patients may initially present with a similar severity of depressive symptoms, their course over 12 months can be quite heterogeneous. Although most primary care patients improve naturalistically over 12 months, many do not remit and it is important for doctors to be able to identify those who are at risk of chronicity. Regular follow-up and greater treatment attention is recommended for patients at risk of chronicity.     

Exploiting the functional and taxonomic structure of genomic data by probabilistic topic modeling

In this paper, we present a method that enable both homology-based approach and composition-based approach to further study the functional core (i.e., microbial core and gene core, correspondingly). In the proposed method, the identification of major functionality groups is achieved by generative topic modeling, which is able to extract useful information from unlabeled data. We first show that generative topic model can be used to model the taxon abundance information obtained by homology-based approach and study the microbial core. The model considers each sample as a “document,” which has a mixture of functional groups, while each functional group (also known as a “latent topic”) is a weight mixture of species. Therefore, estimating the generative topic model for taxon abundance data will uncover the distribution over latent functions (latent topic) in each sample. Second, we show that, generative topic model can also be used to study the genome-level composition of “N-mer” features (DNA subreads obtained by composition-based approaches). The model consider each genome as a mixture of latten genetic patterns (latent topics), while each functional pattern is a weighted mixture of the “N-mer” features, thus the existence of core genomes can be indicated by a set of common N-mer features. After studying the mutual information between latent topics and gene regions, we provide an explanation of the functional roles of uncovered latten genetic patterns. The experimental results demonstrate the effectiveness of proposed method.     

Depression Symptom Trajectories and Associated Risk Factors among Adolescents in Chile

Adolescence is a key period for studying the development of depression, with studies in Europe and North America showing a pattern of elevated risk that begins in early adolescence and continues to increase as adolescents age. Few studies have examined the course of adolescent depression and associated risk factors in low and middle-income countries. This longitudinal cohort study examined depression symptom trajectories and risk factors in a sample of socio-economically disadvantaged adolescents in Chile (n = 2,508). Data were collected over an 18-month period as part of a clinical trial for secondary students aged 12 to 18 (median age 14). Clinical levels of depression were prevalent in this sample at baseline (35% for girls and 28% for boys); yet latent growth models of symptom trajectories revealed a pattern of decreasing symptoms over time. There was evidence of an anxiety-depression developmental pathway for girls, with elevated anxiety levels initially predicting poorer depression outcomes later on. Poor problem-solving skills were associated with initial depression levels but did not predict the course of depressive symptoms. Critically, the declining symptom trajectories raise important methodological issues regarding the effects of repeated assessment in longitudinal studies.     

Self-Rated Health Trajectories in the African American Health Cohort

Background

Self-rated health taps health holistically and dynamically blends prior health histories with current illness burdens and expectations for future health. While consistently found as an independent predictor of functional decline, sentinel health events, physician visits, hospital episodes, and mortality, much less is known about intra-individual changes in self-rated health across the life course, especially for African Americans.

Materials/Methods

Data on 998 African American men and women aged 50–64 years old were taken from a probability-based community sample that was first assessed in 2000–2001 and re-assessed 1, 2, 3, 4, 7, and 9 years later. Using an innovative approach for including decedents in the analysis, semi-parametric group-based mixture models were used to identify person-centered group trajectories of self-rated health over time. Multivariable multinomial logistic regression analysis was then used to differentiate the characteristics of AAH participants classified into the different group trajectories.

Results

Four self-rated health group trajectories were identified: persistently good health, good but declining health, persistently fair health, and fair but declining health. The main characteristics that differentiated the self-rated health trajectory groups from each other were age, education, smoking, morbidity (angina, congestive heart failure, diabetes, and kidney disease), having been hospitalized in the year prior to baseline, depressive symptoms, mobility limitations, and initial self-rated health.

Conclusions

This is the first study to examine self-rated health trajectories separately among African Americans. Four qualitatively distinct self-rated health group trajectories were identified that call into question the accuracy of prior reports that a single, average self-rated health trajectory for African Americans adequately captures their within-group heterogeneity.     

Vocal coordination of troop movement among white-faced capuchin monkeys,Cebus capucinus

Coordinated travel by social groups is well documented, often with evidence that cognitive spatial maps are employed. Yet the mechanisms by which movement decisions are made and implemented within social groups are poorly known. In a field study of white-faced capuchin monkeys in Costa Rica it was demonstrated that a specific call, the “trill,” is used by adults in the initiation and directing of troop movement. The trills of subadults were restricted to vocal exchanges with other subadults. Continuous vocal recordings were collected of the vocalizations of the 14 members of the study troop. A cumulative 33.7 h of continuous samples and 1,892 sonagrams were analyzed. In addition to vocalizations clearly associated with alarm, distress, or agonistic contexts, two distinct call types were identified, trills and huhs. Age-sex classes differed in the rate at which both types of calls were produced in different spatial positions within the troop. Adult females and males produced higher rates of trills when in the leading edge compared to all other spatial positions in a traveling troop. Trills at the edge of a stationary troop represented 36 “successful” and 3 “unsuccessful” start attempts; the troop usually moved in the trajectory predicted by a trilling adult's location on the troop periphery within 10 min of the initiation of trilling. Adults also altered the trajectory of traveling troops by trilling at the side and back of the troop (10 “successful” and 4 “unsuccessful” attempts). Huh vocalizations were most predictably produced when a capuchin is in a dense fruit patch. These results emphasize the role vocalizations serve in the coordination and trajectory of group movement in nonhuman primates, especially those populations that are arboreal or in which visual contact is otherwise impeded. © 1993 Wiley-Liss, Inc.

1 This article is a US Government work and, as such, is in the public domain in the United States of America.

     

Developmental Profiles of Eczema,Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies

Background

The term “atopic march” has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level.

Methods and Findings

Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time.Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts.

Conclusions

The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼7% of those with symptoms) follow trajectory profiles resembling the atopic march. Please see later in the article for the Editors'' Summary     

Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction

Positive and negative affect data are often collected over time in psychiatric care settings, yet no generally accepted means are available to relate these data to useful diagnoses or treatments. Latent class analysis attempts data reduction by classifying subjects into one of K unobserved classes based on observed data. Latent class models have recently been extended to accommodate longitudinally observed data. We extend these approaches in a Bayesian framework to accommodate trajectories of both continuous and discrete data. We consider whether latent class models might be used to distinguish patients on the basis of trajectories of observed affect scores, reported events, and presence or absence of clinical depression.     

Disrupted montane forest recovery hinders biodiversity conservation in the tropical Andes

Aim

Andean montane forests are biodiversity hotspots and large carbon stores and they provide numerous ecosystem services. Following land abandonment after centuries of forest clearing for agriculture in the Andes, there is an opportunity for forest recovery. Field-based studies show that forests do not always recover. However, large-scale and long-term knowledge of recovery dynamics of Andean forests remains scarce. This paper analyses tropical montane forest recovery trajectories over a 15-year time frame at the landscape and tropical Andean scale to inform restoration planning.

Methods

We first detect “potential recovery” as areas that have experienced a forest transition between 2000 and 2005. Then, we use Landsat time series analysis of the normalized difference water index (NDWI) to classify four “realized recovery” trajectories (“ongoing”, “arrested”, “disrupted” and “no recovery”) based on a sequential pattern of 5-yearly Z-score anomalies for 2005–2020. We compare these results against an analysis of change in tree cover to validate against other datasets.

Results

Across the tropical Andes, we detected a potential recovery area of 274 km² over the period. Despite increases in tree cover, most areas of the Andes remained in early successional states (10–25% tree cover), and NDWI levelled out after 5–10 years. Of all potential forest recovery areas, 22% showed “ongoing recovery”, 61% showed either “disrupted” or “arrested recovery”, and 17% showed “no recovery”. Our method captured forest recovery dynamics in a Peruvian arrested succession context and in landscape-scale tree-planting efforts in Ecuador.

Main conclusions

Forest recovery across the Andes is mostly disrupted, arrested or unsuccessful, with consequences for biodiversity recovery and provision of ecosystem services. Low-recovery areas identified in this study might be good candidates for active restoration interventions in this UN Decade on Restoration. Future studies could determine restoration strategies and priorities and suggest management strategies at a local planning scale across key regions in the biodiversity hotspot.     

First detection of “Candidatus Phytoplasma asteris” - and “Candidatus Phytoplasma solani”-related strains in fig trees

In July 2017, a survey was conducted in a fig collection plot at Locorotondo (south of Italy) to investigate the possible presence of phytoplasmas in plants showing yellowing, deformed leaves, short internodes, mottling and mosaic. Samples were collected from symptomatic plants and tested by nested PCR assays using universal and specific primers to amplify the 16S rDNA of these prokaryotes. PCR results detected the presence of phytoplasma sequences in twenty plant samples that resulted clustering two phylogenetically distinct phytoplasmas, i.e., “Candidatus Phytoplasma asteris” and “Candidatus Phytoplasma solani” affiliated to 16SrI and 16SrXII ribosomal groups, respectively. The presence of phytoplasmas belonging to both ribosomal groups was confirmed with group specific quantitative PCR and RFLP assays on 16S ribosomal amplicons. Results of this study indicate for the first time the occurrence of phytoplasmas in fig; however, more work should be carried out to verify their association with the symptoms observed on diseased fig plants.