Assessing the Economic Resilience of Different Management Systems to Severe Forest Disturbance.

Given the drastic changes in the environment, resilience is a key focus of ecosystem management. Yet, the quantification of the different dimensions of resilience remains challenging, particularly for long-lived systems such as forests. Here we present an analytical framework to study the economic resilience of different forest management systems, focusing on the rate of economic recovery after severe disturbance. Our framework quantifies the post-disturbance gain in the present value of a forest relative to a benchmark system as an indicator of economic resilience. Forest values and silvicultural interventions were determined endogenously from an optimization model and account for risks affecting tree survival. We consider the effects of differences in forest structure and tree growth post disturbance on economic resilience. We demonstrate our approach by comparing the economic resilience of continuous cover forestry against a clear fell system for typical conditions in Central Europe. Continuous cover forestry had both higher economic return and higher economic resilience than the clear fell system. The economic recovery from disturbance in the continuous cover system was between 18.2 and 51.5% faster than in the clear fell system, resulting in present value gains of between 1733 and 4535 € ha-1. The advantage of the continuous cover system increased with discount rate and stand age, and was driven by differences in both stand structure and economic return. We conclude that continuous cover systems can help to address the economic impacts of increasing disturbances in forest management.

Screening for depression in psoriasis patients during a dermatological consultation: A first step towards treatment.

BACKGROUND AND OBJECTIVES: Depression is a highly prevalent comorbidity in psoriatic patients. The aim of this prospective study was to follow up psoriasis patients at risk for depression and to evaluate individual pathways to mental health care and the efficacy of depression screening in a real-life setting. PATIENTS AND METHODS: In this prospective multicenter study, 355 patients with psoriasis were screened for depressive symptoms with the revised Beck Depression Inventory (BDI-II). General practitioners of patients at risk for depression were asked for further evaluation. One year later, information on mental health care provision was gathered. RESULTS: 130 patients were screened positive for depressive symptoms, and 71 patients were followed-up (follow-up rate: 54.6 %). Psychiatric treatment was recommended for 28.2 % and accepted by 23.9 % of patients. Parameters of disease activity of psoriasis (PASI: 3.1, ∆: -1.7, P = 0.018), quality of life (Dermatology Life Quality Index [DLQI]: 6.5, ∆: -2.8, P = 0.005), and depressive symptoms (BDI-II: 13.2, ∆: -8.3, P < 0.001) improved significantly. Decrease of the BDI-II score was more pronounced in patients with higher PASI decrease. CONCLUSIONS: Screening for depressive symptoms led to increased utilization of mental health care and improvement of psoriasis, depressive symptoms, and quality of life. Thus, such screening should be implemented in routine care to optimize patient management.

Predicting the future relapse of alcohol-dependent patients from structural and functional brain images.

In alcohol dependence, individual prediction of treatment outcome based on neuroimaging endophenotypes can help to tailor individual therapeutic offers to patients depending on their relapse risk. We built a prediction model for prospective relapse of alcohol-dependent patients that combines structural and functional brain images derived from an experiment in which 46 subjects were exposed to alcohol-related cues. The patient group had been subdivided post hoc regarding relapse behavior defined as a consumption of more than 60 g alcohol for male or more than 40 g alcohol for female patients on one occasion during the 3-month assessment period (16 abstainers and 30 relapsers). Naïve Bayes, support vector machines and learning vector quantization were used to infer prediction models for relapse based on the mean and maximum values of gray matter volume and brain responses on alcohol-related cues within a priori defined regions of interest. Model performance was estimated by leave-one-out cross-validation. Learning vector quantization yielded the model with the highest balanced accuracy (79.4 percent, p < 0.0001; 90 percent sensitivity, 68.8 percent specificity). The most informative individual predictors were functional brain activation features in the right and left ventral tegmental areas and the right ventral striatum, as well as gray matter volume features in left orbitofrontal cortex and right medial prefrontal cortex. In contrast, the best pure clinical model reached only chance-level accuracy (61.3 percent). Our results indicate that an individual prediction of future relapse from imaging measurement outperforms prediction from clinical measurements. The approach may help to target specific interventions at different risk groups.

Virus-like particle formulation optimization by miniaturized high-throughput screening.

Virus-like particles (VLPs) are non-infectious and immunogenic virus-mimicking protein assemblies that are increasingly researched as vaccine candidates. Stability against aggregation is an important determinant dictating the viability of a pipeline VLP product, making multivariable stability data highly desirable especially in early product development stages. However, comprehensive formulation studies are challenging due to low sample availability early in developability assessment. This issue is exacerbated by industry-standard analytical techniques which are low-throughput and/or sample-consuming. This study presents a miniaturized high-throughput screening (MHTS) methodology for VLP formulation by integrating dynamic light scattering (DLS) and asymmetrical flow field-flow fractionation (AF4) in a formulation funnel analysis. Using only 2 µg of sample and 100 s per measurement, a DLS plate reader was deployed to effectively pre-screen a large experimental space, allowing a smaller set of superior formulation conditions to be interrogated at high-resolution with AF4. The stabilizing effects of polysorbate 20, sucrose, trehalose, mannitol and sorbitol were investigated. MHTS data showed that addition of 0.5% w/v polysorbate 20 together with either 40% w/v sucrose or 40% w/v sorbitol could stabilize VLPs at elevated temperatures up to 58 °C. AF4 data further confirmed that the formulation containing 40% w/v sorbitol and 0.5% w/v polysorbate 20 effectively protected VLPs during freeze-thawing and freeze-drying, increasing recoveries from these processes by 80 and 50 percentage points, respectively. The MHTS strategy presented here could be used to rapidly explore a large formulation development space using reduced amounts of sample, without sacrificing the analytical resolution needed for quality control. Such a method paves the way for rapid formulation development and could potentially hasten the commercialization of new VLP vaccines.

Impact of COVID-19 on Influenza and Pneumococcal Vaccination of Psoriatic Patients in Germany: Results from Vac-Pso.

BACKGROUND: Suboptimal influenza and pneumococcal vaccination rates have been reported before the COVID-19 pandemics in certain populations at risk for severe infection. The aim of this longitudinal cohort study was to investigate changes in influenza and pneumococcal vaccination rates and patient perceptions in patients with psoriasis (PsO) before and during the pandemic. METHODS: Data on vaccination, patient and disease characteristics, comorbidity, and patient perceptions were collected with questionnaires before and during the pandemic approximately one year later. RESULTS: Over the whole cohort who participated in the follow-up visit (n = 287; 59.2% male; mean age: 56.3 years), both influenza and pneumococcal lifetime vaccination prevalences increased significantly from 50.5% to 66.2% and from 16.0% to 41.5%, respectively. A total of 88.5% of PsO patients were interested in a COVID-19 vaccination or had already received it. The reasons for and against vaccinations changed significantly before and during the pandemic. CONCLUSIONS: Despite a promising increase in the vaccination prevalence in our PsO cohort, it remains important that awareness for vaccinations is encouraged and closely monitored in future research, particularly in populations at risk.

Low Pneumococcal Vaccination among Patients with Psoriasis in Germany: Results from Vac-Pso.

While suboptimal pneumococcal vaccination rates have been reported in immunosuppressed patients with rheumatic diseases, data for patients with psoriasis (PsO) or atopic dermatitis (AD) are scarce. Pneumococcal vaccination in Germany is recommended in patients with certain comorbidities, immunosuppression, and/or aged 60 years or above. The aim of this multicenter cross-sectional study was to investigate the pneumococcal vaccination rate in patients with PsO compared to patients with AD and to evaluate patient perceptions. All patients completed a questionnaire on vaccination status and perceptions, patient and disease characteristics, as well as comorbidity. Medical records and vaccination certificates were reviewed. Over the whole cohort (n = 327 PsO (41.9% female), n = 98 AD (42.9% female)), 83.8% and 42.9% of PsO and AD patients, respectively, had an indication for pneumococcal vaccination due to immunosuppressive treatment. The pneumococcal vaccination rate was 14.4% and 10.2% in PsO and AD patients, respectively. The vaccination rate depended significantly on age, working status and presence of psoriatic arthritis. The most common reason for nonvaccination was lacking recommendation by physicians. Higher awareness, particularly for vaccination indication due to immunosuppression among dermatologists, general physicians, and patients, is warranted.

Influenza Vaccination in Psoriatic Patients-Epidemiology and Patient Perceptions: A German Multicenter Study (Vac-Pso).

The risk of developing severe complications from an influenza virus infection is increased in patients with chronic inflammatory diseases such as psoriasis (PsO) and atopic dermatitis (AD). However, low influenza vaccination rates have been reported. The aim of this study was to determine vaccination rates in PsO compared to AD patients and explore patient perceptions of vaccination. A multicenter cross-sectional study was performed in 327 and 98 adult patients with PsO and AD, respectively. Data on vaccination, patient and disease characteristics, comorbidity, and patient perceptions was collected with a questionnaire. Medical records and vaccination certificates were reviewed. A total of 49.8% of PsO and 32.7% of AD patients were vaccinated at some point, while in season 2018/2019, 30.9% and 13.3% received an influenza vaccination, respectively. There were 96.6% and 77.6% of PsO and AD patients who had an indication for influenza vaccination due to age, immunosuppressive therapy, comorbidity, occupation, and/or pregnancy. Multivariate regression analysis revealed higher age (p < 0.001) and a history of bronchitis (p = 0.023) as significant predictors of influenza vaccination in PsO patients. Considering that most patients had an indication for influenza vaccination, the rate of vaccinated patients was inadequately low.

A maximum common subgraph kernel method for predicting the chromosome aberration test.

The chromosome aberration test is frequently used for the assessment of the potential of chemicals and drugs to elicit genetic damage in mammalian cells in vitro. Due to the limitations of experimental genotoxicity testing in early drug discovery phases, a model to predict the chromosome aberration test yielding high accuracy and providing guidance for structure optimization is urgently needed. In this paper, we describe a machine learning approach for predicting the outcome of this assay based on the structure of the investigated compound. The novelty of the proposed method consists in combining a maximum common subgraph kernel for measuring the similarity of two chemical graphs with the potential support vector machine for classification. In contrast to standard support vector machine classifiers, the proposed approach does not provide a black box model but rather allows to visualize structural elements with high positive or negative contribution to the class decision. In order to compare the performance of different methods for predicting the outcome of the chromosome aberration test, we compiled a large data set exhibiting high quality, reliability, and consistency from public sources and configured a fixed cross-validation protocol, which we make publicly available. In a comparison to standard methods currently used in pharmaceutical industry as well as to other graph kernel approaches, the proposed method achieved significantly better performance.

A model comparison of COMT effects on central processing of affective stimuli.

The number of studies on imaging genetics has risen considerably over the last few years, and haplotypes are being increasingly applied as a model to increase the explained variance in functional brain activation. Haplotypes, however, are not always the preferable approach. While such highly complex models have a greater capacity for fitting data, they might also lead to over-fitting. This study compares individual single nucleotide polymorphisms (SNPs) with haplotypes by applying both models to effects of catechol-O-methyltransferase (COMT), one of the most extensively studied genes in psychiatric research and imaging genetics, on the central processing of affective cues. To our knowledge, this is the first study to compare haplotypes and SNPs of the COMT gene in an imaging genetics study. The model comparison in this study is based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), introducing the novel concepts of posterior evidence ratio maps and best model maps. Findings reveal the simplest model, comprising only the well studied COMT Val(158)Met polymorphism, to be the most informative one. These results do not necessarily mean that haplotype models are in general inferior to individual SNP analysis. They do underline, however, that techniques for model comparison such as the ones used in this study need to be employed to establish whether the increase in likelihood provided by a more complex haplotype-based model is large enough to warrant the increase in model complexity.