Obligations of Researchers and Managers to Respect Wetlands: Practical Solutions to Minimizing Field Monitoring Impacts

Research and field monitoring can disturb wetland integrity. Adoption of ethical field practices is needed to limit monitoring induced stressors such as trampling, non-native seed and invertebrate dispersal, and disease and fungal spread. We identify a linear pathway of deterioration highlighting stressors that can progress to cumulative impacts, consequences, and losses at the site scale. The first step to minimize disturbance is to assess and classify the current ecosystem quality. We present a tiered framework for wetland classification and link preventative measures to the wetland tier. Preventative measures are recommended at various intensities respective to the wetland tier, with higher tiered wetlands requiring more intense preventative measures. In addition, preventative measures vary by time of implementation (before, during, and after the wetland visit) to mitigate impacts at various temporal scales. The framework is designed to increase transparency of field monitoring impacts and to promote the adoption of preventative measures. Implementing preventative measures can build accountability and foster a greater appreciation for our roles as researchers and managers in protecting wetlands

Grundlagen zur Abschätzung und Bewertung der von Kohlekraftwerken ausgehenden Umweltbelastungen in Entwicklungsländern : Studie im Auftrag des Bundesministeriums für Wirtschaftliche Zusammenarbeit (Nr.: 201-E522-48/78)

Ziel dieses Forschungsberichtes ist es, systematische Bewertungshilfen bereitzustellen, um durch Kohlekraftwerke entstehende Umweltschäden in Entwicklungsländern abzuschätzen. Hierzu müssen von allen denkbaren von Kohlekraftwerken ausgehenden Umweltbelastungen jene ausgesucht werden, die ein hohes Gefährdungspotential besitzen und bei deren Betrieb häufig auftreten. Darüberhinaus sollten aber Ausnahmefälle, bei denen bestimmte Faktorenkombinationen zu für Kohlekraftwerke untypischen Belastungssituationen führen, ebenfalls Berücksichtigung finden. Es besteht also das Problem, Bewertungshilfen zu finden, die sich durch ihre Allgemeingültigkeit und Anwendbarkeit in allen Entwicklungsländern, also in unterschiedlichsten Regionen auszeichnen und die trotzdem im einzelnen Anwendungsfall spezielle Bedingungen beachten

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning

Self-regulation of the dopaminergic reward circuit in cocaine users with mental imagery and neurofeedback

BACKGROUND: Enhanced drug-related reward sensitivity accompanied by impaired sensitivity to non-drug related rewards in the mesolimbic dopamine system are thought to underlie the broad motivational deficits and dysfunctional decision-making frequently observed in cocaine use disorder (CUD). Effective approaches to modify this imbalance and reinstate non-drug reward responsiveness are urgently needed. Here, we examined whether cocaine users (CU) can use mental imagery of non-drug rewards to self-regulate the ventral tegmental area and substantia nigra (VTA/SN). We expected that obsessive and compulsive thoughts about cocaine consumption would hamper the ability to self-regulate the VTA/SN activity and tested if real-time fMRI (rtfMRI) neurofeedback (NFB) can improve self-regulation of the VTA/SN. METHODS: Twenty-two CU and 28 healthy controls (HC) were asked to voluntarily up-regulate VTA/SN activity with non-drug reward imagery alone, or combined with rtfMRI NFB. RESULTS: On a group level, HC and CU were able to activate the dopaminergic midbrain and other reward regions with reward imagery. In CU, the individual ability to self-regulate the VTA/SN was reduced in those with more severe obsessive-compulsive drug use. NFB enhanced the effect of reward imagery but did not result in transfer effects at the end of the session. CONCLUSION: CU can voluntary activate their reward system with non-drug reward imagery and improve this ability with rtfMRI NFB. Combining mental imagery and rtFMRI NFB has great potential for modifying the maladapted reward sensitivity and reinstating non-drug reward responsiveness. This motivates further work to examine the use of rtfMRI NFB in the treatment of CUD

New type of dual-channel PAM chlorophyll fluorometer for highly sensitive water toxicity biotests

A new type of dual-channel PAM chlorophyll fluorometer has been developed, which is specialised in the detection of extremely small differences in photosynthetic activity in algae or thylakoids suspensions. In conjunction with standardised algae cultures or isolated thylakoids, the new device provides an ultrasensitive biotest system for detection of toxic substances in water samples. In this report, major features of the new device are outlined and examples of its performance are presented using suspensions of Phaeodactylum tricornutum (diatoms) and of freeze-dried thylakoids of Lactuca sativa (salad). Investigated and reference samples are exposed to the same actinic intensity of pulse-modulated measuring light. The quantum yields are assessed by the saturation pulse method. Clock-triggered repetitive measurements of quantum yield typically display a standard deviation of 0.1%, corresponding to the inhibition induced by 0.02 mug diuron l(-1). Hence, for diuron or compounds with similar toxicity, the detection limit is well below the 0.1 mug l(-1) defined as the limit for the presence of a single toxic substance in water by the European Commission drinking water regulation. The amounts of water and biotest material required for analysis are very small, as a single assay involves two 1 ml samples, each containing ca. 0.5 mug chlorophyll. Both with Phaeodactylum and thylakoids the relationship between inhibition and diuron concentration is strictly linear up to 10% inhibition, with very similar slopes. Apparent inhibition depends on the actinic effect of the measuring light, showing optima at 6 and 4 mumol quanta m(-2) s(-1) with Phaeodactylum and thylakoids, respectively

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing