Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Connectome-driven neural inventory of a complete visual system

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

Network Statistics of the Whole-Brain Connectome of Drosophila

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective 'teacher' by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the 'student' compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

Local shape descriptors for neuron segmentation

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

Transverse endoplasmic reticulum expansion in hereditary spastic paraplegia corticospinal axons

Hereditary spastic paraplegias (HSPs) comprise a large group of inherited neurologic disorders affecting the longest corticospinal axons (SPG1-86 plus others), with shared manifestations of lower extremity spasticity and gait impairment. Common autosomal dominant HSPs are caused by mutations in genes encoding the microtubule-severing ATPase spastin (SPAST; SPG4), the membrane-bound GTPase atlastin-1 (ATL1; SPG3A) and the reticulon-like, microtubule-binding protein REEP1 (REEP1; SPG31). These proteins bind one another and function in shaping the tubular endoplasmic reticulum (ER) network. Typically, mouse models of HSPs have mild, later onset phenotypes, possibly reflecting far shorter lengths of their corticospinal axons relative to humans. Here, we have generated a robust, double mutant mouse model of HSP in which atlastin-1 is genetically modified with a K80A knock-in (KI) missense change that abolishes its GTPase activity, whereas its binding partner Reep1 is knocked out. Atl1KI/KI/Reep1-/- mice exhibit early onset and rapidly progressive declines in several motor function tests. Also, ER in mutant corticospinal axons dramatically expands transversely and periodically in a mutation dosage-dependent manner to create a ladder-like appearance, on the basis of reconstructions of focused ion beam-scanning electron microscopy datasets using machine learning-based auto-segmentation. In lockstep with changes in ER morphology, axonal mitochondria are fragmented and proportions of hypophosphorylated neurofilament H and M subunits are dramatically increased in Atl1KI/KI/Reep1-/- spinal cord. Co-occurrence of these findings links ER morphology changes to alterations in mitochondrial morphology and cytoskeletal organization. Atl1KI/KI/Reep1-/- mice represent an early onset rodent HSP model with robust behavioral and cellular readouts for testing novel therapies.

Neural network organization for courtship-song feature detection in Drosophila

Animals communicate using sounds in a wide range of contexts, and auditory systems must encode behaviorally relevant acoustic features to drive appropriate reactions. How feature detection emerges along auditory pathways has been difficult to solve due to challenges in mapping the underlying circuits and characterizing responses to behaviorally relevant features. Here, we study auditory activity in the Drosophila melanogaster brain and investigate feature selectivity for the two main modes of fly courtship song, sinusoids and pulse trains. We identify 24 new cell types of the intermediate layers of the auditory pathway, and using a new connectomic resource, FlyWire, we map all synaptic connections between these cell types, in addition to connections to known early and higher-order auditory neurons-this represents the first circuit-level map of the auditory pathway. We additionally determine the sign (excitatory or inhibitory) of most synapses in this auditory connectome. We find that auditory neurons display a continuum of preferences for courtship song modes and that neurons with different song-mode preferences and response timescales are highly interconnected in a network that lacks hierarchical structure. Nonetheless, we find that the response properties of individual cell types within the connectome are predictable from their inputs. Our study thus provides new insights into the organization of auditory coding within the Drosophila brain.

Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set

We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.

Ozonation products of zidovudine and thymidine in oxidative water treatment

Ozonation is an advanced treatment technology that is increasingly used for the removal of organic micropollutants from wastewater and drinking water. However, reaction of organic compounds with ozone can also result in the formation of toxic transformation products. In the present study, the degradation of the antiviral drug zidovudine during ozonation was investigated. To obtain further insights into the reaction mechanisms and pathways, results of zidovudine were compared with the transformation of the naturally occurring derivative thymidine. Kinetic experiments were accompanied by elucidation of formed transformation products using lab-scale batch experiments and subsequent liquid chromatography - high resolution mass spectrometry (LC-HRMS) analysis. Degradation rate constants for zidovudine with ozone in the presence of t-BuOH as radical scavenger varied between 2.8 ∙ 10⁴ M^-1 s^-1 (pH 7) and 3.2 ∙ 10⁴ M^-1 s^-1 (pH 3). The structural difference of zidovudine to thymidine is the exchange of the OH-moiety by the azide function at position 3'. In contrast to inorganic azide, no reaction with ozone was observed for the organic bound azide. In total, nine transformation products (TPs) were identified for both zidovudine and thymidine. Their formation can be attributed to the attack of ozone at the C-C-double bond of the pyrimidine-base. As a result of rearrangements, the primary ozonide decomposed in three pathways forming two different TPs, including hydroperoxide TPs. Rearrangement reactions followed by hydrolysis and subsequent release of H₂O₂ further revealed a cascade of TPs containing amide moieties. In addition, a formyl amide riboside and a urea riboside were identified as TPs indicating that oxidations of amide groups occur during ozonation processes.

Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy

To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM more accessible and affordable to the scientific community.

Dense neuronal reconstruction through X-ray holographic nano-tomography

Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.

Large Scale Image Segmentation with Structured Loss Based Deep Learning for Connectome Reconstruction

We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-Net, trained to predict affinities between voxels, followed by iterative region agglomeration. We train using a structured loss based on Malis, encouraging topologically correct segmentations obtained from affinity thresholding. Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm. Second, we compute the gradient in two separate passes to avoid spurious gradient contributions in early training stages. Our predictions are accurate enough that simple learning-free percentile-based agglomeration outperforms more involved methods used earlier on inferior predictions. We present results on three diverse EM datasets, achieving relative improvements over previous results of 27, 15, and 250 percent. Our findings suggest that a single method can be applied to both nearly isotropic block-face EM data and anisotropic serial sectioned EM data. The runtime of our method scales linearly with the size of the volume and achieves a throughput of $\sim$∼ 2.6 seconds per megavoxel, qualifying our method for the processing of very large datasets.

What you extract is what you see: Optimising the preparation of water and wastewater samples for in vitro bioassays

The assessment of water quality is crucial for safeguarding drinking water resources and ecosystem integrity. To this end, sample preparation and extraction is critically important, especially when investigating emerging contaminants and the toxicity of water samples. As extraction methods are rarely optimised for bioassays but rather adopted from chemical analysis, this may result in a misrepresentation of the actual toxicity. In this study, surface water, groundwater, hospital and municipal wastewater were used to characterise the impacts of common sample preparation techniques (acidification, filtration and solid phase extraction (SPE)) on the outcomes of eleven in vitro bioassays. The latter covered endocrine activity (reporter gene assays for estrogen, androgen, aryl-hydrocarbon, retinoic acid, retinoid X, vitamin D, thyroid receptor), mutagenicity (Ames fluctuation test), genotoxicity (umu test) and cytotoxicity. Water samples extracted using different SPE sorbents (Oasis HLB, Supelco ENVI-Carb+, Telos C18/ENV) at acidic and neutral pH were compared for their performance in recovering biological effects. Acidification, commonly used for stabilisation, significantly altered the endocrine activity and toxicity of most (waste)water samples. Sample filtration did not affect the majority of endpoints but in certain cases affected the (anti-)estrogenic and dioxin-like activities. SPE extracts (10.4 × final concentration), including WWTP effluents, induced significant endocrine effects that were not detected in aqueous samples (0.63 × final concentration), such as estrogenic, (anti-)androgenic and dioxin-like activities. When ranking the SPE methods using multivariate Pareto optimisation an extraction with Telos C18/ENV at pH 7 was most effective in recovering toxicity. At the same time, these extracts were highly cytotoxic masking the endpoint under investigation. Compared to that, extraction at pH 2.5 enriched less cytotoxicity. In summary, our study demonstrates that sample preparation and extraction critically affect the outcome of bioassays when assessing the toxicity of water samples. Depending on the water matrix and the bioassay, these methods need to be optimised to accurately assess water quality.

Analyzing Image Segmentation for Connectomics

Automatic image segmentation is critical to scale up electron microscope (EM) connectome reconstruction. To this end, segmentation competitions, such as CREMI and SNEMI, exist to help researchers evaluate segmentation algorithms with the goal of improving them. Because generating ground truth is time-consuming, these competitions often fail to capture the challenges in segmenting larger datasets required in connectomics. More generally, the common metrics for EM image segmentation do not emphasize impact on downstream analysis and are often not very useful for isolating problem areas in the segmentation. For example, they do not capture connectivity information and often over-rate the quality of a segmentation as we demonstrate later. To address these issues, we introduce a novel strategy to enable evaluation of segmentation at large scales both in a supervised setting, where ground truth is available, or an unsupervised setting. To achieve this, we first introduce new metrics more closely aligned with the use of segmentation in downstream analysis and reconstruction. In particular, these include synapse connectivity and completeness metrics that provide both meaningful and intuitive interpretations of segmentation quality as it relates to the preservation of neuron connectivity. Also, we propose measures of segmentation correctness and completeness with respect to the percentage of "orphan" fragments and the concentrations of self-loops formed by segmentation failures, which are helpful in analysis and can be computed without ground truth. The introduction of new metrics intended to be used for practical applications involving large datasets necessitates a scalable software ecosystem, which is a critical contribution of this paper. To this end, we introduce a scalable, flexible software framework that enables integration of several different metrics and provides mechanisms to evaluate and debug differences between segmentations. We also introduce visualization software to help users to consume the various metrics collected. We evaluate our framework on two relatively large public groundtruth datasets providing novel insights on example segmentations.

TED: A Tolerant Edit Distance for segmentation evaluation

In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.

Identification of transformation products of antiviral drugs formed during biological wastewater treatment and their occurrence in the urban water cycle

The fate of five antiviral drugs (abacavir, emtricitabine, ganciclovir, lamivudine and zidovudine) was investigated in biological wastewater treatment. Investigations of degradation kinetics were accompanied by the elucidation of formed transformation products (TPs) using activated sludge lab experiments and subsequent LC-HRMS analysis. Degradation rate constants ranged between 0.46 L d(-1) gSS(-1) (zidovudine) and 55.8 L d(-1) gSS(-1) (abacavir). Despite these differences of the degradation kinetics, the same main biotransformation reaction was observed for all five compounds: oxidation of the terminal hydroxyl-moiety to the corresponding carboxylic acid (formation of carboxy-TPs). In addition, the oxidation of thioether moieties to sulfoxides was observed for emtricitabine and lamivudine. Antiviral drugs were detected in influents of municipal wastewater treatment plants (WWTPs) with concentrations up to 980 ng L(-1) (emtricitabine), while in WWTP effluents mainly the TPs were found with concentration levels up to 1320 ng L(-1) (carboxy-abacavir). Except of zidovudine none of the original antiviral drugs were detected in German rivers and streams, whereas the concentrations of the TPs ranged from 16 ng L(-1) for carboxy-lamivudine up to 750 ng L(-1) for carboxy-acyclovir. These concentrations indicate an appreciable portion from WWTP effluents present in rivers and streams, as well as the high environmental persistence of the carboxy-TPs. As a result three of the carboxylic TPs were detected in finished drinking water.

Oxypurinol - A novel marker for wastewater contamination of the aquatic environment

The anti-gout agent allopurinol is one of the most prescribed pharmaceuticals in Germany and is widely metabolized into oxypurinol (80%) as well as the corresponding riboside conjugates (10%) within the human body. To investigate the occurrence of allopurinol and oxypurinol in the urban water cycle an analytical method was developed based on solid phase extraction (SPE) and subsequent liquid chromatography electrospray-ionization tandem mass spectrometry (LC-MS/MS). In raw wastewater concentration levels of oxypurinol ranged up to 26.6 μg L(-1), whereas allopurinol was not detected at all. In wastewater treatment plant (WWTP) effluents, concentrations of allopurinol were <LOQ, whereas oxypurinol concentrations ranged from 2.3 μg L(-1) to 21.7 μg L(-1). Elevated concentrations of oxypurinol in biologically treated wastewater originate from the transformation of allopurinol as well as the cleavage of allopurinol-9-riboside, which was confirmed by laboratory experiments with activated sludge taken from a municipal WWTP. Further tracking of oxypurinol in the urban water cycle revealed its presence in rivers and streams (up to 22.6 μg L(-1)), groundwater (up to 0.38 μg L(-1)) as well as in finished drinking water (up to 0.30 μg L(-1)). Due to the high biological stability and the almost ubiquitous presence in the urban water cycle at elevated concentrations, oxypurinol might be used as marker for domestic wastewater in the environment. This was confirmed by correlation analysis to other wastewater markers with strong correlations of the concentrations of oxypurinol and carbamazepine (r(2) = 0.89) as well as of oxypurinol and primidone (r(2) = 0.82).

[Surgical Therapy and Prognostic Factors for Carcinoma of Vater's Papilla]

BACKGROUND

Carcinoma of ampulla of Vater are rare tumours of the GI-tract with an improved prognosis compared to other periampullary tumours. Analysis of survival and prognostic factors are limited due to the low incidence of the carcinoma. The intention of this study in patients with papillary carcinoma was to evaluate short- and long-term survival and to identify prognostic factors for pancreatectomy and reconstruction using pancreatogastrostomy as treatment of carcinoma of Vater's ampulla.

PATIENTS AND METHODS

Between 1989 and 2008 76 patients with a carcinoma of the ampulla of Vater were treated by oncological resection followed by pancreatogastrostomy. Various factors such as demographics, perioperative factors, histopathological findings as well as short- and long-term survival were evaluated retrospectively. Data were analysed statistically using Kaplan-Meier estimates of survival with log-rank test and uni- and multivariate analysis with Cox regression.

RESULTS

The overall 5-year survival was 46 %, the 10-year survival 26 % for resected patients. By univariate analysis we could demonstrate that lymph node metastasis is the only predictor for outcome. In the multivariate analysis, age, sex, grading and especially lymph node status were a significant predictor for the survival of patients.

CONCLUSION

In the current patient cohort lymph node status was the most important independent predictor of outcome after resection of carcinoma of Vater's papilla.

Comprehension of climate change and environmental attitudes across the lifespan

Given the coincidence of the demographic change and climate change in the upcoming decades the aging voter gains increasing importance in climate change mitigation and adaptation processes. It is generally assumed that information status and comprehension of complex processes underlying climate change are prerequisites for adopting pro-environmental attitudes and taking pro-environmental actions. In a cross-sectional study, we investigated in how far (1) environmental knowledge and comprehension of feedback processes underlying climate change and (2) pro-environmental attitudes change as a function of age. Our sample consisted of 92 participants aged 25-75 years (mean age 49.4 years, SD 17.0). Age was negatively related to comprehension of system structures inherent to climate change, but positively associated with level of fear of consequences and anxiousness towards climate change. No significant relations were found between environmental knowledge and pro-environmental attitude. These results indicate that, albeit understanding of relevant structures of the climate system is less present in older age, age is not a limiting factor for being engaged in the complex dilemma of climate change. Results bear implications for the communication of climate change and pro-environmental actions in aging societies.

High cervical spinal cord stimulation (CSCS) increases regional cerebral blood flow after induced subarachnoid haemorrhage in rats

The effects of high cervical spinal cord stimulation (cSCS) on regional cerebral blood flow (rCBF) were investigated after experimentally induced subarachnoid haemorrhage (SAH) in rats by the means of (99m)Tc-HMPAO. The experiments were carried out on a total of 24 Wistar rats, divided in three groups [group I: control without SAH, group II: SAH, group III: SAH and cSCS]. (99m)Tc-HMPAO was administered intravenously (group II/group III) 48 hours after induction of SAH. In group III, (99m)Tc-HMPAO was given after 3 hours of cSCS. All animals were sacrificed 30 minutes after application on (99m)Tc-HMPAO. Radioactivities were determined in blood, cerebrum and cerebellum. The ratio cerebrum/blood and cerebellum/blood was calculated to ascertain "extraction rate" in the sample differentially. The following mean values were calculated for the cerebellum/blood ratio: Group I: 1.06, SD: 0.21; Group II: 0.66, SD: 0.21; Group III: 1.00, SD: 0.37. Comparing the mean values a highly significant difference could be found between group II and III (p = 0.007) and between group I and II (p = 0.0019), respectively. Calculations of the cerebrum/blood ratio revealed similar results. After SAH cSCS enhances cerebral and cerebellar blood flow in rats. Possibly, cSCD constitutes a new therapeutic approach in the treatment of disturbed regional cerebral blood flow after SAH.

Negative correlations between control performance and verbalizable knowledge: indicators for implicit learning in process control tasks?

Negative correlations between task performance in dynamic control tasks and verbalizable knowledge, as assessed by a post-task questionnaire, have been interpreted as dissociations that indicate two antagonistic modes of learning, one being "explicit", the other "implicit". This paper views the control tasks as finite-state automata and offers an alternative interpretation of these negative correlations. It is argued that "good controllers" observe fewer different state transitions and, consequently, can answer fewer post-task questions about system transitions than can "bad controllers". Two experiments demonstrate the validity of the argument by showing the predicted negative relationship between control performance and the number of explored state transitions, and the predicted positive relationship between the number of explored state transitions and questionnaire scores. However, the experiments also elucidate important boundary conditions for the critical effects. We discuss the implications of these findings, and of other problems arising from the process control paradigm, for conclusions about implicit versus explicit learning processes.

An instrument to assess the quality of maternal behavior

Early assessment, detection, and therapeutic intervention in maladaptive maternal behavior can be facilitated by a simple nursing appraisal instrument employed initially on the third postpartum day. The Funke-Irby Interactional Assessment not only discriminates between early adaptive and maladaptive maternal behaviors, but it also illustrates a mother's progress or lack of progress toward adaptive mothering, making the instrument useful to the community health nurse during the early postdelivery weeks. This instrument has been effectively used as a screening device for nurses making decisions about the need for further referral for family therapy.