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Paulus, M.

Publications and source records attributed to Paulus, M..

9 recordsLinked to original sources

Environmental DNA from archived leaves reveals widespread temporal turnover and biotic homogenization in forest arthropod communities

A major limitation of current reports on insect declines is the lack of standardized, long-term, and taxonomically broad time series. Here, we demonstrate the utility of environmental DNA from archived leaf material to characterize plant-associated arthropod communities. We base our work on several multi-decadal leaf time series from tree canopies in four land use types, which were sampled as part of a long-term environmental monitoring program across Germany. Using these highly standardized and well-preserved samples, we analyze temporal changes in communities of several thousand arthropod species belonging to 23 orders using metabarcoding and quantitative PCR. Our data do not support widespread declines of -diversity or genetic variation within sites. Instead, we find a gradual community turnover, which results in temporal and spatial biotic homogenization, across all land use types and all arthropod orders. Our results suggest that insect decline is more complex than mere -diversity loss, but can be driven by {beta}-diversity decay across space and time.

ecology↗

Molecular diet analysis in zebra and quagga mussels (Dreissena spp.) and an assessment of the utility of aquatic filter feeders as biological eDNA filters

Molecular gut content analysis is a popular tool to study food web interactions and was recently also suggested as an alternative source for DNA based biomonitoring. However, the overabundant consumers DNA often outcompetes that of its diet during PCR. Blocking approaches are an efficient means to reduce consumer amplification while retaining broad specificity for dietary taxa. We here designed an assay to monitor the eukaryotic diet of mussels and test their utility as biological eDNA filters to monitor planktonic communities. We designed several rDNA primer sets with a broad taxonomic suitability for eukaryotes, which suppress the amplification of mussels. The primers were tested using mussel DNA extracts and the results were compared to eDNA water samples collected next to the mussel colonies. Taxonomic recovery, as well as patterns of alpha and beta diversity, were compared between mussels and water samples. In addition, we analyzed time series samples of mussel samples from different German rivers. Our primer sets efficiently block the amplification of various mussel genera. The recovered DNA reflects a broad dietary preference across the eukaryotic tree of life and considerable taxonomic overlap with filtered water samples. We also recover various taxa of possible commensals and parasites, associated with the mussels. Our protocol will enable large scale dietary analysis in mussels, facilitate aquatic food web analysis, elucidate the ecological impact of invasive bivalves and the rapid survey of mussel aquacultures for pathogens. Moreover, we show that mussels could serve as an interesting complementary DNA source for biomonitoring.

ecology↗

Electrophysiological investigation of reward anticipation and outcome evaluation during slot machine play

Slot machines are a popular form of gambling, offering a tractable way to experimentally model reward processes. This study used a 3-reel slot paradigm to assess psychologically distinct phases of reward processing, reflecting anticipation, and early and late-stage outcome processing. EEG measures of winning, nearly missing (a losing outcome revealed at the final, third reel), and "totally" missing (a losing outcome revealed earlier, at the second reel) were collected from healthy adults (n=54). Condition effects were evaluated in: i) event-related potential (ERP) components reflecting anticipatory attention (stimulus preceding negativity, SPN) and outcome processing (reward positivity, RewP and late-positive potential, LPP) and ii) total power and phase synchrony of theta and delta band oscillations. Behaviorally, trial initiation was fastest after a near miss outcome and slowest after a winning outcome. As expected, a significant SPN was observed for possible wins (AA) vs. total misses (AB), consistent with reward anticipation. In addition, significantly larger win (AAA) vs. near miss (AAB) amplitudes were observed for the RewP and LPP to wins and LPP to near misses (vs. total misses) reflecting early and late-stage outcome processing effects. There was an effect of reel position on the RewP, with larger effects in the final reel (AAA-AAB) relative to the 2nd-reel locked difference waves (AA-AB). Across all outcomes, near misses elicited the largest and most phase-synchronized theta responses, while wins elicited larger and more phase-synchronized delta responses than total misses, with near misses not differing from wins or total misses. Phase locking measures contrasting win vs. near miss delta and theta synchronization, within time windows corresponding to ERP measurements, covaried with RewP, but not SPN or LPP, amplitude. Lastly, EEG measures showed differential relationships with age and self-reported consummatory pleasure. In the context of slot machine play, where reward anticipation and attainment place minimal demands on effort and skill, ERP and time-frequency methods capture distinct neurophysiological signatures of reward anticipation and outcome processing.

neuroscience↗

High neutralizing potency of swine glyco-humanized polyclonal antibodies against SARS-CoV-2

Perfusion of convalescent plasma (CP) has demonstrated a potential to improve the pneumonia induced by SARS-CoV-2, but procurement and standardization of CP are barriers to its wide usage. Many monoclonal antibodies (mAbs) have been developed but appear insufficient to neutralize SARS-CoV-2 unless two or three of them are being combined. Therefore, heterologous polyclonal antibodies of animal origin, that have been used for decades to fight against infectious agents might represent a highly efficient alternative to the use of CP or mAbs in COVID-19 by targeting multiple antigen epitopes. However, conventional heterologous polyclonal antibodies trigger human natural xenogeneic antibody responses particularly directed against animal-type carbohydrate epitopes, mainly the N-glycolyl form of the neuraminic acid (Neu5Gc) and the Gal 1,3-galactose (Gal), ultimately forming immune complexes and potentially leading to serum sickness or allergy. To circumvent these drawbacks, we engineered animals lacking the genes coding for the cytidine monophosphate-N-acetylneuraminic acid hydroxylase (CMAH) and 1,3-galactosyl-transferase (GGTA1) enzymes to produce glyco-humanized polyclonal antibodies (GH-pAb) lacking Neu5Gc and -Gal epitopes. We found that pig IgG Fc domains fail to interact with human Fc receptors and thereby should confer the safety advantage to avoiding macrophage dependent exacerbated inflammatory responses, a drawback possibly associated with antibody responses against SARS-CoV-2 or to avoiding a possible antibody-dependent enhancement (ADE). Therefore, we immunized CMAH/GGTA1 double knockout (DKO) pigs with the SARS-CoV-2 spike receptor-binding domain (RBD) to elicit neutralizing antibodies. Animals rapidly developed a hyperimmune response with anti-SARS-CoV-2 end-titers binding dilutions over one to a million and end-titers neutralizing dilutions of 1:10,000. The IgG fraction purified and formulated following clinical Good Manufacturing Practices, named XAV-19, neutralized Spike/angiotensin converting enzyme-2 (ACE-2) interaction at a concentration < 1g/mL and inhibited infection of human cells by SARS-CoV-2 in cytopathic assays. These data and the accumulating safety advantages of using glyco-humanized swine antibodies in humans warranted clinical assessment of XAV-19 to fight against COVID-19.

immunology↗

Reward processing electrophysiology in schizophrenia: Effects of age and illness phase

BackgroundReward processing abnormalities may underlie characteristic pleasure and motivational impairments in schizophrenia. Some neural measures of reward processing show strong age-related modulation, highlighting the importance of considering age effects on reward sensitivity. We compared event-related potentials (ERPs) reflecting reward anticipation (stimulus-preceding negativity, SPN) and evaluation (reward positivity, RewP; late-positive potential, LPP) across individuals with schizophrenia (SZ) and healthy controls (HC), with an emphasis on examining effects of chronological age, brain age (i.e., predicted age based on neurobiological measures), and illness phase. MethodsSubjects underwent EEG while completing a slot-machine task for which rewards were not dependent on performance accuracy, speed, or other preparatory demands. Slot-machine task EEG responses were compared between 54 SZ and 54 HC individuals, ages 19 to 65. Reward-related ERPs were analyzed with respect to chronological age, categorically-defined illness phase (early; ESZ versus chronic schizophrenia; CSZ), and were used to model brain age relative to chronological age. ResultsIllness phase-focused analyses indicated there were no group differences in average SPN or RewP amplitudes. However, a group x reward outcome interaction revealed that ESZ differed from HC in later outcome processing, reflected by greater LPP responses following loss versus reward (a reversal of the HC pattern). While brain age estimates did not differ among groups, depressive symptoms in SZ were associated with older brain age estimates while controlling for negative symptoms. ConclusionsESZ and CSZ did not differ from HC in reward anticipation or early outcome processing during a cognitively undemanding reward task, highlighting areas of preserved functioning. However, ESZ showed altered later reward outcome evaluation, pointing to selective reward deficits during the early illness phase of schizophrenia. Further, an association between ERP-derived brain age and depressive symptoms in SZ extends prior findings linking depression with reward-related ERP blunting. Taken together, both illness phase and age may impact reward processing in SZ, and brain aging may offer a promising, novel marker of reward dysfunction that warrants further study.

neuroscience↗

Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention

Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N=223) and healthy controls (N=45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (-6, -54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, -49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.

neuroscience↗

An Active Inference Approach to Dissecting Reasons for Non-Adherence to Antidepressants

BackgroundAntidepressant medication adherence is among the most important problems in health care worldwide. Interventions designed to increase adherence have largely failed, pointing towards a critical need to better understand the underlying decision-making processes that contribute to adherence. A computational decision-making model that integrates empirical data with a fundamental action selection principle could be pragmatically useful in 1) making individual level predictions about adherence, and 2) providing an explanatory framework that improves our understanding of non-adherence. MethodsHere we formulate a partially observable Markov decision process model based on the active inference framework that can simulate several processes that plausibly influence adherence decisions. ResultsUsing model simulations of the day-to-day decisions to take a prescribed selective serotonin reuptake inhibitor (SSRI), we show that several distinct parameters in the model can influence adherence decisions in predictable ways. These parameters include differences in policy depth (i.e., how far into the future one considers when deciding), decision uncertainty, beliefs about the predictability (stochasticity) of symptoms, beliefs about the magnitude and time course of symptom reductions and side effects, and the strength of medication-taking habits that one has acquired. ConclusionsClarifying these influential factors will be an important first step toward empirically determining which are contributing to non-adherence to antidepressants in individual patients. The model can also be seamlessly extended to simulate adherence to other medications (by incorporating the known symptom reduction and side effect trajectories of those medications), with the potential promise of identifying which medications may be best suited for different patients.

neuroscience↗

I know that I don’t know: Structural and functional connectivity underlying meta-ignorance in pre-schoolers

Research Highlights[1] Children develop the ability to report that they do not know something at around five years of age.\n\n[2] Children who could correctly report their own ignorance in a partial-knowledge task showed thicker cortices within medial orbitofrontal cortex.\n\n[3] This region was functionally connected to parts of the default-mode network.\n\n[4] The default-mode network might support the development of correct metacognitive monitoring.\n\nAbstractMetacognition plays a pivotal role in human development. The ability to realize that we do not know something, or meta-ignorance, emerges after approximately five years of age. We aimed at identifying the brain systems that underlie the developmental emergence of this ability in a preschool sample.\n\nTwenty-four children aged between five and six years answered questions under three conditions of a meta-ignorance task twice. In the critical partial knowledge condition, an experimenter first showed two toys to a child, then announced that she would place one of them in a box behind a screen, out of sight from the child. The experimenter then asked the child whether or not she knew which toy was in the box.\n\nChildren who answered correctly both times to the metacognitive question in the partial knowledge condition (n=9) showed greater cortical thickness in a cluster within left medial orbitofrontal cortex than children who did not (n=15). Further, seed-based functional connectivity analyses of the brain during resting state revealed that this region is functionally connected to the medial orbitofrontal gyrus, posterior cingulate gyrus and precuneus, and mid- and inferior temporal gyri.\n\nThis finding suggests that the default mode network, critically through its prefrontal regions, supports introspective processing. It leads to the emergence of metacognitive monitoring allowing children to explicitly report their own ignorance.

neuroscience↗

Cerebral blood flow predicts differential neurotransmitter activity

Application of metabolic magnetic resonance imaging measures such as cerebral blood flow in translational medicine is limited by the unknown link of observed alterations to specific neurophysiological processes. In particular, the sensitivity of cerebral blood flow to activity changes in specific neurotransmitter systems remains unclear. We address this question by probing cerebral blood flow in healthy volunteers using seven established drugs with known dopaminergic, serotonergic, glutamatergic and GABAergic mechanisms of action. We use a novel framework aimed at disentangling the observed effects to contribution from underlying neurotransmitter systems. We find for all evaluated compounds a reliable spatial link of respective cerebral blood flow changes with underlying neurotransmitter receptor densities corresponding to their primary mechanisms of action. The strength of these associations with receptor density is mediated by respective drug affinities. These findings suggest that cerebral blood flow is a sensitive brain-wide in-vivo assay of metabolic demands across a variety of neurotransmitter systems in humans.

neuroscience↗