An annotated bird checklist for Gam island, Raja Ampat, including field notes on species monitoring and conservation

Species checklists are a fundamental component of biodiversity research. They foster understanding of species distributions and habitat preferences, thus reducing gaps of knowledge in geographical occurrences of species. Especially in light of the limited availability of data on species distributions for Tanah Papua, an increasing scientific focus on the region is crucial to foster and refine the knowledge of species occurrences and to inform potential conservation planning. Despite a strong focus on conservation of Raja Ampat´s marine areas, surprisingly few studies have focused on the terrestrial biodiversity of the archipelago. As a consequence, detailed species checklists are largely missing. Here, we provide a preliminary bird species checklist for the island of Gam and its surrounding islands, located in the central Raja Ampat archipelago. During nine sampling periods between 2013 and 2019, we recorded 132 bird species in six distinct habitat types. Of the detected species, six are considered threatened by IUCN Red List criteria. We further recorded three new species for Gam Island, thereby expanding their known extent of occurrence

Cell entry of a host-targeting protein of oomycetes requires gp96

The animal-pathogenic oomycete Saprolegnia parasitica causes serious losses in aquaculture by infecting and killing freshwater fish. Like plant-pathogenic oomycetes, S. parasitica employs similar infection structures and secretes effector proteins that translocate into host cells to manipulate the host. Here, we show that the host-targeting protein SpHtp3 enters fish cells in a pathogen-independent manner. This uptake process is guided by a gp96-like receptor and can be inhibited by supramolecular tweezers. The C-terminus of SpHtp3 (containing the amino acid sequence YKARK), and not the N-terminal RxLR motif, is responsible for the uptake into host cells. Following translocation, SpHtp3 is released from vesicles into the cytoplasm by another host-targeting protein where it degrades nucleic acids. The effector translocation mechanism described here, is potentially also relevant for other pathogen-host interactions as gp96 is found in both animals and plants.This work is supported by the [European Community’s] Seventh Framework Programme [FP7/2007–2013] under grant agreement no. [238550] (L.L., J.D.-U., C.J.S., P.v.W.); BBSRC [BBE007120/1, BB/J018333/1 and BB/G012075/1] (F.T., I.d.B., C.J.S., S.W., P.v.W.); Newton Global Partnership Award [BB/N005058/1] (F.T., P.v.W.), the University of Aberdeen (A.D.T., T.R., C.J.S., P.v.W.) and Deutsche Forschungsgemeinschaft [CRC1093] (P.B., T.S.). We would like to acknowledge the Ministry of Higher Education Malaysia for funding INA. We would like to thank Brian Haas for his bioinformatics support. We would like to acknowledge Neil Gow and Johannes van den Boom for critical reading of the manuscript. We would like to acknowledge Svetlana Rezinciuc for technical help with pH-studies

Skunk River Review Fall 1995, Vol 7

https://openspace.dmacc.edu/skunkriver/1016/thumbnail.jp

TURBOMOLE: Today and Tomorrow

TURBOMOLE is a highly optimized software suite for large-scale quantum-chemical and materials science simulations of molecules, clusters, extended systems, and periodic solids. TURBOMOLE uses Gaussian basis sets and has been designed with robust and fast quantum-chemical applications in mind, ranging from homogeneous and heterogeneous catalysis to inorganic and organic chemistry and various types of spectroscopy, light–matter interactions, and biochemistry. This Perspective briefly surveys TURBOMOLE’s functionality and highlights recent developments that have taken place between 2020 and 2023, comprising new electronic structure methods for molecules and solids, previously unavailable molecular properties, embedding, and molecular dynamics approaches. Select features under development are reviewed to illustrate the continuous growth of the program suite, including nuclear electronic orbital methods, Hartree–Fock-based adiabatic connection models, simplified time-dependent density functional theory, relativistic effects and magnetic properties, and multiscale modeling of optical properties

Seeing eye to eye: social augmented reality and shared decision making in the marketplace

Firms increasingly seek to improve the online shopping experience by enabling customers to exchange product recommendations through social augmented reality (AR). We utilize socially situated cognition theory and conduct a series of five studies to explore how social AR supports shared decision making in recommender–decision maker dyads. We demonstrate that optimal configurations of social AR, that is, a static (vs. dynamic) point-of-view sharing format matched with an image-enhanced (vs. text-only) communicative act, increase recommenders’ comfort with providing advice and decision makers’ likelihood of using the advice in their choice. For both, these effects are due to a sense of social empowerment, which also stimulates recommenders’ desire for a product and positive behavioral intentions. However, recommenders’ communication motives impose boundary conditions. When recommenders have strong impression management concerns, this weakens the effect of social empowerment on recommendation comfort. Furthermore, the stronger a recommender’s persuasion goal, the less likely the decision maker is to use the recommendation in their choice

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards

Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games

Online social interactions in multiplayer games can be supportive and positive or toxic and harmful; however, few methods can easily assess interpersonal interaction quality in games. We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting. We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner. The models can predict both binary and continuous affiliation with up to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with features based on verbal communication demonstrating the highest potential. Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.Comment: CHI '2

Phase Diagrams for Systems Containing Hyperbranched Polymers

Hyperbranched polymers show an outstanding potential for applications ranging from chemistry over nanotechnology to pharmacy. In order to take advantage of this potential, the underlying phase behaviour must be known. From the thermodynamic point of view, the modelling of these phase diagrams is quite challenging, because the thermodynamic properties depend on the architecture of the hyperbranched polymer as well as on the number and kind of present functional end groups. The influence of architecture can be taken into account via the lattice cluster theory (LCT) as an extension of the well-known Flory–Huggins theory. Whereas the Flory–Huggins theory is limited to linear polymer chains, the LCT can be applied to an arbitrary chain architecture. The number and the kind of functional groups can be handled via the Wertheim perturbation theory, applicable for directed forces between the functional groups and the surrounding solvent molecules. The combination of the LCT and the Wertheim theory can be established for the modelling or even prediction of the liquid-liquid equilibria (LLE) of polymer solutions in a single solvent or in a solvent mixture or polymer blends, where the polymer can have an arbitrary structure. The applied theory predicts large demixing regions for mixtures of linear polymers and hyperbranched polymers, as well as for mixtures made from two hyperbranched polymers. The introduction of empty lattice sites permits the theoretical investigation of pressure effects on phase behaviour. The calculated phase diagrams were compared with own experimental data or to experimental data taken from literature