9,499 research outputs found
Human wellbeing responses to species’ traits
People rely on well-functioning ecosystems to provide critical services that underpin human health and wellbeing. Consequently, biodiversity loss has profound negative implications for humanity. Human-biodiversity interactions can deliver individual-level wellbeing gains, equating to substantial healthcare cost-savings when scaled-up across populations. However, critical questions remain about which species and/or traits (e.g. colours, sounds, smells) elicit wellbeing responses. The traits that influence wellbeing can be considered ‘effect’ traits. Using techniques from community ecology, we analyse a database of species’ effect traits articulated by people, to identify those that generate different types of wellbeing (physical, emotional, cognitive, social, spiritual and ‘global’ wellbeing, the latter being akin to ‘whole-person health’). Effect traits have a predominately positive impact on wellbeing, influenced by the identity and taxonomic kingdom of each species. Different sets of effect traits deliver different types of wellbeing. However, traits cannot be considered independently of species because multiple traits can be supported by a single species. Indeed, we find numerous effect traits from across the ecological community can elicit multiple types of wellbeing, illustrating the complexity of biodiversity experiences. Our empirical approach can help implement interdisciplinary thinking for biodiversity conservation and nature-based public health interventions designed to support human wellbeing
Effects of short-term experimental manipulation of captive social environment on uropygial gland microbiome and preen oil volatile composition
IntroductionAvian preen oil, secreted by the uropygial gland, is an important source of volatile compounds that convey information about the sender’s identity and quality, making preen oil useful for the recognition and assessment of potential mates and rivals. Although intrinsic factors such as hormone levels, genetic background, and diet can affect preen oil volatile compound composition, many of these compounds are not the products of the animal’s own metabolic processes, but rather those of odor-producing symbiotic microbes. Social behavior affects the composition of uropygial microbial communities, as physical contact results in microbe sharing. We experimentally manipulated social interactions in captive dark-eyed juncos (Junco hyemalis) to assess the relative influence of social interactions, subspecies, and sex on uropygial gland microbial composition and the resulting preen oil odor profiles.MethodsWe captured 24 birds at Mountain Lake Biological Station in Virginia, USA, including birds from two seasonally sympatric subspecies – one resident, one migratory. We housed them in an outdoor aviary in three phases of social configurations: first in same-sex, same-subspecies flocks, then in male-female pairs, and finally in the original flocks. Using samples taken every four days of the experiment, we characterized their uropygial gland microbiome through 16S rRNA gene sequencing and their preen oil volatile compounds via GC-MS.ResultsWe predicted that if social environment was the primary driver of uropygial gland microbiome composition, and if microbiome composition in turn affected preen oil volatile profiles, then birds housed together would become more similar over time. Our results did not support this hypothesis, instead showing that sex and subspecies were stronger predictors of microbiome composition. We observed changes in volatile compounds after the birds had been housed in pairs, which disappeared after they were moved back into flocks, suggesting that hormonal changes related to breeding condition were the most important factor in these patterns.DiscussionAlthough early life social environment of nestlings and long-term social relationships have been shown to be important in shaping uropygial gland microbial communities, our study suggests that shorter-term changes in social environment do not have a strong effect on uropygial microbiomes and the resulting preen oil volatile compounds
Assessing the species boundary and ecological niche in freshwater gastropods of the family Physidae (Gastropoda, Hygrophila)
The present thesis contributed to increasing the knowledge about the diversity of the neotropical
freshwater mollusks. Through the use of different methodologies for analyzing molecular and
geographical occurrence data, we address important taxonomic issues and show new paths for
future taxonomic research on the Physidae family. This family for a long time had classification
proposals based only on morphological characters of the shell and, later, on the anatomy of the
soft parts. The application of molecular delimitation methods based on coalescence showed the
inadequacy of morphological criteria in discriminating intraspecific variability (overestimating
family diversity) and in detecting the existence of cryptic species complexes (underestimating
family diversity). The data on the occurrence along with the use of georeferencing tools,
modeling, and ecological niche analyses applied to South American physid species, indicated
the possibility of errors in species identification and the need to reassess the distribution of these
physids using other operational criteria such as molecular approaches to access the actual family
diversity and distribution for the continent.A presente tese contribuiu para ampliar o conhecimento sobre a diversidade da malacofauna
dulcÃcola neotropical. Através do emprego de diferentes metodologias de análise de dados
moleculares e de ocorrência geográfica abordamos importantes questões taxonômicas e
mostramos novos caminhos para futuras pesquisas taxonômicas da famÃlia Physidae. FamÃlia
essa que por muito tempo teve propostas de classificação embasadas apenas em caracteres
morfológicos da concha e, posteriormente, na anatomia das partes moles. A aplicação de
métodos de delimitação molecular baseados em coalescência, evidenciou a insuficiência dos
critérios morfológicos em discriminar a variabilidade intraespecÃfica (superestimando a
diversidade da famÃlia) e, em detectar a existência de complexos de espécies crÃpticas
(subestimando a diversidade da famÃlia). A abordagem de busca intensiva por dados de
ocorrência junto a utilização de ferramentas de georreferenciamento, modelagem e análises de
nicho ecológico aplicadas à s espécies de fisÃdeos sul-americanos, indicaram a possibilidade de
erros de identificação de espécies e a necessidade de reavaliar a distribuição desses fisÃdeos
usando outros critérios operacionais, incluindo abordagens moleculares, para acessar a
diversidade e distribuição reais da famÃlia para o continente
Listening Section of the Simulated Toefl Test: Semantic and Pragmatic Context Analysis
Language users are impacted by sociolinguistic factors like semantics and pragmatics in every circumstance. Even in a proficiency test, those two contexts are present because they serve as the test's framework. In order to identify the semantic and pragmatic settings in the TOEFL test simulation for the Listening component, research was done. In this study, the researcher used a qualitative descriptive strategy, using document analysis as the instrument. The study's focus was the FORUM TENTOR INDONESIA publication TOP NO. 1TOEFL SIMULATION. The outcome demonstrates that different kinds of semantic and pragmatic context were present in the test simulation. In 8 of the 30 questions that were analyzed, there were semantic contexts. Semantic contexts of three different types—meaning, semantic feature, and semantic roles—were discovered. Semantic Roles (4 Questions), Semantic Feature (2 Questions), and Meaning are the other predominant semantic types (2 Questions). The Pragmatic context quantities, however, are more prevalent than the Semantic context. Because one test item can contain multiple types of pragmatic language, 40 questions from the 30 studied items were found to have pragmatic contexts. Additionally, the majority of the pragmatic inquiries were of the Reference type since the narrator of the listening section used referring to formulate the question. Context, Politeness, Reference, and Speech Act are the different types of pragmatic context that can be encountered. Reference-type pragmatic contexts are the most common (25 questions)
Bio-inspired Surface Texture Fluid Drag Reduction using Large Eddy Simulation
Skin friction drag can be reduced through the application of bio-inspired riblet surfaces. Numerical simulations were performed using Large Eddy Simulation (LES) to investigate the effect of using riblets on reducing skin friction drag. In this study, three different riblet configurations were used; scalloped, sawtooth and a new design, hybrid, riblet. To validate the effect of using the proposed hybrid riblet design compared with other riblets used in the literature; before applying to complex geometries, they were initially applied to a flat plate in parallel arrangement. Results showed skin friction coefficient reduction of 14% using the proposed hybrid riblet. This reduction was 9.2 times and 1.2 times more compared to sawtooth and scalloped configurations, respectively. The hybrid riblet was then applied partially and fully to NACA 0012 airfoil. Skin friction coefficient reduction of 34.5% was obtained when the hybrid riblet fully applied on the airfoil surface. Furthermore, the Convergent-Divergent (C-D) arrangement was studied, where the riblets were placed fully on the NACA 0012 and aligned with a yaw angle with respect to the flow direction. The convergent lines are inspired by the sensory part of the shark skin, whereas the divergent lines or herringbone are found on the bird feather. The two different riblet configurations, sawtooth and hybrid were modeled with the C-D arrangement and the hybrid riblet with C-D arrangement contributed to higher skin friction coefficient reduction, 34.5%, than the sawtooth riblet shape, 26.75%. Moreover, the C-D arrangement was compared to the parallel arrangement and shown that the C-D arrangement increased the lift coefficient (cl) of the airfoil, the flow separation was delayed and the overall performance of the airfoil was enhanced
Self-empowerment of life through RNA networks, cells and viruses
Our understanding of the key players in evolution and of the development of all organisms in all domains of life has been aided by current knowledge about RNA stem-loop groups, their proposed interaction motifs in an early RNA world and their regulative roles in all steps and substeps of nearly all cellular processes, such as replication, transcription, translation, repair, immunity and epigenetic marking.
Cooperative evolution was enabled by promiscuous interactions between single-stranded regions in the loops of naturally forming stem-loop structures in RNAs. It was also shown that
cooperative RNA stem-loops outcompete selfish ones and provide foundational self-constructive groups (ribosome, editosome, spliceosome, etc.). Self-empowerment from abiotic matter to biological behavior does not just occur at the beginning of biological evolution; it is also essential for all levels of socially interacting RNAs, cells and viruses
Novel approaches to study the design principles of turing patterns
A fundamental concern in biology is the origins of, and the mechanisms responsible for the structures
and patterns observed within, organisms [1]. Turing patterns and the Turing mechanism may explain the
processes behind biological pattern formation. Theoretical studies of the Turing mechanism show that it
is highly sensitive to fluctuations and variations in kinetic parameters. Various experiments have shown
that biochemical processes in living cells are inherently noisy systems, they are subjected to a diverse
range of fluctuations. This ‘robustness problem’ raises the question of how such a seemingly sensitive
mechanism could produce robust patterns amidst noise [2]. Recent computational advances allow for
large-scale explorations of the design space of regulatory networks underpinning pattern production. Such
explorations generate insights into the Turing mechanism’s robustness and sensitivity. Part 1 of the thesis
performs a large-scale exploration within a discrete modelling framework, identifying the same pattern
producing network types identified within previous studies. The equivalence we find across modelling
frameworks suggests that a deeper underlying principle of these Turing mechanisms exist. In contrast
to the continuous case , networks appear to be more robust in the discrete framework we explore here,
suggesting that these networks might be more robust than previously thought. Part 2 of the thesis focuses
on Turing patterns as a inverse problem: is it possible to infer the parameters that most likely produced
a given pattern? Here, we distill the information of a pattern into a one-dimensional representation based
on resistance distances, a concept from electrical networks [3]. We shown this representation to be robust
against fluctuations in the pattern stemming from random initial conditions, or stochasticity of the model,
and therefore permits the application of machine learning methods such as neural networks and support
vector regression for parameter inference. We apply this method to infer one and three parameters for
both deterministic and stochastic models of the Gierer-Meinhardt system. We show that the ’resistance
distance histogram’ method is more robust to noise, and performs better for limited number of data
samples than a vanilla convolutional neural network approach. Robustness of parameter inference with
respect to noise and limited data samples is of particular importance when considering real experimental
data. Overall, this thesis advances our understanding on the design principles of pattern formation, and
provides insight into possible methods for inferring details of regulatory networks behind experimental
evidence of Turing patterns.Open Acces
EXAMINING PROTEIN CONFORMATIONAL DYNAMICS USING COMPUTATIONAL TECHNIQUES: STUDIES ON PHOSPHATIDYLINOSITOL-3-KINASE AND THE SODIUM-IODIDE SYMPORTER
Experimental biophysics techniques used to study proteins, polymers of amino acids that comprise most therapeutic targets of human disease, face limitations in their ability to interrogate the continual structural fluctuations exhibited by these macromolecules in the context of their myriad cellular functions. This dissertation aims to illustrate case studies that demonstrate how protein conformational dynamics can be characterized using computational methods, yielding novel insights into their functional regulation and activity. Towards this end, the work presented here describes two specific membrane proteins of therapeutic relevance: Phosphoinositide 3-kinase (PI3Kα), and the Na+/I- symporter (NIS).
The PI3KCA gene, encoding the catalytic subunit of the PI3Kα protein that phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2) to generate phosphatidylinositol-3,4,5-triphosphate (PIP3), is highly mutated in human cancer. As such, a deeper mechanistic understanding of PI3Kα could facilitate the development of novel chemotherapeutic approaches. The second chapter of this dissertation describes molecular dynamics (MD) simulations that were conducted to determine how PI3Kα conformations are influenced by physiological effectors and the nSH2 domain of a regulatory subunit, p85. The results reported here suggest that dynamic allostery plays a role in populating the catalytically competent conformation of PI3Kα.
NIS, a thirteen-helix transmembrane protein found in the thyroid and other tissues, transports iodide, a required constituent of thyroid hormones T3 and T4. Despite extensive experimental information and clinical data, many mechanistic details about NIS remain unresolved. The third chapter of this dissertation describes the results of unbiased and enhanced-sampling MD simulations of inwardly and outwardly open models of bound NIS under an enforced ion gradient. Simulations of NIS in the absence or presence of perchlorate are also described. The work presented in this dissertation aims to add to our mechanistic understanding of NIS ion transport and elucidate conformational states that occur between the inward and outward transitions of NIS in the absence and presence of bound Na+ and I- ions, which can provide valuable insight into its physiological activity and inform therapeutic interventions.
Taken together, these case studies demonstrate the ability of computational techniques to provide novel insights into the impact of structural dynamics on the functional regulation of therapeutically important biological macromolecules
Imperfectly coordinated water molecules pave the way for homogeneous ice nucleation
Water freezing is ubiquitous on Earth, affecting many areas from biology to
climate science and aviation technology. Probing the atomic structure in the
homogeneous ice nucleation process from scratch is of great value but still
experimentally unachievable. Theoretical simulations have found that ice
originates from the low-mobile region with increasing abundance and persistence
of tetrahedrally coordinated water molecules. However, a detailed microscopic
picture of how the disordered hydrogen-bond network rearranges itself into an
ordered network is still unclear. In this work, we use a deep neural network
(DNN) model to "learn" the interatomic potential energy from quantum mechanical
data, thereby allowing for large-scale and long molecular dynamics (MD)
simulations with ab initio accuracy. The nucleation mechanism and dynamics at
atomic resolution, represented by a total of 36 s-long MD trajectories,
are deeply affected by the structural and dynamical heterogeneity in
supercooled water. We find that imperfectly coordinated (IC) water molecules
with high mobility pave the way for hydrogen-bond network rearrangement,
leading to the growth or shrinkage of the ice nucleus. The hydrogen-bond
network formed by perfectly coordinated (PC) molecules stabilizes the nucleus,
thus preventing it from vanishing and growing. Consequently, ice is born
through competition and cooperation between IC and PC molecules. We anticipate
that our picture of the microscopic mechanism of ice nucleation will provide
new insights into many properties of water and other relevant materials.Comment: 20 pages, 4 figures, under peer revie
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
Existing few-shot segmentation methods are based on the meta-learning
strategy and extract instance knowledge from a support set and then apply the
knowledge to segment target objects in a query set. However, the extracted
knowledge is insufficient to cope with the variable intra-class differences
since the knowledge is obtained from a few samples in the support set. To
address the problem, we propose a multi-information aggregation network
(MIANet) that effectively leverages the general knowledge, i.e., semantic word
embeddings, and instance information for accurate segmentation. Specifically,
in MIANet, a general information module (GIM) is proposed to extract a general
class prototype from word embeddings as a supplement to instance information.
To this end, we design a triplet loss that treats the general class prototype
as an anchor and samples positive-negative pairs from local features in the
support set. The calculated triplet loss can transfer semantic similarities
among language identities from a word embedding space to a visual
representation space. To alleviate the model biasing towards the seen training
classes and to obtain multi-scale information, we then introduce a
non-parametric hierarchical prior module (HPM) to generate unbiased
instance-level information via calculating the pixel-level similarity between
the support and query image features. Finally, an information fusion module
(IFM) combines the general and instance information to make predictions for the
query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet
yields superior performance and set a new state-of-the-art. Code is available
at https://github.com/Aldrich2y/MIANet.Comment: Accepted to CVPR 202
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