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Bowhead whale faeces link increasing algal toxins in the Arctic to ocean warming
Abstract Over the last two decades, ocean warming and rapid loss of sea ice have dramatically changed the Pacific Arctic marine environment1–3. These changes are predicted to increase harmful algal bloom prevalence and toxicity, as rising temperatures and larger open water areas are more favourable for growth of some toxic algal species4. It is well known that algal toxins are transferred through food webs during blooms and can have negative impacts on wildlife and human health5–7. Yet, there are no long-term quantitative reports on algal toxin presence in Arctic food webs to evaluate increasing exposure risks. In the present study, algal toxins were quantified in bowel samples collected from 205 bowhead whales harvested for subsistence purposes over 19 years. These filter-feeding whales served as integrated food web samplers for algal toxin presence in the Beaufort Sea as it relates to changing environmental conditions over two decades. Algal toxin prevalences and concentrations were significantly correlated with ocean heat flux, open water area, wind velocity and atmospheric pressure. These results provide confirmative oceanic, atmospheric and biological evidence for increasing algal toxin concentrations in Arctic food webs due to warming ocean conditions. This approach elucidates breakthrough mechanistic connections between warming oceans and increasing algal toxin exposure risks to Arctic wildlife, which threatens food security for Native Alaskan communities that have been reliant on marine resources for subsistence for 5,000 years (ref. 8).
Reaction dynamics for the [NNO] system from state-resolved and coarse-grained models
The dynamics for the NO(X2Π) + N(4S) ↔ N2(X1Σg+) + O(3P) reaction was followed in the 3A′ electronic state using state-to-state (STS) and Arrhenius-based rates from two different high-level potential energy surfaces represented as a reproducing kernel Hilbert space (RKHS, PESB for “Basel PES”) and permutationally invariant polynomials (PIPs, PESM for “Minnesota PES”). Despite the different number of bound states supported by PESB and PESM, the ignition points from STS and Arrhenius rates are at ∼10−6 s whether or not reverse rates are from assuming microreversibility or explicitly given. Conversion from NO to N2 is incomplete if Arrhenius rates are used, but complete turnover is observed if STS information is used. This is due to non-equilibrium energy flow and state dynamics, which requires a state-based description. Including full dissociation asymptotically leads to the correct 2:1 [N]:[O] concentration with little differences for the species’ dynamics depending on the PES used for the STS information. In conclusion, concentration profiles from coarse-grained simulations are consistent over 14 orders of magnitude in time using STS information based on two different high-level PESs.
On a microdemographic framework for decomposing contemporary fertility dynamics
Dynamic basal ganglia output signals license and suppress forelimb movements
Abstract The basal ganglia are fundamental to motor control and their dysfunction is linked to motor deficits 1–8 . Influential investigations on the primate oculomotor system posited that movement generally depends on transient pauses of tonically firing inhibitory basal ganglia output neurons releasing brainstem motor centres 9,10 . However, prominent increases in basal ganglia output neuron firing observed during other motor tasks cast doubts on the proposed mechanisms of movement regulation through basal ganglia circuitry 11–22 . Here we show that basal ganglia output neurons in the mouse substantia nigra pars reticulata (SNr) represent complex forelimb movements with highly granular and dynamic changes in spiking activity, tiling task execution at the population level. Single SNr neurons exhibit movement-specific firing pauses as well as increases, each occurring in concert with precise and different forelimb movements. Combining optogenetics and simultaneous recordings from basal ganglia output and postsynaptic brainstem neurons, we reveal the functional role of these dynamic firing-rate changes in releasing and suppressing movement through downstream targets. Together, our results demonstrate the existence and function of highly specific and temporally precise movement representations in basal ganglia output circuitry. We propose a model in which basal ganglia output neurons fire dynamically to provide granular and bidirectional movement-specific signals for release and suppression of motor programs to downstream circuits.
The effect of hydration and dynamics on the mass density of single proteins
The density of a protein molecule is a key property within a variety of experimental techniques. We present a computational method for determining protein mass density that explicitly incorporates hydration effects. Our approach uses molecular dynamics simulations to quantify the volume of solvent excluded by a protein. Applied to a dataset of 260 soluble proteins, this yields an average density of 1.296 ± 0.001 g cm−3, notably lower than the widely cited value of 1.35 g cm−3. Contrary to previous suggestions, we find no correlation between protein density and molecular weight. We instead find correlations with residue composition, particularly with hydrophobic amino acid content. Using these correlations, we train a regressor capable of accurately predicting protein density from sequence-derived features alone. Examining the effect of incorporating water molecules on the measured density, we find that water molecules buried in internal cavities have a negligible effect, whereas those at the surface have a profound impact. Furthermore, by calculating the density of a titin domain and of the Bovine Pancreatic Trypsin over molecular dynamics trajectories, we show that individual proteins can occupy states with close but distinguishable densities. Finally, we analyze the density of water in the vicinity of proteins, showing that the first two hydration shells exhibit higher density than bulk water. When included in cumulative density calculations, these hydration layers contribute to a net increase in local solvent density. Overall, we find that proteins are less dense than previously reported, which is offset by their ability to induce a higher density of water in their vicinity.
Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete
A model of protein folding with multiple native states: Metamorphicity, intrinsic disorderness, and folding upon binding of proteins
The sequence–structure–function paradigm in biology states that a protein’s amino acid sequence determines its unique folded state structure, which in turn dictates its unique biological function. This classic concept has been severely challenged by the discovery of metamorphic and intrinsically disordered proteins (IDPs). Metamorphic proteins can fold into multiple native structures and perform multiple functions. IDPs, on the contrary, remain unstructured under physiological conditions but can fold to a unique structure upon binding to a target protein and show functionality. Here, we present a statistical mechanical model of protein folding with multiple native states and analyze their folding phase diagrams. While recovering the classic sequence–structure–function paradigm for a single native state, our model shows metamorphicity at a lower number of native states. An expansion of the unfolded region in the phase diagram at a higher number of native states, making the unfolded state the stable state under physiological conditions, indicates the emergence of an IDP-like scenario. Folding upon binding scenario of IDPs has also been demonstrated when an energetic bias is introduced for a specific native state. A regaining of the folded region upon biasing, increasing the folding propensity of the system, with folding toward a specific native state, is shown. Therefore, our model is general enough to reproduce the classic sequence–structure–function, metamorphicity, intrinsic disorderness, and folding upon binding scenarios of proteins.
Hybrid machine learning techniques for knowledge extraction in communication networks and advanced decision based complex q-Rung orthopair fuzzy frameworks
High-energy reaction dynamics of O3
The high-temperature atom exchange and dissociation reaction dynamics of the O(3P) + O2(Σg−3) system are investigated based on a new reproducing kernel-based representation of high-level multi-reference configuration interaction energies. Quasi-classical trajectory (QCT) simulations find the experimentally measured negative temperature-dependence of the rate for the exchange reaction and describe the experiments within error bars. Similarly, QCT simulations for a recent potential energy surface (PES) at a comparable level of quantum chemical theory reproduce the negative T-dependence. Interestingly, both PESs feature a “reef” structure near dissociation, which has been implicated to be responsible for a positive T-dependence of the rate, inconsistent with experiments. For the dissociation reaction, the T-dependence correctly captures what is known from experiments but underestimates the absolute rates by two orders of magnitude. Accounting for an increased number of accessible electronic states at high temperatures yields near-quantitative agreement. A neural network-based state-to-distribution model is constructed for both PESs and shows good performance in predicting final translational, vibrational, and rotational product state distributions. Such models are valuable for future and more coarse-grained simulations of reactive hypersonic gas flow.
Detection of weeds in teff crops using deep learning and UAV imagery for precision herbicide application
Non-linear Fourier analysis of aperiodic structures: Mass-density-waves in adsorbed monolayers
The adsorption of a “solid-like” monolayer on a crystalline substrate can produce distortions in the monolayer which are describable as mass-density-waves. These mass-density-waves can lead to a fixed alignment between the monolayer and the substrate crystal axes that may not be aligned with any high-symmetry direction of the substrate surface, a phenomenon known as rotational (orientational, Moiré) epitaxy. The driver of rotational epitaxy has been attributed to the partial matching of the symmetry of the substrate surface, to that of the over-layer (i.e., high-order commensurate phases), and to the competition between longitudinal strains (compressions) verses transverse strains (shears) in the mass-density-wave distortions caused by the mismatch between these two symmetries (i.e., the rotated incommensurate phase). In this work, we will examine both these scenarios within a new formalism, one using a fully non-linear approach to the calculation of the Fourier amplitudes that describe these distortions. The effects on the monolayer due to the substrate are found to be well-described by this formalism, which also has important pedagogical and calculational aspects. Within our proposed framework, the high-order commensurate phase shows up (using the language of structural phase transitions) as a partial regaining of the symmetry though a “lock-in transformation.” This work builds upon and extends the earlier work of Novaco and McTague. Both classical and quantum regimes are explored, and the limits of this new approach are tested against other calculations. The extension and application of this framework to other systems, such as two-dimensional van der Waals hetero-structures, is briefly discussed.
Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring
Abstract Pollinating insects provide essential ecosystem services, and using time-lapse photography to automate their observation could improve monitoring efficiency. Computer vision models, trained on clear citizen science photos, can detect insects in similar images with high accuracy, but their performance in images taken using time-lapse photography is unknown. We evaluated the generalisation of three lightweight YOLO detectors (YOLOv5-nano, YOLOv5-small, YOLOv7-tiny), previously trained on citizen science images, for detecting ~ 1,300 flower-visiting arthropod individuals in nearly 24,000 time-lapse images captured with a fixed smartphone setup. These field images featured unseen backgrounds and smaller arthropods than the training data. YOLOv5-small, the model with the highest number of trainable parameters, performed best, localising 91.21% of Hymenoptera and 80.69% of Diptera individuals. However, classification recall was lower (80.45% and 66.90%, respectively), partly due to Syrphidae mimicking Hymenoptera and the challenge of detecting smaller, blurrier flower visitors. This study reveals both the potential and limitations of such models for real-world automated monitoring, suggesting they work well for larger and sharply visible pollinators but need improvement for smaller, less sharp cases.
Effect of thiophenol adsorption on the optical properties of Ag nanoparticles supported on graphene/Rh(111)
Single-layer graphene on a transition metal surface forms a moiré structure that has been used as a template to fabricate metal nanoparticle assemblies. Ag nanoparticles were grown on graphene/Rh(111), and their optical absorption due to localized surface plasmon resonance (LSPR) was observed by optical reflectance spectroscopy. In addition, incoherent electron–hole pair excitation in the nanoparticles was observed using electron energy loss spectroscopy. Using these spectroscopies, the effect of thiophenol (C6H5SH) adsorption on the LSPR characteristics of the Ag nanoparticles was investigated.
mRNA decapping enzyme2 DCP2 expression correlates with progression and prognosis of hepatocellular carcinoma
Flat-panel laser displays through large-scale photonic integrated circuits
Nonconventional growth characteristics of tin silicon oxide grown by thermal atomic layer deposition using H2O as oxidant
Atomic layer deposition (ALD) of tin silicon oxide was performed via an ALD supercycle on an amorphous carbon (a-C) layer, which serves as the mandrel in self-aligned double patterning (SADP) techniques. This approach addresses limitations of conventional ALD SiO2 processes using ozone (O3) as the oxidant, which can lead to degradation of a-C mandrel or collapse of the SiO2 spacer itself under aggressive scaling. In this study, tetrakis(dimethylamino)tin (TDMASn) and bis-diethylaminosilane (BDEAS) were used as Sn and Si precursors, respectively, with H2O as the oxidant to avoid damage to the a-C layer. SiO2 was not grown via a single ALD process due to the low reactivity of BDEAS with H2O. Nevertheless, x-ray photoelectron spectroscopy analyses revealed that Si was incorporated into the film grown by the supercycle of ALD SnOx and SiO2. Notably, it is observed that the growth characteristics of tin silicon oxide exhibited a nonlinear dependence on the cycle ratio. Understanding this unexpected behavior is crucial for SADP, as it affects growth per cycle and film characteristics, such as etch rate and surface roughness. Fourier-transform infrared spectroscopy and density functional theory calculations suggest that hydrogen abstraction between TDMASn and Si–H groups enable the growth of tin silicon oxide. Finally, transmission electron microscopy analysis demonstrated that the a-C layer remained undamaged during the ALD process, whereas a few seconds of ozone exposure caused the ashing of the a-C layer.
Two pathways to resolve relational inconsistencies
Abstract When individuals encounter observations that violate their expectations, when will they adjust their expectations and when will they maintain them despite these observations? For example, when individuals expect objects of type A to be smaller than objects B, but observe the opposite, when will they adjust their expectation about the relationship between the two objects (to A being larger than B)? Naively, one would predict that the larger the violation, the greater the adaptation. However, experiments reveal that when violations are extreme, individuals are more likely to hold on to their prior expectations rather than adjust them. To address this puzzle, we tested the adaptation of artificial neural networks (ANNs) capable of relational learning and found a similar phenomenon: Standard learning dynamics dictates that small violations would lead to adjustments of expected relations while larger ones would be resolved using a different mechanism—a change in object representation that bypasses the need for adaptation of the relational expectations. These results suggest that the experimentally-observed stability of prior expectations when facing large expectation violations is a natural consequence of learning dynamics and does not require any additional mechanisms. We conclude by discussing the effect of intermediate adaptation steps on this stability.
Include Indigenous Knowledge systems in climate reports
Exotic Harmonium model: Exploring correlation effects of attractive Coulomb interaction
Simple few-body systems often serve as theoretical laboratories across various branches of theoretical physics. A prominent example is the two-electron Harmonium model, which has been widely studied over the past three decades to gain insights into the nature of the electron–electron correlations in many-electron quantum systems. Building on our previous work [Riyahi et al., Phys. Rev. B 108, 245155 (2023)], we introduce an analogous model consisting of an electron and a positively charged particle (PCP) with variable mass, interacting via Coulomb forces while confined by external harmonic potentials. Termed the exotic Harmonium model, this provides insights into the electron–PCP correlations, a cornerstone of the emerging field of the ab initio study of multi-component many-body quantum systems. Through a systematic exploration of the parameter space and numerical solutions of the corresponding Schrödinger equation, we identify two extreme regimes: the atom-like and the particle-in-trap-like behavior. The electron–PCP correlation dominates in the atom-like regime, significantly influencing physical observables, while its role diminishes in the particle-in-trap-like limit. Between these two extremes lies a complex intermediate regime that challenges qualitative interpretation. Overall, the exotic Harmonium model offers a powerful framework to unravel the electron–PCP correlations across diverse systems, spanning particles of varying masses and conditions, from ambient to high-pressure environments.