Miracle-dz/UniFAR-FTUs huggingface dataset:
Physics of sliding on water predicts morphological and behavioral allometry across a wide range of body sizes in water striders (Gerridae)

Laws of physics shape morphological and behavioral adaptations to locomotion at different body sizes. Water striders serve as a model taxon to study how simple physical constraints of water-surface habitats affect their behavior and morphology, and hydrodynamics of rowing by midlegs on the surface is well understood. However, the physics of the subsequent passive sliding has been less explored. We created a model of sliding on the water surface to simulate the effect of body mass, striding type, and wetted leg lengths on an insect’s ability to float on the surface and on the sliding resistance. The model predicts that to support their weight on the surface during sliding, the heavy species should either develop long forelegs that support the frontal part of its body during symmetrical striding (when two midlegs thrust) or use asymmetrical striding (when one forward-extended midleg supports the body while the other midleg and contra-lateral hindleg thrust). These predictions are confirmed by the behavior and morphology of various Gerridae species. Hence, the results illustrate how simple physical processes specific to a certain habitat type have far-reaching consequences for the evolution of morphological and behavioral diversification associated with body size among biological organisms in these habitats.
Rapid evolution of flower phenology and clonality in restored populations of multiple grassland species

Restoration of terrestrial ecosystems often requires re-introduction of plants. In restored sites, the plants often face environment that differs from the one in natural populations. This which can affect plant traits, reduce performance and impose novel selection pressures. As a response, restored populations might rapidly evolve and adapt to the novel conditions. This may enhance population survival and contribute to restoration success, but has been rarely tested so far. Here, we focused on populations of three grassland species restored 20 years ago (Galium wirtgenii, Inula salicina and Centaurea jacea) by the transfer of green hay, and compared them with populations that were source of the hay. We measured plants both in-situ, and in common garden under control and three stress conditions. In-situ, restored and natural populations differed in flowering phenology in two out of the three species. In the common garden, plants of the restored population flowered earlier (in Galium) or showed increased plasticity of clonal propagation in response to clipping (Inula). Both these traits suggest rapid adaptation to the contrasting mowing regimes in restored in comparison to the natural donor sites. In Centaurea, we detected no differentiation, neither in-situ, nor in the common garden. Rapid evolution in two out of three species indicates that evolution in restoration may be rather common, yet not ubiquitous across species.
How density dependence, genetic erosion and the extinction vortex impact evolutionary rescue

Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.
The neural basis of cost-benefit trade-offs in effort investment: a quantitative activation likelihood estimation meta-analysis

Prominent theories of cognitive effort-based decision-making posit that shared brain regions process both potential reward and task demand, supporting the idea that effort allocation are informed by a cost-benefit trade-off, weighing the expected benefits of successful control against the inherent costs of effort exertion. While the dorsal anterior cingulate cortex (dACC) has been proposed as a candidate region supporting this decision, it remains unclear whether dACC activity tracks rewards and costs as independent quantities, or it reflects the effort intensity worth the integrated costs and benefits. While recent accounts of dACC function posit a crucial role the region in negotiating cost-benefit trade-offs, empirical evidence for this account remains scarce across single studies. To address this, we conducted a systematic meta-analysis review of neuroimaging studies, using activation-likelihood estimation method to quantify brain activity across 45 studies (N = 1273 participants) investigating reward-guided effort. We found reliable recruitment of the dACC, putamen, and anterior insula for processing both larger rewards and increasing task demands. However, the dACC clusters sensitive to task demands and rewards were anatomically distinct with no significant overlap: caudal dACC activity tracked increasing task demands, while rostral dACC activity tracked increasing rewards. Critically, we also observed that caudal dACC activity tracked the integration of costs and benefits, compatible with mental effort intensity account. These findings suggest there are distinct signals for demand and effort in the dACC which are also integrated to support the decision to invest effort, supporting recent computational accounts of cost-benefit value integration in effort-based choice.
Openness to Experience is associated with neural and performance measures of memory in older adults

Age-related decline in episodic memory performance is a well-replicated finding across numerous studies. Recent studies focusing on aging and individual differences found that the Big Five personality trait Openness to Experience is associated with better episodic memory performance in older adults, but the associated neural mechanisms are largely unclear. Here we investigated the relationship between Openness and memory network function in a sample of 352 participants (143 older adults, 50-80 years; 209 young adults, 18-35 years). Participants underwent functional magnetic resonance imaging (fMRI) during a visual memory encoding task. Functional memory brain-network integrity was assessed using the SAME scores (similarity of activations during memory encoding), which reflect the similarity of a participant’s memory network activity compared to prototypical fMRI activity patterns of young adults. Openness was assessed using the NEO Five Factor Inventory (NEO-FFI). Older vs. young adults showed lower memory performance and higher deviation of fMRI activity patterns (i.e., lower SAME scores). Specifically in older adults, high Openness was associated with better memory performance, and mediation analysis showed that this relationship was partially mediated by higher SAME scores. Our results suggest that trait Openness may constitute a protective factor in cognitive aging by better preservation of the brain’s memory network.
Long-term bird abundance in a threatened grassland ecosystem: an assessment using daily bird lists

: Open Natural Ecosystems (ONEs), such as tropical grasslands, are among the most threatened habitats on Earth today. The long-term monitoring of ONEs is an important research domain that is essential for understanding anthropogenic impacts and facilitating conservation action. Using a simple day-listing method over a 12-year period, we studied bird abundance in a central Indian grassland-agriculture mosaic experiencing several land-use changes. Our results indicate that some grassland species (such as the Great Indian Bustard Ardeotis nigriceps ) show steep declines during the study period, while woodland species (such as the Small Minivet Pericrocotus cinnamomeus) show highly fluctuating populations with no discernable trend. Daily listing also reveals distinct seasonal patterns, and we discuss the Great Indian Bustard and Eurasian Marsh Harrier Circus aeruginosus as examples. Our study highlights the utility of consistent checklist surveys to monitor abundance trends of bird communities within a changing landscape.
Excessive Firing of Dyskinesia-Associated Striatal Direct Pathway Neurons is Gated By Dopamine and Excitatory Synaptic Input

The striatum integrates dopaminergic and glutamatergic inputs to select preferred versus alternative actions, but the precise mechanisms remain unclear. One way to study action selection is when it breaks down. Here, we explored the cellular and synaptic mechanisms of levodopa-induced dyskinesia (LID), a complication of Parkinson’s disease therapy characterized by involuntary movements. We used an activity-dependent tool (FosTRAP) in conjunction with a mouse model of LID to investigate functionally distinct subsets of striatal direct pathway medium spiny neurons (dMSNs). In vivo, levodopa differentially activates dyskinesia-associated (TRAPed) dMSNs compared to other dMSNs. This activation is likely to be driven by two cellular mechanisms we identified through ex vivo electrophysiology: higher sensitivity to dopamine and stronger excitatory input from the motor cortex and thalamus. Together, these findings suggest how intrinsic and synaptic properties of heterogeneous dMSN subpopulations integrate to support action selection.
Automated simulation-based membrane-protein refinement into cryo-EM data

The resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins – a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane-protein cryo-EM maps. Using adaptive-force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best fit model which balances stereochemistry and goodness-of-fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the X-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential (GOAP) was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane-protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins. II. STATEMENT OF SIGNIFICANCE Cryo-EM is an increasingly critical method of structure determination. As data collection and model generation become more efficient, iteratively fitting an experimental density can still require considerable time and expertise. Membrane proteins are particularly important targets in pharmacology and bioengineering, but can present distinctive challenges to data quality and modeling. Here, we tested a new tool to drive density fitting with molecular dynamics simulations, in context of a new structure of the membrane protein maltoporin. Fitting performed well in detergent, lipids, or solution, offering simpler options for fully automated simulation protocols. We were also able to apply fitting to adjust the microscope’s pixel size. The approach described here should be applicable to rapid, accurate refinement of a variety of membrane-protein structures.
Unexpected robustness of the band gaps of TiO2 under high pressures

Titanium dioxide (TiO2) is a wide band gap semiconducting material which is promising for photocatalysis. Here we present first-principles calculations to study the pressure dependence of structural and electronic properties of two TiO2 phases: the cotunnite-type and the Fe2P-type structure. The band gaps are calculated using density functional theory with the generalized gradient approximation, as well as the many-body perturbation theory with the GW approximation. The band gaps of both phases are found to be unexpectedly robust across a broad range pressures. The corresponding pressure coefficients are significantly smaller than that of diamond and silicon carbide, whose pressure coefficient is the smallest value ever measured by experiment. The robustness originates from the synchronous change of valence band maximum and conduction band minimum with nearly identical rates of changes. A step-like jump of band gaps around the phase transition pressure point is expected and understood in light of the difference in crystal structures.
Refining structural models of membrane proteins with disordered domains in phospholipid nanodiscs

Small-angle scattering can be used to derive structural information about membrane proteins reconstituted in suitable carrier systems enabling solubilization of the membrane proteins in question. Since the studies are done in solution, there is no need for crystallization or deposition on sample grids, and it is in principle possible to obtain structural information about intrinsically disordered regions which cannot be resolved by crystallography or the quantitative link to which is hard to establish using e.g. electron microscopy methods. In this study, tetramers of the gated spinach aquaporin SoPIP2;1 were reconstituted into nanodiscs and small-angle x-ray scattering data were recorded. From these data, we refine structural models of the entire nanodisc-membrane protein complex including the flexible regions using newly developed models based on Fast Debye sums. We introduce software for these computations available via online repositories and discuss the implications and limitations of these methods. Author summary When it comes to investigating the structure and function of the proteins, a particular class of proteins are known to be cumbersome and problematic: membrane proteins that reside in the cell membrane and regulate and facilitate a number of critical biological processes. Such proteins can often not be studied by conventional means as they unravel and denature structurally or even precipitate in solution. To add insult to injury, such membrane proteins also often contain parts that are intrinsically disordered rendering them irresolvable by e.g. traditional crystallographic techniques and hard to describe structurally. Here, we present a combined computational and experimental approach (as well as the necessary software) to analyze and determine the structure of such proteins in close-to-native conditions in so-called nanodiscs, a biological carrier systems, using small-angle scattering and molecular simulations.
Sleep waves in a large‐scale corticothalamic model constrained by activities intrinsic to neocortical networks and single thalamic neurons

Aim Many biophysical and non-biophysical models have been able to reproduce the corticothalamic activities underlying different EEG sleep rhythms but none of them included the known ability of neocortical networks and single thalamic neurons to generate some of these waves intrinsically. Methods We built a large-scale corticothalamic model with a high fidelity in anatomical connectivity consisting of a single cortical column and first- and higher-order thalamic nuclei. The model is constrained by different neocortical excitatory and inhibitory neuronal populations eliciting slow (<1 Hz) oscillations and by thalamic neurons generating sleep waves when isolated from the neocortex. Results Our model faithfully reproduces all EEG sleep waves and the transition from a desynchronized EEG to spindles, slow (<1 Hz) oscillations and delta waves by progressively increasing neuronal membrane hyperpolarization as it occurs in the intact brain. Moreover, our model shows that slow (<1 Hz) waves most often start in a small assembly of thalamocortical neurons though occasionally they originate in cortical layer 5. Moreover, thalamocortical neuron input increases the frequency of slow (<1 Hz) waves compared to those generated by isolated cortical networks. Conclusion Our simulations challenge current mechanistic understanding of the temporal dynamics of sleep wave generation and suggest testable predictions.
Zic3 enables bimodal regulation of tyrosine hydroxylase expression in dopaminergic neurons of olfactory bulb and midbrain

Dopaminergic (DA) neurons in the Olfactory bulb (OB) are involved in odor detection and discrimination. Transcription factor (TF) regulatory network responsible for their fate specification remains poorly understood and the spatial regulation of DA neurons remains elusive. In this study, mice exposed to odor stimulant exhibited specific upregulation of Zinc finger transcription factor of Cerebellum (ZIC) 3 along with Tyrosine Hydroxylase (TH). Stringent co-expression analysis showed ZIC3 and TH dual positive neurons in OB. Genetic manipulation showed ZIC3 to be both essential and sufficient to drive TH expression and essential for odor perception. ZIC3 interacts with ER81 and binds to region encompassing ER81 binding site in DA neurons and is indispensable for TH expression. In midbrain (MB), in the absence of ER81, ZIC3 switches its molecular partner and binds to Pitx3 promoter-a DA fate determinant. Under ectopic expression of ER81 in MB DA neurons, propensity of ZIC3 binding to Pitx3 promoter is compromised and its occupancy on Th promoter encompassing ER81 binding site is established, finally culminating in desired TH expression. Together, these findings reveal a unique ZIC3 mediated bimodal regulation of TH in OB and MB to ultimately facilitate DAergic fate.
Using spatio-temporal information in weather radar data to detect and track communal bird roosts

The exodus of swallows from communal nighttime roosts is often visible as an expanding ring-shaped pattern in weather radar data. The WSR-88D network operated by the National Weather Service archives more than 25 years of data across 143 stations in the contiguous US. However, access to information about the roosting behavior of swallows is limited by the cost of manual annotation of these scans. We develop an AI system to detect and track swallow roosts in weather radar data. Our model is based on the Faster R-CNN architecture and is customized to incorporate multiple spatial and temporal channels in volumetric radar scans using novel adaptor layers. We systematically study the impact of network architecture and input representation for this task. We incorporate our detection outputs into an AI-assisted system with an interface for human screening to collect research-grade data about roosting behavior. We deploy the system to collect information from 12 radar stations in the Great Lakes region of the US spanning 21 years. The addition of temporal information improves roost detection performance from 47.0% mean average precision to 54.7%. Temporal information helps the model recognize the expanding pattern of roosts and filter false positives due to rain and static structures. Our system allowed the annotation of 15,628 roost signatures with 64,620 single-frame detections in 612,786 radar scans with 183.6 total hours of human screening, or 1.08 seconds per radar scan. Our AI-assisted system provides research-quality roost data with far less human effort than manual annotation of radar scans. The data contains critical information about the phenology and population trends of swallows and martins, a declining group of aerial insectivores. Our successful deployment to collect historical data for 8% of the radar stations in the contiguous US lays the groundwork for continentscale analysis of swallow roosts, and provides a starting point for analysis of other family-specific phenomena in weather radar, such as bat roosts and mayfly hatches.
'Buy the Rumor, Sell the News': Liquidity Provision by Bond Funds Following Corporate News Events

Using a comprehensive database of corporate news, we examine how bond mutual funds trade on the sentiment of news releases. We find that bond funds trade against the direction of news sentiment (e.g., selling after good news about a firm). The results are more pronounced in bonds that lie within a fund’s investment objective sector, and in bonds with high turnover and low information asymmetry, and in credit-rating news and news with positive sentiment. Funds that most frequently trade against news sentiment produce a higher alpha, and a source of such alpha is bond price reversals post news events. Fixed income mutual funds, dealers, and insurance companies complement each other in news trading, with mutual funds trading against news largely in the absence of dealers. Our study indicates that bond mutual funds represent a significant liquidity provider, upon corporate news events, in the market for corporate bonds.
A minimum control ancilla driven quantum computation scheme with repeat-until-success style gate generation

Some two qubit interactions are singly sufficient for universal quantum computation but not without the use of an ancilla. Recent schemes for universal quantum computation have focused on hybrid physical systems using ancillae. In them, the application of resources is shifted to the ancilla system. We consider which 2-qubit interactions are universal in ancilla schemes where direct connections between main register qubits are forbidden. By the use of ancilla driven operations and repeat-until-success style random gates, a single fixed symmetric gate can be universal be control of the number of repetitions alone.
An Alternate Approach to Measure Specific Star Formation Rates at

We trace the specific star formation rate (sSFR) of massive star-forming galaxies ( ) from to 7. Our method is substantially different from previous analyses, as it does not rely on direct estimates of star formation rate, but on the differential evolution of the galaxy stellar mass function (SMF). We show the reliability of this approach by means of semianalytical and hydrodynamical cosmological simulations. We then apply it to real data, using the SMFs derived in the COSMOS and CANDELS fields. We find that the is proportional to at , in agreement with other observations but in tension with the steeper evolution predicted by simulations from to 2. We investigate the impact of several sources of observational bias, which, however, cannot account for this discrepancy. Although the SMF of high-redshift galaxies is still affected by significant errors, we show that future large-area surveys will substantially reduce them, making our method an effective tool to probe the massive end of the main sequence of star-forming galaxies.