Miracle-dz/UniFAR-FTUs huggingface dataset:
Insights on the GPCR helix 8 solution structure and orientation using a neurotensin receptor 1 peptide

G-protein coupled receptors (GPCRs) are the largest class of membrane proteins in the human genome with high pharmaceutical relevance and implications to human health. These receptors share a prevalent architecture of seven transmembrane helices followed by an intracellular, amphipathic helix 8 (H8) and a disordered C-terminus. Technological advancements have led to over 1000 receptor structures in the last two decades, yet frequently H8 and the C-tail are conformationally heterogeneous or altogether absent. Here we synthesize a peptide comprising the neurotensin receptor 1 (NTS1) H8 and C-terminus (H8-Ctail) to investigate its structural stability, conformational dynamics and orientation in the presence of detergent and phospholipid micelles, which mimic the membrane. Circular dichroism (CD) and nuclear magnetic resonance (NMR) measurements confirm that zwitterionic 1,2-diheptanoyl-sn-glycero-3-phosphocholine is a potent stabilizer of H8 structure, whereas the commonly-used branched detergent lauryl maltose neopentyl glycol (LMNG) is unable to completely stabilize the helix – even at amounts four orders of magnitude greater than its critical micellar concentration. We then used NMR spectroscopy to assign the backbone chemical shifts. A series of temperature and lipid titrations were used to define the H8 boundaries as F376-R392 from chemical shift perturbations, changes in resonance intensity, and chemical-shift derived phi/psi angles. Finally, the H8 azimuthal and tilt angles, defining the helix orientation relative of the membrane normal were measured using paramagnetic relaxation enhancement (PRE) NMR. Taken together, our studies reveal the H8C-tail region is sensitive to membrane physicochemical properties and is capable of more adaptive behavior than previously suggested by static structural techniques.
A detailed dissection of the expression, localization, structure, and diagnostic potential of cyst wall proteins of the eye pathogen Acanthamoeba

The cyst wall of the eye pathogen Acanthamoeba castellanii contains cellulose and chitin and has ectocyst and endocyst layers connected by conical ostioles. Previously, we used mass spectrometry of purified walls to identify an abundant laccase and three families of lectins (Jonah, Luke, and Leo). Here we show that frameshifts in the protein prediction in AmoebaDB, which incorrectly add 12 transmembrane helices, cause Jonah to mislocalize to a ring around ostioles rather than to the ectocyst layer. RT-PCR, double labels with GFP and RFP or mCherry, and promoter swaps show that ectocyst localization does not just correlate with but is caused by earlier expression, while localization in the endocyst layer and ostioles is caused by later expression. A chitin-binding domain from an Entamoeba chitinase shows chitin forms thick fibrils in the ectocyst layer and a honeycomb in the endocyst layer. AlphaFold shows Ac wall proteins originate from bacteria by horizontal gene transfer (β-helical folds of Jonah and three cupredoxin-like domains of the laccase), share common ancestry with wall proteins of slime molds (β-jelly-roll folds of Luke), or are unique to Acanthamoeba (four disulfide knots of Leo). Ala mutations show linear arrays of aromatic amino acids in β-jelly-roll folds of Luke and disulfide knots of Leo are necessary for binding cellulose and proper localization of proteins in the cyst wall. Finally, rabbit antibodies to recombinant Jonah, Luke, Leo, and laccase efficiently detect calcoflour white-labeled cysts of 10 of 11 Acanthamoeba isolates tested, suggesting all four proteins are excellent diagnostic targets. IMPORTANCE Acanthamoebae are free-living amoeba in the soil and water that cause Acanthamoeba keratitis in under-resourced countries, where water for washing hands may be scarce. Acanthamoeba is an emerging pathogen in the United States, because of its association with contact lens use. Here we show early expression during encystation causes a Jonah lectin and a laccase to localize to the outer layer of the cyst wall, while later expression cause Luke and Leo lectins to localize to the inner layer and the conical ostioles that connect the layers. We used structural predictions to identify the aromatic amino acids of Luke and Leo necessary for binding cellulose in the wall and to identify domains of Jonah and laccase useful for making recombinant proteins to immunize rabbits. Rabbit antibodies to Jonah, Luke, Leo, and laccase all efficiently detected cysts of ten Acanthamoeba isolates, including five T4 genotypes that cause most keratitis cases.
The role of NMDA receptors in memory and prediction in cultured neural networks

Memory has been extensively studied at the behavioural as well as the cellular level. Spike timing dependent plasticity (STDP) is associated with N-methyl-D-aspartate (NMDA) receptor activation and is widely accepted to be essential for long-term memory. However, experimental evidence remains sparse, probably due to the required complex combination of cellular and functional readouts. Recent work showed that in-vitro cortical networks memorize and predict inputs. The initial dependency of prediction on short-term memory decreased during the formation of long-term memory traces. Here, we stimulated in-vitro networks to investigate memory and prediction under control conditions, or using the NMDA antagonist APV. APV did not significantly reduce network excitability, but did impede long-term memory trace formation. In APV-treated cultures short-term memory of stimuli persisted, and they were still able to predict. In contrast to control cultures, prediction remained fully dependent on short-term memory. This confirms that NMDA receptor activation is essential for the formation of long-term memory traces and supports the notion that, as control cultures learn to memorize the stimulus, long-term memory starts to contribute to their predictive capability.
Brachiopod Synecology in a Time of Crisis (Late Ordovician-Early Silurian)

The extinction of endemic brachiopods in North America at the end of the Ordovician and recolonization by European species has been related to glacio-eustatic lowering of sea level which disrupted conditions in epicontinental seas. North American species may have been narrowly adapted to relatively stable conditions of broad, tropical shallow seas. European invaders may have been less specialized because they were adapted to conditions in both the open ocean and in narrow European epicontinental seas. Being less narrowly adapted, European species probably were better able to cope with changing environmental conditions than were North American species. During the Lower and Middle Llandovery, shallow water, low diversity communities of Pentamerus Community depth were unstable and characterized by repeated extinctions and invasions. Following the crisis at the Ordovician-Silurian boundary 3 to 5 million years were needed to reestablish communities that were persistent in geologic time.
Influence of nicotine and alcohol on sleep latency: the reward-attention circuit model

We previously developed a neurocomputational model called the reward-attention circuit, which has been used to investigate the separate effects of nicotine and alcohol on attention focus. The model is based on known circuits linking midbrain dopaminergic neurons to the thalamocortical loop and is sufficiently versatile to capture diverse phenomena. In this work, we used the model to study the influence of both nicotine and alcohol on sleep latency (time to fall asleep). In line with findings from other studies, our simulations suggest that nicotine can have a stimulating effect that increases sleep latency, promoting wakefulness, and, in contrast, alcohol can have a sedating effect that induces sleep. Together, our results suggest insights into alterations in mesothalamic dopamine activity and sleep. Also, they raise speculations about aspects of insomnia and low mesothalamic dopamine.
Towards a Scalable In Situ Fast Fourier Transform

The Fast Fourier Transform (FFT) is a numerical operation that transforms a function into a form comprised of its constituent frequencies and is an integral part of scientific computation and data analysis. The objective of our work is to enable use of the FFT as part of a scientific in situ processing chain to facilitate the analysis of data in the spectral regime. We describe the implementation of an FFT endpoint for the transformation of multi-dimensional data within the SENSEI infrastructure. Our results show its use on a sample problem in the context of a multi-stage in situ processing workflow.
Decentralized Finite-Sum Optimization over Time-Varying Networks

We consider decentralized time-varying stochastic optimization problems where each of the functions held by the nodes has a finite sum structure. Such problems can be efficiently solved using variance reduction techniques. Our aim is to explore the lower complexity bounds (for communication and number of stochastic oracle calls) and find optimal algorithms. The paper studies strongly convex and nonconvex scenarios. To the best of our knowledge, variance reduced schemes and lower bounds for time-varying graphs have not been studied in the literature. For nonconvex objectives, we obtain lower bounds and develop an optimal method GT-PAGE. For strongly convex objectives, we propose the first decentralized time-varying variance-reduction method ADOM+VR and establish lower bound in this scenario, highlighting the open question of matching the algorithms complexity and lower bounds even in static network case.
Relative yield of thermal and nonthermal emission during weak flares observed by STIX during September 20-25, 2021

The disparate nature of thermal-nonthermal energy partition during flares, particularly during weak flares, is still an open issue. Following the Neupert effect, quantifying the relative yield of X-ray emission in different energy bands can enable inferring the underlying energy release mechanism. During September 20-25, 2021, the Solar Orbiter mission - being closer to the Sun ($\sim$0.6 AU) and having a moderate separation angle (<40$^{\circ}$) from the Sun-Earth line provided a unique opportunity to analyze multi-wavelength emission from $\sim$200 (mostly weak) flares, commonly observed by the Spectrometer Telescope for Imaging X-rays (STIX), STEREO-A, GOES, and SDO observatories. Associating the quotient (q$_{f}$) of hard X-ray fluence (12-20 keV) and soft X-ray flux (4-10 keV) with the peak SXR flux enabled us to identify strongly non-thermal flares. Multi-wavelength investigation of spectral and imaging mode observations of the 20 strongly non-thermal weak flares reveals an inverse relationship of q$_{f}$ with the emission measure (EM) (and density), and a positive relationship with the flare plasma temperature. This indicates that plasma in tenuous loops attains higher temperatures compared to that in the denser loops, in response to nonthermal energy deposition. This is in agreement with the plasma parameters of the coronal loops, as derived by applying the one-dimensional Palermo Harvard (PH) hydrodynamical code to the coronal loop plasma having different initial coronal loop base pressures when subjected to similar heating input. Our investigation, therefore, indicates that the plasma parameters of the flaring loop in the initial phase have a decisive role in thermal-nonthermal energy partitioning.
Evaluating Datalog Tools for Meta-reasoning over OWL 2 QL

Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015, Description Logics) and Lenzerini et al. (2020, Information Systems 88, 101294), Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi and Faber (2021, International Joint Conference on Rules and Reasoning, Springer, 218–233.) by a more detailed experimental analysis and more background.
Why is the Dynamics of Glasses Super-Arrhenius?

The steep increase of the relaxation time of glass forming liquids upon cooling is traditionally ascribed to an impending entropy crisis: since the system has"nowhere to go", dynamics must come to a halt. This classic argument, due to Adam \&Gibbs, has been bolstered and refined by the development of the Random First Order Transition (RFOT) theory, which fares remarkably well at reproducing most salient experimental facts of super-cooled liquids. All static predictions of RFOT, including the existence of a point-to-set length and the role of pinning sites, have been vindicated by detailed numerical simulations. Yet, there is no consensus that the basic mechanism explaining the glass transition is the one captured by RFOT. Strong doubts have emerged following the observation that adding or removing kinetic constraints can change the relaxation time by orders of magnitude, while leaving thermodynamics unchanged. This is at odds with the idea of a one-to-one mapping between excess entropy and relaxation time. In the following discussion paper presented at the Solvay conference in October 2023, we review areas of consensus and dissent of RFOT with other competing theoretical proposals, and propose possible paths for (partial) reconciliation. We further argue that extensive numerical simulations of the non-linear susceptibility of glasses, in particular in the aging regime, should shed important light on the mechanism at the origin of the super-Arrhenius behaviour of the relaxation time. In any case, more imagination is still needed to come up with experimental, theoretical or numerical ideas that would allow to finally settle the question of why glasses do not flow.
Presymplectic minimal models of local gauge theories

We elaborate on the recently proposed notion of a weak presymplectic gauge PDE. It is a $\mathbb{Z}$-graded bundle over the space-time manifold, equipped with a degree $1$ vector field and a compatible graded presymplectic structure. This geometrical data naturally defines a Lagrangian gauge field theory. Moreover, it encodes not only the Lagrangian of the theory but also its full-scale Batalin-Vilkovisky (BV) formulation. In particular, the respective field-antifield space arises as a symplectic quotient of the super-jet bundle of the initial fiber bundle. A remarkable property of this approach is that among the variety of presymplectic gauge PDEs encoding a given gauge theory we can pick a minimal one that usually turns out to be finite-dimensional, and unique in a certain sense. The approach can be considered as an extension of the familiar AKSZ construction to not necessarily topological and diffeomorphism-invariant theories. We present a variety of examples including $p$-forms, chiral Yang-Mills theory, Holst gravity, and conformal gravity. We also explain the explicit relation to the non-BV-BRST version of the formalism, which happens to be closely related to the covariant phase space and the multisymplectic approaches.
Alternate cleavage structure and electronic inhomogeneity in Ca-doped YBa$_2$Cu$_3$O$_{7-\delta}$

YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO) has favorable macroscopic superconducting properties of $T_\mathrm{c}$ up to 93 K and $H_{c2}$ up to 150 T. However, its nanoscale electronic structure remains mysterious because bulk-like electronic properties are not preserved near the surface of cleaved samples for easy access by local or surface-sensitive probes. It has been hypothesized that Ca-doping at the Y site could induce an alternate cleavage plane that mitigates this issue. We use scanning tunneling microscopy (STM) to study both Ca-free and 10% Ca-doped YBCO, and find that the Ca-doped samples do indeed cleave on an alternate plane, yielding a spatially-disordered partial (Y,Ca) layer. Our density functional theory calculations support the increased likelihood of this new cleavage plane in Ca-doped YBCO. On this surface, we image a superconducting gap with average value 24 $\pm$ 3 meV and characteristic length scale 1-2 nm, similar to Bi-based high-$T_\mathrm{c}$ cuprates, but the first map of gap inhomogeneity in the YBCO family.
Defining Neural Network Architecture through Polytope Structures of Dataset

Current theoretical and empirical research in neural networks suggests that complex datasets require large network architectures for thorough classification, yet the precise nature of this relationship remains unclear. This paper tackles this issue by defining upper and lower bounds for neural network widths, which are informed by the polytope structure of the dataset in question. We also delve into the application of these principles to simplicial complexes and specific manifold shapes, explaining how the requirement for network width varies in accordance with the geometric complexity of the dataset. Moreover, we develop an algorithm to investigate a converse situation where the polytope structure of a dataset can be inferred from its corresponding trained neural networks. Through our algorithm, it is established that popular datasets such as MNIST, Fashion-MNIST, and CIFAR10 can be efficiently encapsulated using no more than two polytopes with a small number of faces.
Resource-efficient In-orbit Detection of Earth Objects

With the rapid proliferation of large Low Earth Orbit (LEO) satellite constellations, a huge amount of in-orbit data is generated and needs to be transmitted to the ground for processing. However, traditional LEO satellite constellations, which downlink raw data to the ground, are significantly restricted in transmission capability. Orbital edge computing (OEC), which exploits the computation capacities of LEO satellites and processes the raw data in orbit, is envisioned as a promising solution to relieve the downlink burden. Yet, with OEC, the bottleneck is shifted to the inelastic computation capacities. The computational bottleneck arises from two primary challenges that existing satellite systems have not adequately addressed: the inability to process all captured images and the limited energy supply available for satellite operations. In this work, we seek to fully exploit the scarce satellite computation and communication resources to achieve satellite-ground collaboration and present a satellite-ground collaborative system named TargetFuse for onboard object detection. TargetFuse incorporates a combination of techniques to minimize detection errors under energy and bandwidth constraints. Extensive experiments show that TargetFuse can reduce detection errors by 3.4 times on average, compared to onboard computing. TargetFuse achieves a 9.6 times improvement in bandwidth efficiency compared to the vanilla baseline under the limited bandwidth budget constraint.
Approximating Accelerator Impedances with Resonator Networks

It is common in the accelerator community to use the impedance of accelerator components to describe wake interactions in the frequency domain. However, it is often desirable to understand such wake interactions in the time domain in a general manner for excitations that are not necessarily Gaussian in nature. The conventional method for doing this involves taking the inverse Fourier Transform of the component impedance, obtaining the Green's Function, and then convolving it with the desired excitation distribution. This method can prove numerically cumbersome, for a convolution integral must be evaluated for each individual point in time when the wake function is desired. An alternative to this method would be to compute the wake function analytically, which would sidestep the need for repetitive integration. Only a handful of cases, however, are simple enough for this method to be tenable. One of these cases is the case where the component in question is an RLC resonator, which has a closed-form analytical wake function solution. This means that a component which can be represented in terms of resonators can leverage this solution. As it happens, common network synthesis techniques may be used to map arbitrary impedance profiles to RLC resonator networks in a manner the accelerator community has yet to take advantage of. In this work, we will use Foster Canonical Resonator Networks and partial derivative descent optimization to develop a technique for synthesizing resonator networks that well approximate the impedances of real-world accelerator components. We will link this synthesis to the closed-form resonator wake function solution, giving rise to a powerful workflow that may be used to streamline beam dynamics simulations.
Gravitational Wave Displacement and Velocity Memory Effects

In this article, we compare in detail the linear and nonlinear approach to the Gravitational Waves Displacement and Velocity Memory (GWDM and GWVM) effects. We consider astrophysical situations that give rise to gravitational waves with GWVM effect, i.e. with a residual velocity (the so-called ``velocity-coded memory") and discuss the possibility of future detection of the GWVM effect.
Online Transfer Learning for RSV Case Detection

Transfer learning has become a pivotal technique in machine learning, renowned for its effectiveness in various real-world applications. However, a significant challenge arises when applying this approach to sequential epidemiological data, often characterized by a scarcity of labeled information. To address this challenge, we introduce Predictive Volume-Adaptive Weighting (PVAW), a novel online multi-source transfer learning method. PVAW innovatively implements a dynamic weighting mechanism within an ensemble model, allowing for the automatic adjustment of weights based on the relevance and contribution of each source and target model. We demonstrate the effectiveness of PVAW through its application in analyzing Respiratory Syncytial Virus (RSV) data, collected over multiple seasons at the University of Pittsburgh Medical Center. Our method showcases significant improvements in model performance over existing baselines, highlighting the potential of online transfer learning in handling complex, sequential data. This study not only underscores the adaptability and sophistication of transfer learning in healthcare but also sets a new direction for future research in creating advanced predictive models.