Miracle-dz/UniFAR-FTUs huggingface dataset:
The structure of Lepidoptera-plant interaction networks across clades, life stages, and environmental gradients

Aim Integrate biogeographic and ecological knowledge to understand the spatial-structural patterns of plant-insect interaction networks at the landscape scale. Location The 36,000 km2 German state of Baden-Württemberg, Central Europe. Methods We integrated extensive data of Lepidoptera-plant occurrences and interactions to inferentially construct local interaction networks across Baden-Württemberg, considering in total 3148 plant and 980 Lepidoptera species, covering butterflies, Noctuid moths, Geometrid moths and Bombycoid moths. We quantified clade- and life-stage-specific network structures and related these features to GIS-informed environmental conditions, thereby revealing the spatial (environmental) patterns and potential drivers of networks’ structural variation across the landscape. Results Spanning the same environmental gradients, Lepidoptera clades and life stages can form various interaction structures with food plants and exhibit distinct spatial-structural patterns. For all major Lepidopteran groups, except Geometrid moths, potential diet across life stages tended to broaden toward low-elevation farmlands. The larval and adult networks of butterflies became less modular with farmland coverage; the same for adult Noctuid moths, but the inverse for adult Geometrid moths. With increasing elevation, the larval and adult networks of Noctuid moths became less and more modular, respectively, whereas Geometrid adult networks became more modular. While the adult dietary niche of butterflies was more overlapped at low elevation, those of Noctuid and Geometrid moths further associated with land cover and were more overlapped toward low- and high-elevation farmlands, respectively. Main conclusions Environmental factors and biotic interactions together shape ecological communities. By particularly accounting for species-interaction contexts, we revealed the spatial-structural patterns of Lepidoptera-plant networks along geo-climate and land-cover gradients, where the shaping mechanisms likely include both evolutionary (e.g., resource-consumer co-evolution) and ecological (e.g., competitive exclusion) processes and are specific to Lepidoptera’s clade or life stage. Such biogeographical structural patterns provide ecological and conservation implications at both species and community levels, and can indicate the potential response of Lepidoptera-plant communities to environmental changes.
A Modular, Adaptive, Deep-Learning-Based Brain-VR Interface

Brain-Computer Interfaces (BCIs) may open up new possibilities for Virtual Reality (VR) applications: BCIs may be used for active brain control of VR avatars, or to make VR content passively-adaptive based on information decoded from ongoing brain activity. Application domains for such Brain-VR Interfaces (BVRI) include medical and healthcare, entertainment, and education. Conversely, VR technology also opens up new possibilities for BCI research and development: E.g., gamified immersive BCI paradigms may improve subject engagement and long-term motivation, helping to study learning and adaptivity in the BCI-control context. Previously, we have demonstrated a first adaptive, deep-learning-based online BCI for the control of robotic assistants. Here, we describe the extension of this setup to a modular, extensible, VR-compatible online BCI setup. We describe how we integrated a classical active BCI control paradigm using motor imagery into a gamified interactive VR scenario, designed to enhance the long-term motivation of subjects. We also present an initial quality assessment of electroencephalographic (EEG) signals acquired with a dry-electrode system. We anticipate that the presented modular adaptive Brain-VR Interface will help to understand and facilitate (co-)adaptivity during long-term BCI usage.
The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling

Background Randomized clinical trials (RCT) are limited in reflecting observable results out of controlled settings, which requires the execution of further lengthy observational studies. The usage of real-world data (RWD) has been recently considered to be a viable alternative to overcome these issues and complement certain clinical conclusions. Transcriptomics and other high-throughput data contain a molecular description of medical conditions and disease states. When linked to RWD, including demographical information, transcriptomics data is capable of elucidating nuances in disease pathways in specific patient populations. This work focuses on the construction of a patient repository database with clinical information resulting from the integration of publicly available transcriptomics datasets. Results Patient data were integrated into the patient repository by using a novel post-processing technique allowing for the usage of samples originating from different/multiple Gene Expression Omnibus (GEO) datasets. RWD was mined from GEO samples’ metadata and a clinical and demographical characterization of the database was obtained. Our post-processing technique, which we have named MACAROON – MicroArray Cross-plAtfoRm pOst-prOcessiNg, aims to uniformize and integrate transcriptomics data (considering batch effects and possible processing-originated artefacts). This process was able to better reproduce the down streaming biological conclusions in a 10% improvement when compared to other methods available. RWD mining was done through a manually curated synonym dictionary allowing for the correct assignment (95.33% median accuracy; 80.14% average) of medical conditions. Conclusions Our strategy produced an RWD repository, which includes molecular information as well as clinical and demographical RWD. The exploration of these data facilitates the discovery of clinical outcomes and pathways specific to predetermined populations of patients by integrating multiple public datasets.
Energy Cascades in Astrophysical Plasma

A local turbulence model is developed to study energy cascades in the interstellar medium (ISM) based on self-consistent two-dimensional fluid simulations. The model describes a partially ionized magnetofluid interstellar medium (ISM) that couples a neutral hydrogen fluid with a plasma primarily through charge exchange interactions. Charge exchange interactions are ubiquitous in warm ISM plasma and the strength of the interaction depends largely on the relative speed between the plasma and the neutral fluid. Unlike small length-scale linear collisional dissipation in a single fluid, charge exchange processes introduce channels that can be effective on a variety of length-scales that depend on the neutral and plasma densities, temperature, relative velocities, charge exchange cross section and the characteristic length scales. We find, from scaling arguments and nonlinear coupled fluid simulations, that charge exchange interactions modify spectral transfer associated with large scale energy containing eddies. Consequently, the warm ISM turbulent cascade rate prolongs spectral transfer amongst inertial range turbulent modes. Turbulent spectra associated with the neutral and plasma ISM fluids are therefore steeper than those predicted by Kolmogorov's phenomenology.
ScholarWorks @ Georgia State University ScholarWorks @ Georgia State University Prototype Nerve-Specific Near-Infrared Fluorophores Prototype Nerve-Specific Near-Infrared Fluorophores

Nerve preservation is an important issue during most surgery because accidental transection or injury results in significant morbidity, including numbness, pain, weakness, or paralysis. Currently, nerves are still identified only by gross appearance and anatomical location during surgery, without intraoperative image guidance. Near-infrared (NIR) fluorescent light, in the wavelength range of 650-900 nm, has the potential to provide high-resolution, high-sensitivity, and real-time avoidance of nerve damage, but only if nerve-specific NIR fluorophores can be developed. In this study, we evaluated a series of Oxazine derivatives to highlight various peripheral nerve structures in small and large animals. Among the targeted fluorophores, Oxazine 4 has peak emission near into the NIR, which provided nerve-targeted signal in the brachial plexus and sciatic nerve for up to 12 h after a single intravenous injection. In addition, recurrent laryngeal nerves were successfully identified and highlighted in real time in swine, which could be preserved during the course of thyroid resection. Although optical properties of these agents are not yet optimal, chemical structure analysis provides a basis for improving these prototype nerve-specific NIR fluorophores even further.
Numerics for the spin orbit equation of Makarov with constant eccentricity

We present an algorithm for the rapid numerical integration of a time-periodic ODE with a small dissipation term that is $C^1$ in the velocity. Such an ODE arises as a model of spin-orbit coupling in a star/planet system, and the motivation for devising a fast algorithm for its solution comes from the desire to estimate probability of capture in various solutions, via Monte Carlo simulation: the integration times are very long, since we are interested in phenomena occurring on times similar to the formation time of the planets. The proposed algorithm is based on the High-order Euler Method (HEM) which was described in~\cite{hem}, and it requires computer algebra to set up the code for its implementation. The pay-off is an overall increase in speed by a factor of about $7.5$ compared to standard numerical methods. Means for accelerating the purely numerical computation are also discussed
Generalized orthogonal matching pursuit for multiple measurements - A structural approach

Sparse data approximation has become a popular research topic in signal processing. However, in most cases only a single measurement vector (SMV) is considered. In applications, the multiple measurement vector (MMV) case is more usual, i.e., the sparse approximation problem has to be solved for several data vectors coming from closely related measurements. Thus, there is an unknown inter-vector correlation between the data vectors. Using SMV methods typically does not return the best approximation result as the correlation is ignored. In the past few years several algorithms for the MMV case have been designed to overcome this problem. Most of these techniques focus on the approximation quality while quite strong assumptions to the type of inter-vector correlation are made. While we still want to find a sparse approximation, our focus lies on preserving (possibly complex) structures in the data. Structural knowledge is of interest in many applications. It can give information about e.g., type, form, number or size of objects of interest. This may even be more useful than information given by the non-zero amplitudes itself. Moreover, it allows efficient post processing of the data. We numerically compare our new approach with other techniques and demonstrate its benefits in two applications.
Fermi-LAT constraints on dark matter annihilation cross section from observations of the Fornax cluster

We analyze 2.8-yr data of 1? 100 GeV photons for clusters of galaxies, collected with the Large Area Telescope onboard the Fermi satellite. By analyzing 49 nearby massive clusters located at high Galactic latitudes, we find no excess gamma-ray emission towards directions of the galaxy clusters. Using flux upper limits, we show that the Fornax cluster provides the most stringent constraints on the dark matter annihilation cross section. Stacking a large sample of nearby clusters does not help improve the limit for most dark matter models. This suggests that a detailed modeling of the Fornax cluster is important for setting robust limits on the dark matter annihilation cross section based on clusters. We therefore perform the detailed mass modeling and predict the expected dark matter annihilation signals from the Fornax cluster, by taking into account effects of dark matter contraction and substructures. By modeling the mass distribution of baryons (stars and gas) around a central bright elliptical galaxy, NGC 1399, and using a modified contraction model motivated by numerical simulations, we show that the dark matter contraction boosts the annihilation signatures by a factor of 4. For dark matter masses around 10 GeV, the upper limit obtained on the annihilation cross section times relative velocity is ? (2? 3) ? 10? 25? cm3? s? 1, which is within a factor of 10 from the value required to explain the dark matter relic density. This effect is more robust than the annihilation boost due to substructure, and it is more important unless the mass of the smallest subhalos is much smaller than that of the Sun.
Telescopic projective integration for kinetic equations with multiple relaxation times

We study a general, high-order, fully explicit numerical method for simulating kinetic equations with a BGK-type collision model with multiple relaxation times. In that case, the problem is stiff and its spectrum consists of multiple separated eigenvalue clusters. Projective integration methods are explicit integration schemes that first take a few small (inner) steps with a simple, explicit method, after which the solution is extrapolated forward in time over a large (outer) time step. These are very efficient schemes, provided there are only two clusters of eigenvalues. Telescopic projective integration methods generalize the idea of projective integration methods by constructing a hierarchy of projective levels. Here, we show how telescopic projective integration methods can be used to efficiently integrate kinetic equations with multiple relaxation times. We show that the required number of projective levels depends on the number of clusters, which in turn depends on the stiffness of the BGK source term. The size of the outer level time step only depends on the slowest time scale present in the model and is independent of the stiffness of the problem. We discuss stability and illustrate the approach with simulations in one and two spatial dimensions.
The quest for electronic ferroelectricity in organic charge-transfer crystals

Organic ferroelectric materials are in demand in the growing field of environmentally friendly, lightweight electronics. Donor-Acceptor charge transfer crystals have been recently proposed as a new class of organic ferroelectrics, which may possess a new kind of ferroelectricity, the so-called electronic ferroelectricity, larger and with faster polarity switching in comparison with conventional, inorganic or organic, ferroelectrics. The current research aimed at achieving ambient conditions electronic ferroelectricity in organic charge transfer crystals is shortly reviewed, in such a way to evidence the emerging criteria that have to be fulfilled to reach this challenging goal.
An Immersed Method Based on Cut-Cells for the Simulation of 2D Incompressible Fluid Flows Past Solid Structures

We present a cut-cell method for the simulation of 2D incompressible flows past obstacles. It consists in using the MAC scheme on cartesian grids and imposing Dirchlet boundary conditions for the velocity field on the boundary of solid structures following the Shortley-Weller formulation. In order to ensure local conservation properties, viscous and convecting terms are discretized in a finite volume way. The scheme is second order implicit in time for the linear part, the linear systems are solved by the use of the capacitance matrix method for non-moving obstacles. Numerical results of flows around an impulsively started circular cylinder are presented which confirm the efficiency of the method, for Reynolds numbers 1000 and 3000. An example of flows around a moving rigid body at Reynolds number 800 is also shown, a solver using the PETSc-Library has been prefered in this context to solve the linear systems.
An algorithmic approach to perverse derived equivalences: Brou\'e's Conjecture for $\Omega^{+}_8(2)$

Following Craven and Rouquier's computational method to tackle Brou\'e's abelian defect group conjecture, we present two algorithms implementing that procedure in the case of principal blocks of defect $D \cong C_{\ell} \times C_{\ell}$ for a prime $\ell$; the first describes a stable equivalence between $B_0(G)$ and $B_0(N_G(D))$, and the second tries to lift a such stable equivalence to a perverse derived equivalence. As an application, we show that Brou\'e's conjecture holds for the principal $5$-block of the simple group $\Omega^{+}_8(2)$.
Chiral d -wave superconductivity in SrPtAs

Recent muon spin-rotation ($\ensuremath{\mu}$SR) measurements on SrPtAs revealed time-reversal-symmetry breaking with the onset of superconductivity [Biswas et al., Phys. Rev. B 87, 180503(R) (2013)], suggesting an unconventional superconducting state. We investigate this possibility via the functional renormalization group and find a chiral $(d+id)$-wave order parameter favored by the multiband fermiology and hexagonal symmetry of SrPtAs. This $(d+id)$-wave state exhibits significant gap anisotropies as well as gap differences on the different bands, but only has point nodes on one of the bands at the Brillouin zone corners. We study the topological characteristics of this superconducting phase, which features Majorana-Weyl nodes in the bulk, protected surface states, and an associated thermal Hall response. The lack of extended nodes and the spontaneously broken time-reversal symmetry of the $(d+id)$-wave state are in agreement with the $\ensuremath{\mu}$SR experiments. Our theoretical findings, together with the experimental evidence, thus suggest that SrPtAs is an example of chiral $d$-wave pairing and a Weyl superconductor.
A Preliminary Pharmacodynamic Study for the Management of Alzheimer’s Disease Using Memantine-Loaded PLGA Nanoparticles

Alzheimer’s disease is becoming a common disorder of the elderly population due to shrinkage of the brain size with age and many other neurological complications. To provide an effective treatment option, memantine-encapsulated polymeric nanoparticles were prepared in the study. The nanoparticles were prepared by using nanoprecipitation followed by homogenization and ultrasonication methods, characterized on the basis of particle size, polydispersity index, and zeta potential. Further, in vitro release profile, cytotoxicity analysis, and Giemsa staining were conducted. To observe the efficacy of nanoparticles in scopolamine-induced Alzheimer models in vivo studies were also carried out. The results showed that nanoparticles were in the nano range with a particle size of 58.04 nm and − 23 mV zeta potential. The in vitro release was also sustained till 24 h with ~ 100% release in selected media phosphate buffer saline, simulated nasal fluid, and artificial cerebrospinal fluid. The cytotoxicity results with ~ 98 to 100% cell viability and no morphological changes through Giemsa staining indicated that nanoparticles were not leading to cell toxicity. The gamma scintigraphy studies showed higher uptake of the drug in the target site through the intranasal route and pharmacodynamic studies indicated that nanoparticles were able to inhibit the spatial memory impairment significantly as compared to the control group. The findings clearly indicated that the developed memantine nanoparticles could act as an alternative approach for the management of Alzheimer’s disease.
Symmetry-protected topological orders for interacting fermions: Fermionic topological nonlinear σ models and a special group supercohomology theory

Symmetry-protected topological (SPT) phases are gapped short-range-entangled quantum phases with a symmetry $G$, which can all be smoothly connected to the trivial product states if we break the symmetry. It has been shown that a large class of interacting bosonic SPT phases can be systematically described by group cohomology theory. In this paper, we introduce a (special) group supercohomology theory which is a generalization of the standard group cohomology theory. We show that a large class of short-range interacting fermionic SPT phases can be described by the group supercohomology theory. Using the data of super cocycles, we can obtain the ideal ground state wave function for the corresponding fermionic SPT phase. We can also obtain the bulk Hamiltonian that realizes the SPT phase, as well as the anomalous (ie, non-on-site) symmetry for the boundary effective Hamiltonian. The anomalous symmetry on the boundary implies that the symmetric} boundary must be gapless for 1+1D boundary, and must be gapless or topologically ordered beyond 1+1D. As an application of this general result, we construct a new SPT phase in 3D, for interacting fermionic superconductors with coplanar spin order (which have $T^2=1$ time-reversal $Z_2^T$ and fermion-number parity $Z_2^f$ symmetries described by a full symmetry group $Z_2^T\times Z_2^f$). Such a fermionic SPT state can neither be realized by free fermions nor by interacting bosons (formed by fermion-pairs), and thus are not included in the K-theory classification for free fermions or group cohomology description for interacting bosons. We also construct three interacting fermionic SPT phases in 2D with a full symmetry group $Z_2\times Z_2^f$. Those 2D fermionic SPT phases all have central-charge $c=1$ gapless edge excitations, if the symmetry is not broken.
The role of advection in phase-separating binary liquids

Using the advective Cahn-Hilliard equation as a model, we illuminate the role of advection in phase-separating binary liquids. The advecting velocity is either prescribed, or is determined by an evolution equation that accounts for the feedback of concentration gradients into the flow. Here, we focus on passive advection by a chaotic flow, and coupled Navier-Stokes Cahn-Hilliard flow in a thin geometry. Our approach is based on a combination of functional-analytic techniques, and numerical analysis. Additionally, we compare and contrast the Cahn-Hilliard equation with other models of aggregation; this leads us to investigate the orientational Holm-Putkaradze model. We demonstrate the emergence of singular solutions in this system, which we interpret as the formation of magnetic particles. Using elementary dynamical systems arguments, we classify the interactions of these particles.
Factors Inﬂuencing Consumers’ Purchase Intention toward Accommodation via Lodging Websites: Evidence from Binh Duong Province of Vietnam

This paper empirically studies factors influencing consumers ’ purchase intention toward accommodation via lodging websites and their impact magnitudes in Binh Duong province of Vietnam. The results show that Lodging information (LI), Online lodging reviews (OR), Trust with the host (HT), Website usability (WU), and Perceived privacy and security (PS) significantly have positive impacts on consumers ’ purchase intention (PI) toward accommodation via lodging websites for a sample size of 400 respondents in Binh Duong province. Remarkably, OR has the strongest impact on PI, followed by WU, PS, HT, and LI. However, Perceived lodging value (PV) and Perceived lodging price (PP) have insignificant impacts on PI. The findings provide marketers and practitioners with useful information for promoting purchase intention toward accommodation via lodging websites from the perspective of consumers in an emerging economy like Vietnam.