Miracle-dz/UniFAR-FTUs huggingface dataset:
Migratory patterns of two major influenza virus host species on tropical islands

Animal migration is a major driver of infectious agent dispersal. Duck and seabirds migrations, for instance, play a key role in the spatial transmission dynamics and gene flows of avian influenza viruses (AIV), worldwide. On tropical islands, brown and lesser noddies (Anous stolidus and A. tenuirostris) may be important AIV hosts, but the lack of knowledge on their migratory behaviour limits our understanding of virus circulation in island networks. Here we show that high connectivity between islands generated by post-breeding behaviours could be a major driver in the spread and the maintenance of AIV among tropical islands. Tracking data highlight two types of behaviours during the non-breeding season: birds either staying in the vicinity of their breeding ground (on Bird Island, Seychelles), or moving to and roosting on other islands in the Indian Ocean. Differences in roosting places are detected between migrant birds, ranging from the Tanzanian coast to the Maldives archipelago and Tromelin Island. Epidemiological data further support that brown and lesser noddies are major hosts for AIV, although significant variation of seroprevalence between species suggest that other drivers are involved in virus infection and transmission dynamics.
The International Journal of Biostatistics An Improved Bland-Altman Method for Concordance Assessment

It is often necessary to compare two measurement methods in medicine and other experimental sciences. This problem covers a broad range of data with applications arising from many different fields. The Bland-Altman method has been a favorite method for concordance assessment. However, the Bland-Altman approach creates a problem of interpretation for many applications when a mixture of fixed bias, proportional bias and/or proportional error occurs. In this paper, an improved Bland-Altman method is proposed to handle more complicated scenarios in practice. This new approach includes Bland-Altman's approach as its special case. We evaluate concordance by defining an agreement interval for each individual paired observation and assessing the overall concordance. The proposed interval approach is very informative and offers many advantages over existing approaches. Data sets are used to demonstrate the advantages of the new method.
Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences. Teaser We present a global effort to devise harmonization procedures necessary to meaningfully leverage big data.
Neuroanatomical characterization of the Nmu-Cre knock-in mice reveals an interconnected network of unique neuropeptidergic cells

Neuromedin U (NMU) is an evolutionary conserved neuropeptide that has been implicated in multiple processes, such as circadian regulation, energy homeostasis, reward processing and stress coping. Although central expression of NMU has been addressed previously, the lack of specific and sensitive tools has prevented a comprehensive characterization of NMU-expressing neurons in the brain. We have generated a knock-in mouse model constitutively expressing Cre recombinase under the Nmu promoter. We have validated the model using a multi-level approach based on quantitative reverse-transcription polymerase chain reactions, in situ hybridization, a reporter mouse line and an adenoviral vector driving Cre-dependent expression of a fluorescent protein. Using the Nmu-Cre mouse, we performed a complete mapping of NMU expression in adult mouse brain, unveiling a potential midline NMU modulatory circuit with the ventromedial hypothalamic nucleus (VMH) as a key node. Moreover, immunohistochemical analysis suggested that NMU neurons in the VMH mainly constitute a unique population of hypothalamic cells. Taken together, our results suggest that Cre expression in the Nmu-Cre mouse model largely reflects NMU expression in the adult mouse brain, without altering endogenous NMU expression. Thus, the Nmu-Cre mouse model is a powerful and sensitive tool to explore the role of NMU neurons in mice.
GraphGPSM: a global scoring model for protein structure using graph neural networks

The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles, and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14, and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D, and DeepAccNet etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2% and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation.
Hierarchical Bayesian modeling identifies key considerations in the development of quantitative loop-mediated isothermal amplification assays

Motivation Loop-mediated isothermal amplification (LAMP) is a rapidly growing, fast, and cost-effective technique for detection of DNA/RNA in point-of-care biomedical applications. However, it remains unclear what factors affect LAMP’s quantitative resolution, and experimental optimization of primers presents a major bottleneck in assay design. A lack of model-based frameworks to characterize LAMP data and address these questions presents an unmet need for LAMP assay development. Results We present hierarchical Bayesian models of LAMP amplification based on Gompertz functions, and use these models to infer the effect of RNA variation and other factors on LAMP amplification curves derived from 80 blood samples of patients with suspected acute infection. Our analysis uncovers associations between LAMP assay resolution and characteristics such as primer sequence composition and thermodynamic properties. In addition to correlations between RNA input abundance and time shift of the the LAMP amplification curve, we also detect RNA-dependent assocations with amplification rate. We further investigate associations between primer/target properties and quantitative performance of the assay by generating a set of synthetic RNA samples with systematically varied primer sequences and applying our framework. We find evidence that the associations observed are driven by across-target rather than within-target variation, an important observation for study design. Our findings represent important first steps towards guided development of quantitative LAMP assays. Availability and Implementation Analysis and modeling code is available upon reasonable request.
Use of multimodal sensory cues in predator avoidance by wild-caught zebrafish shoals

Shoaling in fishes is regulated by factors like predation, vegetation cover, water flow and food availability. Shoals detect and respond to changes in these ecological factors using a multimodal sensory system. Here, we examine the immediate response of wild-caught zebrafish (Danio rerio) shoals to cues from its natural predator, the snakehead (Channa sp.). Zebrafish shoals were recorded upon exposure to (1) olfactory predator cues, (2) visual predator cues, (3) both cues together, and (4) no cue. We tracked individuals and analysed shoal responses across these treatments. We found that compared to control treatments, shoals receiving either visual or olfactory cues had significantly greater: (i) cohesion, (ii) polarization and (iii) velocity. Interestingly, when the shoals received both cues simultaneously, the cohesion, polarization and velocity decreased and a significantly greater number of individual freezing events occurred. Therefore, zebrafish relied on both visual and olfactory cues to escape predation. However, when shoals were presented with both cues together, while freezing frequency increased, other responses were comparable to control treatments where no predator cue was provided. While this study indicates that multimodal cues elicit a different anti-predator response than the cues singly, more experiments are required to identify the underlying cause of this behaviour.
Neurobiology of Aging

Topographic maps form a critical feature of cortical organization, yet are poorly described with respect to their microstructure in the living aging brain. We acquired quantitative structural and functional 7T-MRI data from younger and older adults to characterize layer-wise topographic maps of the primary motor cortex (M1). Using parcellation-inspired techniques, we show that quantitative T1 and Quantitative Susceptibility Maps values of the hand, face, and foot areas differ significantly, revealing microstructurally distinct cortical fields in M1. We show that these fields are distinct in older adults and that myelin borders between them do not degenerate. We further show that the output layer 5 of M1 shows a particular vulnerability to age- related increased iron, while layer 5 and the superficial layer show increased diamagnetic substance, likely reflecting calcifications. Taken together, we provide a novel 3D model of M1 microstructure, where body parts form distinct structural units, but layers show specific vulnerability toward increased iron and calcium in older adults. Our findings have implications for understanding sensorimotor organization and aging, in addition to topographic disease spread.
PBFT Blockchain-Based OpenStack Identity Service

Openstack is widely used as a representative open-source infrastructure of the service (IaaS) platform. The Openstack Identity Service is a centralized approach component based on the token including the Memcached for cache, which is the in-memory key-value store. Token validation requests are concentrated on the centralized server as the number of differently encrypted tokens increases. This paper proposes the practical Byzantine fault tolerance (PBFT) blockchain-based Openstack Identity Service, which can improve the performance efficiency and reduce security vulnerabilities through a PBFT blockchain framework-based decentralized approach. The experiment conducted by using the Apache JMeter demonstrated that latency was improved by more than 33.99% and 72.57% in the PBFT blockchain-based Openstack Identity Service, compared to the Openstack Identity Service, for 500 and 1,000 differently encrypted tokens, respectively.
On the Steiner Problem

Let M be a metric space with metric ρ which has the following properties. 1. M is finitely compact. 2. There exists a geodesic in M joining each two points of M. 3. For all a, b∈M, ρ(a, b) is equal to the length of a geodesic joining a and b.
Application of an Optimized Support Vector Regression Algorithm in Short-Term Traffic Flow Prediction

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.
Critical Rsos Models and Conformal Field Theory

The eigenvalues of the transfer matrix of the generalized RSOS model are exactly calculated. From the consideration of the thermodynamics of the quantum system on the one-dimensional chain connected with the RSOS model, we calculate the central charges of the effective conformal field theories describing the critical behavior of the model in different regimes.
Shape Design for Thin-Walled Beam Cross Sections Using Rational B Splines

Shape optimization of thin-walled beams is discussed. The cross-sectional shape of the beam is described by nonuniform rational B-spline curves. The optimization problem is defined in terms of cross-sectional properties and stresses. Design variables are the control parameters of the rational B-spline curves and the wall thickness of the beam. Cross-sectional properties and stresses are obtained from the elasticity solution for the beam. Consideration of torsion and pure shear requires the solution of Laplace equations which is accomplished using finite elements. A design sensitivity analysis is given for the cross-sectional properties and stresses. A sample problem is presented to illustrate the formulations.
ROBUST MEASUREMENT-BASED OPTIMIZATION WITH STEADY-STATE DIFFERENTIAL EQUATION MODELS

A robust model-based iterative feedback optimization methodolgy has been introduced in previous work for the steady-state optimization of chemical process operations without requiring cumbersome model updating. Here we extend the methodology to utilize differential equation models directly. The adjoint system method that is often used in dynamic optimization is modified to explicitly incorporate plant measurements in the gradient computation during each iteration, resulting in robustness with respect to model-plant mismatch. In the case that the states cannot be measured along the entire spatial direction, estimated profiles based on boundary measurements are utilized. The methodology is tested with simulations of an ammonia synthesis reactor in autothermal operation and is shown to be robust in the presence of modeling error, input error, and measurement noise.
Learning Interpretable Representations of Images

Computers represent images with pixels and each pixel contains three numbers for red, green and blue colour values. These numbers are meaningless for humans and they are mostly useless when used directly with classical machine learning techniques like linear classifiers. Interpretable representations are the attributes that humans understand: the colour of the hair, viewpoint of a car or the 3D shape of the object in the scene. Many computer vision tasks can be viewed as learning interpretable representations, for example a supervised classification algorithm directly learns to represent images with their class labels. In this work we aim to learn interpretable representations (or features) indirectly with lower levels of supervision. This approach has the advantage of cost savings on dataset annotations and the flexibility of using the features for multiple follow-up tasks. We made contributions in three main areas: weakly supervised learning, unsupervised learning and 3D reconstruction. In the weakly supervised case we use image pairs as supervision. Each pair shares a common attribute and differs in a varying attribute. We propose a training method that learns to separate the attributes into separate feature vectors. These features then are used for attribute transfer and classification. We also show theoretical results on the ambiguities of the learning task and the ways to avoid degenerate solutions. We show a method for unsupervised representation learning, that separates semantically meaningful concepts. We explain and show ablation studies how the components of our proposed method work: a mixing autoencoder, a generative adversarial net and a classifier. We propose a method for learning single image 3D reconstruction. It is done using only the images, no human annotation, stereo, synthetic renderings or ground truth depth map is needed. We train a generative model that learns the 3D shape distribution and an encoder to reconstruct the 3D shape. For that we exploit the notion of image realism. It means that the 3D reconstruction of the object has to look realistic when it is rendered from different random angles. We prove the efficacy of our method from first principles.
WICKED PROBLEMS AND NEW WAYS OF ORGANIZING: HOW FE Y ALEGRIA CONFRONTED CHANGING MANIFESTATIONS OF POVERTY

Wicked problems are causally complex, lack definite solutions, and re-emerge in different guises. This paper discusses how new ways of organizing emerge to tackle changing manifestations of wicked problems. Focusing on the wicked problem of poverty, we conducted a longitudinal study of Fe y Alegria (FyA), one of the world’s largest non-governmental organization, which provides education for the poor across 21 countries in Latin America and Africa. Drawing on archival and ethnographic data, we trace the historical narratives of how FyA defined poverty as a problem and developed new ways of organizing, from its foundation by a Jesuit priest in 1955 to its current networked structure. Our findings reveal the ongoing cycle of interpretive problem definition and organizing solutions for wicked problems. First, since there is no “true” formulation of a wicked problem, actors construct narrative explanations based on their understanding of the problem. Second, organizational solutions to a wicked problem are thus reflections of these narrative constructions. Third, emerging
Room-temperature terahertz mixer based on the simultaneous electronic and optical excitations of plasma waves in a field effect transistor

A method for the heterodyne detection of terahertz (THz) signals is proposed. A high electron mobility transistor is used as a nonlinear element, while the optical beating of two laser beams exciting plasma waves in the transistor channel plays the role of the THz local oscillator. High efficiency and room-temperature operation of such a mixer are demonstrated by numerical simulations.
Influence of K 24 on the structure of nematic liquid crystal droplets

Abstract A phenomenological free energy is used to describe the stable ordering of nematic liquid crystals confined to supramicron spherical cavities. In particular the effects of the saddle splay elastic constant, K 24, on the equilibrium structures and phase diagram of droplets with homeotropic surface anchoring are discussed. Some structures are illustrated by the corresponding simulated polarization microscope textures. Possibilities for an experimental determination of the saddle-splay elastic constant and surface anchoring strength by studying the radial-axial structural transition in such droplets are analysed. It is shown that the K 24 term in the elastic free energy stabilizes a deformed droplet structure even in the limit of the zero anchoring strength.
Photonic band structures of 1-D plasma photonic crystal with time-variation plasma density

The photonic band structures of one-dimensional plasma photonic crystal (1-D PPC) with time-variation plasma density are studied by using differential transfer matrix method. Taking an exponential functional form of time as an illustration of the time-varying plasma density, we examine the photonic band structures of the 1-D PPC. The results show that the photonic band structures can be tuned correspondingly by changing the time which modifies the plasma dielectric constant in different frequency regions, especially in the range of 0<ω<ωp0; whereas well above ωp0, the effects of the time-variation are small. The parameter dependence of the effects is also calculated and discussed.
Conjugate gradient techniques for adaptive filtering

The technique of conjugate gradients is applied to the adaptive filtering problem. The method provides convergence comparable to that of RLS (recursive-least-squares) schemes and has a computational requirement that is intermediate between the LMS (least-mean-square) and the RLS methods. It does not suffer from any known stability problems. <<ETX>>
Meridional distribution of stratospheric trace constituents

Abstract Vertically stratified stratospheric tracers such as methane and nitrous oxide tend to have constant mixing ratio surfaces that slope downward toward the poles in the meridional plane. The equilibrium tracer slope results from the competition between the slope steepening effects of advection by the diabatic circulation and the slope flattening effects of quasi-isentropic eddy transport and photochemical loss. The diabatic circulation itself is, however, driven primarily by eddy transports, which maintain the departure of stratosphere temperatures from radiative equilibrium. If the eddy transports are weak, the diabatic circulation is also weak and the slope is small. Using a simple beta-plane channel model and an eddy diffusion parameterization for the eddy potential vorticity and tracer transports, we show that the slope is a maximum for a value of eddy diffusion such that the dynamical time scale is between the radiative and chemical time scales.