Miracle-dz/UniFAR-FTUs huggingface dataset:
AlphaDAPR: An AI-based Explainable Expert Support System for Art Therapy

Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairment or mental disorders. Along with self-report measures based on a questionnaire, psychological drawing assessments can augment information about an individual psychological state. However, the interpretation of the drawing assessments requires much time and effort, especially in a large-scale group such as schools or companies, and depends on the experience of the art therapists. To address this issue, we propose an AI-based expert support system, AlphaDAPR, to support art therapists and psychologists in conducting a large-scale automatic drawing assessment. Our survey results with 64 art therapists showed that 64.06% of the participants indicated a willingness to use the proposed system. The results of structural equation modeling highlighted the importance of explainable AI embedded in the interface design to affect perceived usefulness, trust, satisfaction, and intention to use eventually. The interview results revealed that most of the art therapists show high levels of intention to use the proposed system while expressing some concerns about AI’s possible limitations and threats as well. Discussion and implications are provided, stressing the importance of clear communication about the collaborative role of AI and users.
Effect of finite magnetic moment on threshold amplitude of electrostatic waves in nonlinear dynamics

An ensemble of electrostatic waves propagating obliquely to the external uniform magnetic field accelerates the plasma electrons, giving rise to a mechanical energy in terms of finite magnetic moment (μ). In nonlinear analysis, this magnetic moment modifies the resonances among the harmonics (l) and the overlapping parameter (K) leading to stochasticity. It is shown that the threshold amplitude (φk) of electrostatic waves at the onset of chaos increases slowly for l=0 harmonics and sharply for higher harmonics (l⩾1), owing to the effect of magnetic moment. A sharp decrease of threshold amplitude with wave vector (k) pertaining to the ensemble of waves for higher harmonics (l⩾1) is also delineated. The findings may have astrophysical and cosmological signatures in the high energy particle regimes.
An explainable deep learning classifier of bovine mastitis based on whole genome sequence data - circumventing the p>>>n problem

The most serious drawback underlying the biological annotation of Whole Genome Sequence data is the p>>n problem, meaning that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). Therefore, the major aim of the study was to propose a way to circumvent the problem by combining a LASSO logistic regression model with Deep Learning (DL). That was illustrated by a practical biological problem of classification of cows into mastitis-susceptible or mastitis-resistant, based on genotypes of Single Nucleotide Polymorphisms (SNPs) identified in their WGS. Among several DL architectures proposed via optimisation of DL hyperparameters using the Optuna software, imposed on different SNP sub-sets defined by LASSO logistic regressions with different penalty values, the architecture with 204,642 SNPs was selected as the best one. This architecture was composed of 2 layers with respectively 7 and 46 units per layer as well as respective drop-out rates of 0.210 and 0.358. The classification of the test data set resulted in the AUC=0.750, accuracy=0.650, sensitivity=0.600, and specificity=0.700 was selected as the best model and thus proceeded to genomic and functional annotations. Significant SNPs were selected based on the SHapley Additive exPlanation values transformed to Z-scores to assess the underlying type I-error. These SNPs were annotated to genes. As a final result, a single GO term related to the biological process and thirteen GO terms related to the molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Author Summary Our objective is to distinguish between cows that are susceptible and resistant to bovine mastitis by analysing their genomic data. However, we face a significant challenge due to the large number of single nucleotide polymorphisms (SNPs) and limited sample size. To address this challenge, we utilize two methods: feature selection algorithms and deep learning. We experiment with various ways of implementing these techniques and evaluate their performance on a validation set. Our findings reveal that the optimal approach can accurately predict a cow’s susceptibility or resistance status around 65% of the time. Additionally, we employ a technique to identify the most crucial SNPs and their biological functions. Our results indicate that some of these SNPs are related to immune response or protein synthesis pathways, implying that they may affect the cow’s health and productivity.
Visual working memory models of delayed estimation do not generalize to whole-report tasks

Whole-report working memory tasks provide a measure of recall for all stimuli in a trial, and afford single-trial analyses that are not possible with single-report delayed estimation tasks. However, most whole-report studies assume that trial stimuli are encoded and reported independently, and do not consider the relationships between stimuli presented and reported within the same trial. Here, we present the results of two independently conducted whole-report experiments. The first dataset was recorded by Adam, Vogel, and Awh, 2017, and required participants to report color and orientation stimuli using a continuous response wheel. We recorded the second dataset, which required participants to report color stimuli using a set of discrete buttons. We find that participants often group their reports by color similarity, contradicting the assumption of independence implicit in most encoding models of working memory. Next, we show that this behavior is consistent across participants and experiments when reporting color but not orientation, two circular variables often assumed to be equivalent. Finally, we implement an alternative to independent encoding where stimuli are encoded as a hierarchical Bayesian ensemble, and show that this model predicts biases that are not present in either dataset. Our results suggest that assumptions made by both independent and hierarchical ensemble encoding models—which were developed in the context of single-report delayed estimation tasks—do not hold for the whole-report task. This failure to generalize highlights the need to consider variations in task structure when inferring fundamental principles of visual working memory.
Spatial Signatures of Biological Soil Crusts and Community Level Self-Organization in Drylands

In dryland landscapes, patches of vascular plants can respond to environmental stress by adjusting their spatial pattern to intercept runoff more effectively, i.e., spatially self-organize, and maintain productivity. However, vegetation patch dynamics in drylands often assumes interspaces of plant patches are composed only of bare soil. Biological soil crusts (BSCs) are complex communities, largely of cyanobacteria, algae, lichens, and bryophytes, living in the soil surface in drylands and often cover more area than vascular plants. BSCs often occur in patches of light cyanobacteria and dark-mixed aggregates and can significantly affect and respond to ecohydrological feedbacks in dryland ecosystems. However, little is known about their spatial patterns and dynamics. In this study, we investigate spatial attributes of BSC patches, their spatial interactions with vascular plants, and factors that drive variation in these attributes. We collected ultra-high-resolution (1-cm) data on spatial patterns of BSCs and vascular plants at 26 sites across three ecoregions of the Southwest of the United States of America. Our analysis shows that light cyanobacterial BSCs vary most in their patch shape complexity along the aridity gradient, while dark-mixed BSCs vary strongly in their abundance. The abundance of dark-mixed BSCs is significantly affected by the soil template, namely soil texture and calcareousness, as well as vascular plants to persist under stress. Furthermore, species associations also change with environmental stress. Light cyanobacteria BSCs, likely a significant source of runoff, may act as a buffer for woody plants against drying, as spatial interactions between these biota become more positive (i.e., spatially aggregated) with greater aridity. While dark-mixed BSCs rely significantly on soil conditions and reduce in abundance as a response to aridity stress, we find evidence that they may have some capacity to spatially adjust under conditions of constant aridity. The interaction of dark-mixed BSCs with light cyanobacteria patches becomes more positive with slope. We conclude that light cyanobacteria BSCs can likely change patch shape in response to water limitation, while dark-mixed BSCs have a reduced capacity to do so – providing further evidence that the abundance of dark-mixed BSCs will decline in the future under drying. BSCs and vascular plants coordinate in space in response to resource availability, suggesting the need to consider self-organization of multiple assemblages to fully understand dryland response to climatic change.
The detection of X-ray variability in O stars

Seven O stars known to have strong, and sometimes variable, stellar winds have been observed repeatedly with the Imaging Proportional Counter on the Einstein Observatory, in a program designed to determine whether the X-ray fluxes from these stars are variable. In three cases, definite changes were seen, either on a time scale of a year (Iota Ori and Delta Ori) or five days (15 Mon). In two of these cases, the X-ray spectrum was harder when the overall flux was higher, indicating that some of the fluctuations may take place in a hot (approximately 10 to the 7th K) emitting region at the bottom of the winds.
Introduction to the Special Issue: Moving Beyond Traditional Psychometric Approaches

Researchers frequently employ outdated statistical procedures to evaluate their measure’s psychometric properties or simply fail to capture the full potential of the data. This special issue reviews the utility of several statistical approaches researchers can apply to uncover the factor structure, test for measurement equivalence across groups, and evaluate internal consistency reliability. Specifically, these manuscripts review and demonstrate the following: (a) statistical and methodological decisions when evaluating measures; (b) how to best model the latent constructs and factor structure; (c) methods to assess measurement equivalence across groups; and (d) procedures to assessing instrument reliability. This special issue also encourages researchers to take a deeper exploration into their measure’s psychometric properties and carefully consider their modeling framework and statistical assumptions.
Lepidopteran Synteny Units (LSUs) reveal deep conservation of macrosynteny in butterflies and moths

Advances in DNA sequencing technologies have, for the first time, provided us with enough whole chromosome-level genomes to understand in detail how chromosome number and composition change over time. Here, we use the genomes of butterflies and moths to look at the levels and age of macrosynteny in the Lepidoptera and Trichoptera. We used comparative BUSCO analsysis to define reproducible units of macrosynteny which we term ‘Lepidopteran Synteny Units’ or LSUs. The 31 chromosomes of the model butterfly Melitaea cinxia served as a reference point. The results show that chromosome-wide macrosynteny extends from the most basal branches of the Lepidopteran phylogeny to the most distal. This synteny also extends to the order Trichoptera, a sister group of the Lepidoptera. Thus, chromosome-wide macrosynteny has been conserved for a period of >200 My in this group of insects. We found no major interchromosomal translocations, reciprocal or non-reciprocal, in the genomes studied. Intrachromosomal rearrangements, in contrast, were abundant. Beyond its use in defining LSUs, this type of homology-based analysis will be useful in determining the relationships between chromosomal elements in different animals and plants. Further, by more precisely defining the breakpoints of chromosomal rearrangements we can begin to look at their potential roles in chromosomal evolution. Statement The authors declare no conflicting interests Contributions Conceptualisation: W.T. , R.H. f. ; data analysis: W.T. ; writing & editing: W.T. , K.S. , R.H. f All authors read and approved the final manuscript.
Extensive variation in germline de novo mutations in Poecilia reticulata

The rate of germline mutation is fundamental to evolutionary processes, as it generates the variation upon which selection acts. The guppy, Poecilia reticulata, is a model of rapid adaptation, however the relative contribution of standing genetic variation versus de novo mutation to evolution in this species remains unclear. Here, we use pedigree-based approaches to quantify and characterize de novo mutations (DNMs) in three large guppy families. Our results suggest germline mutation rate in the guppy varies substantially across individuals and families. Most DNMs are shared across multiple siblings, suggesting they arose during early zygotic development. DNMs are randomly distributed throughout the genome, and male-biased mutation rate is low, as would be expected from the short guppy generation time. Overall, our study demonstrates remarkable variation in germline mutation rate and provides insights into rapid evolution of guppies.
An intact oculomotor neural circuit in congenital blindness

In the past three decades, multiple studies revealed that congenital blindness is associated with functional and structural reorganization in early visual areas and its interaction with other neural systems. Among the most reproducible findings is the weaker connectivity between the visual and sensorimotor cortices, which in sighted individuals plays a role in eye-motor coordination. Here we demonstrate an important exception to this reorganization phenomena: we find that in congenitally blind individuals (as for normally sighted ones), spontaneous, non-controlled eye movements correlate with connectivity between visual and sensorimotor cortices. Furthermore, using time-lagged regression, we show that eye movements drive activity in the visual cortex, which subsequently drives sensorimotor activity. Remarkably, this phenomenon persists even though blind participants often exhibit unsynchronized eye movements and cannot sense or regulate their eye positions. These findings provide evidence of a preserved, “non-functional” connectivity in congenitally blind individuals and reveal a firm, hard-wired constraint on connectivity that remains immune to any reorganization phenomena produced by lack of visual experience and oculomotor control.
FLONE: fully Lorentz network embedding for inferring novel drug targets

Motivation To predict novel drug targets, graph-based machine learning methods have been widely used to capture the relationships between drug, target, and disease entities in heterogeneous biological networks. However, most of the existing methods cannot explicitly consider disease types when predicting targets. More importantly, drug-disease-target (DDT) networks could exhibit multi-relational hierarchical sub-structures with the information of biological interactive functions, but these methods, especially those based on Euclidean space embedding cannot fully utilize such topology information, which might lead to sub-optimal prediction results. We hypothesized that, by properly importing hyperbolic space embedding specifically for modeling hierarchical DDT networks, graph-based machine learning algorithms could better capture the relationships between aforementioned entities, which ultimately improves the target prediction performance. Results To test our hypothesis, we formulated the drug target prediction problem as a knowledge graph triple completion task explicitly considering disease types. To solve this problem, we proposed FLONE, a novel hyperbolic Lorentz space embedding-based method to capture the hierarchical structural information in the DDT network when inferring drug targets. We evaluated it on two different DDT networks, our experimental results showed that by introducing Lorentz space, one of the isomorphic models of hyperbolic space, FLONE generates more accurate candidate target predictions given the drug and disease than the Euclidean translation-based counterparts, which supports our hypothesis. Beyond these experiments, we also devised the hyperbolic encoders to fuse drug and target similarity information into FLONE, to make it capable to handle triples corresponding to previously unseen drugs and targets for more challenging prediction scenarios. Availability and implementation Source code and dataset information are at: https://github.com/arantir123/DDT_triple_prediction. Contact s.he@cs.bham. ac.uk
Parametric instabilities study in a shock ignition relevant regime

Inertial Confinement Fusion with Shock Ignition relies on a very strong shock created by a laser pulse at an intensity of the order of 1016W/cm2. In this context, an experimental campaign at the Prague Asterix Laser System (PALS) has been carried out within the frame of the HiPER project. Two beams have been used, the first to create an extended preformed plasma (scale length of the order of hundreds of micrometers) on a planar target, the second to generate a strong shock wave. Different diagnostics were used to study both the shock breakout at the rear surface of the target and the laserplasma coupling and parametric instabilities. This paper is focused on back-scattering analysis to measure the backreflected energy and to characterize parametric instabilities such as stimulated Brillouin and Raman scattering. Our experimental data show that parametric instabilities do not play a strong role in the laser plasma coupling. Moreover, preliminary analysis of the back reflected light from the interaction region shows that less than 5% of the total incident laser energy was back-reflected, with only a small fraction of that light was originating from parametric instabilities.
Pope Francis' Vision and Whole Child Education: Exploring the Foundational Beliefs of Cura Personalis and Accompaniment

Abstract This paper is a response to Pope Francis’ invitation to dialogue on how Catholic educators can help shape the future of the planet by focusing on integral human development. Offering a discussion of the theological foundations of two primary beliefs of whole child education—cura personalis, the care for the whole person, and accompaniment, the process of journeying with the child and their caregivers throughout the educational process—this paper advances Pope Francis’ vision for integral human development and advances a framework for practitioners and scholars to be attentive to in their practice and research.
Simulated Results for Neutron Radiations Shielding Using Monte Carlo

This report demonstrates the results of the experiment that was carried out in the Radiation Laboratory in University of Liverpool, United Kingdom. The radiation source used for the Neutron experiment was Americium Beryllium (Am/Be) put in water tank. Materials used as moderators were 2cm by 100cm by 100cm Polythene and 1cm by 100cm by 100cm. Cadmium sheet was also used to filter out thermal neutrons with energies of 0.025eV or less. The cadmium sheet was placed by the tank so that it is backed by adequate material for attenuating the capture neutrons. The detector used for the neutron experiment was 3 He-RSP41640203 Detector (100cm long x 5cm diameter).
Dynamic Multimode Response of Composite Plates to Sonic Boom and Blast Loadings

In this study, the linear and nonlinear displacement responses of an antisymmetric angle-ply composite plate subjected to sonic boom and air blast loadings are addressed. The first-order shear deformation theory is used for the governing equations so that the shear deformations can be taken into account for the responses of moderately thick plates. The von Karman type of geometric nonlinearities are considered and the rectangular composite plate is assumed to be simply supported on all edges. A multimode Galerkin approach is used to obtain the equation of motion in the time domain, and those equations are solved by a Runge-Kutta fifth-order method. The influences of a large number of parameters are investigated, such as damping ratio, geometric nonlinearity, number of modes, characteristic of the blast, plate geometry, and fiber orientation.
The geometric properties of high-Schmidt-number passive scalar iso-surfaces in turbulent boundary layers

The geometric properties are quantified for concentration iso-surfaces of a high-Schmidt-number passive scalar field produced by an iso-kinetic source with an initial finite characteristic length scale released into the inertial layer of fully developed open-channel-flow turbulent boundary layers. The coverage dimension and other measures of two-dimensional transects of the passive scalar iso-surfaces are found to be scale dependent. The coverage dimension is around 1.0 at the order of the Batchelor length scale and based on our data increases in a universal manner to reach a local maximum at a length scale around the Kolmogorov scale. We introduce a new parameter called the coverage length underestimate, which demonstrates universal behaviour in the viscous–convective regime for these data and hence is a potentially useful practical tool for many mixing applications. At larger scales (in the inertial–convective regime), the fractal geometry measures are dependent on the Reynolds number, injection length scale, and concentration threshold of the iso-surfaces. Finally, the lacunarity of the iso-surface structure shows that the instantaneous scalar field is most inhomogenous around the length scale corresponding to the Kolmogorov scale.
The Σ D relation for supernova remnants in nearby galaxies

This paper examines relations between the radio surface brightness £ and the diameter D (also known as Σ-D relations) for a sample of extragalactic supernova remnants (SNRs) as constructed from a combination of published data and data from our own surveys. Our sample of extragalactic SNRs is the largest ever devised for the purpose of analyzing Σ-D relations. The main results of this paper may be summarized as follows: (i) the empirical relations for SNRs in 10 of the 11 nearby galaxies studied have the approximately trivial Σ D -2 form, therefore limiting their interpretation as physically meaningful relations. In addition, these relations are subject to selection effects rendering them even less useful. Further Monte Carlo simulations suggest that the effect of survey sensitivity has the opposite effect of volume selection (e.g. Malinquist bias, a volume selection effect that shapes the Galactic sample) by tending to flatten the slopes toward a trivial relation. In this case, the true slopes may be steeper than the observed slopes; (ii) compact M 82 SNRs appear to follow a uniquely different Σ-D relation in comparison to the larger, older SNRs in the other 10 galaxies. Monte Carlo simulations suggest that the probability of this difference arising by chance is 1% to 10%, depending on what is assumed regarding the underlying SNR population; (iii) three candidate hypernova remnants were identified in our sample of 11 nearby galaxies.
Radio emissions from the planets Earth, Jupiter, and Saturn

We review the major observed and inferred features of the three currently known nonthermal radio planets, the earth, Jupiter, and Saturn. Several of the properties of the three planets are quite similar, suggesting that the same processes take place at all three. In addition, Jupiter has two radio components in the low-frequency range which may not have analogs at the earth or Saturn. We speculate on the expected addition of Uranus to this list of radio planets.