Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular markers for the assessment of chicken biodiversity

Three main aspects of chicken biodiversity are dealt within this report: (a) cluster analysis based on autosomal microsatellites, (b) microsatellites on the sex chromosomes, and (c) SNP-based biodiversity. (a) Cluster analysis of autosomal microsatellites: We used 29 microsatellites to genotype 2000 chickens randomly selected from 65 different populations representing various chicken types and various geographical regions. The computer program Structure placed the 65 populations into clusters that are in agreement with their geographic origin and breed history. Only at two predefined clusters, there is little admixture between non-commercial populations originating from Asia and those from Europe. In contrast, commercial broilers and brown egg layers appeared as admixed populations of these two main gene pools. Increasing the number of clusters resulted in generation of specific clusters of commercial lines, having very low admixture with other clusters. In addition, we identified seven mixed populations, each of which shared portions of their genome with several other genetic clusters. (b) Microsatellites on the sex chromosomes: We predicted 173 potential microsatellites on chromosome W by in-silico analysis of the chicken genome assembly (version WASHUC1). Twenty five microsatellites of the highest sequence quality were tested in the lab for gender specificity. Unexpectedly, PCR products were generated in both sexes. Moreover, 14 selected microsatellites were mapped (using the East Lansing reference panel) and in all cases, the “W specific” microsatellites were mapped to chromosome Z and except for one locus, to the same ∼6 cM region. We conclude that the draft assembly for chromosome W is quite inaccurate. (c) SNP-based biodiversity: Ten distinct chicken breeds were genotyped at 145 single nucleotide polymorphisms (SNPs) located at 14 random DNA fragments and twenty five, each from different and unlinked genes. Microsatellite genotypes of the same ten breeds were used for comparison. Applying bootstrap values as the criterion for tree's reliability, we found that: (1) increasing the number of SNPs had a higher impact on the reliability of the analysis than increasing the number of individuals per population, and (2) the bootstrap values of phylogenetic un-rooted trees based on microsatellites were relatively low.
Spatial patterns in pond invertebrate communities: separating environmental and distance effects

1. The nature and extent of spatial pattern in communities has important implications for their dynamics and conservation. Previous studies of pond ecosystems, over relatively small spatial scales, have found little evidence of spatial autocorrelation of community composition. Patterns in community composition over greater spatial distances have not been documented. 2. Here, data on macroinvertebrate communities and physico-chemical characteristics of 102 ponds over a 60 x 6.0km area of Oxfordshire, UK, were used to examine evidence for spatial autocorrelation in community composition and to separate the effects of environmental similarity and physical distance on community similarity. 3. Overall similarity between communities was low, but showed significant positive spatial autocorrelation. There was evidence for both environmental and physical distance effects on spatial autocorrelation of community similarity. Community similarity was negatively related to differences in environmental conditions, but effects were only significant for large environmental differences. 4. When environmental effects were accounted for, there was significant positive spatial autocorrelation of community composition over inter-site distances of up to 13 km. These results suggest that interactions between pond sites, potentially through dispersal, are evident over larger spatial scales than has previously been appreciated, and emphasize the need to consider spatial issues when developing strategies for pond conservation.
A New Instrument for Multicultural Studies

Institutions of higher education want to diversify their learning climates, and many offer courses in multiculturalism, yet these courses still do not meet the needs of attitudinal change. A new instrument was developed, the Munroe Multicultural Attitude Scale Questionnaire (MASQUE), that was theoretically based in Banks’s transformative approach, which specifically measured multicultural attitudes. Psychometric properties of the instrument’s scores are discussed. Exploratory factor analysis supported the “know,” “act,” and “care” domains of Banks’s transformative approach, and the instrument was sensitive to detecting group differences on several demographic variables. The MASQUE’s potential uses for affecting multicultural research and instruction are discussed.
Nest Site Selection and Breeding Success in Three Turdus Thrush Species Coexisting in an Urban Environment

Abstract The process of establishing breeding populations of birds in small towns of Central Europe provides a unique opportunity to study them during synurbization in statu nascendi. Over the years 2006–2011, we investigated the breeding ecology of three coexisting thrush species Turdus spp. in the urban habitats of the town of Bardejov (NE Slovakia). We studied nest distribution, nest predation in relation to nest placement and the breeding success of the Common Blackbird T. merula, Fieldfare T. pilaris and Song Thrush T. philomelos. The study species differed significantly in terms of microhabitat characteristics and vertical spatial distribution, expressed as the nest location height (Blackbird < Song Thrush < Fieldfare), the distance from the town centre (Fieldfare < Song Thrush < Blackbird), the distance from the nest tree to human paths and buildings (Blackbird < Song Thrush < Fieldfare) and the average distance between breeding conspecific pairs (Fieldfare < Blackbird < Song Thrush). We also found significant differences in nesting microhabitats (conifers, deciduous trees and shrubs) usage (breeding in conifers: Song Thrush < Blackbird < Fieldfare). On the other hand, no significant differences were found in breeding success and predation between species. A major factor affecting the predation rate was the distance between nests and the distance to human paths and buildings, and with Fieldfares and Common Blackbirds also the height of trees and the distance to the town centre. Our results suggest that ecological segregation among closely related species can also be common in a changed, urban environment.
Identification and determination antimicrobial resistance pattern of Haemophilus influenza isolated from patients with otitis media

Background: Otitis media is one of the most infections diseases in children. Approximately 85% of children will have otitis media at least once by the age of 3 months, and nearly 50% of children would have more than three times. The objective of the this research was to detect Haemophilus influenza the most cause of otitis media in children to determine the antibiotic susceptibility pattern's of this organism. Methods : In total 102 specimens were collected during 6 months period from patients with otitis media referred to Amir Alam hospitalin Tehran. Specimens were evaluated for Haemophilus influenzae by microscopic examination and culture. The antibiotic susceptibility test was performed by disk diffusion method of Kirby-Bauer and sensitivity rate of tested antibiotics were assessed by evaluation diameter of the growth inhibitory zone. Results : Out of 102 tested specimens, 7 (6.9%) were identified as Haemophilus influenzae. The rate of sensitivity of isolated Haemophilus influenzae to different antibiotics were trimethoprim (100%), vancomycin (85%), streptomycin (57%), tetracycline (57%), and doxycycline (56%),and the rate of resistance to gentamycin (85%), carbenicillin (70%) and cefixime (70%), clindamycin (70%) amikacin (57%) amoxicillin (57%). Conclusion : With respect to new resistance species especially to clinidomycin, amikiacin and ampicilin ,it is recommended to perform antibiotic susceptibility tests before treatment.
Online Boosting with Bandit Feedback

We consider the problem of online boosting for regression tasks, when only limited information is available to the learner. We give an efficient regret minimization method that has two implications: an online boosting algorithm with noisy multi-point bandit feedback, and a new projection-free online convex optimization algorithm with stochastic gradient, that improves state-of-the-art guarantees in terms of efficiency.
Sensor Clusterization in D-optimal Design in Infinite Dimensional Bayesian Inverse Problems

We investigate the problem of sensor clusterization in optimal experimental design for infinite-dimensional Bayesian inverse problems. We suggest an analytically tractable model for such designs and reason how it may lead to sensor clusterization in the case of iid measurement noise. We also show that in the case of spatially correlated measurement error clusterization does not occur. As a part of the analysis we prove a matrix determinant lemma analog in infinite dimensions, as well as a lemma for calculating derivatives of $\log \det$ of operators.
Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learning

Patients with diabetes who are self-monitoring have to decide right before each meal how much insulin they should take. A standard bolus advisor exists, but has never actually been proven to be optimal in any sense. We challenged this rule applying Reinforcement Learning techniques on data simulated with T1DM, an FDA-approved simulator developed by Kovatchev et al. modeling the gluco-insulin interaction. Results show that the optimal bolus rule is fairly different from the standard bolus advisor, and if followed can actually avoid hypoglycemia episodes.
Chandra X-ray study confirms that the magnetic standard Ap star KQ Vel hosts a neutron star companion

Context. KQ Vel is a peculiar A0p star with a strong surface magnetic field of about 7.5 kG. It has a slow rotational period of nearly 8 years. Bailey et al. (A&A, 575, A115) detected a binary companion of uncertain nature and suggested that it might be a neutron star or a black hole. Aims. We analyze X-ray data obtained by the Chandra telescope to ascertain information about the stellar magnetic field and/or interaction between the star and its companion. Methods. We confirm previous X-ray detections of KQ Vel with a relatively high X-ray luminosity of 2 × 1030 erg s−1. The X-ray spectra suggest the presence of hot gas at > 20 MK and, possibly, of a nonthermal component. The X-ray light curves are variable, but data with better quality are needed to determine a periodicity, if any. Results. We interpret the X-ray spectra as a combination of two components: the nonthermal emission arising from the aurora on the A0p star, and the hot thermal plasma filling the extended shell that surrounds the “propelling” neutron star. Conclusions. We explore various alternatives, but a hybrid model involving the stellar magnetosphere along with a hot shell around the propelling neutron star seems most plausible. We speculate that KQ Vel was originally a triple system and that the Ap star is a merger product. We conclude that KQ Vel is an intermediate-mass binary consisting of a strongly magnetic main-sequence star and a neutron star.
An integral-based spectral method for inextensible slender fibers in Stokes flow

Every animal cell is filled with a cytoskeleton, a dynamic gel made of inextensible fibers, such as microtubules, actin fibers, and intermediate filaments, all suspended in a viscous fluid. Numerical simulation of this gel is challenging because the fiber aspect ratios can be as large as $10^4$. We describe a new method for rapidly computing the dynamics of inextensible slender filaments in periodically-sheared Stokes flow. The dynamics of the filaments are governed by a nonlocal slender body theory which we partially reformulate in terms of the Rotne-Prager-Yamakawa hydrodynamic tensor. To enforce inextensibility, we parameterize the space of inextensible fiber motions and strictly confine the dynamics to the manifold of inextensible configurations. To do this, we introduce a set of Lagrange multipliers for the tensile force densities on the filaments and impose the constraint of no virtual work in an $L^2$ weak sense. We augment this approach with a spectral discretization of the local and nonlocal slender body theory operators which is linear in the number of unknowns and gives improved spatial accuracy over approaches based on solving a line tension equation. For dynamics, we develop a second-order semi-implicit temporal integrator which requires at most a few evaluations of nonlocal hydrodynamics and a few block diagonal linear solves per time step. After demonstrating the improved accuracy and robustness of our approach through numerical examples, we apply our formulation to a permanently cross-linked actin mesh in a background oscillatory shear flow. We observe a characteristic frequency at which the network transitions from quasi-static, primarily elastic behavior to dynamic, primarily viscous behavior. We find that far-field hydrodynamics increases the viscous modulus by as much as 25%, even for semi-dilute fiber suspensions.
Effect of degree of soil disturbance on mycorrhizal colonization and phosphorus absorption by maize in growth chamber and field experiments

SUMMARY Prior studies have established that phosphorus absorption by maize from undisturbed soil is greater than from soil which has been disturbed since the previous period of growth. Those experiments were conducted under controlled conditions and with only two disturbance treatments, namely disturbance of the soil by hand sufficient that it will pass a 5 mm sieve, and no disturbance at all. We present here two experiments in which additional treatments of intermediate degrees of disturbance are included. In a growth chamber experiment, cutting the soil into 1, 2 or 4 cm cubes reduced the phosphorus absorption and shoot dry matter, but the reduction was not as great as that after hand disturbance of the soil. In a field experiment, the phosphorus absorption and shoot dry matter found in a conventional tillage and no-till treatments were less than in hand-planted no-till plots, but greater than with severe soil disturbance. In both experiments increases in shoot dry mass and shoot phosphorus concentration in the less disturbed treatments were not accompanied by an increase in mycorrhizal colonization. This is inconsistent with earlier studies in which colonization was greater in undisturbed soil. Since it has now been demonstrated that increased colonization is not a necessary component in the process whereby lack of disturbance results in improved plant performance, the role of mycorrhizas in this effect should be reconsidered. If mycorrhizas are important in this respect then this can only be by means of changes in the external mycelium. However, without experimental confirmation this interpretation is speculative.
Causal analysis of Covid-19 Spread in Germany

In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policies that were applied by the different federal states. We propose and prove a new theorem for a causal feature selection method for time series data, robust to latent confounders, which we subsequently apply on Covid-19 case numbers. We present findings about the spread of the virus in Germany and the causal impact of restriction measures, discussing the role of various policies in containing the spread. Since our results are based on rather limited target time series (only the numbers of reported cases), care should be exercised in interpreting them. However, it is encouraging that already such limited data seems to contain causal signals. This suggests that as more data becomes available, our causal approach may contribute towards meaningful causal analysis of political interventions on the development of Covid-19, and thus also towards the development of rational and data-driven methodologies for choosing interventions.
On the orbital evolution of binaries with circumbinary discs

Circumbinary discs are generally thought to take up angular momentum and energy from the binary orbit over time through gravitational torques that are mediated by orbital resonances. This process leads to the shrinkage of the binary orbit over time, and it is important in a variety of astrophysical contexts including the orbital evolution of stellar binaries, the migration of planets in protoplanetary discs, and the evolution of black hole binaries (stellar and supermassive). The merger of compact object binaries provides a source of gravitational waves in the Universe. Recently, several groups have reported numerical simulations of circumbinary discs that yield the opposite result, finding that the binary expands with time. Here we argue that this result is primarily due to the choice of simulation parameters, made for numerical reasons, which differ from realistic disc parameters in many cases. We provide physical arguments, and then demonstrate with 3D hydrodynamical simulations, that thick (high pressure, high viscosity) discs drive sufficient accretion of high angular momentum material to force binary expansion, while in the more realistic case of thin (low pressure, low viscosity) discs there is less accretion and the binary shrinks. In the latter case, tides, which generally transfer angular momentum and energy from the more rapidly rotating object (the binary) to the less rapidly rotating object (the disc), are the dominant driver of disc-binary evolution. This causes the binary to shrink. We therefore conclude that for common circumbinary disc parameters, binaries with non-extreme mass ratios are expected to shrink over time. Expansion of the binary can occur if the disc viscosity is unusually high, which may occur in the very thick discs encountered in, for example, circumplanetary discs, super-Eddington AGN, or the outer regions of passive protostellar discs that are heated by the central protostar. We also provide discussion of the impact that some simplifications to the problem, that are prevalent in the literature and usually made for numerical convenience, have on the disc-binary evolution.
Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy. Method This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. Conclusion As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.
Bot Development for Social Engineering Attacks on Twitter

A series of bots performing simulated social engineering attacks using phishing in the Twitter platform was developed to identify potentially unsafe user behavior. In this work different bot versions were developed to collect feedback data after stimuli directed to 1,287 twitter accounts for 38 consecutive days. The results were not conclusive about the existence of preceptors for unsafe behavior, but we conclude that despite Twiter's security this kind of attack is still feasible.
Deep Active Learning by Model Interpretability

Recent successes of Deep Neural Networks (DNNs) in a variety of research tasks, however, heavily rely on the large amounts of labeled samples. This may require considerable annotation cost in real-world applications. Fortunately, active learning is a promising methodology to train high-performing model with minimal annotation cost. In the deep learning context, the critical question of active learning is how to precisely identify the informativeness of samples for DNN. In this paper, inspired by piece-wise linear interpretability in DNN, we introduce the linearly separable regions of samples to the problem of active learning, and propose a novel Deep Active learning approach by Model Interpretability (DAMI). To keep the maximal representativeness of the entire unlabeled data, DAMI tries to select and label samples on different linearly separable regions introduced by the piece-wise linear interpretability in DNN. We focus on modeling Multi-Layer Perception (MLP) for modeling tabular data. Specifically, we use the local piece-wise interpretation in MLP as the representation of each sample, and directly run K-Center clustering to select and label samples. To be noted, this whole process of DAMI does not require any hyper-parameters to tune manually. To verify the effectiveness of our approach, extensive experiments have been conducted on several tabular datasets. The experimental results demonstrate that DAMI constantly outperforms several state-of-the-art compared approaches.
Clustering of Social Media Messages for Humanitarian Aid Response during Crisis

Social media has quickly grown into an essential tool for people to communicate and express their needs during crisis events. Prior work in analyzing social media data for crisis management has focused primarily on automatically identifying actionable (or, informative) crisis-related messages. In this work, we show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness and encourage the field to adopt them for their research or even deployment. We also extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well.