Miracle-dz/UniFAR-FTUs huggingface dataset:
Scoring protein function predictions

Due to advances in genome sequencing technology, more and more fully sequenced genomes are becoming available at an ever accelerating rate. However, the relationship between genomic sequence and biological function is complex, and the key to understanding it, are the gene products [11]. One important class of gene products are the proteins. Databases like the UniProtKB (www.uniprot.org) strive to archive all the available information about proteins, such as their amino acid sequence and annotations that describe their function. They use the structured vocabulary of the Gene Ontology [1] to describe gene products in terms of their molecular function, biological process and cellular location. Since these databases are still incomplete [12], and experimentally determining the function of uncharacterized proteins is very expensive, prediction algorithms are used to infer new annotations for poorly and unannotated proteins from the existing knowledge base ([3], [8]). These inferred annotations can then be used as a hypothesis for further work. Based on the assumption that such an algorithm might have more supporting evidence for some predictions than for others, it is logical to conclude that some predictions are more likely to be correct than others. Since manual verification of such predictions can be a very time-consuming process, it is useful to be able to differentiate between them. The different levels of confidence an algorithm has in its predictions can be expressed by confidence scores.
Feature Engineering and a Proposed Decision-Support System for Systematic Reviewers of Medical Evidence

Objectives Evidence-based medicine depends on the timely synthesis of research findings. An important source of synthesized evidence resides in systematic reviews. However, a bottleneck in review production involves dual screening of citations with titles and abstracts to find eligible studies. For this research, we tested the effect of various kinds of textual information (features) on performance of a machine learning classifier. Based on our findings, we propose an automated system to reduce screeing burden, as well as offer quality assurance. Methods We built a database of citations from 5 systematic reviews that varied with respect to domain, topic, and sponsor. Consensus judgments regarding eligibility were inferred from published reports. We extracted 5 feature sets from citations: alphabetic, alphanumeric+, indexing, features mapped to concepts in systematic reviews, and topic models. To simulate a two-person team, we divided the data into random halves. We optimized the parameters of a Bayesian classifier, then trained and tested models on alternate data halves. Overall, we conducted 50 independent tests. Results All tests of summary performance (mean F3) surpassed the corresponding baseline, P<0.0001. The ranks for mean F3, precision, and classification error were statistically different across feature sets averaged over reviews; P-values for Friedman's test were . 045, . 002, and . 002, respectively. Differences in ranks for mean recall were not statistically significant. Alphanumeric+ features were associated with best performance; mean reduction in screening burden for this feature type ranged from 88% to 98% for the second pass through citations and from 38% to 48% overall. Conclusions A computer-assisted, decision support system based on our methods could substantially reduce the burden of screening citations for systematic review teams and solo reviewers. Additionally, such a system could deliver quality assurance both by confirming concordant decisions and by naming studies associated with discordant decisions for further consideration.
Kernel Based Functional Gene Grouping

During the last years, high throughput experiments have become very popular. During the analysis of such data the need for a functional grouping of genes arises. In this paper, we propose grouping genes according to their biological function by means of kernel functions, which are similarity measures having special mathematical properties and play a crucial role e.g. in SVM classification. Thereby our kernel functions rely on functional information on the genes provided by Gene Ontology annotation. We investigate and compare several provably symmetric, positive semidefinite kernel functions in combination with spectral clustering, dual k-means and average linkage and demonstrate that our approach leads to good clustering results.
EDUHEM 2018 VIII International conference on intercultural education and International conference on transcultural health: THE VALUE OF EDUCATION AND HEALTH FOR A GLOBAL, TRANSCULTURAL WORLD PILOT STUDY INTO THE EXECUTIVE FUNCTIONS OF CHILDREN AGED 8-9 BAPNE METHOD

This study aims to generate interest in matters regarding executive functions in children in primary education, and to increase our understanding as to whether the development of these functions in pupils in their third year of primary school can be improved using rhythm and body percussion through the BAPNE method. The study was based on a research protocol with a total of n=51 subjects, divided between an experimental group and a control group in the province of Valencia (Spain). The two groups were similar in nature and were of average sociocultural and economic levels. The methodology used was experimental and used measurements and collection of preand post-test sample measurements, before and after the BAPNE method was used with the experimental group. Subsequently, statistical analysis was carried out which was factorial in design and analysed the measurements taken before and after the intervention. The results obtained show that BAPNE sessions has a significant positive effects on the executive functions. © 2019 Published by Future Academy www.FutureAcademy. org.UK
Evolving diverse Ms. Pac-Man playing agents using genetic programming

This paper uses genetic programming (GP) to evolve a variety of reactive agents for a simulated version of the classic arcade game Ms. Pac-Man. A diverse set of behaviours were evolved using the same GP setup in three different versions of the game. The results show that GP is able to evolve controllers that are well-matched to the game used for evolution and, in some cases, also generalise well to previously unseen mazes. For comparison purposes, we also designed a controller manually using the same function set as GP. GP was able to significantly outperform this hand-designed controller. The best evolved controllers are competitive with the best reactive controllers reported for this problem.
Understanding the context of network traffic alerts

For the protection of critical infrastructures against complex virus attacks, automated network traffic analysis and deep packet inspection are unavoidable. However, even with the use of network intrusion detection systems, the number of alerts is still too large to analyze manually. In addition, the discovery of domain-specific multi stage viruses (e.g., Advanced Persistent Threats) are typically not captured by a single alert. The result is that security experts are overloaded with low-level technical alerts where they must look for the presence of an APT. In this paper we propose an alert-oriented visual analytics approach for the exploration of network traffic content in multiple contexts. In our approach CoNTA (Contextual analysis of Network Traffic Alerts), experts are supported to discover threats in large alert collections through interactive exploration using selections and attributes of interest. Tight integration between machine learning and visualization enables experts to quickly drill down into the alert collection and report false alerts back to the intrusion detection system. Finally, we show the effectiveness of the approach by applying it on real world and artificial data sets.
chapter three Role of life-history trade-offs in the equalization and differentiation of tropical tree species

Introduction Early observations of the remarkable diversity in life-history and morphology exhibited by tropical plants have strongly influenced our views on how these species coexist. Initially plants were classified according to size and life form, and subsequently into ecological species groups that recognized the importance of variation in light availability associated with the forest growth cycle (Richards 1952; Swaine & Whitmore 1988; Burslem & Swaine 2002). These ecological species groups reflect the existence of adaptive strategies that trees adopt during regeneration, and are the consequence of unavoidable trade-offs among suites of traits influencing growth, survival and fecundity. In tropical forests, light availability has typically been identified as the primary limiting condition to growth. Adaptive strategies influencing the colonization of canopy openings and the capture or use of light are manifested as combinations of traits influencing dispersal, germination, seedling establishment, and allocation of resources to growth, storage and defence (van Steenis 1958; Budowski 1965; Whitmore 1975; Bazzaz & Pickett 1980; Coley et al. 1985; Kitajima 1994). This review addresses two issues: first, the extent to which individual trade-offs linking these traits can lead to the differentiation of tree species accounting for interspecific differences in the requirements for light and other resources; and second, the extent to which the action of multiple opposing trade-offs has a contrary effect of equalizing overall recruitment success so that no particular trait combination provides a recruitment advantage. Classical views of species coexistence are founded upon niche differentiation as the mechanism preventing competitive exclusion, with limiting similarity in niche requirements providing an upper bound on local species richness (Hutchinson 1959; MacArthur & Levins 1967; Grubb 1977). Interspecific competition for limiting resources might be expected to select for adaptations that
VIRAL ANTIBODIES IN BLOOD IN OBSESSIVE COMPULSIVE DISORDER

IgG viral antibodies for herpes simplex virus -1, varicella zoster virus, cytomegalovirus, measles and mumps were studied in 76 subjects with obsessive compulsive disorder and compared with a control population. There was a significantly higher titre for some of the antibodies, most specifically for herpes simplex virus type 1 and mumps. This suggests a possible role for these viral infections in the pathogenesis of obsessive compulsive disorder.
Suggestion analysis for food recipe improvement

Suggestion analysis for food recipe improvement is to identify helpful suggestions from user comments to improve the recipes. Consequently, user comments about food recipes are classified into two groups that are comments with suggestions or without suggestions. The word information from modified lexicons and created rules for interpreting meaning are applied to analyze those comments or opinions. Natural language processing and text analysis are included in the proposed analysis technique. The automate comment analysis can help both users to choose the preferred food recipes and recipe authors to develop their own creative recipes. To summarize food recipe improvement, the user comments are collected and grouped into suggestion comments and other comments. The evaluation of proposed suggestion analysis shows that the accuracy and precision of comment classification are more than 70%.
Relations between Risk for Sleep-Disordered Breathing, Cognitive and Executive Functioning, and Academic Performance in Six-Year-Olds

ABSTRACT Research Findings: Optimal sleep is important for children’s learning and development. Sleep disordered breathing (SDB) refers to a spectrum of conditions from simple snoring to obstructive sleep apnea that is common in childhood and interrupts sleep. We examined pathways between SDB and academic performance of children (N = 163, M age = 6.2 years) one year after school entry. Measures included parent questionnaire and clinical assessment of SDB, standardized tests and rating scales of cognitive and executive functioning, researcher-administered literacy and numeracy tasks, and teacher-reported academic performance. Structural equation modeling (SEM) revealed direct and indirect paths between SDB and poorer academic performance. In indirect models, children’s SDB severity score was significantly linked to poorer executive functioning (β = .38, p < .01) and negatively associated with nonverbal reasoning (β = −.21, p < .01). Poorer executive functioning, in turn, was negatively related to the verbal composite (β = −.61, p < .01), with verbal and nonverbal composites associated with academic performance (βs = .56, .27, respectively, p’s < .01). Practice or Policy: These findings point to the need for attention to SDB and its links to potential cognitive sequelae across early development. Practitioners equipped with knowledge of SDB symptoms can facilitate referral for appropriate consultation and evaluation.
Heuristic Assignment of CPDs for Probabilistic Inference in Junction Trees

Extensive research has been done for efficient computation of probabilistic queries posed to Bayesian networks (BNs). One popular architecture for exact inference on BNs is the Junction Tree (JT) based architecture. Among all variations developed, HUGIN is the most efficient JT-based architecture. The Global Propagation (GP) method used in the HUGIN architecture is arguably one of the best methods for probabilistic inference in BNs. Before the propagation, initialization is done to obtain the potential for each cluster in the JT. Then with the GP method, each cluster potential is transformed into cluster marginal through passing messages with its neighboring clusters. Improvements have been proposed to make the message propagation more efficient. Still, the GP method can be very slow for dense networks. As BNs are applied to larger, more complex and realistic applications, the design of more efficient inference algorithm has become increasingly important. Towards this goal, in this paper, we present a heuristic for initialization that avoids unnecessary message passing among clusters of a JT, therefore improving the performance of the architecture by passing fewer messages.
Performance analysis of multi-cell MMSE based receivers in MU-MIMO systems with very large antenna arrays

Uplink minimum mean-square-error (MMSE) detection in multi-user multiple-input multiple-output (MU-MIMO) systems with multi-cell setup is investigated in this paper. We first derive a closed form lower bound on the ergodic achievable uplink rate for each user, assuming perfect channel state information available at the base station.We further analyze and compare the asymptotic performance of an uplink with multi-cell MMSE based detection to its special case of single-cell MMSE detection. The analysis results indicate that the use of large number of base station antennas in multi-cell MU-MIMO systems leads to an increase of energy and spectral efficiency. Our analysis is based on results from random matrix theory and is verified by the simulations.
Multimodal gesture recognition for robot musical accompaniment ∗

Listening and watching are important skills for accompanists to play in time with fellow musicians. By detecting subtle visual cues and listening to other players, musicians can start together, stop together, and follow faster or slower changes in tempo. In this paper, we formalize this non-verbal language for the case of flutists, and describe how our thereminist robot accompanist system detects them. Initial experiments show over 83% detection rates for our 3 types of visual cues. Additionally, by coupling visual cues and acoustic beat detection, the robot can extract tempo in less than half a second.
Potential of Polycyclic Aromatic Hydrocarbon-Degrading Bacterial Isolates to Contribute to Soil Fertility

Restoration of polycyclic aromatic hydrocarbon- (PAH-) polluted sites is presently a major challenge in agroforestry. Consequently, microorganisms with PAH-degradation ability and soil fertility improvement attributes are sought after in order to achieve sustainable remediation of polluted sites. This study isolated PAH-degrading bacteria from enriched cultures of spent automobile engine-oil polluted soil. Isolates' partial 16S rRNA genes were sequenced and taxonomically classified. Isolates were further screened for their soil fertility attributes such as phosphate solubilization, atmospheric nitrogen fixation, and indoleacetic acid (IAA) production. A total of 44 isolates were obtained and belong to the genera Acinetobacter, Arthrobacter, Bacillus, Flavobacterium, Microbacterium, Ochrobactrum, Pseudomonas, Pseudoxanthomonas, Rhodococcus, and Stenotrophomonas. Data analysed by principal component analysis showed the Bacillus and Ochrobactrum isolates displayed outstanding IAA production. Generalized linear modelling statistical approaches were applied to evaluate the contribution of the four most represented genera (Pseudomonas, Acinetobacter, Arthrobacter, and Rhodococcus) to soil fertility. The Pseudomonas isolates were the most promising in all three soil fertility enhancement traits evaluated and all isolates showed potential for one or more of the attributes evaluated. These findings demonstrate a clear potential of the isolates to participate in restorative bioremediation of polluted soil, which will enhance sustainable agricultural production and environmental protection.
SATzilla : An Algorithm Portfolio for SAT ∗

Inspired by the success of recent work in the constraint programming community on typical-case complexity, in [3] we developed a new methodology for using machine learning to study empirical hardness of hard problems on realistic distributions. In [2] we demonstrated that this new approach can be used to construct practical algorithm portfolios. In brief, the fact that algorithms for solving NP-hard problems are often relatively uncorrelated means that it is possible for a portfolio to outperform all of its constituent algorithms. However, such uncorrelation is a knife that cuts both ways: a portfolio that makes bad choices among its constituent algorithms will often have much worse performance than any of its constituent algorithms. Our methodology can be outlined as follows:
Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke

After brain lesions caused by tumors or stroke, or after lasting loss of input (deafferentation), inter- and intra-regional brain networks respond with complex changes in topology. Not only areas directly affected by the lesion but also regions remote from the lesion may alter their connectivity—a phenomenon known as diaschisis. Changes in network topology after brain lesions can lead to cognitive decline and increasing functional disability. However, the principles governing changes in network topology are poorly understood. Here, we investigated whether homeostatic structural plasticity can account for changes in network topology after deafferentation and brain lesions. Homeostatic structural plasticity postulates that neurons aim to maintain a desired level of electrical activity by deleting synapses when neuronal activity is too high and by providing new synaptic contacts when activity is too low. Using our Model of Structural Plasticity, we explored how local changes in connectivity induced by a focal loss of input affected global network topology. In accordance with experimental and clinical data, we found that after partial deafferentation, the network as a whole became more random, although it maintained its small-world topology, while deafferentated neurons increased their betweenness centrality as they rewired and returned to the homeostatic range of activity. Furthermore, deafferentated neurons increased their global but decreased their local efficiency and got longer tailed degree distributions, indicating the emergence of hub neurons. Together, our results suggest that homeostatic structural plasticity may be an important driving force for lesion-induced network reorganization and that the increase in betweenness centrality of deafferentated areas may hold as a biomarker for brain repair.
Mathematical Modelling as Problem Solving for Children in the Singapore Mathematics Classrooms.

The newly revised mathematics curriculum in Singapore has recently factored Applications and Modelling to be part of the teaching and learning of mathematics. Its implication is that even children should now be involved in works of mathematical modelling. However, to be able to implement modelling activities in the primary mathematics classroom, it is at the outset needful for teachers to have an understanding of what it is, how it is different from conventional pedagogies, and what the learning outcomes look like. Since the goal of the Singapore mathematics curriculum is problem solving, I discuss mathematical modelling as problem solving and examine the mathematical reasoning of two groups of high-ability Primary 6 students in their engagement of a model-eliciting task based on a classroom research. Comparisons of the two groups within the modelling stages show that primary school students were able to reason mathematically towards constructing models.
Independent Component Analysis in Spiking Neurons

Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.
Detect Stepping-Stone Insider Attacks by Network Traffic Mining and Dynamic Programming

Stepping-stone is the most popular way used to attack other computers. Some insiders use stepping-stone to launch their attacks pretending to be outsiders. In this paper, we propose a novel algorithm to detect stepping-stone insider attacks through comparing outgoing and incoming connections. We modify the existing packet matching algorithm by introducing sliding window to make the algorithm more efficient and practicable. The algorithm to compute the similarity between two time-pair sequences through finding the longest common sub sequence is proposed. The stepping-stone insider attacks detection algorithm is easy to be implemented and to use since there is no threshold needed. The experimental results showed the effectiveness of the algorithm to detect stepping-stone insider attacks.
Comparison of the Transmission Potential of Two Genetically Distinct Sindbis Viruses After Oral Infection of Aedes aegypti (Diptera: Culicidae)

Abstract Within mosquitoes, arboviruses encounter barriers to infection and dissemination that are critical determinants of vector competence. The molecular mechanisms responsible for these barriers have yet to be elucidated. The prototype Sindbis (SIN) strain, AR339, and viruses derived from this strain, such as TR339 virus, have limited infection and transmission potential in the medically important arthropod vector, Aedes aegypti (L.). However, the Malaysian SIN virus strain, MRE16, disseminates in nearly 100% of Ae. aegypti 14 d after oral infection. Here, we compare the spatial and temporal infection patterns of MRE16 and TR339 viruses in Ae. aegypti. The results indicate that a midgut escape barrier is primarily responsible for the significantly lower dissemination and transmission potentials observed after oral infection with TR339 virus. MRE16 and TR339 viruses now represent a well-characterized model system for the further study of virus determinants of vector infection, particularly determinants affecting the midgut escape barrier in Ae. aegypti.