Miracle-dz/UniFAR-FTUs huggingface dataset:
Main Molecular Markers of Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) represents a worldwide problem, with a poor prognosis. Despite advances in the OSCC therapy, it remains as a disease of later diagnosis and high level of recurrence, what deeply implicates in the life quality. Therefore, it is necessary to define molecular markers to allow early diagnosis, and to identify new therapeutic targets. In this review, we have collected scientific data on main OSCC biomarkers that have been published since 2004. They were classified into different categories, according to their main role: cell proliferation/cell cycle regulators, apoptosis, angiogenesis, and cellular adhesion/metastasis.
Generalized Pearson correlation squares for capturing mixtures of bivariate linear dependences

Motivated by the pressing needs for capturing complex but interpretable variable relationships in scientific research, here we generalize the squared Pearson correlation to capture a mixture of linear dependences between two real-valued random variables, with or without an index variable that specifies the line memberships. We construct generalized Pearson correlation squares by focusing on three aspects: the exchangeability of the two variables, the independence of parametric model assumptions, and the availability of population-level parameters. For the computation of the generalized Pearson correlation square from a sample without line-membership specification, we develop a K-lines clustering algorithm, where K, the number of lines, can be chosen in a data-adaptive way. With our defined population-level generalized Pearson correlation squares, we derive the asymptotic distributions of the sample-level statistics to enable efficient statistical inference. Simulation studies verify the theoretical results and compare the generalized Pearson correlation squares with other widely-used association measures in terms of power. Gene expression data analysis demonstrates the effectiveness of the generalized Pearson correlation squares in capturing interpretable gene-gene relationships missed by other measures. We implement the estimation and inference procedures in an R package gR2.
Incidence of Verticillium and Plant Development in Pepper as Affected by the Bioactivator Harpin

There is a group of compounds containing proteins that activate responses to stress in plants. One of these proteins is harpin, used for an alternative control of insects and fungi, as well as to increase yield and quality for vegetable production. Harpin was applied to foliage to study effects on induced resistance in pepper (Capsicum annuum L.), cvs. Demre and Yalova Charleston, inoculated with Verticillium dahliae Kleb. , a pathogen of pepper. Plants grown in a greenhouse were treated with three applications of harpin at 50 g/100 L in water. Changes in stem dry weight, root dry weight, leaf water content and leaves/plant, total chlorophyll content in leaves, leaf color, and leaf defoliation were determined. Reduction in total leaf chlorophyll content was caused by V. dahliae. Values of total leaf chlorophyll content (cv. Demre 13.10 mg/100 L, cv. Yalova Charleston 12.96 mg/100 L) in plants subjected to +harpin +V. dahliae were higher than for those treated with −harpin +V. dahliae (cv. Demre, 6.76 mg/100 L; cv. Yalova Charleston, 7.05 mg/100 L). Leaves/plant and plant height values were lower for V. dahliae inoculated plants. The results indicate that harpin may induce resistance to V. dahliae in peppers.
ML Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL and PhysNet.

Machine Learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a comparative assessment of ML models based on state-of-the-art variants of deep neural networks (NN), reproducing kernel Hilbert space (RKHS+F), and kernel ridge regression (KRR) is presented. Learning curves for energies and atomic forces indicate rapid convergence towards excellent predictions for B3LYP, MP2, and CCSD(T)-F12 reference results for modestly sized (in the hundreds) training sets. Typically, learning curve off-sets decay as one goes from NN (PhysNet) to RKHS+F to KRR (FCHL). Conversely, the predictive power for extrapolation of energies towards new geometries increases in the same order with RKHS+F and FCHL performing almost equally. For harmonic vibrational frequencies, the picture is less clear, with PhysNet and FCHL yielding respectively flat learning at ∽1 and ∼0.2 cm-1 no matter which reference method, while RKHS+F models level off for B3LYP, and exhibit continued improvements for MP2 and CCSD(T)-F12. Finite-temperature molecular dynamics (MD) simulations with the same initial conditions yield indistinguishable infrared spectra with good performance compared with experiment except for the high-frequency modes involving hydrogen stretch motion which is a known limitation of MD for vibrational spectroscopy. For sufficiently large training set sizes all three models can detect insufficient convergence (``noise'') of the reference electronic structure calculations in that the learning curves level off. Transfer learning (TL) from B3LYP to CCSD(T)-F12 with PhysNet indicates that additional improvements in data efficiency can be achieved.
A microarray-based approach for identifying genes for resistance to Fusarium Graminearum in maize ( Zea Mays L.)

Fusarium graminearum causes gibberella ear rot in maize. The incidence and severity of the disease is strongly influenced by environmental factors and can reach epidemic levels. The infection can result in severe reduction in crop quality because of contamination of the grain with trichothecene mycotoxins. The best strategy for control of the disease is to develop resistant hybrids. Unfortunately, most commercial maize hybrids currently grown in Canada have little or no resistance to infection by Fusarium graminearum . In a previous study, we identified several Quantitative Trait Loci ( QTL) for resistance to gibberella ear rot in maize. The focus of this work was to identify genes underlying those QTLs by using microarray-based Comparative Genomic Hybridization (CGH). The 46K maize 70-mer oligo microarray (University of Arizona) was hybridized with the Alu I digested genomic DNA from CO387 (resistant) and CG62 (susceptible) parental lines and with the pooled resistant and susceptible recombinant inbred lines from the CG62 x CO387 population. Approximately one hundred of genes (t-test) had significant hybridization intensity differences between resistant and susceptible parents and pools. Some of these genes (chitinase, protein kinase) were similar to previous gene-based markers that were identified to cosegregate with Fusarium resistance QTLs. The identified resistance genes will be converted into markers that can easily be scored to allow rapid introgression of gibberella ear rot resistance into elite germplasm.
PriorGAN: Real Data Prior for Generative Adversarial Nets

Generative adversarial networks (GANs) have achieved rapid progress in learning rich data distributions. However, we argue about two main issues in existing techniques. First, the low quality problem where the learned distribution has massive low quality samples. Second, the missing modes problem where the learned distribution misses some certain regions of the real data distribution. To address these two issues, we propose a novel prior that captures the whole real data distribution for GANs, which are called PriorGANs. To be specific, we adopt a simple yet elegant Gaussian Mixture Model (GMM) to build an explicit probability distribution on the feature level for the whole real data. By maximizing the probability of generated data, we can push the low quality samples to high quality. Meanwhile, equipped with the prior, we can estimate the missing modes in the learned distribution and design a sampling strategy on the real data to solve the problem. The proposed real data prior can generalize to various training settings of GANs, such as LSGAN, WGAN-GP, SNGAN, and even the StyleGAN. Our experiments demonstrate that PriorGANs outperform the state-of-the-art on the CIFAR-10, FFHQ, LSUN-cat, and LSUN-bird datasets by large margins.
Counting Homomorphisms to K4-minor-free Graphs, modulo 2

We study the problem of computing the parity of the number of homomorphisms from an input graph $G$ to a fixed graph $H$. Faben and Jerrum [ToC'15] introduced an explicit criterion on the graph $H$ and conjectured that, if satisfied, the problem is solvable in polynomial time and, otherwise, the problem is complete for the complexity class $\oplus\mathrm{P}$ of parity problems. We verify their conjecture for all graphs $H$ that exclude the complete graph on $4$ vertices as a minor. Further, we rule out the existence of a subexponential-time algorithm for the $\oplus\mathrm{P}$-complete cases, assuming the randomised Exponential Time Hypothesis. Our proofs introduce a novel method of deriving hardness from globally defined substructures of the fixed graph $H$. Using this, we subsume all prior progress towards resolving the conjecture (Faben and Jerrum [ToC'15]; Gobel, Goldberg and Richerby [ToCT'14,'16]). As special cases, our machinery also yields a proof of the conjecture for graphs with maximum degree at most $3$, as well as a full classification for the problem of counting list homomorphisms, modulo $2$.
Slide polynomials and subword complexes

Subword complexes were defined by Knutson and Miller in 2004 to describe Gröbner degenerations of matrix Schubert varieties. Subword complexes of a certain type are called pipe dream complexes. The facets of such a complex are indexed by pipe dreams, or, equivalently, by monomials in the corresponding Schubert polynomial. In 2017 Assaf and Searles defined a basis of slide polynomials, generalizing Stanley symmetric functions, and described a combinatorial rule for expanding Schubert polynomials in this basis. We describe a decomposition of subword complexes into strata called slide complexes. The slide complexes appearing in such a way are shown to be homeomorphic to balls or spheres. For pipe dream complexes, such strata correspond to slide polynomials. Bibliography: 14 titles.
Sub-clinical necrotic enteritis: its aetiology and predisposing factors in commercial broiler production

Sub-clinical necrotic enteritis (NE) is a major economic cost to the broiler production industry due to poor growth and feed conversion efficiency of broiler chicken flocks, higher condemnation of livers or rarely the whole carcasses at the slaughter house and an increased risk of microbial contamination of poultry meat. Sub-clinical NE is a multifactorial disease although Clostridium perfringens plays a major role in its pathogenesis. Its diagnosis and confirmation are quite different from those of general infectious diseases. Disease confirmation is from the presence of necrotic or ulcerative lesions on the small intestinal mucosa with identifiable aggregates of pathogenic C. perfringens. C. perfringens numbers in the small intestinal digesta or the mucosal scrapings are not correlated with disease severity, whereas counts above 106 cfu/g in the caecal contents indicate an increased probability of NE-specific gut lesions. Not only does the presence and counts of C. perfringens strains capable of producing related toxins affect the incidence of the disease but also a number of predisposing factors are important in the aetiology of NE. The major factors that predispose growing chickens to sub-clinical NE are diet variables, diseases that cause mucosal damage of the intestine and environmental factors that either alter the development of gut associated lymphoid tissue or change litter quality within the production house. Dietary variables may be a major cause of variation in sub-clinical NE in commercial broiler production; differences in polysaccharides, lipids, protein sources, protein digestibility and the presence of antinutritive factors have been identified as variables affecting the incidence of the disease.
Relative Growth Rates of the Noble Pen Shell Pinna nobilis Throughout Ontogeny Around the Balearic Islands (Western Mediterranean, Spain)

ABSTRACT In this study, we evaluated the growth of adults and juveniles of the endangered noble pen shell Pinna nobilis, calculated the growth rates of newly recruited juveniles, and described relative directional growth during ontogeny. We described growth using posterior adductor muscle scars (PAMS) and fit of the von Bertalanffy growth curve for adult population data at shallow depths (5–10 m) around the Mallorca and Ibiza islands, and compared the parameters with other Mediterranean populations. Populations around Mallorca could be fitted according to the von Bertalanffy function Lt = 65.31(1 - e-0.11(t + 0.53)), whereas for Ibiza the function was Lt = 154.55(1 - e-0.03(t + 4.31)). We compared first-year growth rates obtained in the laboratory and from field collections with newly settled individuals. We found that experimentally evaluated growth rates of juveniles in the laboratory of 0.18 mm/day are low compared with minimum values obtained in the field, which range, on average, from 0.28–0.32 mm/day, indicating food availability can limit growth. Furthermore we investigated relative allometric growth patterns during ontogeny using a unique data set ranging from measurements of newly recruited juveniles with shell length less than 1 cm to measurements on adult shells of 69.8 cm length. Fitting a sigmoidal function to our data and solving for the allometric exponent b, we found 3 distinct growth periods: preferential growth in the length direction up to 5.82 cm in shell length, then widening of the shell up to 14.64 cm, after which the shape of the shell becomes more elongated once again.
Estimating genetic flow between exotic and native poplar species in Québec

The goal of this study was to detect and quantify, when necessary, the presence of gene flow between poplars with exotic components and their native relatives. Seeds were collected from native mother trees, Populus balsamifera and P. deltoides, located in the surroundings of plantations of poplars with exotic components, in two different sites (Matane and Sorel) and during two to three consecutive years. We genotyped these seeds using genetic markers specific for these species. This method allowed us to detect, in the seeds collected from native mother trees present around these plantations, the presence of alleles brought about from male trees with exotic components located within the plantations. Our results revealed the presence of hybridization between native trees and trees with exotic components from the plantations. This spontaneous hybridization could affect in the long term the genetic constitution of local populations of native species.
Fast OSCAR and OWL Regression via Safe Screening Rules

Ordered Weighted $L_{1}$ (OWL) regularized regression is a new regression analysis for high-dimensional sparse learning. Proximal gradient methods are used as standard approaches to solve OWL regression. However, it is still a burning issue to solve OWL regression due to considerable computational cost and memory usage when the feature or sample size is large. In this paper, we propose the first safe screening rule for OWL regression by exploring the order of the primal solution with the unknown order structure via an iterative strategy, which overcomes the difficulties of tackling the non-separable regularizer. It effectively avoids the updates of the parameters whose coefficients must be zero during the learning process. More importantly, the proposed screening rule can be easily applied to standard and stochastic proximal gradient methods. Moreover, we prove that the algorithms with our screening rule are guaranteed to have identical results with the original algorithms. Experimental results on a variety of datasets show that our screening rule leads to a significant computational gain without any loss of accuracy, compared to existing competitive algorithms.
Learning Post-Hoc Causal Explanations for Recommendation

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models. The lack of transparency has highlighted the critical importance of improving the explainability of recommender systems. In this paper, we propose to extract causal rules from the user interaction history as post-hoc explanations for the black-box sequential recommendation mechanisms, whilst maintain the predictive accuracy of the recommendation model. Our approach firstly achieves counterfactual examples with the aid of a perturbation model, and then extracts personalized causal relationships for the recommendation model through a causal rule mining algorithm. Experiments are conducted on several state-of-the-art sequential recommendation models and real-world datasets to verify the performance of our model on generating causal explanations. Meanwhile, We evaluate the discovered causal explanations in terms of quality and fidelity, which show that compared with conventional association rules, causal rules can provide personalized and more effective explanations for the behavior of black-box recommendation models.
The Partial Response Network: a neural network nomogram

A method is proposed to infer a Generalised Additive Neural Network (GANN) from an inital Multi-layer Perceptron (MLP). The novelty in the method consists in iterative estimation of the partial responses of the MLP followed by feature selection with the Lasso. The features are re-calibrated in a second iteration which also refines feature selection. The Partial Response Network (PRN) does not require an additional explanation because it is expressed in terms of non-linear functions of one or two variables. While the concept of the GANN is not new, to our knowledge this is the first efficient method to design and estimate its component functions, without having to pre-set its functional form. The novelty can also be seen in the derivation of a neural network nomogram. Moreover, the interpretable model has comparable or superior performance to the original MLP. The performance of the PRN matches Gradient Boosting Machines, Support Vector Machines and Random Forests on six data sets from the UCI repository. It is also benchmarked against a Generalised Additive Model, the Sparse Additive Model. For tabular data, typical of many risk models involving binary classification, the proposed method achieves state-of-the-art performance with small and fully interpretable models.
Enabling Continual Learning with Differentiable Hebbian Plasticity

Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process. Thus, neural networks that are deployed in the real world often struggle in scenarios where the data distribution is non-stationary (concept drift), imbalanced, or not always fully available, i.e., rare edge cases. We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity (DHP) Softmax layer that adds a rapid learning plastic component (compressed episodic memory) to the fixed (slow changing) parameters of the softmax output layer; enabling learned representations to be retained for a longer timescale. We demonstrate the flexibility of our method by integrating well-known task-specific synaptic consolidation methods to penalize changes in the slow weights that are important for each target task. We evaluate our approach on the Permuted MNIST, Split MNIST and Vision Datasets Mixture benchmarks, and introduce an imbalanced variant of Permuted MNIST — a dataset that combines the challenges of class imbalance and concept drift. Our proposed model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.
An introduction to the lichen flora of Khao Yai National Park, Thailand

Summary A workshop in tropical forests of Khao Yai National Park on lichenized fungi, identified major components of the lichen flora on corticolous, foliicolous and saxicolous substrata. 161 taxa were recorded in three areas of the park including a riverine site on the Lam Takhong river, a montane forest at Khao Khieo and areas of tropical forest around the park headquarters. Species new to Thailand included foliicolous and corticolous species of equatorial forests, and others yet to be identified or described.
Algorithms and complexity for geodetic sets on planar and chordal graphs

We study the complexity of finding the \emph{geodetic number} on subclasses of planar graphs and chordal graphs. A set $S$ of vertices of a graph $G$ is a \emph{geodetic set} if every vertex of $G$ lies in a shortest path between some pair of vertices of $S$. The \textsc{Minimum Geodetic Set (MGS)} problem is to find a geodetic set with minimum cardinality of a given graph. The problem is known to remain NP-hard on bipartite graphs, chordal graphs, planar graphs and subcubic graphs. We first study \textsc{MGS} on restricted classes of planar graphs: we design a linear-time algorithm for \textsc{MGS} on solid grids, improving on a $3$-approximation algorithm by Chakraborty et al. (CALDAM, 2020) and show that it remains NP-hard even for subcubic partial grids of arbitrary girth. This unifies some results in the literature. We then turn our attention to chordal graphs, showing that \textsc{MGS} is fixed parameter tractable for inputs of this class when parameterized by its \emph{tree-width} (which equals its clique number). This implies a polynomial-time algorithm for $k$-trees, for fixed $k$. Then, we show that \textsc{MGS} is NP-hard on interval graphs, thereby answering a question of Ekim et al. (LATIN, 2012). As interval graphs are very constrained, to prove the latter result we design a rather sophisticated reduction technique to work around their inherent linear structure.
Complex Sparse Signal Recovery with Adaptive Laplace Priors

Because of its self-regularizing nature and uncertainty estimation, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most methods are based on the real-value signal model, with the complex-value signal model rarely considered. Typically, the complex signal model is adopted so that phase information can be utilized. Therefore, it is non-trivial to develop Bayesian models for the complex-value signal model. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning (SBL) framework, a hierarchical model with adaptive Laplace priors is proposed for applications of complex sparse signal recovery in this paper. The proposed hierarchical Bayesian framework is easy to extend for the case of multiple measurement vectors. Moreover, the space alternating principle is integrated into the algorithm to avoid using the matrix inverse operation. In the experimental section of this work, the proposed algorithm is concerned with both complex Gaussian random dictionaries and directions of arrival (DOA) estimations. The experimental results show that the proposed algorithm offers better sparsity recovery performance than the state-of-the-art methods for different types of complex signals.