Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Insights in Human Cancer Mutational Patterns: Similarity-Based Cancer Classification Using Siamese Networks

: In recent years, a number of innovations concerning the diagnosis and treatment of diseases through the application of genomics have opened the door to the detailed analysis of somatic mutation patterns in human cancers. Several AI-based systems have been proposed to identify correlations between mutations and type of cancer. However, the use of AI in Bioinformatics still presents two main limitations: (i) the explainability , i.e., the ability of the methods to partially explain and motivate their behavior, and (ii) the usability , i.e., about the strong limitations that are found in the actual use of such methods in real bio-medical contexts and scenarios. In this work, we propose a novel ML-based cancer-type detection system which integrates explainability and usability techniques. To this aim, we first formulate the cancer-type detection problem using the similarity-based classification paradigm. Then, given a cancer sample, we assume to have a set of somatic mutation features available which can be interpreted as cancer mutational view of the sample itself. Finally, we propose the use of a special Machine Learning model defined for learning similarity functions, namely the Siamese Neural Network (SNN). The proposed SNN learns to take a pair of cancer mutational views as input, and to compute a similarity score that can be used to verify whether such samples are similar or not. Preliminary experiments carried out to assess the effectiveness of the proposed system show high performance reaching f1 score 97 . 61%, and highlight how the similarity-based classification paradigm could be more suitable than the commonly used classification paradigm for the formulation of the cancer-type detection problem.
Electroretinograms in Drosophila: A Robust and Genetically Accessible Electrophysiological System for the Undergraduate Laboratory

Laboratory courses in neurophysiology fulfill a critical need for inquiry-based training in undergraduate programs in neuroscience and biology. These courses typically use classical electrophysiological preparations to explore the basic features of neuronal function. However, current neuroscience research also focuses on elucidating the molecular and genetic mechanisms of neuronal function, using model systems that include mutant and transgenic animals. To bridge laboratory training in neurophysiology with modern molecular genetics, we describe a teaching model based on electroretinography of the fruit fly Drosophila melanogaster, a long-established model system for basic neuroscience research. Drosophila are easily maintained, economical, and have hundreds of neurophysiologically relevant mutant strains and genetic tools readily available. The Drosophila electroretinogram (ERG) is a simple and accessible extracellular recording of a neural signal in the fly eye in response to flashes of light. The signal is multifaceted and the response is sensitive to stimulation parameters such as intensity, duration and wavelength, thus forming a rich source of analysis for students. Most importantly, different mutations affecting key components of intracellular signaling, synaptic transmission or neuronal function can affect the ERG waveform in characteristic ways. Recording wild type and mutant ERGs allows students to examine firsthand the connection between genetics, biochemical pathways, and electrophysiology. This neurophysiology laboratory course can facilitate and enhance an understanding of the cellular and molecular contributions to neurophysiological recordings.
Solar Domestic Hot Water Design and Optimisation in the United Kingdom

Solar energy is being increasingly implemented for domestic uses, such as hot water systems. However, the poor economic gains, particularly in cold climates and winter months, make this environmentally beneficial technology a less attractive investment. So far, research into optimising these designs have focussed on warmer climates and optimising the gains made during the summer months. Furthermore, most high budget research is geared towards industrial implementation which has limited use at the domestic, home-owner scale. To investigate an economically and energetically optimal domestic hot water system design for cold climates, mathematical methods and TRNSYS simulations were used to experiment with the parameters influencing efficiency. This was achieved by creating a parametric model to reveal losses experienced by the system. Next, a fully validated TRNSYS model was built to allow for experimentation from which the system could be adapted for cold climates. The key impact of this research will be to improve the affordability, such as rate of return, and appeal of solar domestic hot water systems for the public. Ultimately, developments such as this will reduce dependence upon fossil fuels in the United Kingdom, and other environmentally conscious nations, and allow large-scale implementation of sustainable technologies.
merging multiple reassortant H 5 N 5 avian influenza viruses ducks , China , 2008 un -

Three highly pathogenic H5N5 avian influenza viruses (HPAI), A/duck/Guangdong/wy11/ 2008 (WY11), A/duck/Guangdong/wy19/2008 (WY19), and A/duck/Guangdong/wy24/ 2008 (WY24) were isolated from ducks in southern China in April 2008. Here, we characterized these viruses by performing sequencing and phylogenetic analyses of their viral genes, assessing their virulence in ducks and mice, and performing cross-protection experiments in chickens. Sequence analysis revealed that the HA genes of these H5N5 viruses showed 97.1–97.8% homology to A/wild duck/Hunan/211/2005 (H5N1) influenza virus and that their NA genes showed 96.4–96.8% nucleotide identity to the NA gene of A/ duck/Hunan/5613/2003 (H6N5) influenza virus, which belongs to the Eurasian lineage. Genotypic analysis indicated that these H5N5 viruses were multiple reassortants among H5N1, H5N2, H6N2, and H6N5 viruses. The analysis of HA clade showed that these H5N5 viruses are clustering into clade 2.3. 4. In animal experiments, these H5N5 viruses caused 50% mortality in ducks and 100% mortality in chickens. In cross-protection experiments, the clade 2.3. 2 avian influenza vaccine could provide only 75% protection with chickens against H5N5 virus challenge. Moreover, the H5N5 virus replicated efficiently in the lungs of mice, which suggested that the H5N5 viruses have the potential to infect mammalian hosts. Since ducks have served as reassortant vessels, playing pivotal roles in the generation of new subtypes of influenza viruses, it is important to monitor the emergence of this novel subtype of influenza viruses in waterfowl to understand their ecology and evolution and to control the spread of new viruses. 2013 Elsevier B.V. All rights reserved. Corresponding author at: Harbin Veterinary Research Institute, ngang District, No. 427 Maduan Street, Harbin 150001, China. Tel. : 6 45182765628; fax: +86 45182733132. Corresponding author at: Harbin Veterinary Research Institute, ngang District, No. 427 Maduan Street, Harbin 150001, China. E-mail addresses: liuming04@126.com (M. Liu), nzhang03@yahoo.com (Y. Zhang).
The Effects of Reality vs . Fantasy Based First-Person Shooting Video Games on Adolescent Behavior

This conceptual paper reviewed the literature on violent video games and aggression. Using the General Aggression Model as a framework, this study provides evidence to support the relationship between video game violence and aggressiveness. To address the lack of research on first-person shooter (FPS) video games found in the literature, a future study will be proposed observing different types of first-person shooter video games (reality and fantasy) and the effects they may have on adolescent behavior. Consistent with the General Aggression Model, findings showed increases in aggression for adolescents exposed to violent video games. While research shows first-person shooter video games increase adolescent aggressiveness, evidence on different types of first-person shooter games and their effects on adolescent behavior were inconclusive. However, research reported by Potter (1988) showed that viewers tend to experience more emotional and behavioral issues when viewing reality based media. In addition, Anderson and Bushman (2002) reported that empirical evidence shows that violent video games and aggressiveness have a positive and significant relationship. The grouping of Anderson and Bushman (2002) and Potter (1988), provide evidence that adolescent aggressiveness will be higher when playing reality based first-person shooter games in comparison to fantasy based first-person shooter video games.
Seasonal variation of indoor radon–thoron levels in dwellings of four districts of Haryana, India

In the present work, seasonal (winter, rainy, summer) variation of indoor radon and thoron was measured for 120 days from 91 dwellings in different villages and towns in four districts (Rohtak, Sonipat, Panipat, and Jind) of Haryana using single-entry pinhole-based radon–thoron-discriminating dosimeters. The present study is imperative because a thermal power plant is present in Panipat district, and the Yamuna River passes through the Sonipat and Rohtak districts, and a high-heat-producing granite (HHPG) region is in the vicinity of the study area. Annual effective dose due to radon and thoron has been calculated by using an equilibrium factor as determined by other studies in the neighboring region. Concentrations for one year were estimated (March 2015–February 2016) from data collected for 4 months. The result shows that the annual indoor radon levels vary from 9.12 ± 1.14 to 94.23 ± 9.94 Bq/m3 with an average value of 21.66 ± 3.08 Bq/m3. The annual indoor thoron levels vary from 15.18 ± 2.85 to 105.35 ± 15.17 Bq/m3 with an average value of 52.82 ± 7.15 Bq/m3. The annual effective dose varies from 0.85 to 4.73 mSv with an average value of 2.41 mSv. The average value of annual indoor radon and thoron is lower than the limits of 100 Bq/m3 as recommended by the World Health Organization and the thoron value is higher than the worldwide average value of 40 Bq/m3. Indoor radon and thoron concentrations are highest in the winter and lowest in the summer. Concentrations of these gases in the rainy season are higher compared to the summer season. When we compare the rainy and winter seasons, concentrations of these gases are higher in the winter season. The differences in average concentrations of radon and thoron values are presented by the ratio of average radon and thoron concentrations in three seasons. Hence, the winter–summer ratio, winter–rainy season ratio, and rainy season–summer ratio of average radon concentrations in the study region are 1.72, 1.41, and 1.50, respectively. For the thoron concentration, winter–summer ratio, winter–rainy season ratio, and rainy season–summer ratio are 1.38, 1.05, and 1.38, respectively. This study area is safe from radiological effects of radon and thoron gases in confined spaces because the annual effective dose is lower than established safe limits.
Scalable mining of small visual objects

This paper presents a scalable method for automatically discovering frequent visual objects in large multimedia collections even if their size is very small. It first formally revisits the problem of mining or discovering such objects, and then generalizes two kinds of existing methods for probing candidate object seeds: weighted adaptive sampling and hashing-based methods. The idea is that the collision frequencies obtained with hashing-based methods can actually be converted into a prior probability density function given as input to a weighted adaptive sampling algorithm. This allows for an evaluation of any hashing scheme effectiveness in a more generalized way, and a comparison with other priors, e.g. guided by visual saliency concerns. We then introduce a new hashing strategy, working first at the visual level, and then at the geometric level. This strategy allows us to integrate weak geometric constraints into the hashing phase itself and not only neighborhood constraints as in previous works. Experiments conducted on a new dataset introduced in this paper will show that using this new hashing-based prior allows a drastic reduction of the number of tentative probes required to discover small objects instantiated several times in a large dataset.
A markov chain approximation to choice modeling

Assortment planning is an important problem that arises in many industries such as retailing and airlines. One of the key challenges in an assortment planning problem is to identify the "right model" for the substitution behavior of customers from the data. Error in model selection can lead to highly sub-optimal decisions. In this paper, we present a new choice model that is a simultaneous approximation for all random utility based discrete choice models including the multinomial logit, the nested logit and mixtures of multinomial logit models. Our model is based on a new primitive for substitution behavior where substitution from one product to another is modeled as a state transition of a Markov chain. In particular, we consider a Markov chain where there is a state for each product, and model the substitution behavior as follows: a customer arrives in the state corresponding to his most preferred product. If that product is not available, he/she transitions to other product states according to the transition probabilities of the Markov chain. Therefore, the preferences of the customers are approximated by Markovian transitions in this choice model. We show that the choice probabilities computed by our model are a good approximation to the true choice probabilities of any random utility discrete based choice model under mild conditions. Moreover, they are exact if the underlying model is a Multinomial logit model. We also give a data-driven procedure to estimate the parameters of the Markov chain model that does not require any knowledge of the latent choice model. Furthermore, we show that the assortment optimization problem under our choice model can be solved efficiently in polynomial time. In an assortment optimization problem, the goal is to find an assortment (or offer set) that maximizes the total expected revenue. The result is quite surprising as we can not even express the choice probabilities for different offer sets using a simple functional form in the Markov chain model. In addition to the theoretical bounds, we also conduct numerical experiments and observe that the average maximum relative error of the choice probabilities of our model with respect to the true probabilities for any offer set is less than 3% (the average being taken over different offer sets). Therefore, our model provides a tractable data-driven approach to choice modeling and assortment optimization that is robust to model selection errors. Moreover, the state transition primitive for substitution provides interesting insights to model the substitution behavior in many real-world applications.
Indefinite Mean-Field Stochastic Linear-Quadratic Optimal Control: From Finite Horizon to Infinite Horizon

In this paper, the finite-horizon and the infinite-horizon indefinite mean-field stochastic linear-quadratic optimal control problems are studied. Firstly, the open-loop optimal control and the closed-loop optimal strategy for the finite-horizon problem are introduced, and their characterizations, difference and relationship are thoroughly investigated. The open-loop optimal control can be defined for a fixed initial state, whose existence is characterized via the solvability of a linear mean-field forward-backward stochastic difference equation with stationary conditions and a convexity condition. On the other hand, the existence of a closed-loop optimal strategy is shown to be equivalent to any one of the following conditions: the solvability of a couple of generalized difference Riccati equations, the finiteness of the value function for all the initial pairs, and the existence of the open-loop optimal control for all the initial pairs. It is then proved that the solution of the generalized difference Riccati equations converges to a solution of a couple of generalized algebraic Riccati equations. By studying another generalized algebraic Riccati equation, the existence of the maximal solution of the original ones is obtained together with the fact that the stabilizing solution is the maximal solution. Finally, we show that the maximal solution is employed to express the optimal value of the infinite-horizon indefinite mean-field linear-quadratic optimal control. Furthermore, for the question whether the maximal solution is the stabilizing solution, the necessary and the sufficient conditions are presented for several cases.
Multi‐proxy constraints on the significance of covariant δ13C values in carbonate and organic carbon during the early Mississippian

This study investigates the covariation between carbonate and organic δ13C values in a proximal to distal transect of four outcrops in the Madison Limestone in the Western United States Rockies, combined with δ34S values of carbonate associated sulphate, the concentration of acid‐insoluble material and measurements of total organic carbon. These new geochemical datasets not only allow for an evaluation of carbon isotope covariance during one of the largest perturbations to the global carbon cycle over the past 550 Myr, but also constrain the cause of the excursion in carbonate δ13C values. The results support the hypothesis that a period of anoxia did not play a role in generating the positive carbonate δ13C values, but rather favour interpretations by previous workers that the proliferation of land plants destabilized the Carboniferous carbon cycle, setting the stage for a significant change in the carbonate δ13C values of contemporaneous marine carbonates. These results also demonstrate that one of the largest perturbations to the global carbon cycle did not produce synchronous variations in carbonate and organic δ13C values, emphasizing the importance of local depositional controls on carbon isotope covariance in the geological record in both modern and ancient environments.
Differential evolution for bilevel programming

Bilevel programming problems are characterized by two optimization problems which are hierarchically related, where to each feasible upper level solution an optimal solution in the lower level problem must be associated. These problems appear in many practical applications, and a variety of solution techniques can be found in the literature. In this paper, an algorithm is proposed which uses differential evolution to solve both the upper and lower level optimization problems. Several test problems from the literature are solved in order to assess the performance of the proposed method.
Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology

In the last decade, advances in high-throughput technologies such as DNA microarrays have made it possible to simultaneously measure the expression levels of tens of thousands of genes and proteins. This has resulted in large amounts of biological data requiring analysis and interpretation. Nonnegative matrix factorization (NMF) was introduced as an unsupervised, parts-based learning paradigm involving the decomposition of a nonnegative matrix V into two nonnegative matrices, W and H, via a multiplicative updates algorithm. In the context of a p×n gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a metagene, and each column of H represents the metagene expression pattern of the corresponding sample. NMF has been primarily applied in an unsupervised setting in image and natural language processing. More recently, it has been successfully utilized in a variety of applications in computational biology. Examples include molecular pattern discovery, class comparison and prediction, cross-platform and cross-species analysis, functional characterization of genes and biomedical informatics. In this paper, we review this method as a data analytical and interpretive tool in computational biology with an emphasis on these applications.
Adaptation of human motion capture data to humanoid robots for motion imitation using optimization

The interactions between a humanoid robot and human are important, when the humanoid robot is requested to serve people with human-friendly services. For such interactions, the imitation of human arm motions by a humanoid robot is discussed as the first step for imitating the full body motion of a human. The human motions captured by a motion capture system may not be applied directly to the humanoid robot because of the differences in geometric aspect, dynamics and kinematic capabilities between the robot and human. To overcome this difficulty, a method to adapt captured motions to the humanoid robot is developed. The geometric difference in the arm length is resolved by scaling the arm length of the robot with a constant based on a length ratio. The imitation of human arm motion is then realized by solving an inverse kinematics problem which is formulated as an optimization task. The errors between the captured trajectories of human arms and the approximated trajectories of robot's arms are minimized. The dynamics capabilities of the joint motors such as limits of joint position and velocity, are imposed on the optimization problem. Several human motions are imitated by the humanoid robots developed in our institute.
Sub-micron, metal gate, high-к dielectric, implant-free, enhancement-mode III-V mosfets

The performance of 300 nm, 500 nm and 1 mum metal gate, implant free, enhancement mode III-V MOSFETs are reported. Devices are realised using a 10 nm MBE grown Ga2O3/(GaxGd1-x)2O3 high-kappa (kappa=20) dielectric stack grown upon a delta-doped AlGaAs/InGaAs/AlGaAs/GaAs heterostructure. Enhancement mode operation is maintained across the three reported gate lengths with a reduction in threshold voltage from 0.26 V to 0.08 V as the gate dimension is reduced from 1 mum to 300 nm. An increase in transconductance is also observed with reduced gate dimension. Maximum drain current of 420 muA/mum and extrinsic transconductance of 400 muS/mum are obtained from these devices. Gate leakage current of less than 100 pA and subthreshold slope of 90 mV/decade were obtained for all gate lengths. These are believed to be the highest performance sub-micron enhancement mode III-V MOSFETs reported to date.
On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.