Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.
The “ atypical ” protein kinase , SsoPK 5 , an archaeal member of the piD 261 / Bud 32 subfamily January

Open reading frame (ORF) sso0433 from the archaeon Sulfolobus solfataricus encodes a protein kinase, SsoPK5 that exhibits 33% sequence identity to p53 related protein kinase (PRPK) from Homo sapiens and 26% sequence identity to piD261/Bud32 from Saccharomyces cerevisiae. Given this high degree of similarity, the objectives of this thesis were to (a) clone and purify recombinant SsoPK5, (b) examine its commonalities and differences with its eukaryotic homologues, and (c) determine if it was regulated by nucleotides or related compounds. Substantial progress was achieved on each objective. After successful cloning of ORF sso0433 and purification of its protein product, SsoPK5, it was determined that SsoPK5 was cold labile and incubation at 4oC for an extended period of time rendered SsoPK5 incapable of phosphotransferase activity. When stored at room temperature, SsoPK5 was capable of transferring the γ-phosphate from ATP to casein, reduced carboxyamidomethylated and maleylated (RCM) lysozyme, and p53. SsoPK5 phosphotransferase activity required a divalent metal cofactor; like pid261/Bud32, SsoPK5 preferred Mn over the more commonly preferred Mg. SsoPK5 was shown to phosphorylate itself on threonine and serine residues; one of the specific amino acid residues modified is threonine-151.
Statistical methods for studying disease subtype heterogeneity

A fundamental goal of epidemiologic research is to investigate the relationship between exposures and disease risk. Cases of the disease are often considered a single outcome and assumed to share a common etiology. However, evidence indicates that many human diseases arise and evolve through a range of heterogeneous molecular pathologic processes, influenced by diverse exposures. Pathogenic heterogeneity has been considered in various neoplasms such as colorectal, lung, prostate, and breast cancers, leukemia and lymphoma, and non‐neoplastic diseases, including obesity, type II diabetes, glaucoma, stroke, cardiovascular disease, autism, and autoimmune disease. In this article, we discuss analytic options for studying disease subtype heterogeneity, emphasizing methods for evaluating whether the association of a potential risk factor with disease varies by disease subtype. Methods are described for scenarios where disease subtypes are categorical and ordinal and for cohort studies, matched and unmatched case–control studies, and case–case study designs. For illustration, we apply the methods to a molecular pathological epidemiology study of alcohol intake and colon cancer risk by tumor LINE‐1 methylation subtypes. User‐friendly software to implement the methods is publicly available. Copyright © 2015 John Wiley & Sons, Ltd.
Why the child cannot be happy

Neuter forms of adjectives in Norwegian and Swedish are formed through the regular and productive process of suffixing the neuter morpheme /-t/ to the adjective stem. A number of adjectives however do not follow this rule and appear to have no usable neuter form at all. The master thesis investigates these missing neuter forms through the use of a lexical decision task (Meyer and Schvaneveldt, 1971) where expected but missing neuter forms of adjectives where used as priming stimulus for their stem forms. Masked priming (Forster, 1998) was the method used. The experiment measures how well participants can connect neuter forms to their stem forms which were used as target stimuli for the lexical decision. The adjectives that do not form neuters share commonalities in phonological form and in semantic content, and constitute a fairly small but seemingly productive class. Adjectives with blocked neuter gender forms in the experiment were controlled against adjectives that regularly form neuters and pseudowords with corresponding neuters of similar
SparkNG: Interactive MATLAB Tools for Introduction to Speech Production, Perception and Processing Fundamentals and Application of the Aliasing-Free L-F Model Component

This article introduces a set of interactive tools for studying fundamentals of speech production, perception and processing. In addition to this voice production simulator, it consists of interactive time-frequency representation, auditory representation visualizer and a vocal tract shape visualizer for introductory materials. It consists of compiled executables for Windows and Mac environment, which do not require MATLAB license. The MATLAB sources of the tools and their constituent functions are publicly accessible under open source license.
Graphene Networks and Its Role on Free-Volume Properties of Graphene / Epoxidied Natural Rubber Composites with a Segregated Structure : Rheological and Positron Annihilation Studies

a Chinese Agricultural Ministry Key Laboratory of Tropical Crop Product Processing, Agricultural Product Processing Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang 524001, PR China b College of Materials and Chemical Engineering, Hainan university, Haikou, 571737, PR China c Institute for Frontier Materials, Deakin University, Waurn Ponds, Geelong VIC 3216, Australia
Firm Size Transmission Effect and Price-Volume Relationship Analysis During Financial Tsunami Periods

Investors attend importance to forecast the price of financial assets, thus, the factors affecting the stock price are usually the focus of financial research in the field, in which the most important factors to scholars are firm size transmission effect and price-volume relationship. In this study, the analysis of these two items in the Taiwan stock market is conducted. The results indicate that the firm size transmission effect is almost significant, and the reversal phenomenon also exists. However, before the financial tsunami, the firm size transmission effect does not significantly exist; this result also indirectly proves the directional asymmetry of the market returns, proposed by McQueen, Pinegar, and Thorley (1996). For price and volume relationship, big cap index reveals that volume leads to price before the financial tsunami, and small cap index appears that price leads to volume in 2010.
An Efficient Approach for Detecting Non-Bound layers using Advance Segment Intelligence Chronicle Data Detection ( ASICDD )

The modern world all of them using internet and searching data according to their needs but the result of the data is not accurate some added impurities added in the data that impurities has removing is the important concept of the data mining in the dataset. In our new approach we are going to eliminate the impurities in the data. Those impurities are called as the outliers. We are going to use the Advance Segment Intelligence Chronicle Data Detection (ASICDD) algorithm to eliminate the outliers in the searching data’s and give the best result to the user’s need in this methodology the data searching is the best performance and it going give the exact result to the user. During the process of cluster the data has comes like bunch of the data that data we are going to eliminate the impurities. Basically the clustering two type of clustering first one data mining and spatial mining these are the two types. In that we are going to use the data mining and eliminate the impurities like fraud detection and removing the unwanted data’s. Here we are going to use the three values to find the outliers. First value tendency this tendency value base on the local behaviour and second value has used in the kernel k clustering the type of local outlier factor based method and third value has the threshold. It used to give the better performance of the data clustering.
Breathing bands due to molecular order in CH 3 NH 3 PbI

CH3NH3PbI3 perovskite is nowadays amongst the most promising photovoltaic materials for energy conversion. We have studied by ab-initio calculations, using several levels of approximation -namely density functional theory including spin-orbit coupling and quasi-particle corrections by means of the GW method, as well as pseudopotential self-interaction corrections-, the role of the methylammonium orientation on the electronic structure of this perovskite. We have considered many molecular arrangements within 2 × 2 × 2 supercells, showing that the relative orientation of the organic molecules is responsible for a huge band gap variation up to 2 eV. The band gap sizes are related to distortions of the PbI3 cage, which are in turn due to electrostatic interactions between this inorganic frame and the molecules. The strong dependence of the band gap on the mutual molecular orientation is confirmed at all levels of approximations. Our results suggest then that the coupling between the molecular motion and the interactions of the molecules with the inorganic cage could help to explain 1Corresponding author Preprint submitted to Computational Materials Science November 10, 2017 ar X iv :1 61 2. 02 62 4v 3 [ co nd -m at . m tr lsc i] 9 N ov 2 01 7 the widening of the absorption spectrum of CH3NH3PbI3 perovskite, consistent with the observed white spectrum.
State of the Art on : Generative models for reinforcement learning

Machine learning (ML) is the computational field that leverages statistical techniques to develop algorithms able to learn from data. Reinforcement learning (RL) [59] is a subfield of ML that deals with the problem of training an agent to maximize a reward signal while acting in an environment. The study of generative models is another ML subarea, with the aim of describing in probabilistic terms the process that generated the data. Despite several families of algorithms can be applied to RL and generative modeling, in recent years deep learning approaches have demonstrated great capabilities for some problems: they aim at learning distributed, highly compositional representations of data through end-to-end gradient-based optimization. Relevant conferences for this research topic, according to experts’ opinion and rankings, are AAAI and IJCAI, in the broader field of artificial intelligence, NIPS and ICML, specifically for machine learning, and ICLR, specialized in deep learning; relevant journals are instead the Journal of Machine Learning Research (Microtome), Transactions on Pattern Analysis and Machine Intelligence (IEEE), Machine Learning (Springer), Transactions on Neural Networks and Learning Systems (IEEE).
Fisher sequential classifiers

This paper presents further discussion and development of the two-parameter Fisher criterion and describes its two modifications (weighted criterion and another multiclass form). The criteria are applied in two algorithms for training linear sequential classifiers. The main idea of the first algorithm is separating the outermost class from the others. The second algorithm, which is a generalization of the first one, is based on the idea of linear division of classes into two subsets. As linear division of classes is not always satisfactory, a piecewise-linear version of the sequential algorithm is proposed as well. The computational complexity of different algorithms is analyzed. All methods are verified on artificial and real-life data sets.
Human detection system by fusing depth map-based method and convolutional neural network-based method

In this paper, the depth images and the colour images provided by Kinect sensors are used to enhance the accuracy of human detection. The depth-based human detection method is fast but less accurate. On the other hand, the faster region convolutional neural network-based human detection method is accurate but requires a rather complex hardware configuration. To simultaneously leverage the advantages and relieve the drawbacks of each method, one master and one client system is proposed. The final goal is to make a novel Robot Operation System (ROS)-based Perception Sensor Network (PSN) system, which is more accurate and ready for the real time application. The experimental results demonstrate the outperforming of the proposed method compared with other conventional methods in the challenging scenarios.
Can facial metrology predict gender?

We investigate the question of whether facial metrology can be exploited for reliable gender prediction. A new method based solely on metrological information from facial landmarks is developed. Here, metrological features are defined in terms of specially normalized angle and distance measures and computed based on given landmarks on facial images. The performance of the proposed metrology-based method is compared with that of a state-of-the-art appearance-based method for gender classification. Results are reported on two standard face databases, namely, MUCT and XM2VTS containing 276 and 295 images, respectively. The performance of the metrology-based approach was slightly lower than that of the appearance-based method by only about 3.8% for the MUCT database and about 5.7% for the XM2VTS database.
CONSTRAINING THE MILKY WAY POTENTIAL WITH A SIX-DIMENSIONAL PHASE-SPACE MAP OF THE GD-1 STELLAR STREAM

The narrow GD-1 stream of stars, spanning 60° on the sky at a distance of ∼10 kpc from the Sun and ∼15 kpc from the Galactic center, is presumed to be debris from a tidally disrupted star cluster that traces out a test-particle orbit in the Milky Way halo. We combine Sloan Digital Sky Survey (SDSS) photometry, USNO-B astrometry, and SDSS and Calar Alto spectroscopy to construct a complete, empirical six-dimensional (6D) phase-space map of the stream. We find that an eccentric orbit in a flattened isothermal potential describes this phase-space map well. Even after marginalizing over the stream orbital parameters and the distance from the Sun to the Galactic center, the orbital fit to GD-1 places strong constraints on the circular velocity at the Sun's radius Vc = 224 ± 13 km s−1 and total potential flattening qΦ = 0.87+0.07−0.04. When we drop any informative priors on Vc, the GD-1 constraint becomes Vc = 221 ± 18 km s−1. Our 6D map of GD-1, therefore, yields the best current constraint on Vc and the only strong constraint on qΦ at Galactocentric radii near R ∼ 15 kpc. Much, if not all, of the total potential flattening may be attributed to the mass in the stellar disk, so the GD-1 constraints on the flattening of the halo itself are weak: qΦ,halo > 0.89 at 90% confidence. The greatest uncertainty in the 6D map and the orbital analysis stems from the photometric distances, which will be obviated by GAIA.
Sexual antagonism in the pistil varies among populations of a hermaphroditic mixed‐mating plant

Sexual conflicts and their evolutionary outcomes may be influenced by population‐specific features such as mating system and ecological context; however, very few studies have investigated the link between sexual conflict and mating system. The self‐compatible, mixed‐mating hermaphrodite Collinsia heterophylla (Plantaginaceae) is thought to exhibit a sexual conflict over timing of stigma receptivity. This conflict involves (i) delayed stigma receptivity, which intensifies pollen competition, and (ii) early fertilization forced by pollen, which reduces seed set. We investigated the potential for the conflict to occur under field conditions and performed glasshouse crosses within eight populations to assess its consistency across populations. Flowers were visited, and produced seeds after pollination, at all developmental stages, suggesting that the conflict can be of significance under natural conditions. In the glasshouse, early pollination imposed costs in all populations. Overall, the timing of first seed set was most strongly affected by the maternal parent, denoting stronger female than male ability to influence the onset of stigma receptivity. Crosses also revealed a negative relationship between donor‐ and recipient‐related onset of receptivity within individuals, a novel result hinting at trade‐offs in sex allocation or a history of antagonistic selection. Neither timing of stigma receptivity, timing of first seed set, nor pollen competitive ability covaried with population outcrossing rate. In conclusion, these results indicate that sexually antagonistic selection may be present in varying degrees in different populations of C. heterophylla, but this variation does not appear to be directly related to mating system variation.
Estimating the Public’s Preferences for Sustainable Aquaculture: A Country Comparison

Integrated Multi-Trophic Aquaculture (IMTA) is an alternative to the monoculture of fin fish species, in which several species are combined in the production process. This can have environmental advantages such as a lower environmental impact through nutrient cycling and natural filters; and can have economic advantages consisting of increased efficiency, product diversification and potential price premiums. In this paper, a choice experiment (CE) was conducted through an online survey in Ireland, the UK, Italy, Israel and Norway, to assess how the public makes decisions on what type of salmon or sea bream to buy based on the attributes of the product. Analysis assessed the Willingness-to-Pay (WTP) for more sustainable produced seafood using a Latent Class multinomial logit modelling approach. In the experiment, an ecolabel was used to distinguish between regularly produced (monoculture) products and sustainably produced (IMTA) products. The general public in each country showed a positive attitude towards the development of such an ecolabel and towards the payment of a price premium for the more sustainably produced salmon or sea bream.
Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning

As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) is based on a discounted reward reinforcement learning algorithm, bucket-brigade algorithm, which optimizes the discounted total reward received by an agent but is not suitable for all multi-step problems, especially large-size ones. There are some undiscounted reinforcement learning methods available, such as R-learning, which optimize the average reward per time step. In this paper, R-learning is used as the reinforcement learning employed by ZCS, to replace its discounted reward reinforcement learning approach, and tournament selection is used to replace roulette wheel selection in ZCS. The modification results in classifier systems that can support long action chains, and thus is able to solve large multi-step problems.
Equality versus equity based pay systems and their effects on rational altruism motivation in teams: wicked masked altruism

This paper analyses the design of optimal incentives in teams both with and without rational altruism. The main contribution of the paper is to study the influence of the incentive function on the altruism parameter chosen by team members. We find that optimal incentive is independent of the presence of rational altruism. Secondly, we compare the welfare loss of equal sharing rules versus second best optimal sharing rules (based on equity). Finally, we distinguish between two sources of rational altruism: 1) the strategic component, and 2) an additional component that reflects wicked behaviour by some agents, reducing the firm’s efficiency.
Study of Subchronic Effect of Myristicafragrans Houtt. On Histopathology of Testes of Swiss Albino Mice

Plants and plant materials are consumed as condiments and spices, which not only enhance the flavors of food but also show preservative and medicinal properties. The spice NutmegMyristicafragransHoutt.has several medicinal properties. However it may prove to be toxic if consumed in larger quantities over a longer time period. Hence present paper aims at studying the sub-chronic effect of aqueous and methnolic extracts of Nutmeg on histopathology of testes of Swiss Albino Mice. Marked degenerative changes in the seminiferous tubules were observed in the sections of testis treated with both aqueous and methanolic extracts of Nutmeg
Open circuit voltage decay as a tool to assess the reliability of organic solar cells: P3HT:PCBM vs. HBG1:PCBM

We developed a model that explain the open circuit voltage decay in polymeric solar cells to analyze the effects of the degradation induced by accelerated life-tests. The model accounts for the polaron pairs separation and recombination, the Langevin recombination, and the trap assisted recombination. The model permits to calculate several parameters. Among them, for the first time, we were able to extrapolate the Energy Gap of the blend from electrical, non-destructive measurements. We performed electrical stresses on organic solar cells based on two different active layers (P3HT:PC61BM, HBG1:PC61BM) applying specific characterizations, including open circuit voltage decay. We showed the better reliability of HBG1 based devices with respect to the analogous systems containing P3HT as donor material.