Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing User Similarity by Combining SimRank++ and Cosine Similarities to Improve Collaborative Filtering

This paper addresses the sparsity problem in collaborative filtering (CF) by developing an aggregated useruser similarity measure suitable for the user-based CF model. The aggregated similarity measure is a weighted aggregation of the SimRank++ similarity on the user-item bipartite graph and the cosine similarity of the Linked Open Data (LOD)-based user profiles derived from both the rating data and the items' descriptive attributes found from LOD resources. To validate the effectiveness of the aggregated similarity and evaluate the accuracy of rating predictions with the user-based CF method, comparative experiments between four similarity measures, the Pearson correlation coefficient, the SimRank++ similarity, the cosine similarity and the aggregated similarity, were conducted on the MovieLens 100k dataset and DBpedia. The experimental results indicate that the proposed aggregated similarity measure overall outperforms the other three similarity measures in terms of both Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), especially in the cases of 30-100 nearest neighbors.
Is the Basis of the Stock Index Futures Markets Nonlinear

A considerable amount of papers use a cost-carry model in modelling the relationship between future and spot index prices. The cost-carry model defines basis, bt;T , at time t and maturity date of the future contract at T as bt;T = ft � st = r(T � t), where ft, st and r denote the log of future prices, the log of spot index prices and the difference between interest rate and dividend rate, respectively. Using daily data time series on future contracts of the S&P 500 index and the FTSE 100 index, as well as the price levels of the corresponding underlying cash indices over the sample period from January 1, 1988 to December 31, 1998, [1] argued that there is significant nonlinearity in the dynamics of the basis due to the existence of transaction costs or agents heterogeneity. They found that the basis follows a nonlinear stationary ESTAR (Exponential Smooth Transition Autoregressive) model. However, based on the study with the S&P 500 data series from January 1, 1998 to December 31, 2009, we conclude that there is no significant difference between a linear AR(p) model and a nonlinear STAR model in fitting the data.
Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate

Objective. Electrocorticography (ECoG), used as a neural recording modality for brain-machine interfaces (BMIs), potentially allows for field potentials to be recorded from the surface of the cerebral cortex for long durations without suffering the host-tissue reaction to the extent that it is common with intracortical microelectrodes. Though the stability of signals obtained from chronically implanted ECoG electrodes has begun receiving attention, to date little work has characterized the effects of long-term implantation of ECoG electrodes on underlying cortical tissue. Approach. We implanted and recorded from a high-density ECoG electrode grid subdurally over cortical motor areas of a Rhesus macaque for 666 d. Main results. Histological analysis revealed minimal damage to the cortex underneath the implant, though the grid itself was encapsulated in collagenous tissue. We observed macrophages and foreign body giant cells at the tissue-array interface, indicative of a stereotypical foreign body response. Despite this encapsulation, cortical modulation during reaching movements was observed more than 18 months post-implantation. Significance. These results suggest that ECoG may provide a means by which stable chronic cortical recordings can be obtained with comparatively little tissue damage, facilitating the development of clinically viable BMI systems.
UnibucKernel: A kernel-based learning method for complex word identification

In this paper, we present a kernel-based learning approach for the 2018 Complex Word Identification (CWI) Shared Task. Our approach is based on combining multiple low-level features, such as character n-grams, with high-level semantic features that are either automatically learned using word embeddings or extracted from a lexical knowledge base, namely WordNet. After feature extraction, we employ a kernel method for the learning phase. The feature matrix is first transformed into a normalized kernel matrix. For the binary classification task (simple versus complex), we employ Support Vector Machines. For the regression task, in which we have to predict the complexity level of a word (a word is more complex if it is labeled as complex by more annotators), we employ v-Support Vector Regression. We applied our approach only on the three English data sets containing documents from Wikipedia, WikiNews and News domains. Our best result during the competition was the third place on the English Wikipedia data set. However, in this paper, we also report better post-competition results.
Lack of Pannexin 1 Alters Synaptic GluN2 Subunit Composition and Spatial Reversal Learning in Mice

Long-term potentiation (LTP) and long-term depression (LTD) are two forms of synaptic plasticity that have been considered as the cellular substrate of memory formation. Although LTP has received considerable more attention, recent evidences indicate that LTD plays also important roles in the acquisition and storage of novel information in the brain. Pannexin 1 (Panx1) is a membrane protein that forms non-selective channels which have been shown to modulate the induction of hippocampal synaptic plasticity. Animals lacking Panx1 or blockade of Pannexin 1 channels precludes the induction of LTD and facilitates LTP. To evaluate if the absence of Panx1 also affects the acquisition of rapidly changing information we trained Panx1 knockout (KO) mice and wild type (WT) littermates in a visual and hidden version of the Morris water maze (MWM). We found that KO mice find the hidden platform similarly although slightly quicker than WT animals, nonetheless, when the hidden platform was located in the opposite quadrant (OQ) to the previous learned location, KO mice spent significantly more time in the previous quadrant than in the new location indicating that the absence of Panx1 affects the reversion of a previously acquired spatial memory. Consistently, we observed changes in the content of synaptic proteins critical to LTD, such as GluN2 subunits of N-methyl-D-aspartate receptors (NMDARs), which changed their contribution to synaptic plasticity in conditions of Panx1 ablation. Our findings give further support to the role of Panx1 channels on the modulation of synaptic plasticity induction, learning and memory processes.
Influence Of Root Canal Irrigants On Dental Tissues: A Review Of Literature

The success of endodontic therapy depends on thorough cleaning and three-dimensional obturation of root canal. Successful cleaning entails the use of instruments to physically remove substances, irrigating systems to flush loosened materials away and to dissolve contents from inaccessible region. The most commonly used root canal irrigants are sodium hypochlorite, hydrogen peroxide, ethylene diamine tetra acetic acid, normal saline, citric acid and MTAD. Irrigation solutions used during endodontic treatment react with organic and inorganic components of dental hard tissues, thereby, altering the physical and chemical properties like adhesion, modulus of elasticity, microhardness, tensile & flexural strength etc., of the tooth thereby predisposing to fracture. Also, the cytotoxicity of these agents is concentration dependent, higher the concentration more is the side-effects. Therefore, to attain maximum benefits of the irrigating solutions only minimal required concentration should be used. The literature search used the MEDLINE database. Reference lists of potentially relevant articles and review articles were also screened for the search strategy. The keywords searched were root canal irrigants, endodontics, side-effects, cytotoxicity, smear-layer, dentin hardness, and pulpdissolution.
French Word Recognition through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM

Optical character recognition (OCR) is a fundamental problem in computer vision. Research studies have shown significant progress in classifying printed characters using deep learning-based methods and topologies. Among current algorithms, recurrent neural networks with long-short term memory blocks called RNN-LSTM have provided the highest performance in terms of accuracy rate. Using the top 5,000 French words collected from the internet including all signs and accents, RNN-LSTM models were trained and tested for several cases. Six fonts were used to generate OCR samples and an additional dataset that included all samples from these six fonts was prepared for training and testing purposes. The trained RNN-LSTM models were tested and achieved the accuracy rates of 99.98798% and 99.91889% for edit distance and sequence error, respectively. An accurate preprocessing followed by height normalization (standardization methods in deep learning) enabled the RNN-LSTM model to be trained in the most efficient way. This machine learning work also revealed the robustness of RNN-LSTM topology to recognize printed characters.
Parameter-free adaptive step-size multiobjective optimization applied to remote photoplethysmography

In this work, we propose to reformulate the objective function of Independent Component Analysis (ICA) to make it a better posed problem in the context of Remote photoplethysmography (rPPG). In recent previous works, linear combination coefficients of RGB channels are estimated maximizing the non-Gaussianity of ICA output components. However, in the context of rPPG a priori knowledge of the pulse signal can be incorporated into the component extraction algorithm. To this end, the contrast function of regular ICA is extended with a measure of periodicity formulated using autocorrelation. This novel semi-blind source extraction method for measuring rPPG has the interesting property of being free from manual parameter adjustment. The tedious selection of the step-size parameter in the gradient-ascent algorithm has been advantageously replaced by an adaptive step size. Our method has been validated against our large in-house video database UBFC-RPPG.
Protein Synthesis in vitro: Cell-Free Systems Derived from Human Cells

When researchers wish to obtain recombinant proteins, a primary choice of the method is in most cases the expression in E. coli. If this system does not work for the protein of interest, they may turn to insect or mammalian cells. Protein expression in vitro may be chosen if these in vivo expression systems do not give the protein satisfactorily. There are several reasons why expression of some recombinant proteins in living cells is poor. If the protein to be expressed is toxic to host cells or inhibitory for growth, it should be difficult to express the protein to a high level. The cell-free system is derived from the extract from broken cells, and therefore the above-mentioned problems that occur in the living cells, if not all, are avoidable.
Using Goal Programming on Estimated Pareto Fronts to Solve Multiobjective Problems

Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple conflicting objectives. Also, different instances of real-world problems often share similar fitness landscapes because key parts of the data are the same across these instances. We we propose a novel methodology that consists of solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We propose three goal-based objective functions and show that on a real-world home healthcare planning problem the methodology can produce improved results in a shorter computation time.
UWB wireless sensor networks: UWEN - a practical example

The research topic of sensor networks has been around for some time. With improvements in device size, power consumption, communications, and computing technology, sensor networks are becoming more popular for an ever increasing range of applications. Since 2002, there has been an increased in the popularity of commercial applications based on ultra wideband. This, in turn, has ignited interest in the use of this technology for sensor networks and fuelled research in the area. Impulse-radio-based UWB technology has a number of inherent properties that are well suited to sensor network applications. In particular, UWB systems have potentially low complexity and low cost, have noise-like signal, are resistant to severe multipath and jamming, and have very good time domain resolution allowing for location and tracking applications. This article examines one example of a UWB sensor network for outdoor sport and lifestyle applications.
4 INSULIN – LEPTIN SIGNALING IN THE BRAIN

Energy homeostasis, a term used to designate the physiological processes that regulate the metabolic state, is controlled by different organs, including the brain. Leptin and insulin play a key role in the central control of feeding and body weight, with interconnected signaling pathways at various levels. In addition, central resistance to their actions has a primary role in the pathogenesis of obesity and type 2 diabetes. Studies performed in knockout mice for insulin targets show that the impairment of insulin signaling is related to changes in learning and memory. Moreover, circadian neurological alterations induce metabolic processes that lead to obesity and insulin resistance in patients with mental disorders. Thus, leptin and insulin signaling may be a link between the pathophysiological states affecting energy homeostasis and the appearance of mental illnesses. Pharmacological targeting for these signaling pathways could lead to important medical benefits in these diseases.
A Perspective on Modern Pesticides, Pelagic Fish Declines, and Unknown Ecological Resilience in Highly Managed Ecosystems

Pesticides applied on land are commonly transported by runoff or spray drift to aquatic ecosystems, where they are potentially toxic to fishes and other nontarget organisms. Pesticides add to and interact with other stressors of ecosystem processes, including surface-water diversions, losses of spawning and rearing habitats, nonnative species, and harmful algal blooms. Assessing the cumulative effects of pesticides on species or ecological functions has been difficult for historical, legal, conceptual, and practical reasons. To explore these challenges, we examine current-use (modern) pesticides and their potential connections to the abundances of fishes in the San Francisco Estuary (California). Declines in delta smelt (Hypomesus transpacificus), Chinook salmon (Oncorhynchus tshawytscha), and other species have triggered mandatory and expensive management actions in the urbanizing estuary and agriculturally productive Central Valley. Our inferences are transferable to other situations in which toxics may drive changes in ecological status and trends.
Mutation update for the GPC3 gene involved in Simpson‐Golabi‐Behmel syndrome and review of the literature

Simpson‐Golabi‐Behmel syndrome (SGBS) is an X‐linked multiple congenital anomalies and overgrowth syndrome caused by a defect in the glypican‐3 gene (GPC3). Until now, GPC3 mutations have been reported in isolated cases or small series and the global genotypic spectrum of these mutations has never been delineated. In this study, we review the 57 previously described GPC3 mutations and significantly expand this mutational spectrum with the description of 29 novel mutations. Compiling our data and those of the literature, we provide an overview of 86 distinct GPC3 mutations identified in 120 unrelated families, ranging from single nucleotide variations to complex genomic rearrangements and dispersed throughout the entire coding region of GPC3. The vast majority of them are deletions or truncating mutations (frameshift, nonsense mutations) predicted to result in a loss‐of‐function. Missense mutations are rare and the two which were functionally characterized, impaired GPC3 function by preventing GPC3 cleavage and cell surface addressing respectively. This report by describing for the first time the wide mutational spectrum of GPC3 could help clinicians and geneticists in interpreting GPC3 variants identified incidentally by high‐throughput sequencing technologies and also reinforces the need for functional validation of non‐truncating mutations (missense, in frame mutations, duplications).
Modulation of Active Gut Microbiota by Lactobacillus rhamnosus GG in a Diet Induced Obesity Murine Model

Gut microbiota play a key role in the development of metabolic disorders. Defining and correlating structural shifts in gut microbial assemblages with conditions related to metabolic syndrome have, however, been proven difficult. Results from 16S genomic DNA and 16S ribosomal RNA analyses of fecal samples may differ widely, leading to controversial information on the whole microbial community and metabolically active microbiota. Using a C57BL/6J murine model, we compared data from 16S genomic DNA and ribosomal RNA of the fecal microbiota. The study included three groups of experimental animals comprising two groups with high fat diet induced obesity (DIO) while a third group (control) received a low fat diet. One of the DIO groups was treated with the probiotic Lactobacillus rhamnosus GG (LGG). Compared to the data obtained by DNA analysis, a significantly higher abundance of OTUs was accounted for by RNA analysis. Moreover, rRNA based analysis showed a modulation of the active gut microbial population in the DIO group receiving LGG, thus reflecting a change in the induced obesity status of the host. As one of the most widely studied probiotics the functionality of LGG has been linked to the alleviation of metabolic syndrome, and, in some cases, to an impact on the microbiome. Yet, it appears that no study has reported thus far on modulation of the active microbiota by LGG treatment. It is postulated that the resulting impact on calorie consumption affects weight gain concomitantly with modulation of the functional structure of the gut microbial population. Using the 16S rRNA based approach therefore decisively increased the precision of gut microbiota metagenome analysis.
Personalized PageRank dimensionality and algorithmic implications

Many systems, including the Internet, social networks, and the power grid, can be represented as graphs. When analyzing graphs, it is often useful to compute scores describing the relative importance or distance between nodes. One example is Personalized PageRank (PPR), which assigns to each node $v$ a vector whose $i$-th entry describes the importance of the $i$-th node from the perspective of $v$. PPR has proven useful in many applications, such as recommending who users should follow on social networks (if this $i$-th entry is large, $v$ may be interested in following the $i$-th user). Unfortunately, computing $n$ such PPR vectors (where $n$ is the number of nodes) is infeasible for many graphs of interest. In this work, we argue that the situation is not so dire. Our main result shows that the dimensionality of the set of PPR vectors scales sublinearly in $n$ with high probability, for a certain class of random graphs and for a notion of dimensionality similar to rank. Put differently, we argue that the effective dimension of this set is much less than $n$, despite the fact that the matrix containing these vectors has rank $n$. Furthermore, we show this dimensionality measure relates closely to the complexity of a PPR estimation scheme that was proposed (but not analyzed) by Jeh and Widom. This allows us to argue that accurately estimating all $n$ PPR vectors amounts to computing a vanishing fraction of the $n^2$ vector elements (when the technical assumptions of our main result are satisfied). Finally, we demonstrate empirically that similar conclusions hold when considering real-world networks, despite the assumptions of our theory not holding.
Paradigm Shift to Neuroimmunomodulation for Translational Neuroprotection in Stroke

The treatment of acute ischemic stroke is still an unresolved clinical problem since the only approved therapeutic intervention relies on early blood flow restoration through pharmacological thrombolysis, mechanical thrombus removal, or a combination of both strategies. Due to their numerous complications and to the narrow time-window for the intervention, only a minority of stroke patients can actually benefit from revascularization procedures, highlighting the urgent need of identifying novel strategies to prevent the progression of an irreversible damage in the ischemic penumbra. During the past three decades, the attempts to target the pathways implicated in the ischemic cascade (e.g., excitotoxicity, calcium channels overactivation, reactive oxygen species (ROS) production) have failed in the clinical setting. Based on a better understanding of the pathobiological mechanisms and on a critical reappraisal of most failed trials, numerous findings from animal studies have demonstrated that targeting the immune system may represent a promising approach to achieve neuroprotection in stroke. In particular, given the dualistic role of distinct components of both the innate and adaptive arms of the immune system, a strategic intervention should be aimed at establishing the right equilibrium between inflammatory and reparative mechanisms, taking into consideration their spatio-temporal recruitment after the ischemic insult. Thus, the application of immunomodulatory drugs and their ability to ameliorate outcomes deserve validation in patients with acute ischemic stroke.
SSMART: Sequence-structure motif identification for RNA-binding proteins

Motivation RNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized. Results We developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3‘UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP. Availability SSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/ Contact uwe.ohler@mdc-berlin.de