Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Hemagglutinin MHC-II Ligand Analogues in Anti-TNFα Biologics: Implications for Immunogenicity of Pharmaceutical Proteins

The purpose of this study was to evaluate the extent of overlapping immunogenic peptides between three pharmaceutical biologics and influenza viruses. Clinical studies have shown that subsets of patients with rheumatoid arthritis (RA) develop anti-drug antibodies towards anti-TNFα biologics. We postulate that common infectious pathogens, including influenza viruses, may sensitize RA patients toward recombinant proteins. We hypothesize that embedded within infliximab (IFX), adalimumab (ADA), and etanercept (ETN) are ligands of class II major histocompatibility complex (MHC-II) that mimic T cell epitopes derived from influenza hemagglutinin (HA). The rationale is that repeated administration of the biologics would reactivate HA-primed CD4 T cells, stimulating B cells to produce cross-reactive antibodies. Custom scripts were constructed using MATLAB to compare MHC-II ligands of HA and the biologics; all ligands were predicted using tools in Immune Epitope Database and Resources (IEDB). We analyzed three HLA-DR1 alleles (0101, 0401 and 1001) that are prominent in RA patients, and two alleles (0103 and 1502) that are not associated with RA. The results indicate that 0401 would present more analogues of HA ligands in the three anti-TNFα biologics compared to the other alleles. The approach led to identification of potential ligands in IFX and ADA that shares sequence homology with a known HA-specific CD4 T cell epitope. We also discovered a peptide in the complementarity-determining region 3 (CDR-3) of ADA that encompasses both a potential CD4 T cell epitope and a known B cell epitope in HA. The results may help generate new hypotheses for interrogating patient variability of immunogenicity of the anti-TNFα drugs. The approach would aid development of new recombinant biologics by identifying analogues of CD4 T cell epitopes of common pathogens at the preclinical stage.
Secure multi-party consensus gossip algorithms

Information fusion is the keystone of many surveillance systems, in which the security of the information is a crucial aspect. This paper proposes a method to fuse information exchanging only encrypted data, through a secure extension of the popular consensus gossip algorithm using secure multi-party computation methodology. Sensor entities exchange only encrypted information and never have direct access to the data while iteratively reaching consensus. The agents do not have access to the final value and can just retrieve partial information, for instance a binary decision. An innovative implementation of the consensus algorithm in the encrypted domain is proposed and analyzed.
Behavioural Economics and Public Policy: Lessons for Effective and Efficient Developmental Schemes

Why do we often want things we do not need, and need things we do not want? This paper reviews the very core process of human psyche and the associated biases that make us what we are not: Homo economicus. It also proposes the many ways public policies could be redesigned – based on the recent findings in behavioural economics – in order to change the way people behave in an optimal way so that an ideal level of individual and social welfare can be achieved for any given society. A paper written for the Thai Student Academic Conference in Cambridge, UK (7 th -8 th February, 2009)
ToxiCat: Hybrid Named Entity Recognition services to support curation of the Comparative Toxicogenomic Database

We report on the original implementation of named entity recognition (NER) modules based on an automatic text categorization pipeline, so-called ToxiCat (Toxicogenomic Categorizer), developed to perform biomedical documents classification and prioritization for the previous Biocreative campaign in order to speed up the curation of the Comparative Toxicogenomics Database (CTD). ToxiCat NER modules are a group of components that analyse text for enclosed information. These modules are based on an information retrieval engine for MEDLINE (EAGLi), a gene normalization (GN) service (NormaGene) developed for a previous BioCreative campaign, gene ontology categorizer (GOCat) and finally an entity recognizer for diseases and chemicals. The NER services are publically available as RESTful web services at http://pingu.unige. ch:8080/Toxicat.
Notice of RetractionInternet usage and teens' psychological well-being in China

The aim of this study was to explore the evolution of the relationship between Internet use on psychological well-being from elementary school, middle school, to high schools. Randomized stratified sampling was used. Participants were 3, 185 teens in Kunming, a city located in southwest China. They were asked to complete a questionnaire on Internet Use and Well-Being Scale. The result indicated a relationship existed between Internet use and psychological well-being. Higher grade students used Internet earlier, spent more total time online per week, and scored lower psychological well-being. Students of middle and high school shared more similarity on Internet usage behavior and content preference of social and study services. Significant difference existed on loneliness and anxiety among elementary-, middle-, and high-schools. Compared with females, males significantly used the Internet earlier, more frequently, more for all Internet services, and scored higher psychological well-being. The difference between genders was also discussed.
SELECTED DRUG DELIVERY SYSTEMS BASED ON NANOEMULSION

A nanoemulsion is recognized as an emulsion with a droplet size ranging from 20 to 200 nm. It can provide greater encapsulation efficiency for delivery. Nanoemulsions have unique characteristics such as extremely small size, biocompatibility, relative stability, ability to solubilize high quantities of hydrophobic compounds, ability to reduce toxicity of cytotoxic drugs and ability to protect drugs from hydrolysis and enzymatic degradation under physiological conditions. Nanoemulsions can be used in many drug delivery systems. This paper summarises utilization of selected nanoemulsions as anti– inflammatory, anti–cancer and anti–microbial in some drug delivery systems.
What is in a text, what isn’t, and what this has to do with lexical semantics

This paper queries which aspects of lexical semantics can reasonably be expected to be modelled by corpus-based theories such as distributional semantics or techniques such as ontology extraction. We argue that a full lexical semantics theory must take into account the extensional potential of words. We investigate to which extent corpora provide the necessary data to model this information and suggest that it may be partly learnable from text-based distributions, partly inferred from annotated data, using the insight that a concept’s features are extensionally interdependent.
Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices

Peer prediction mechanisms are often adopted to elicit truthful contributions from crowd workers when no ground-truth verification is available. Recently, mechanisms of this type have been developed to incentivize effort exertion, in addition to truthful elicitation. In this paper, we study a sequential peer prediction problem where a data requester wants to dynamically determine the reward level to optimize the trade-off between the quality of information elicited from workers and the total expected payment. In this problem, workers have homogeneous expertise and heterogeneous cost for exerting effort, both unknown to the requester. We propose a sequential posted-price mechanism to dynamically learn the optimal reward level from workers' contributions and to incentivize effort exertion and truthful reporting. We show that (1) in our mechanism, workers exerting effort according to a non-degenerate threshold policy and then reporting truthfully is an equilibrium that returns highest utility for every worker, and (2) The regret of our learning mechanism w.r. t. offering the optimal reward (price) is upper bounded by Õ(T{3/4) where T is the learning horizon. We further show the power of our learning approach when the reports of workers do not necessarily follow the game-theoretic equilibrium.
An Approach to Software Architecture Description Using UML Revision 2 . 0

This document presents a practicabke way of describing software architectures using the Unified Modeling Language. The approach is based on a “3+1” structure in which three viewpoints on the described system are used – module, component & connector, and allocation – are used to describe a solution for a set of architectural requirements.
Where to apply dropout in recurrent neural networks for handwriting recognition?

The dropout technique is a data-driven regularization method for neural networks. It consists in randomly setting some activations from a given hidden layer to zero during training. Repeating the procedure for each training example, it is equivalent to sample a network from an exponential number of architectures that share weights. The goal of dropout is to prevent feature detectors to rely on each other. Dropout has successfully been applied to Deep MLPs and to convolutional neural networks, for various tasks of Speech Recognition and Computer Vision. We recently proposed a way to use dropout in MDLSTM-RNNs for handwritten word and line recognition. In this paper, we show that further improvement can be achieved by implementing dropout differently, more specifically by applying it at better positions relative to the LSTM units.
Metropolitan smart-growth centers: An assessment of incentive policies in four regions

Across the United States, metropolitan areas face challenges related to transportation and land use. An emerging policy in many regions is to promote development around higher-density, mixed-use (smart-growth) centers that create locally accessible nodes; many of these nodes are also linked to transit stops. Some metropolitan planning organizations (MPOs) have developed regional plans and incentive programs to encourage local governments to develop these centers. Incentives include grants as well as funding criteria in transportation improvement programs (TIP) that favor projects supporting centers. This paper assesses these policies as they have been applied in: (1) Puget Sound, Washington; (2) Portland, Oregon; (3) Denver, Colorado; and (4) San Diego, California. For the four regions we reviewed documents, conducted 40 interviews with key individuals, administered an online survey of 450 experts (response rate = 44 percent), and held a two-day forum involving 40 participants. We found that incentive policies by themselves were having a limited impact because they are new and offer small amounts of funding relative to local government needs and market forces. However, when incentives are combined with plans, policies, and transit investment, they provide a significant foundation for promoting growth around centers. There are a number of ways these policies can be improved, and many policies are transferable to other metropolitan regions.
Intelligent anti-grouping in real-time strategy games

Assembling suitable groups of fighting units to combat incoming enemy groups is a tactical necessity in real-time strategy (RTS) games. Furthermore it heavily influences future strategic decisions like unit building. Here, we demonstrate how to efficiently (offline) solve the problem of finding matches for the current enemy group(s) based on self-organizing maps (SOMs), powered by a simple evolutionary algorithm. The concept is implemented and thoroughly experimentally investigated in the RTS game Glest. We show that the offline learning is reliable and can be sped up considerably by employing a very simple substitute objective function instead of game simulations, making it a nearly universal, simple, and transparent technique.
Natural Selection in Synthetic Communities Highlights the Roles of Methylococcaceae and Methylophilaceae and Suggests Differential Roles for Alternative Methanol Dehydrogenases in Methane Consumption

We describe experiments that follow species dynamics and gene expression patterns in synthetic bacterial communities including species that compete for the single carbon substrate supplied, methane, and species unable to consume methane, which could only succeed through cooperative interactions. We demonstrate that these communities mostly select for two functional guilds, methanotrophs of the family Methylococcaceae and non-methanotrophic methylotrophs of the family Methylophilaceae, these taxonomic guilds outcompeting all other species included in the synthetic mix. The metatranscriptomics analysis uncovered that in both Methylococcaceae and Methylophilaceae, some of the most highly transcribed genes were the ones encoding methanol dehydrogenases (MDH). Remarkably, expression of alternative MDH genes (mxaFI versus xoxF), previously shown to be subjects to the rare Earth element switch, was found to depend on environmental conditions such as nitrogen source and methane and O2 partial pressures, and also to be species-specific. Along with the xoxF genes, genes encoding divergent cytochromes were highly expressed in both Methylophilaceae and Methylococcaceae, suggesting their function in methanol metabolism, likely encoding proteins serving as electron acceptors from XoxF enzymes. The research presented tested a synthetic community model that is much simplified compared to natural communities consuming methane, but more complex than the previously utilized two-species model. The performance of this model identifies prominent species for future synthetic ecology experiments and highlights both advantages of this approach and the challenges that it presents.
Open source information retrieval: a report on the SIGIR 2012 workshop

On August 16, 2012 the SIGIR 2012 Workshop on Open Source Information Retrieval was held as part of the SIGIR 2012 conference in Portland, Oregon, USA. There were 2 invited talks, one from industry and one from academia. There were 6 full papers and 6 short papers presented as well as demonstrations of 4 open source tools. Finally there was a lively discussion on future directions for the open source Information Retrieval community. This contribution discusses the events of the workshop and outlines future directions for the community.
An Adaptive Multi-objective Evolutionary Algorithm Based on Differential Evolution

Multi-objective Optimization Evolutionary Algorithm can be applied to problems in Economies, Management and Engineering; actually, most of design problem in real world can be reduced to multi-objective optimization problem. This paper proposed a new Evolutionary Algorithm based on Differential Evolution. The experimental results as the test-bed show that this algorithm can obtain quite fine approximated Pareto fronts and the fronts cover the theoretical Pareto front; these results imply that the proposed algorithm performs well.
Tumor progression in murine leukemia virus-induced T-cell lymphomas: monitoring clonal selections with viral and cellular probes

Clonal selections occurring during the progression of Moloney murine leukemia virus (MuLV)-induced T-cell lymphomas in mice were examined in primary and transplanted tumors by monitoring various molecular markers: proviral integration patterns, MuLV insertions near c-myc and pim-1, and rearrangements of the immunoglobulin heavy chain and beta-chain T-cell receptor genes. The results were as follows. Moloney MuLV frequently induced oligoclonal tumors with proviral insertions near c-myc or pim-1 in the independent clones. Moloney MuLV acted as a highly efficient insertional mutagen, able to activate different (putative) oncogenes in one cell lineage. Clonal selections during tumor progression were frequently marked by the acquisition of new proviral integrations. Independent tumor cell clones exhibited a homing preference upon transplantation in syngeneic hosts and were differently affected by the route of transplantation.
Successive Interference Mitigation in Multiuser MIMO Channels

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN.
Adaptation to flavomycin in the ruminal bacterium, Prevotella bryantii

Aims: The recent EU ban of growth‐promoting antibiotics in animal production was based on fears concerning antibiotic resistance being transmitted to human pathogens. This paper explores the adaptation mechanism of a common ruminal bacterium, Prevotella bryantii, to one of the banned compounds, flavomycin (flavophospholipol).
Milk Thistle Nomenclature: Why It Matters in Cancer Research and Pharmacokinetic Studies

Extracts of milk thistle have been recognized for centuries as “liver tonics” and are well-known to prevent or reverse hepatotoxicity of reactive drug metabolites or naturally occurring toxins. Milk thistle extracts are now under intense study in the experimental therapeutics of cancer for chemoprevention, treatment, and amelioration of chemotherapy side effects. Precision in nomenclature, however, has lagged behind this progress. The crude commercial product of milk thistle is termed silymarin, a complex of at least 7 flavonolignans and 1 flavonoid that comprises 65% to 80% of milk thistle extract. From silymarin is derived silibinin, a semipurified fraction once thought to be a single compound but now recognized as a 1:1 mixture of 2 diastereoisomers, silybin A and silybin B. The distinction between silymarin and silibinin is not only important to understanding the historical literature, but thorough characterization and use of chemically defined mixtures in preclinical and clinical studies are essential to the progress of these botanical compounds as human therapeutics. As a result, we urge clinicians and preclinical investigators alike to exercise rigor in nomenclature and use pure compounds or precisely defined chemical mixtures in subsequent studies. Herein, we provide a guide to the proper nomenclature and composition of milk thistle extracts and discuss the known pharmacokinetic studies of these botanical medicines. The drug-interaction potential of these extracts appears to be quite low, and in fact, silibinin appears to synergize with the antitumor effects of some commonly used chemotherapeutics. However, some precautions are advised as high-dose, phase II studies are conducted.
Movement behaviour and dynamics of an aquatic insect in a stream benthic landscape

Understanding not just where organisms move but how they move is an important step towards integrating animal behaviour into landscape ecology. The three-dimensional landscape of a streambed provides an ideal setting for forging this integration because of the persuasive effects of flowing water. In this study, we experimentally examine the larval movement of the case-building caddisfly Agapetus boulderensis Milne, 1936 in response to two current velocities in each of five levels of contrasting habitat types (i.e., smooth patches that facilitate movement and thick algal patches that constrain movement). Detailed behavioural observations showed that larvae employed two distinctly different strategies of movement in different current velocities: faster crawling and slower pivoting. Our results suggest that individual decision-making between crawling and pivoting is related to the magnitude of current velocity across the streambed, and the frequency at which larvae employ these behaviours translates into dif.
Block Design and Neurocognitive Impairment in Children

Learning disabled children were subgrouped according to etiology: neurocognitively impaired and neurocognitively intact. The two groups were found to be intellectually different from one another, as the impaired scored much lower on a general intelligence test than the intact. In addition, the impaired appeared to have a specific cognitive deficit, as they differed from the intact on the Block Design subtest when IQ was controlled through a difference of differences technique. Nonetheless, neither Block Design nor general intelligence was strongly related to reading deviation, though there was a suggestion of a relationship between Verbal IQ and reading within the impaired group. Despite the finding that the impaired group suffers from both global and specific intellectual deficits, it must be posited that nonintellective factors also much affect school performance. IQ and Block Design do not explain failure to read in either group. These findings and conclusions, it must be cautioned, pertain to abnormal populations.
Application of Tast-to-Method Transform to Laser Seam Welding

Intelligent machines are supposed to automatically set process parameters when faced with a task to be processed. The intelligence is often realized by databases which link the task with process parameters. This paper reviews a flexible and portable (to various processes) system to find (optimized) process parameters which force the process outcome to pre-defined quality under given variable conditions. In addition, extensions to the original system are presented and the whole concept is applied to laser seam welding (LSW). Experimental results based on real process executions demonstrate the applicability in industrial environments. Keywords–Machine Intelligence; Task-to-Method-Mapping; Support Vector Regression; Data Domain Description; Laser Welding
Effects of Measurements and Pseudomeasurements Correlation in Distribution System State Estimation

Distribution system state estimation (DSSE) is one of the key elements of the monitoring activity of an active distribution network, and is the basis for every control and management application. The DSSE relies on real measurements collected by the distributed measurement system and on other available information, mainly obtained from historical data that help in obtaining observability. This prior information is necessary to derive the so called pseudomeasurements. Accurate input data are fundamental for an accurate estimation, as well as knowledge on possible correlation in the measured and pseudomeasured data. A degree of correlation can exist in the measured data, due to measurement devices, and among power consumptions or generations of some particular nodes. This paper presents an exhaustive analysis on the influence of correlations on the quality of the estimation. The importance of including correlation in the weighted least square estimation approach is discussed using both traditional and synchronized measurements. Results obtained on a 95-bus distribution network are presented and discussed.