Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating influenza antigenic dynamics with molecular evolution

Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution. DOI: http://dx.doi. org/10.7554/eLife.01914. 001
CALCULATION OF INCLUSION COMPLEX OF A BINAPHTYL DERIVATIVE AND β-CYCLODEXTRIN USING DENSITY FUNCTIONAL THEORY

We investigated the interaction of the host-guest β-cyclodextrin2,2’-Dihydroxy-1,1’-binaphthyl complex by means of molecular dynamics simulation using a density functional based tight-binding code. We focused in particular on the investigation of the most stable conformation of this complex by investigating some of the structural properties that change with the time. This includes the hydrogen bonding formation of the active agent guest molecule with the torus-like macro ring of the host β-cyclodextrin leading to the formation of the stable adduct in the lipophilic cavity of the biopolimeric matrix. The role of solvent, water in stabilizing the complex was also discussed. We relate our results of the final stable optimized geometry of the complex to the UV/Vis and circular dichroism spectral study.
Dextran sulfate acts as an endothelial cell protectant and inhibits human complement and natural killer cell-mediated cytotoxicity against porcine cells

Background. The innate immune system, including complement and natural killer (NK) cells, plays a critical role in activation and damage of endothelial cells (ECs) during xenograft rejection. The semisynthetic proteoglycan analog dextran sulfate (DXS, molecular weight 5,000) is known to inhibit the complement and coagulation cascades. We hypothesized that DXS may act as an “EC-protectant” preventing complement and NK lysis by functionally replacing heparan sulfate proteoglycans that are shed from the EC surface on activation of the endothelium. Methods. Binding of DXS to ECs, deposition of human complement, cytotoxicity, and heparan sulfate expression after exposure to normal human serum were analyzed by flow cytometry. The efficacy of DXS to protect ECs from xenogeneic NK cell-mediated cytotoxicity was tested in standard 51Cr-release assays. Results. DXS dose-dependently inhibited all three pathways of complement activation. Binding of DXS to porcine cells increased on treatment with human serum or heparinase I and correlated positively with the inhibition of human complement deposition. This cytoprotective effect of DXS was still present when the challenge with normal human serum was performed up to 48 hr after DXS treatment of the cells. DXS incubation of porcine ECs with and without prior tumor necrosis factor-&agr; stimulation reduced xenogeneic cytotoxicity mediated by human NK cells by 47.3% and 25.3%, respectively. Conclusions. DXS binds to porcine cells and protects them from complement- and NK cell-mediated injury in vitro. It might therefore be used as a novel therapeutic strategy to prevent xenograft rejection and has potential for clinical application as an “EC protectant. ”
Profiling cloud applications with hardware performance counters

Virtualization is a key enabler technology for cloud computing. It allows applications to share computing, memory, storage, and network resources. However, physical resources are not standalone and the server infrastructure is not homogeneous. The CPU cores are commonly connected to the shared memory, caches, and computational units. As a result, the performance of cloud applications can be greatly affected if, while being executed at different computing cores, they compete for the same shared cache or network resource. The performance degradation can be as high as 50%. In this work we present a methodology which predicts the performance problems of cloud applications during their concurrent execution by looking at the hardware performance counters collected during their standalone execution. The proposed methodology fosters design of novel solutions for efficient resource allocation and scheduling.
Teachers’ adaptations to and orientations towards an adolescent literacy curriculum

Abstract This study examines teacher implementation of an adolescent literacy curriculum designed as an intervention for students reading two years below grade level. Specifically, this work focuses on the adaptations made by four experienced teachers in a single school that sustained implementation of this curriculum after the intervention trial had ended. Data were collected through observation and interview. An accounts of teaching practice methodology was used to define each teacher’s orientation towards the curriculum, and then to determine whether this orientation demonstrated assimilation or accommodation to intervention principles. I found that the vast majority of time spent implementing the curriculum included adaptations, and that each teacher’s adaptations were different, and reflected her pre-existing orientation towards literacy teaching. Although one teacher demonstrated assimilation and accommodation to intervention principles, the other three primarily demonstrated assimilation. These findings suggest the importance of understanding teachers’ orientations towards curriculum in order to provide more tailored professional development which may help teachers accommodate to the most critical pedagogical features of a curriculum. This may be especially significant in considering sustained implementation, after research-related supports have been withdrawn.
Towards vessel characterisation in the vicinity of the optic disc in digital retinal images

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel patterns in the retina. This paper describes progress towards the development of an integrated automated analyser of the retinal blood vessels in the vicinity of the optic disc using digital colour retinal images. First the optic disc was detected using a combination of Butterworth filtering, canny edge detection and morphological filters. After finding initial points using a median filter, blood vessels were tracked at one optic disc radius from the optic disc boundary, by two-dimensional fitting of a physically inspired model to a local region of a vessel. The last step involved the classification of the segmented vessels into arteries and veins, by using colour and hue features as inputs to a variety of classifier algorithms. The optic disc was located to within 2.5% of one optic-disc diameter in 13 of 20 images when compared to the manually identified optic disc centre. Using the median filter we obtained good accuracy for locating blood vessels. Optimisation of the blood vessel classifier using the naïve Bayes rule, resulted in a mean accuracy of 70% (s.d.=17.6%) over eight images analysed.
Scalability analysis and flow admission control in mininet-based SDN environment

In this demo, we provide a Mininet-based Software-Defined Network (SDN) simulation environment that improves the total flow throughput and scalability of the overall network. Mininet is a suitable and manageable tool to implement the proposed SDN based flow admission control module in order to configure the entire topology since it has already built in Open-Flow switches and virtual controllers. This open-source platform is also easily configurable via its drag and drop capabilities. In this Demo, for the Control Plane, OpenDaylight controller is used in order to simulate flow admission control module that fairly admits flows into the OpenFlow switches. OpenFlow version, 1.3. for communication between separated Data and Control plane and Linux based operating system to build Mininet 2.1. 0 are deployed in the simulator environment.
Graph-Based Bilingual Word Embedding for Statistical Machine Translation

Bilingual word embedding has been shown to be helpful for Statistical Machine Translation (SMT). However, most existing methods suffer from two obvious drawbacks. First, they only focus on simple contexts such as an entire document or a fixed-sized sliding window to build word embedding and ignore latent useful information from the selected context. Second, the word sense but not the word should be the minimal semantic unit; however, most existing methods still use word representation. To overcome these drawbacks, this article presents a novel Graph-Based Bilingual Word Embedding (GBWE) method that projects bilingual word senses into a multidimensional semantic space. First, a bilingual word co-occurrence graph is constructed using the co-occurrence and pointwise mutual information between the words. Then, maximum complete subgraphs (cliques), which play the role of a minimal unit for bilingual sense representation, are dynamically extracted according to the contextual information. Consequently, correspondence analysis, principal component analyses, and neural networks are used to summarize the clique-word matrix into lower dimensions to build the embedding model. Without contextual information, the proposed GBWE can be applied to lexical translation. In addition, given contextual information, GBWE is able to give a dynamic solution for bilingual word representations, which can be applied to phrase translation and generation. Empirical results show that GBWE can enhance the performance of lexical translation, as well as Chinese/French-to-English and Chinese-to-Japanese phrase-based SMT tasks (IWSLT, NTCIR, NIST, and WAT).
An Introduction to the CSAR Package

We present here a R package for the statistical detection of protein-bound genomic regions, where, considering the average DNA fragment size submitted to sequencing, single-nucleotide enrichment values are obtained. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control false discovery rate are obtained through random permutation. The computational efficiency is achieved implanting the most time-consuming functions in C++ language, and integrating them in the R package. Standard outputs of the package are tables of genomic coordinates of significantly enriched region locations, level of enrichment per nucleotide position and the distance of enriched regions to annotated genomic features. The algorithm is described in detail in [1], [2], [3].
OpenAirInterface-an effective emulation platform for LTE and LTE-Advanced

The OpenAirInterface (OAI) emulation platform is an effective tool to evaluate the performance of LTE and LTE-Advanced system, and test related algorithms and strategies. This platform aims at building a platform that is accuracy, repeatable and scalable. The complete features of OAI emulation platform are presented and the key technologies are introduced. In order to develop the platform for the further application, some new modules have been added and some validation of the platform has been provided. The emulation comes in two flavors: single machine mode and multi-machine mode. PHY abstraction with the core methodology of effective SINR mapping improves the platform's efficiency and the capability to build a large-scale emulation with multiple nodes. The key function random access and scheduling of MAC layer and the basic call procedure of the platform are provided. The validation results shown in this paper illustrate that the emulation platform is correctly operating and PHY abstraction reduce the emulation time significantly on the basis of accuracy. This emulation platform can be used to test, evaluate and validate of the real LTE system, and with the efficiency operation, it can be improved to support the systemlevel emulation of a large-scale LTE and LTE-A system.
Recommendation of Multimedia Items by Link Analysis and Collaborative Filtering

We investigate two recommendation approaches suitable for online multimedia sharing services. Our first approach, UserRank, recommends items by global interestingness irrespective of user preferences and is based on the analysis of ownership and evaluation link structure. We also present a personalized interestingness algorithm that combines UserRank with collaborative filtering which enables a single parameter to control the degree of personalization in the recommendations. Our initial results from an informal user study are encouraging.
A patient with traumatic chylothorax

Chylothorax refers to the accumulation of chyle in the pleural cavity. Although rare, it is the most frequent cause of pleural effusion in the neonatal period. Its incidence is estimated at one in 15,000 neonates. The causes of chylothorax are multiple, and there are several major types of chylothorax defined by origin, ie, traumatic (and iatrogenic) chylothorax, medical (spontaneous) chylothorax, and congenital chylothorax. A case of neonatal chylothorax following surgery for esophageal atresia and our therapeutic approach to this entity are presented. Conservative therapy with total enteral nutrition and drainage were sufficient. Treatment for chylothorax is essentially medical, ie, pleural drainage, removal of dietary fats, treatment of any medical cause, and use of drugs to reduce production of chyle. In the event of failure or reappearance of a large effusion, surgical treatment is needed.
Statistical Primer for Cardiovascular Research Introduction to the Analysis of Survival Data in the Presence of Competing Risks

601 Abstract—Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression models in the presence of competing risks, researchers can choose from 2 different families of models: modeling the effect of covariates on the cause-specific hazard of the outcome or modeling the effect of covariates on the cumulative incidence function. The former allows one to estimate the effect of the covariates on the rate of occurrence of the outcome in those subjects who are currently event free. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. The former family of models may be better suited for addressing etiologic questions, whereas the latter model may be better suited for estimating a patient's clinical prognosis. We illustrate the application of these methods by examining cause-specific mortality in patients hospitalized with heart failure. Statistical software code in both R and SAS is provided. under the terms of the Creative Commons Attribution Non-Comercial-NoDervis License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.
Constructing the world: Active causal learning in cognition

Humans are adept at constructing causal models of the world that can support prediction, explanation, simulation-based reasoning, planning and control. In this thesis I explore how people learn about the causal world interacting with it, and how they represent and modify their causal knowledge as they gather evidence. Over 10 experiments and modelling, I show that interventional and temporal cues, along with top-down hierarchical constraints, inform the gradual evolution and adaptation of increasingly rich causal representations. Chapters 1 and 2 develop a rational analysis of the problems of learning and representing causal structure, and choosing interventions, that perturb the world in ways that reveal its structure. Chapters 3--5 focus on structure learning over sequences of discrete trials, in which learners can intervene by setting variables within a causal system and observe the consequences. The second half of the thesis generalises beyond the discrete trial learning case, exploring interventional causal learning in situations where events occur in continuous time (Chapters 6 and 7); and in spatiotemporally rich physical "microworlds" (Chapter 8). Throughout the experiments, I find that both children and adults are robust active causal learners, able to deal with noise and complexity even as normative judgment and intervention selection become radically intractable. To explain their success, I develop scalable process level accounts of both causal structure learning and intervention selection inspired by approximation algorithms in machine learning. I show that my models can better explain patterns of behaviour than a range of alternatives as well as shedding light on the source of common biases including confirmatory testing, anchoring effects and probability matching. Finally, I propose a close relationship between active learning and active aspects of cognition including thinking, decision making and executive control.
Social Media Data Integration for Community Detection

Community detection is an unsupervised learning task that discovers groups such that group members share more similarities or interact more frequently among themselves than with people outside groups. In social media, link information can reveal heterogeneous relationships of various strengths, but often can be noisy. Since different sources of data in social media can provide complementary information, e.g., bookmarking and tagging data indicates user interests, frequency of commenting suggests the strength of ties, etc., we propose to integrate social media data of multiple types for improving the performance of community detection. We present a joint optimization framework to integrate multiple data sources for community detection. Empirical evaluation on both synthetic data and real-world social media data shows significant performance improvement of the proposed approach. This work elaborates the need for and challenges of multi-source integration of heterogeneous data types, and provides a principled way of multi-source community detection.
Histone H1 Poly[ADP]-Ribosylation Regulates the Chromatin Alterations Required for Learning Consolidation

Memory formation requires changes in gene expression, which are regulated by the activation of transcription factors and by changes in epigenetic factors. Poly[ADP]-ribosylation of nuclear proteins has been postulated as a chromatin modification involved in memory consolidation, although the mechanisms involved are not well characterized. Here we demonstrate that poly[ADP]-ribose polymerase 1 (PARP-1) activity and the poly[ADP]-ribosylation of proteins over a specific time course is required for the changes in synaptic plasticity related to memory stabilization in mice. At the molecular level, histone H1 poly[ADP]-ribosylation was evident in the hippocampus after the acquisition period, and it was selectively released in a PARP-1-dependent manner at the promoters of cAMP response element-binding protein and nuclear factor-κB dependent genes associated with learning and memory. These findings suggest that histone H1 poly[ADP]-ribosylation, and its loss at specific loci, is an epigenetic mechanism involved in the reprogramming of neuronal gene expression required for memory consolidation.
An empirical study on combining instance based and rule based classifiers

One of the most important challenges in developing problem solving methods is to combine and synergistically utilize general and specific knowledge. This paper presents one possible way of performing this integration that might be generally described as follows: "To solve a problem, first try to use the conventional rulebased approach. If it does not work, try to find a similar problem you have solved in the past and adapt the old solution to the new situation". We applied this heuristic for a classification task. The background concepts of this heuristic are standard cases (the source of data for the rules) and exceptional cases (representative of the specific knowledge). The presented empirical study has not shown this attempt to be successful in accuracy when it is compare to its parents methods: instance-based and rulebased approach, but a careful policy in the distribution of standard and exceptional cases might provide a competitive classifier in terms of accuracy and comprehensibility.
Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties

Scheduling of multi-microgrids (MMGs) is one of the important tasks in MMG operation and it faces new challenges as the integration of demand response (DR) programs and renewable generation (wind and solar) sources increases. In order to address these challenges, robust optimization (RO)-based scheduling has been proposed in this paper considering uncertainties in both renewable energy sources and forecasted electric loads. Initially, a cost minimization deterministic model has been formulated for the MMG system. Then, it has been transformed to a min-max robust counterpart and finally, a traceable robust counterpart has been formulated using linear duality theory and Karush–Kuhn–Tucker (KKT) optimality conditions. The developed model provides immunity against the worst-case realization within the provided uncertainty bounds. Budget of uncertainty has been used to develop a trade-off between the conservatism of solution and probability of unfeasible solution. The effect of uncertainty gaps on internal and external trading, operation cost, unit commitment of dispatchable generators, and state of charge (SOC) of battery energy storage systems (BESSs) have also been analyzed in both grid-connected and islanded modes. Simulations results have proved the robustness of proposed strategy.
Collaborated Directional Chamfer Matching for 3D Hand Pose Estimation

We describe a shape-based chamfer matching method for recovering 3D hand pose using a monocular camera. Although some variants of chamfer matching methods incorporated edge direction information, of which the direction term is set empirically, so they may fail in different application scenario. In this paper, a collaborated directional chamfer matching (CDCM) is proposed. The direction term can be directly converted to a distance term, so a normalization factor is no more needed, and the similarity measurement can be directly calculated in scale space without adjusting the normalization factor. At the same time, a two layered structure is constructed using both the Active shape models (ASM) and the proposed CDCM to get the precise pose. Experimental results show the effectiveness of the proposed method.