Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal TLR signaling is required for prenatal asthma protection by the nonpathogenic microbe Acinetobacter lwoffii F78

The pre- and postnatal environment may represent a window of opportunity for allergy and asthma prevention, and the hygiene hypothesis implies that microbial agents may play an important role in this regard. Using the cowshed-derived bacterium Acinetobacter lwoffii F78 together with a mouse model of experimental allergic airway inflammation, this study investigated the hygiene hypothesis, maternal (prenatal) microbial exposure, and the involvement of Toll-like receptor (TLR) signaling in prenatal protection from asthma. Maternal intranasal exposure to A. lwoffii F78 protected against the development of experimental asthma in the progeny. Maternally, A. lwoffii F78 exposure resulted in a transient increase in lung and serum proinflammatory cytokine production and up-regulation of lung TLR messenger RNA. Conversely, suppression of TLRs was observed in placental tissue. To investigate further, the functional relevance of maternal TLR signaling was tested in TLR2/3/4/7/9−/− knockout mice. The asthma-preventive effect was completely abolished in heterozygous offspring from A. lwoffii F78–treated TLR2/3/4/7/9−/− homozygous mother mice. Furthermore, the mild local and systemic inflammatory response was also absent in these A. lwoffii F78–exposed mothers. These data establish a direct relationship between maternal bacterial exposures, functional maternal TLR signaling, and asthma protection in the progeny.
Computing Strongest Necessary and Weakest Sufficient Conditions of First-Order Formulas

A technique is proposed for computing the weakest sufficient (wsc) and strongest necessary (snc) conditions for formulas in an expressive fragment of first-order logic using quantifier elimination techniques. The efficacy of the approach is demonstrated by using the techniques to compute snc's and wsc's for use in agent communication applications, theory approximation and generation of abductive hypotheses. Additionally, we generalize recent results involving the generation of successor state axioms in the propositional situation calculus via snc's to the first-order case. Subsumption results for existing approaches to this problem and a re-interpretation of the concept of forgetting as a process of quantifier elimination are also provided.
Epigenetic: A missing paradigm in cellular and molecular pathways of sulfur mustard lung: a prospective and comparative study

Sulfur mustard (SM, bis- (2-chloroethyl) sulphide) is a chemical warfare agent that causes DNA alkylation, protein modification and membrane damage. SM can trigger several molecular pathways involved in inflammation and oxidative stress, which cause cell necrosis and apoptosis, and loss of cells integrity and function. Epigenetic regulation of gene expression is a growing research topic and is addressed by DNA methylation, histone modification, chromatin remodeling, and noncoding RNAs expression. It seems SM can induce the epigenetic modifications that are translated into change in gene expression. Classification of epigenetic modifications long after exposure to SM would clarify its mechanism and paves a better strategy for the treatment of SM-affected patients. In this study, we review the key aberrant epigenetic modifications that have important roles in chronic obstructive pulmonary disease (COPD) and compared with mustard lung.
Characterization of Rainfall Patterns in the Semiarid Brazil

The Semiarid Brazil (SAB) is the wettest semiarid region of the world, and its economy is strongly based on family farming, predominantly in non-irrigated conditions. Dryland farming in this region is influenced by climate variability, which increases the risk of crop losses during periods of drought. Thus, the objective of this study is to identify rainfall and number of rainy days patterns in the period from 1979 to 2014, using data provided by the Global Precipitation Climatology Project (GPCP). Cluster analysis was used in order to define homogeneous groups in terms of precipitation and number of rainy days within each month. The statistical technique of discriminant analysis was used to verify the classification of homogeneous groups as defined by the cluster analysis. Four precipitation groups were created in the SAB, namely SAB I, SAB II, SAB III and SAB IV, where SAB I is the wettest one. It comprises the southern and eastern portions of the SAB and has an average annual accumulated rainfall of about 1,000 mm. The SAB IV is the second wettest group and extends throughout the east of Alagoas, Pernambuco, Paraíba and Rio Grande do Norte states, Ceará and western Piauí, with an average annual total of 820 mm. SAB II and SAB III are the driest groups, with 748 and 570 mm respectively. The accuracy of the classification of precipitation groups was 94%. Regarding the number of rainy days, the east and the north of the SAB present the largest number of rainy days, in JFMA, with a total of 310 mm distributed in 81 days. In the western and southern portions of the SAB the same rainfall and number of rainy days behavior patterns were observed. The highest means were registered in NDJ, with a total of 451 mm in 64 days and 55 days, respectively. As for the dry months, July and August registered rainfall means of 8 mm in 11 and 9 days, respectively. The accuracy of the classification of groups by number of rainy days was 95%.
The specialising or sampling debate: a retrospective analysis of adolescent sports participation in the UK

Abstract Whether young people should specialise in one competitive sport at an early age, or pursue a wider range of sports during adolescence is a topic of some debate (Baker, Cobley, & Fraser-Thomas, 2009) and is fundamental within sports policy and coaching practice. The purpose of this retrospective recall study was to identify whether early specialisation or sporting diversification (sampling) throughout childhood and adolescence can influence performance levels prior to adulthood. An online questionnaire was used to collect the sport participation histories of 1006 UK sports people, which were then compared with the developmental framework provided by the Developmental Model of Sport Participation (DMSP, Côté & Fraser-Thomas, 2007). A significant association between the number of sports participated in at the ages of 11, 13, and 15 and the standard of competition between 16 and 18 years was found. Individuals who competed in three sports aged 11, 13, and 15 were significantly more likely to compete at a national compared with club standard between the ages of 16 and 18 than those who practised only one sport. The findings reported here provide some empirical support for the sampling performance pathway DMSP model in a UK context.
Removing communications in clustered microarchitectures through instruction replication

The need to communicate values between clusters can result in a significant performance loss for clustered microarchitectures. In this work, we describe an optimization technique that removes communications by selectively replicating an appropriate set of instructions. Instruction replication is done carefully because it might degrade performance due to the increased contention it can place on processor resources. The proposed scheme is built on top of a previously proposed state-of-the-art modulo-scheduling algorithm. Though this algorithm has been proved to be very effective at reducing communications, results show that the number of communications can be further decreased by around one-third through replication, which results in a significant speedup. IPC is increased by 25% on average for a four-cluster microarchitecture and by as much as 70% for selected programs. We also show that replicating appropriate sets of instructions is more effective than doubling the intercluster connection network bandwidth.
Multiple Incremental Decremental Learning of Support Vector Machines

We propose a multiple incremental decremental algorithm of support vector machines (SVM). In online learning, we need to update the trained model when some new observations arrive and/or some observations become obsolete. If we want to add or remove single data point, conventional single incremental decremental algorithm can be used to update the model efficiently. However, to add and/or remove multiple data points, the computational cost of current update algorithm becomes inhibitive because we need to repeatedly apply it for each data point. In this paper, we develop an extension of incremental decremental algorithm which efficiently works for simultaneous update of multiple data points. Some analyses and experimental results show that the proposed algorithm can substantially reduce the computational cost. Our approach is especially useful for online SVM learning in which we need to remove old data points and add new data points in a short amount of time.
User Vulnerability and Its Reduction on a Social Networking Site

Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breaches with dire consequences. With the continuous expansion of a user’s social network, privacy settings alone are often inadequate to protect a user’s profile. In this research, we aim to address some critical issues related to privacy protection: (1) How can we measure and assess individual users’ vulnerability? (2) With the diversity of one’s social network friends, how can one figure out an effective approach to maintaining balance between vulnerability and social utility? In this work, first we present a novel way to define vulnerable friends from an individual user’s perspective. User vulnerability is dependent on whether or not the user’s friends’ privacy settings protect the friend and the individual’s network of friends (which includes the user). We show that it is feasible to measure and assess user vulnerability and reduce one’s vulnerability without changing the structure of a social networking site. The approach is to unfriend one’s most vulnerable friends. However, when such a vulnerable friend is also socially important, unfriending him or her would significantly reduce one’s own social status. We formulate this novel problem as vulnerability minimization with social utility constraints. We formally define the optimization problem and provide an approximation algorithm with a proven bound. Finally, we conduct a large-scale evaluation of a new framework using a Facebook dataset. We resort to experiments and observe how much vulnerability an individual user can be decreased by unfriending a vulnerable friend. We compare performance of different unfriending strategies and discuss the security risk of new friend requests. Additionally, by employing different forms of social utility, we confirm that the balance between user vulnerability and social utility can be practically achieved.
Protein Structural Motif Prediction in Multidimensional ø-Psi Space Leads to Improved Secondary Structure Prediction

A significant step towards establishing the structure and function of a protein is the prediction of the local conformation of the polypeptide chain. In this article, we present systems for the prediction of three new alphabets of local structural motifs. The motifs are built by applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for multiple phi-psi angle values collected from high-resolution protein structures. The predictive systems, based on ensembles of bidirectional recurrent neural network architectures, and trained on a large non-redundant set of protein structures, achieve 72%, 66%, and 60% correct motif prediction on an independent test set for di-peptides (six classes), tri-peptides (eight classes) and tetra-peptides (14 classes), respectively, 28-30% above baseline statistical predictors. We then build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into a traditional 3-class (helix, strand, coil) secondary structure. This system achieves 79.5% correct prediction using the "hard" CASP 3-class assignment, and 81.4% with a more lenient assignment, outperforming a sophisticated state-of-the-art predictor (Porter) trained in the same experimental conditions. The structural motif predictor is publicly available at: http://distill.ucd. ie/porter+/.
Adoptive T-cell Transfer Therapy and Oncogene-Targeted Therapy for Melanoma: The Search for Synergy

The clinical strengths of immunotherapy and small-molecule inhibitors targeting the mitogen-activated protein kinase (MAPK) pathway appear to be largely complementary for the treatment of advanced melanoma. In current practice, most patients with BRAF V600 mutant melanomas will see both modalities. Several in vitro and in vivo studies suggest that combining immunotherapy with MAPK inhibition may have synergistic effects. First, mouse models show that adoptive cell therapy (ACT) can be enhanced by vaccination. Rapid tumor destruction by vemurafenib could provide a vaccine-like stimulus to adoptively transferred T cells. Second, both in mice and in early clinical trials, melanoma metastases treated with MAPK inhibitors seem to display increased T-cell infiltrates. Third, MAPK inhibition upregulates the expression of some melanoma antigens and, therefore, may enhance T-cell recognition of vemurafenib-treated melanomas. Fourth, vemurafenib may sensitize tumor cells to immune destruction. Finally, some investigators have found that an optimal antitumor effect from MAPK inhibition is dependent on an intact host immune response. Currently, the Surgery Branch of the National Cancer Institute has initiated a phase II trial combining the BRAF inhibitor vemurafenib with ACT using tumor-infiltrating lymphocytes in patients with BRAF-mutant tumors to investigate the safety and efficacy of this combination. The proposed mechanisms for synergy between these two modalities can be complex, and their optimal combination may require testing a variety of sequences and schedules. Clin Cancer Res; 19(19); 5292–9. ©2013 AACR.
Average consensus on networks with transmission noise or quantization

In this note we study the average consensus algorithm in a distributed system of agents which are allowed to communicate according to a directed graph. Moreover, the communication between connected agents is not perfect, but affected by some error, which can be either a random additive noise or produced by a quantization. We investigate the effects of these constraints on the performance of the average consensus algorithms.
The Contribution of Long‐Term Research at Gombe National Park to Chimpanzee Conservation

Abstract: Long‐term research projects can provide important conservation benefits, not only through research specifically focused on conservation problems, but also from various incidental benefits, such as increased intensity of monitoring and building support for the protection of an area. At Gombe National Park, Tanzania, long‐term research has provided at least four distinct benefits to wildlife conservation. (1) Jane Goodall's groundbreaking discoveries of chimpanzee (Pan troglodytes) tool use, hunting, and complex social relationships in what was then a game reserve drew attention to the area and created support for upgrading Gombe to national park status in 1968. (2) The highly publicized findings have earned Gombe and Tanzania the attention of a worldwide public that includes tourists and donors that provide financial support for Gombe, other parks in Tanzania, and chimpanzee conservation in general. (3) Crucial information on social structure and habitat use has been gathered that is essential for effective conservation of chimpanzees at Gombe and elsewhere. (4) A clear picture of Gombe's chimpanzee population over the past 40 years has been determined, and this has helped identify the greatest threats to the viability of this population, namely disease and habitat loss outside the park. These threats are severe and because of the small size of the population it is extremely vulnerable. Research at Gombe has led to the establishment of conservation education and development projects around Gombe, which are needed to build local support for the park and its chimpanzees, but saving these famous chimpanzees will take a larger integrated effort on the part of park managers, researchers, and the local community with financial help from international donors.
In vitro evaluation of chondroitin sulphate-chitosan microspheres as carrier for the delivery of proteins

A novel formulation based on chondroitin sulphate/chitosan microspheres (CS/CH) has been investigated for oral delivery of macromolecules using ovalbumin as the model protein (OVA). The microspheres were prepared by a new emulsion-complex coacervation method. Physico-chemical properties of the polymers constituting microparticulate matrix were investigated by IR, DSC, TGA and X-ray diffraction analyses. In vitro tests were performed to evaluate the drug delivery system degradation and the protein release under conditions simulating the intestinal fluids. The ability of colonic enzymes to degrade the microparticulate systems was simulated employing the chondroitinase ABC enzyme. Results showed that the different CS/CH compositions influenced both microparticles stability and the protein release rate. Only the microspheres composed by 1:1 chondroitin sulphate–chitosan ratio achieved an OVA release profile suitable to a possible colon targeting. These microspheres released ∼30% of ovalbumin encapsulated in 24 h in the different aqueous media tested, while they released 100% of protein in the presence of chondroitinase. The preliminary results demonstrated that chondroitin sulphate-chitosan microspheres can be a suitable delivery system for protein drug envisaged to oral administration.
Does mouse size affect study and evaluation results?: a study comparing preschool children's performance with small and regular-sized mice

When researchers and practitioners conduct research studies or software evaluations where children use mice, it is unclear whether using different mouse sizes would affect the results of these studies and evaluations. To answer this question, we conducted a study with 50 four and five year olds who were randomly assigned to use either small or regular-sized mice. The children in the study had been using regular-sized mice 35 minutes a day, four times a week, for two months previous to the study. In analyzing the results of the study we found no statistically significant differences in accuracy or efficiency between the children who used small and regular-sized mice. We also found no statistically significant differences in accuracy or efficiency between the four and five year olds. The latter result was somewhat surprising given previous research results. In spite of the participants being frequent users of mice, they still had low accuracy rates when pointing and clicking on the smallest target size we tested (16 pixels in diameter). This result provides further evidence of preschool children's need for interactions designed specifically for their developing motor skills.
Inferring social ties from common activities in twitter

We investigate the extent to which we can infer social ties between a pair of users in an online social network Twitter, based on their common activities defined by the number of common celebrity profiles they are following. In this work, we analyze the list of celebrities that a set of Twitter users are following in December 2013 to infer the social ties that existed between these users till July 2009. We use two probabilistic models given by Kossinets et al. [Science, 2006] and Crandall et al. [PNAS, 2010] for this purpose. The model of Kossinets et al. is meant to give an upper bound for the probability of friendship between a pair of users, whereas the model by Crandall et al. is supposed to give an almost accurate estimate of the same. We observe that the model of Kossinets et al. is able to give an upper bound whereas the model given by Crandall et al. is unable to give an almost accurate estimate for our dataset. However, the model by Crandall et al. is observed to provide a correct estimate of the probability of friendship between the users when we consider following a particular type of celebrity profile, e.g. CEO, Author etc., as the activity of a user.
Model-Based Automated Generation of User Interfaces

User interface design and development for knowledge-based systems and most other types of applications is a resource-consuming activity. Thus, many attempts have been made to automate, to certain degrees, the construction of user interfaces. Current tools for automated design of user interfaces are able to generate the static layout of an interface from the application's data model using an intelligent program that applies design rules. These tools, however, are not capable of generating the dynamic behavior of the interface, which must be specified programmatically, and which constitutes most of the effort of interface construction. Mecano is a model-based user-interface development environment that uses a domain model to generate both the static layout and the dynamic behavior of an interface. A knowledge-based system applies sets of dialog design and layout rules to produce interfaces from the domain model. Mecano has been used successfully to completely generate the layout and the dynamic behavior of relatively large and complex, domain-specific, form- and graph-based interfaces for applications in medicine and several other domains.
On the Advantages of Cooperative and Social Smart Route Control

Smart route control is being increasingly used as a way to dynamically improve the end-to-end performance of the outbound traffic of multihomed stub domains. However, all the solutions available at present have in common two drawbacks which are key motivations for this work. First, all solutions are standalone, so no routing control interactions exist between the domains sourcing and sinking the traffic. The consequences of this lack of interactions are quite coarse route control over the outbound traffic from the domains, and the inability to smartly control how traffic flows into the domains. The second drawback is that all available solutions behave in a fully selfish way, that is, they operate without considering the effects of their decisions in the performance of the network. Given these limitations, we propose to extend the existing route control model from standalone and selfish to a cooperative and social route control model. Our main contribution in this paper is to show that when several route controllers compete for the same network resources, the conventional ones are outperformed by those using a cooperative and social approach and this becomes especially noticeable as the network utilization increases. Our results reveal that it is possible to reduce the frequency of traffic relocations by more than a 50% on average and still obtain slightly better end-to-end traffic performance for delay-sensitive applications. A key advantage is that our extension can be installed and used today by simply performing software upgrades to any of the existing route control solutions.