Miracle-dz/UniFAR-FTUs huggingface dataset:
Injectable Cartilage Shaving: An Autologous and Long Lasting Filler Material for Correction of Minor Contour Deformities in Rhinoplasty

BACKGROUND Filler materials are gaining popularity in nonsurgical rhinoplasty the major advantages are the ability to camouflage the surface deformities, and also the soft and malleable consistency; while the major drawback of the safe fillers such as hyaluronic acid is short durability. In this study, we evaluated the injectable cartilage shaving as an autologous filler material for correction of minor contour deformities in rhinoplasty. METHODS Injectable cartilage shaving was used for correction of surface irregularities in primary or secondary rhinoplasty, and long term results of 128 patients were evaluated. The source of cartilage was autologous septum, rib or less frequently, the ear concha. The material was injected with 14 to 18 gauge needles or blunted tip lipofilling cannulas with 1.3-1.7 mm internal diameters. It was performed whether during the septorhinoplasty or as a separate single procedure without elevation of the flap. Success was defined as the long term survival of the graft in the desired site and absence of recurrent deformity or complications such as extrusion, infection or displacement. RESULTS Twenty seven males and 101 females underwent the procedure from May 2008 to January 2014. Mean follow up period was 31 (13-58) months. Ninety five percent of patients were satisfied or very satisfied with the results at the last follow up visits and touch up procedure was performed for the unsatisfied patients. CONCLUSION Injectable cartilage shaving is a reliable filler to correct and camouflage the surface irregularities, and it is durable and predictable in long term follow ups.
Large-Scale Biasing and the Primordial Gravitational Potential

We modified the Press-Schechter (PS) formalism and then analytically derived a constrained mass distribution function n(M|φ) for the regions having some specified value of the primordial gravitational potential φ. The resulting modified PS theory predicts that gravitationally bound clumps with masses corresponding to rich clusters are significantly biased toward the regions of negative primordial potential—the troughs of the potential. The prediction is quantitative, depending on the mass and the depth of the troughs, which can be tested in large N-body simulations. As an illustration of the magnitude of the effect, we calculate the constrained mass function for the CDM model with Γ=Ωh=0.25 normalized to σ8=1. In particular, we show that the probability of finding a clump of mass 1014-1015 h-1 M☉ in the region of negative initial potential is 1.3-3 times greater (depending on the mass) than that in the region of positive initial potential. The scale of the potential fluctuations Rφ=√3σφ/σφ′ is shown to be ≈ 120 h-1 Mpc for the spectrum in question. The rms mass density contrast on this scale is only about σδ(Rφ) ≈ 0.03. Assuming that the modified PS theory is statistically correct, we conclude that clusters are significantly biased (b≥10; b is a bias factor defined by Δncl/ncl=bΔρm/ρm) toward the regions having negative initial potential.
Pioneering neural implant work at the University of Reading , UK

The Cybernetics Intelligence Research Group (CIRG) at the University of Reading, UK, is made up of seven full-time academics plus various research assistants and PhD students. We broadly define cybernetic intelligence as the study of intelligence and its applications. Members of CIRG are internationally active in the following areas: embodied machine intelligence (for example, learning mobile robots), neural networks (novel types, their properties and applications), intelligent search (stochastic diffusion, genetic algorithms), intelligent control (including novel optimal control, non-linear control and modelling techniques), applied cognitive systems (including pattern classification and learning classifiers), neuroscience (models of information processing in the brain), and neural implant technology.
PS25 Discontinuation of Antiepileptic Drugs in Pregnancy: A UK Population Based Study in the Health Improvement Network (THIN)

Background Since the thalidomide tragedy in the 1960s, many women are concerned with the use of drugs in pregnancy. Antiepileptic drugs (AEDs) are commonly used to treat epilepsy and bipolar disorders in women of child bearing age. However, some AEDs are associated with an increased risk of major congenital malformations. Untreated epilepsy and bipolar disorders in pregnancy may lead to devastating consequences for both mother and child. This leaves women and health care professionals with a dilemma as whether to continue AED medication in pregnancy. Using data from THIN, a UK primary care database, we examined pregnancy as a determinant for ceasing AED treatment. Methods A cohort study of pregnant women receiving AEDs in UK primary care was conducted. We identified women who were prescribed AEDs in the three months before pregnancy. Time to last consecutive AED prescription in pregnancy was estimated whereby discontinuation of therapy was defined by a gap of more than three months between prescriptions. We identified a control group of twice as many non-pregnant women receiving AEDs. These women were randomly selected, but stratified within five year age bands and matched on indication for AEDs as for the pregnant women Cox’s regression was used to compare the likelihood of discontinuing AEDs between pregnant and non-pregnant women. Results In total, 934 women were prescribed AEDs three months before pregnancy. Pregnant women with epilepsy were twice as likely to cease AEDs compared to non-pregnant women (Hazard Ratio (HR):2.00, 95% confidence interval (CI:1.62–2.47). Of 745 women with epilepsy, 601 (80.7%) continued treatment into pregnancy and 465 (62.4%) to the end of the second trimester. Of 1,490 non-pregnant women with epilepsy, 1,242 (83.4%) and 1,071 (71.9%) continued for comparable time periods. Pregnant women with bipolar disorder or depression were three times as likely to cease AEDs compared to non-pregnant women (HR:3.07, 95% CI:2.04–4.62). Of 54 pregnant women with bipolar disorder, 27 (50.0%) continued into pregnancy, and only 8 (14.8%) to the end of the second trimester. In 108 non-pregnant women with bipolar disorder, 82 (75.9%) and 58 (53.7%) continued for comparable periods. Conclusion Pregnancy is a determinant for the discontinuation of AEDs during pregnancy, especially in women with bipolar disorder or depression despite the potentially severe consequences associated with not treating the underlying illness in pregnancy.
Astrocytic Toll-Like Receptor 3 Is Associated with Ischemic Preconditioning- Induced Protection against Brain Ischemia in Rodents

Background Cerebral ischemic preconditioning (IPC) protects brain against ischemic injury. Activation of Toll-like receptor 3 (TLR3) signaling can induce neuroprotective mediators, but whether astrocytic TLR3 signaling is involved in IPC-induced ischemic tolerance is not known. Methods IPC was modeled in mice with three brief episodes of bilateral carotid occlusion. In vitro, IPC was modeled in astrocytes by 1-h oxygen-glucose deprivation (OGD). Injury and components of the TLR3 signaling pathway were measured after a subsequent protracted ischemic event. A neutralizing antibody against TLR3 was used to evaluate the role of TLR3 signaling in ischemic tolerance. Results IPC in vivo reduced brain damage from permanent middle cerebral artery occlusion in mice and increased expression of TLR3 in cortical astrocytes. IPC also reduced damage in isolated astrocytes after 12-h OGD. In astrocytes, IPC or 12-h OGD alone increased TLR3 expression, and 12-h OGD alone increased expression of phosphorylated NFκB (pNFκB). However, IPC or 12-h OGD alone did not alter the expression of Toll/interleukin receptor domain-containing adaptor-inducing IFNβ (TRIF) or phosphorylated interferon regulatory factor 3 (pIRF3). Exposure to IPC before OGD increased TRIF and pIRF3 expression but decreased pNFκB expression. Analysis of cytokines showed that 12-h OGD alone increased IFNβ and IL-6 secretion; 12-h OGD preceded by IPC further increased IFNβ secretion but decreased IL-6 secretion. Preconditioning with TLR3 ligand Poly I:C increased pIRF3 expression and protected astrocytes against ischemic injury; however, cells treated with a neutralizing antibody against TLR3 lacked the IPC- and Poly I:C-induced ischemic protection and augmentation of IFNβ. Conclusions The results suggest that IPC-induced ischemic tolerance is mediated by astrocytic TLR3 signaling. This reprogramming of TLR3 signaling by IPC in astrocytes may play an important role in suppression of the post-ischemic inflammatory response and thereby protect against ischemic damage. The mechanism may be via activation of the TLR3/TRIF/IRF3 signaling pathway.
Class A Scavenger Receptor 1 (MSR1) Restricts Hepatitis C Virus Replication by Mediating Toll-like Receptor 3 Recognition of Viral RNAs Produced in Neighboring Cells

Persistent infections with hepatitis C virus (HCV) may result in life-threatening liver disease, including cirrhosis and cancer, and impose an important burden on human health. Understanding how the virus is capable of achieving persistence in the majority of those infected is thus an important goal. Although HCV has evolved multiple mechanisms to disrupt and block cellular signaling pathways involved in the induction of interferon (IFN) responses, IFN-stimulated gene (ISG) expression is typically prominent in the HCV-infected liver. Here, we show that Toll-like receptor 3 (TLR3) expressed within uninfected hepatocytes is capable of sensing infection in adjacent cells, initiating a local antiviral response that partially restricts HCV replication. We demonstrate that this is dependent upon the expression of class A scavenger receptor type 1 (MSR1). MSR1 binds extracellular dsRNA, mediating its endocytosis and transport toward the endosome where it is engaged by TLR3, thereby triggering IFN responses in both infected and uninfected cells. RNAi-mediated knockdown of MSR1 expression blocks TLR3 sensing of HCV in infected hepatocyte cultures, leading to increased cellular permissiveness to virus infection. Exogenous expression of Myc-MSR1 restores TLR3 signaling in MSR1-depleted cells with subsequent induction of an antiviral state. A series of conserved basic residues within the carboxy-terminus of the collagen superfamily domain of MSR1 are required for binding and transport of dsRNA, and likely facilitate acidification-dependent release of dsRNA at the site of TLR3 expression in the endosome. Our findings reveal MSR1 to be a critical component of a TLR3-mediated pattern recognition receptor response that exerts an antiviral state in both infected and uninfected hepatocytes, thereby limiting the impact of HCV proteins that disrupt IFN signaling in infected cells and restricting the spread of HCV within the liver.
Building Successful Online Communities: Evidence-Based Social Design

Online communities are among the most popular destinations on the Internet, but not all online communities are equally successful. For every flourishing Facebook, there is a moribund Friendster--not to mention the scores of smaller social networking sites that never attracted enough members to be viable. This book offers lessons from theory and empirical research in the social sciences that can help improve the design of online communities. The social sciences can tell us much about how to make online communities thrive, offering theories of individual motivation and human behavior that, properly interpreted, can inform particular design choices for online communities. The authors draw on the literature in psychology, economics, and other social sciences, as well as their own research, translating general findings into useful design claims. They explain, for example, how to encourage information contributions based on the theory of public goods, and how to build members' commitment based on theories of interpersonal bond formation. For each design claim, they offer supporting evidence from theory, experiments, or observational studies.The book focuses on five high-level design challenges: starting a new community, attracting new members, encouraging commitment, encouraging contribution, and regulating misbehavior and conflict. By organizing their presentation around these fundamental design features, the authors encourage practitioners to consider alternatives rather than simply adapting a feature seen on other sites.
Efficient deep neural networks for speech synthesis using bottleneck features

This paper proposes a cascading deep neural network (DNN) structure for speech synthesis system that consists of text-to-bottleneck (TTB) and bottleneck-to-speech (BTS) models. Unlike conventional single structure that requires a large database to find complicated mapping rules between linguistic and acoustic features, the proposed structure is very effective even if the available training database is inadequate. The bottle-neck feature utilized in the proposed approach represents the characteristics of linguistic features and its average acoustic features of several speakers. Therefore, it is more efficient to learn a mapping rule between bottleneck and acoustic features than to learn directly a mapping rule between linguistic and acoustic features. Experimental results show that the learning capability of the proposed structure is much higher than that of the conventional structures. Objective and subjective listening test results also verify the superiority of the proposed structure.
OO-FSG: An Object-Oriented Approach to Mine Frequent Subgraphs

Frequent subgraph mining (FSG) has always been an important issue in data mining. Several frequent subgraph mining methods have been developed for mining graph data. However, most of these are main memory algorithms in which scalability is a bigger issue. A few algorithms have opted for a relational approach that stores the graph data in relational tables. However, relational databases have their own space as well computing constraints when it comes to storing large databases. Moreover, relational databases do not preserve semantic information as they represent simple entities and in order to preserve the relationship between two entities additional tables are necessary. Object-oriented databases, on the other hand, do not have these constraints. In this paper, we present an object-oriented database approach to mining frequent sub-graphs. We use Db4o, a popular open-source object database system, to store the input graph data as well as intermediate results. Db4o can save all the information about an entity in a single class in an object form. Application domains such as protein-protein interaction data, social network data, and chemical compound structure data require mining frequent subgraphs while preserving the meaning. This paper proposes a novel idea for using object oriented database db4o to store graph data, which can support large graph data as well as preserve semantic information.
Pedagogy-Based-Technology and Chemistry Students' Performance in Higher Institutions: A Case of Debre Berhan University.

Many students have difficulty in learning abstract and complex lessons of chemistry. This study investigated how students develop their understandings of abstract and complex lessons in chemistry with the aid of visualizing tools: animation, simulation and video that allow them to build clear concepts. Animation, simulation and video enable learning by doing and provide opportunity to explore the abstract and complex lessons of chemistry. They also enable to present information in more dynamic, compelling and interactive way with engaging environment. The study applied animation, simulation and video supporting, on student-centered learning activities, in electrochemistry for second year students organized with state of art technology flash and micro media player and evaluated for its effectiveness. A particular focus of this investigation was to what extent the uses of animation, simulation and video influenced student-centered learning. Pre- and post- tests were administrated on the target group and concurrent embedded mixed case study design was employed. Total quality of the developed package was evaluated by administrating a separate schedule containing open and closed ended question. The comments and ratings obtained in the learners’ insights provided the basis for the learning impact of the study. The result obtained from the experiment and responses of the schedule showed that technology integrated with appropriate pedagogy improves the performance of students.
Reducing the Optimum: Superexponential in Opt Time, Fixed Parameter Inapproximability for Clique and Setcover

An (r, t)-FPT-hardness in opt is finding some instance I whose optimum value opt is known and proving that the problem admits no r(opt) approximation that runs in time t(opt)|I| O(1). The usual definition uses r(k)-inapproximability in time t(k) · |I| O(1) , for some k ≥ opt in minimization problems, and opt ≤ k for maximization problems. If opt is known then inapproximability in opt implies inapproximability in k (but not vice versa) thus inapproximability in opt is stronger. We restrict our tools to gap reductions from 3-SAT and always assuming the Exponential Time Hypothesis (eth). In all our reductions opt is known as its the " yes " instance in a gap reduction from 3-SAT. In is obvious that as we want to prove (r, s)-FPT-hardness for larger and larger r, t it is required to change the optimum into smaller and smaller. However, no paper previous to ours proved Fixed Parameter Inapproximability by systematically reducing the optimum value. We develop a general technique to reduce the value of the optimum, which is of independent interest. The technique is robust and we apply it to three completely different problems. By reducing the value of the optimum, we give the first host of (r, t)-FPT-hardness results for clique and setcover so that t is much larger than super exponential in opt. All previous FPT-hardness [4] had time strictly subexponential in opt. In fact, allowing subexponential time in opt can make some FPT-hardness proofs trivial. One of our results for clique is proving that there is no 1/(1 −) approximation for clique, for some > 0 that runs in time t(opt) · n O(1) time, for any t, however huge. The time can also be set to 2 o(n) for any o(n) term. This result improves in two ways the main result in a paper by Feige and Killian [11]. We also show that the The Minimum Maximal Independent Set (mmis) problem is (r(opt, t(opt))-FPT-hard for any r, s however huge. This was already known in terms of k. While hardness in opt is stronger, we mainly present this proof because the reduction in the optimum in this case, is very instructive and its ideas may find further applications.
Explicit Dynamic Scheduling : A Practical MicroData owArchitectureCarl

This paper introduces Explicit Dynamic Scheduling (EDS), a practical implementation of dataaow on a chip. By combining RISC design principles with well-known compiler dependence analysis techniques, EDS combines a straightforward hardware design, suitable for high speed implementation, with the performance advantages of dataaow at the instruction level. EDS uniies pipeline and memory latency tolerance in a single paradigm, and is able to exploit parallelism at ner grain sizes than recent hybrid dataaow approaches. The scope of parallelism exploited by EDS is limited to inner loops and ne-grained parallelism, leaving coarse-grained parallelism to be exploited between processors with explicit scheduling software. This leads to a more straightforward hardware implementation than traditional micro-dataaow schemes. The dynamic scheduling provided by EDS has measurable performance advantages over superscalar and superpipelined RISC processors relying on static instruction scheduling.
An Ontology-Based Conceptual Framework to Improve Rock Data Quality in Reservoir Models

In the petroleum industry, a huge amount of data is generated every day by many different sources. The petroleum industry relies on the efficient use of this data to build computational models that represent subsurface petroleum reservoirs. A problem that arises when one is building reservoir models is the fact that the existent data is hardly interoperable. Many companies produce data, but the data is not easily integrated. In this work, we present a novel ontology-based conceptual framework to enhance well logs data quality, in order to improve the construction of reservoir models. Better reservoir models results in a better prediction of the petroleum reservoir quality, supporting a better evaluation of the reservoir economic value. We propose the use of the geological concept of reservoir petrofacies to make explicit the attributes that most contribute to the reservoir porosity and permeability. Thus, with the help of an expert, it is possible identify these attributes in well logs, enabling the possibility to extrapolate these known attributes to other logs in which the expert did not evaluate. The result is the possibility to trace this data along the different modeling activities.
Task planning using physics-based heuristics on manipulation actions

In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge. The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.
A COMPILER GENERATOR PRODUCED BY A SELF-APPLICABLE SPECIALIZER CAN HAVE A SURPRISINGLY NATURAL AND UNDERSTANDABLE STRUCTURE

This paper describes the structure of, and the ideas behind, a self-applicable specializer of programs, as well as the principles of operation of a compiler generator that has been produced automatically by specializing the specializer with respect to itself. It has been found that the structure of the compilers produced can be improved by making use of such devices as introducing different representations for the values of Kand U-parameters, splitting the subject program into Kand U-program, and automatically raising the arity of functions in the residual program.
Software components selection using the fuzzy set theory

In this paper, we addressed a problem on which depends closely the success of developing component-based applications. The problem consists in how are selected appropriate software components from several alternatives. The paper presents a solution in which we used the concept of fuzzy sets. The proposed solution consists in creating a fuzzy set of suitable components in which each component is associated with a degree of membership that presents the degree of appropriateness of the component with required specifications. To measure the membership degrees of components, we proposed a fuzzy multidimensional measure based on appropriateness indicators which each one reflects a particular aspect of appropriateness. Afterwards, we presented possibilities of using agents to realize a distributed implementation of the proposed approach.