CD40-CD40L Interaction in Immunity Against Protozoan Infections

Activation of the immune system against protozoan infections relies particularly on two specific signals provided by cognate interaction of T cells with antigen presenting cells (APCs). The first signal is attributed to binding of the T-cell receptor (TCR) to peptide/MHC complexes on the surface of APCs, whereas the second signal is triggered through binding of several costimulatory molecules on the surface of APCs with their corresponding receptors on T cells. Among these costimulatory signallings, CD40/CD40L interactions have been particularly investigated in protozoan infection models with regard to their potential to amplify cell-mediated immunity against intracellular parasites. This article reviews current studies of the potential role of CD40/CD40L interaction in the modulation of immune responses against some protozoan parasites and highlights recent developments regarding manipulation of this interaction for promoting control of parasite infections

Genetically Engineered Bacteria in Gene Therapy — Hopes and Challenges

The main concern of gene therapy is to target the gene of interest to intended cell tissues for optimizing treatment efficiency. Genetically engineered bacteria have been developed as shuttle vectors for localized delivery of therapeutics. Their success depends upon their tropism to target cells and the efficiency of the engaged delivery system. Bodies of evidence clearly indicate the great potential of recombinant bacteria in gene therapy, although most of the studies were just looking for proof-of-concept rather than a ready-to-use final product. This part will provide an overview of our current understanding of bacteria-based delivery of therapeutic genes and heterologous antigens for prophylactic strategies

Ways Artificial Intelligence Will Shape eLearning

The paper seeks to highlight the significance of artificial intelligence (AI) in eLearning. The research problem for analysis is how eLearning and the use of artificial intelligence make education more accessible and cost-effective. Therefore, the research investigates whether Artificial Intelligence in the e-learning will improve the quality of education offered in various universities. The method used to address the problem is a theoretical lens and quantitative research that investigates the significance of Artificial intelligence in e-learning. The study indicates that artificial intelligence in eLearning provides learners with skills without looking for an instructor. The artificial intelligence machine provides relevant information that prepares a learner in the workplace environment. The significance of the research project is making education more accessible and improving the lives of disabled persons

Impact of Virtual Reality on Modern Education

Virtual reality in education is at its center stage. For students to gain relevant skills there is a need to introduce a virtual world so that one can achieve the practical aspect needed in the workplace environment. Memorizing facts makes students bored; hence, the need for virtual reality that will help students gain expertise. The paper seeks to discuss the impact of virtual reality in modern education. To understand the effects of virtual reality in the education sector, the theoretical approach and quantitative research have been used. The results of the study show that virtual reality assists students with special education needs and ensure skills are obtained. Virtual reality improves student self-esteem

Mathematical and numerical study of the concentration effect of red cells in blood

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A theory for bone resorption based on the local rupture of osteocytes cells connections: A finite element study

In this work, a bone damage resorption finite element model based on the disruption of the inhibitory signal transmitted between osteocytes cells in bone due to damage accumulation is developed and discussed. A strain-based stimulus function coupled to a damage-dependent spatialfunction is proposed to represent the connection between two osteocytes embedded in the bone tissue. The signal is transmitted to the bone surface to activate bone resorption. The proposed modelis based on the idea that the osteocyte signal reduction is not related to the reduction of the stimulus sensed locally by osteocytes due to damage, but to the difficulties for the signal in travelling along a disrupted area due to microcracks that can destroy connections of the intercellular network between osteocytes and bone-lining cells. To check the potential of the proposed model to predict the damage resorption process, two bone resorption mechano-regulation rules corresponding to twomechanotransduction approaches have been implemented and tested: 1) Bone resorption based on a coupled strain-damage stimulus function without ruptured osteocyte connections (NROC); and 2) Bone resorption based on a strain stimulus function with ruptured osteocyte connections (ROC). The comparison between the results obtained by both models, shows that the proposed model based on ruptured osteocytes connections predicts realistic results in conformity with previously published findings concerning the fatigue damage repair in bone

Effects of nitrogen rates on grain yield and nitrogen agronomic efficiency of durum wheat genotypes under different environments

Durum wheat is an important staple food crop in Tunisia and other Mediterranean countries and is grown in various climatic conditions. Production and yield are however severely limited not only by drought events but also by reduced levels of nitrogen fertilisation. A study was carried out at two locations in the sub-humid area of Tunisia: Mateur in 2009–10 and 2010–11 and Beja in 2011–12 and 2012–13 under rainfed conditions. Four durum wheat genotypes (landraces: Bidi, Azizi; improved: Om Rabia, Khiar) were evaluated for nitrogen agronomic efficiency and related agronomic traits under various nitrogen rates: 0, 50, 100, 150, 200 and 250 kgNha−1, with three replications. There was a significant interaction effect (P ≤ 0.001) environments × genotypes ×N treatments for grain yield (GY), biomass yield (BY), harvest index (HI), partial factor productivity of applied nitrogen (PFPN) and nitrogen agronomic use efficiencies (NAE). GY was the most affected trait by nitrogen applied showing an increase of 94% under high N treatment (250 kgNha−1) compared to control plots without N treatments. A significant linear regression exists between GY (0 N) and GY for the different N rates (r =0.70; P < 0.001). This effect was more pronounced for improved genotypes than landraces for all parameters excepting BY and NAEBY. BY showed +11% increase in landraces than improved genotypes. PFPN showed an average decrease of 65% under high-N fertilisation with 10% prevalence for improved genotypes. Landraces tend to promote vegetative growth while grain filling efficiency was higher for improved genotype

A review of Group B Streptococcus maternal-fetal infection

For a long time, infectious diseases have been a major public health problem, mainly maternal fetal infections linked to neonate’s mortality. Streptococcus agalactiae (GBS) infection is one of the main infections, which threat mother-infant health. One of the major challenges that remains to be addressed is therapeutic care strategy, further, the emergence of antibiotic resistant bacteria which constitute a major challenge for clinicians. Concerning GBS an antibiotic prophylaxis regimen is adopted to reduce the vertical transmission of bacteria from mother to neonate and avoid the appearance of complications related to GBS infection such as early onset disease and late onset disease that can lead to stillbirths. Like most bacteria, GBS is susceptible to first-line antibiotics, and in case of resistance, therapy is based on second and third-line antibiotics. The drug susceptibility testing of microorganisms is therefore essential in the therapeutic strategy, because it not only facilitates the orientation of treatment but also help to set up a system supervising the expansion of resistant strains. This present paper constitutes a literature review on Streptococcus agalactiae maternal-fetal infection and summarizes some epidemiological studies on the emergence of this bacterium as well as it provides the prevalence of its resistance to antibiotics and outlines some vaccine development strategies

A nash game algorithm for the solution of coupled conductivity identification and data completion in cardiac electrophysiology

International audienceWe consider the identification problem of the conductivity coefficient for an elliptic operator using an incomplete over specified measures on the surface. Our purpose is to introduce an original method based on a game theory approach, and design a new algorithm for the simultaneous identification of conductivity coefficient and data completion process. We define three players with three corresponding criteria. The two first players use Dirichlet and Neumann strategies to solve the completion problem, while the third one uses the conductivity coefficient as strategy, and uses a cost which basically relies on an identifiability theorem. In our work, the numerical experiments seek the development of this algorithm for the electrocardiography imaging inverse problem, dealing with in-homogeneities in the torso domain. Furthermore, in our approach, the conductivity coefficients are known only by an approximate values. we conduct numerical experiments on a 2D torso case including noisy measurements. Results illustrate the ability of our computational approach to tackle the difficult problem of joint identification and data completion. Mathematics Subject Classification. 35J25, 35N05, 91A80. The dates will be set by the publisher

A Nash-game approach to solve the Coupled problem of conductivity identification and data completion

International audienceWe consider the identification problem of the conductivity coefficient for an elliptic operator using an incomplete over-specified measurements on the surface (Cauchy data). Data completion problems are widely discussed in literature by several methods (see, e.g., for control and game oriented approaches [1, 2], and references therein). The identification of conductivity and permit-tivity parameters has also been investigated in many studies (see, e.g., [3, 4]). In this work, our purpose is to extend the method introduced in [1], based on a game theory approach, to develop a new algorithm for the simultaneous identification of conductivity coefficient and missing boundary data. We shall say that there are three players and we define three objective functions. Each player controls one variable and minimizes his own cost function in order to seek a Nash equilibrium which is expected to approximate the inverse problem solution. The first player solves the elliptic equation (div(k.(u)) = 0) with the Dirichlet part of the Cauchy data prescribed over the accessible boundary and a variable Neumann condition (which we call first player's strategy) prescribed over the inaccessible part of the boundary. The second player makes use correspondingly of the Neumann part of the Cauchy data, with a variable Dirichlet condition prescribed over the inaccessible part of the boundary. The first player then minimizes the gap related to the non used Neumann part of the Cauchy data, and so does the second player with a corresponding Dirichlet gap. The two players consider a response of the unknown conductivity of the third player. The third player controls the conductivity coefficient, and uses the over specified Dirichlet condition as well as the second's player Dirichlet condition strategy prescribed over the inaccessible part of the boundary