Bioavailability of Eight Brands of Ampicillin Capsules

An in vivo absorption study was performed in a crossover fashion on 6 healthy volunteers (4 males and 2 females) to compare the bioavailability of 8 brands of ampicillin capsules. Absorption was assessed by a urinary excretion method in which the drug was assayed chemically. Statistical analysis of the results was carried out to evaluate the significance of differences between brands and between subjects. Results of the analysis of variance indicated no significant differences between the tested brands of ampicillin capsules. However, significant differences between brand A and brand B were found on using the student t-test. A significant intersubject variation was also found between the volunteers participated in the present study.     

Relevance feedback revisited: dealing with content and structure in XML documents

Relevance feedback (RF) is a technique that allows to enrich an initial query according to the user feedback. The goal is to express more precisely the user’s needs. Some open issues arise when considering semi-structured documents like XML documents. They are mainly related to the form of XML documents which mix content and structure information and to the new granularity of information. Indeed, the main objective of XML retrieval is to select relevant elements in XML documents instead of whole documents. Most of the RF approaches proposed in XML retrieval are simple adaptation of traditional RF to the new granularity of information. They usually enrich queries by adding terms extracted from relevant elements instead of terms extracted from whole documents. In this article, we describe a new approach of RF that takes advantage of two sources of evidence: the content and the structure. We propose to use the query term proximity to select terms to be added to the initial query and to use generic structures to express structural constraints. Both sources of evidence are used in different combined forms. Experiments were carried out within the INEX evaluation campaign and results show the effectiveness of our approaches.     

Genomic and Targeted Approaches Unveil the Cell Membrane as a Major Target of the Antifungal Cytotoxin Amantelide A

Amantelide A, a polyhydroxylated macrolide isolated from a marine cyanobacterium, displays broad-spectrum activity against mammalian cells, bacterial pathogens, and marine fungi. We conducted comprehensive mechanistic studies to identify the molecular targets and pathways affected by amantelide A. Our investigations relied on chemical structure similarities with compounds of known mechanisms, yeast knockout mutants, yeast chemogenomic profiling, and direct biochemical and biophysical methods. We established that amantelide A exerts its antifungal action by binding to ergosterol-containing membranes followed by pore formation and cell death, a mechanism partially shared with polyene antifungals. Binding assays demonstrated that amantelide A also binds to membranes containing epicholesterol or mammalian cholesterol, thus suggesting that the cytotoxicity to mammalian cells might be due to its affinity to cholesterol-containing membranes. However, membrane interactions were not completely dependent on sterols. Yeast chemogenomic profiling suggested additional direct or indirect effects on actin. Accordingly, we performed actin polymerization assays, which suggested that amantelide A also promotes actin polymerization in cell-free systems. However, the C-33 acetoxy derivative amantelide B showed a similar effect on actin dynamics in vitro but no significant activity against yeast. Overall, these studies suggest that the membrane effects are the most functionally relevant for amantelide A mechanism of action.     

Hybrid Deep Neural Network-Based Text Representation Model to Improve Microblog Retrieval

Abstract

Retrieving relevant information from Twitter is always a challenging task given its vocabulary mismatch, sheer volume and noise. Representing the content of text tweets is a critical part of any microblog retrieval model. For this reason, deep neural networks can be used for learning good representations of text data and then conduct to a better matching. In this paper, we are interested in improving both representation and retrieval effectiveness in microblogs. For that, a Hybrid-Deep Neural-Network-based text representation model is proposed to extract effective features’ representations for clustering oriented microblog retrieval. HDNN combines recurrent neural network and feedforward neural network architectures. Specifically, using a bi-directional LSTM, we first generate a deep contextualized word representation which incorporates character n-grams form FasText. However, these contextual embedded existing in a high-dimensional space are not all important. Some of them are redundant, correlated and sometimes noisy making the learning models over-fitting, complex and less interpretable. To deal with these problems, we proposed a Hybrid-Regularized-Autoencoder-based method which combines autoencoder with Elastic Net regularization for an effective unsupervised feature selection and extraction. Our experimental results show that the performance of clustering and especially information retrieval in microblogs depend heavily on features’ representation.     

Global warming’s impact on the performance of GSHP

Since heating and cooling systems of buildings consume 30–50% of the global energy consumption, increased efficiency of such systems means a considerable reduction in energy consumption. Ground source heat pumps (GSHP) are likely to play a central role in achieving this goal due to their high energy efficient performance. The efficiency of GSHP depends on the ground temperature, heating and cooling demands, and the distribution of heating and cooling over the year. However, all of these are affected by the ongoing climatic change. Consequently, global warming has direct effects on the GSHP performance. Within the framework of current study, heating and cooling demands of a reference building were calculated for different global warming scenarios in different climates i.e. cold, mild and hot climate. The prime energy required to drive the GSHP system is compared for each scenario and two configurations of ground heat exchangers. Current study shows that the ongoing climatic change has significant impact on GSHP systems.     

Lewis Carroll's contributions to artificial intelligence

Lewis Carroll, author of “Alice in Wonderland”, wrote about “Symbolic Logic”, which is equivalent to Propositional or Predicate Logic used in Artificial Intelligence today. He used logical symbols such as negation, conjunction, implication plus logical charts to solve problems. This study will present his contributions to Artificial Intelligence today.     

A generalized family of memristor‐based voltage controlled relaxation oscillator

Recently, memristive oscillators are a significant topic in the nonlinear circuit theory where there is a possibility to build relaxation oscillators without existence of reactive elements. In this paper, a family of voltage‐controlled memristor‐based relaxation oscillator including two memristors is presented. The operation of two memristors‐based voltage relaxation oscillator circuits is demonstrated theoretically with the mathematical analysis and with numerical simulations. The generalized expressions for the oscillation frequency and conditions are derived for different cases, where a closed form is introduced for each case. The effect of changing the circuit parameters on the oscillation frequency and conditions is investigated numerically. In addition, the derived equations are verified using several transient PSPICE simulations. The power consumption of each oscillator is obtained numerically and compared with its PSPICE counterpart. Furthermore, controlling the memristive oscillator with a voltage grants the design an extra degree of freedom which increases the design flexibility. The nonlinear exponential model of memristor is employed to prove the oscillation concept. As an application, two examples of voltage‐controlled memristor‐based relaxation oscillator are provided to elaborate the effect of the reference voltage on the output voltage. This voltage‐controlled memristor‐based relaxation oscillator has nano size with storage property that makes it more efficient compared with the conventional one. It would be helpful in many communication applications.     

On The Optimization of Fractional Order Low-Pass Filters

This paper presents three different optimization cases for normalized fractional order low-pass filters (LPFs) with numerical, circuit and experimental results. A multi-objective optimization technique is used for controlling some filter specifications, which are the transition bandwidth, the stop band frequency gain and the maximum allowable peak in the filter pass band. The extra degree of freedom provided by the fractional order parameter allows the full manipulation of the filter specifications to obtain the desired response required by any application. The proposed mathematical model is further applied to a case study of a practical second- generation current conveyor (CCII)-based fractional low-pass filter. Circuit simulations are performed for two different fractional order filters, with orders 1.6 and 3.6, with cutoff frequencies 200 and 500 Hz, respectively. Experimental results are also presented for LPF of 4.46 kHz cutoff frequency using a fabricated fractional capacitor of order 0.8, proving the validity of the proposed design approach.