Miracle-dz/UniFAR-FTUs huggingface dataset:
Halothane, isoflurane and sevoflurane inhibit NADH: ubiquinone oxidoreductase (complex I) of cardiac mitochondria

We have investigated the effects of volatile anaesthetics on electron transport chain activity in the mammalian heart. Halothane, isoflurane and sevoflurane reversibly increased NADH fluorescence (autofluorescence) in intact ventricular myocytes of guinea‐pig, suggesting that NADH oxidation was impaired. Using pig heart submitochondrial particles we found that the anaesthetics dose‐dependently inhibited NADH oxidation in the order: halothane > isoflurane = sevoflurane. Succinate oxidation was unaffected by either isoflurane or sevoflurane, indicating that these agents selectively inhibit complex I (NADH:ubiquinone oxidoreductase). In addition to inhibiting NADH oxidation, halothane also inhibited succinate oxidation (and succinate dehydrogenase), albeit to a lesser extent. To test the hypothesis that complex I is a target of volatile anaesthetics, we examined the effects of these agents on NADH:ubiquinone oxidoreductase (EC 1.6.99.3) activity using the ubiquinone analogue DBQ (decylubiquinone) as substrate. Halothane, isoflurane and sevoflurane dose‐dependently inhibited NADH:DBQ oxidoreductase activity. Unlike the classical inhibitor rotenone, none of the anaesthetics completely inhibited enzyme activity at high concentration, suggesting that these agents bind weakly to the ‘hydrophobic inhibitory site’ of complex I. In conclusion, halothane, isoflurane and sevoflurane inhibit complex I (NADH:ubiquinone oxidoreductase) of the electron transport chain. At concentrations of ≈2 MAC (minimal alveolar concentration), the activity of NADH:ubiquinone oxidoreductase was reduced by about 20 % in the presence of halothane or isoflurane, and by about 10 % in the presence of sevoflurane. These inhibitory effects are unlikely to compromise cardiac performance at usual clinical concentrations, but may contribute to the mechanism by which volatile anaesthetics induce pharmacological preconditioning.
Biocontrol Activities of Gamma Induced Mutants of Trichoderma harzianum against some Soilborne Fungal Pathogens and their DNA Fingerprinting

Background Random induced mutation by gamma radiation is one of the genetic manipulation strategies to improve the antagonistic ability of biocontrol agents. Objectives This study aimed to induce mutants with more sporulation, colonization rate leading to enhanced antagonistic ability (in vitro assay) comparing to wild type (WT) and the assessment of genetic differences (in situ evaluation) using molecular markers. The superior mutants could be appropriate biocontrol agents against soil borne fungal diseases. Materials and Methods In this research sampling and isolation of Trichoderma isolates were performed from soils with low incidence of soil borne disease. T. harzianum 65 was selected and irradiation was conducted with gammacell at optimal dose 250 Gray/s. Mutants (115) were obtained from the WT. The antagonistic abilities of twenty-four mutants were evaluated using dual culture and culture filtrate tests. Results The results of in vitro assays revealed that Th15, Th11 and Th1 mutants exhibited stronger growth inhibition (GI) and colonization rate on Macrophomina phaseolina and Rhizoctonia solani AG4 compared to the wild type. Th15 and Th11 mutants exhibited stronger GI and colonization rate on Sclerotinia sclerotiorum in dual culture and culture filtrate tests and Th1 and Th11 mutants exhibited stronger GI on Fusarium grminearum in culture filtrate test. The DNA fingerprinting was carried out using RAPD and rep-PCR markers. Two (Th9 and Th17) out of the 24 mutants categorized distantly from the rest based on different polymorphism obtained by molecular markers. However, Th9 was different in GI% from Th17. RAPD analysis separated WT from mutants, Th9 from Th17 and also phenotypically superior mutants from other mutants. Meanwhile, rep-PCR analysis categorized WT isolate and mutants according to their antagonistic properties. Conclusions The latter marker (rep-PCR) appeared to be reproducible and simple to distinguish mutants from a single isolate of T. harzianum. Mutants (3 isolates) were phenotypically and genotypically distinct from WT. These mutants demonstrated a pronounced biocontrol activities against soilborne fungal phytopathogens.
Semidefinite Programming Approaches for Sensor Network Localization With Noisy Distance Measurements

A sensor network localization problem is to determine the positions of the sensor nodes in a network given incomplete and inaccurate pairwise distance measurements. Such distance data may be acquired by a sensor node by communicating with its neighbors. We describe a general semidefinite programming (SDP)-based approach for solving the graph realization problem, of which the sensor network localization problems is a special case. We investigate the performance of this method on problems with noisy distance data. Error bounds are derived from the SDP formulation. The sources of estimation error in the SDP formulation are identified. The SDP solution usually has a rank higher than the underlying physical space which, when projected onto the lower dimensional space, generally results in high estimation error. We describe two improvements to ameliorate such a difficulty. First, we propose a regularization term in the objective function that can help to reduce the rank of the SDP solution. Second, we use the points estimated from the SDP solution as the initial iterate for a gradient-descent method to further refine the estimated points. A lower bound obtained from the optimal SDP objective value can be used to check the solution quality. Experimental results are presented to validate our methods and show that they outperform existing SDP methods. Note to Practitioners-Wireless sensor networks consist of a large number of inexpensive wireless sensors deployed in a geographical area with the ability to communicate with their neighbors within a limited radio range. Wireless sensor networks are finding increasing applicability to a range of monitoring applications in civil and military scenarios, such as biodiversity and geographical monitoring, smart homes, industrial control, surveillance, and traffic monitoring. It is often very useful in the applications of sensor networks to know the locations of the sensors. Global positioning systems suffer from many drawbacks in this scenario, such as high cost, line-of-sight issues, etc. Therefore, there is a need to develop robust and efficient algorithms that can estimate or "localize" sensor positions in a network by using only the mutual distance measures (received signal strength, time of arrival) that the wireless sensors receive from their neighbors. This paper describes an algorithm that solves the sensor network localization problem using advanced optimization techniques. We also study the effect of using very noisy measurements and propose robust methods to deal with high noise. Finally, simulation results for the algorithms are presented to demonstrate their performance in terms of computational effort and accuracy
A Multi-Agent Based Implementation of a Delphi Process (Short Paper)

The Delphi protocol is applied when a community of experts is required to reach a consensus and to deliver an answer. In these cases, consensus stands for reaching an agreement among the experts about what the answer should be. This consensus reaching problem has been already considered in the literature, though its automatisation remains as a challenge. Intuitively, the experts should dialogue, interchange ideas, and change their mind as the discussion progresses. This paper presents the first complete-implementation of the Delphi process. This implementation is achieved with a Multi-agent System(MAS), in which the experts are implemented with agents. The presented case study solves the document relevance evaluation problem where a community of experts decide whether a document is relevant or not. In conclusion, this paper makes an important contribution to people using Delphi processes, because the presented system is the first complete-computerised Delphi process. With respect to multi-agent systems, it has the potential to solve coordination in an original way.
Categorical selection of the antibody repertoire in splenic B cells

In the bone marrow, the passage of developing B cells through critical checkpoints of differentiation is associated with a reduction of specific categories of CDR3 of the Ig heavy chain (CDR‐H3), particularly those with excessive hydrophobic or charged amino acids and those with a length of eight or fewer residues. To gain insight into the role of CDR‐H3 content in the development of B cells in the spleen, we compared the sequences of VH7183DJCμ transcripts from sorted transitional T1, marginal zone, and follicular B cell subsets to those expressed by immature IgM+IgD– and mature IgMloIgDhi B cells in the bone marrow. Although differences in VH utilization were noted, the T1 CDR‐H3 repertoire showed extensive similarity to that of immature bone marrow B cells, and the follicular CDR‐H3 repertoire most resembled that of mature bone marrow B cells. Unlike the splenic follicular and bone marrow mature B cell CDR‐H3 repertoires, the marginal zone B cell CDR‐H3 repertoire retained both short and highly charged amino acid motifs, including those with two arginines. Our findings suggest that antigen binding sites containing specific categories of CDR‐H3 sequence content may inhibit, permit, or even facilitate passage of the host B cell through critical checkpoints in peripheral as well as central development.
Chinese Nominal Entity Recognition Based on Inducted Context Patterns

Since whether or not a character sequence refers to an object in real word is determined mostly by its context, the context pattern induction plays an important role in entity recognition, which is an important task in the field of natural language processing (NLP). We present a nominal entity recognition method based on the context pattern induction. It induces high-precision context patterns in an unsupervised way. Then it uses the matched context patterns directly, instead of extracted entities by inducted patterns, as the features of a maximum entropy(ME) based recognition model. The experiments show that the proposed method improves the performance of the high quality nominal entity recognizer, and achieves higher accuracy and recall rate.
LDTS: A Lightweight and Dependable Trust System for Clustered Wireless Sensor Networks

The resource efficiency and dependability of a trust system are the most fundamental requirements for any wireless sensor network (WSN). However, existing trust systems developed for WSNs are incapable of satisfying these requirements because of their high overhead and low dependability. In this work, we proposed a lightweight and dependable trust system (LDTS) for WSNs, which employ clustering algorithms. First, a lightweight trust decision-making scheme is proposed based on the nodes' identities (roles) in the clustered WSNs, which is suitable for such WSNs because it facilitates energy-saving. Due to canceling feedback between cluster members (CMs) or between cluster heads (CHs), this approach can significantly improve system efficiency while reducing the effect of malicious nodes. More importantly, considering that CHs take on large amounts of data forwarding and communication tasks, a dependability-enhanced trust evaluating approach is defined for cooperations between CHs. This approach can effectively reduce networking consumption while malicious, selfish, and faulty CHs. Moreover, a self-adaptive weighted method is defined for trust aggregation at CH level. This approach surpasses the limitations of traditional weighting methods for trust factors, in which weights are assigned subjectively. Theory as well as simulation results shows that LDTS demands less memory and communication overhead compared with the current typical trust systems for WSNs.
Tracing High-Density Gas in M82 and NGC 4038

We present the first detection of CS in the Antennae galaxies toward the NGC 4038 nucleus, as well as the first detections of two high-J (5-4 and 7-6) CS lines in the center of M82. The CS(7-6) line in M82 shows a profile that is surprisingly different from those of other low-J CS transitions we observed. This implies the presence of a separate, denser and warmer molecular gas component. The derived physical properties and the likely location of the CS(7-6) emission suggest an association with the supershell in the center of M82.
State of the art in knowledge extraction from online polls: a survey of current technologies

The ongoing research and development in the field of Natural Language Processing has lead to a great number of technologies in its context. There have been major benefits when it comes to bringing together the worlds of natural language and semantic technologies, so more and more potential areas of application emerge. One of these is the subject of this paper, in particular the possible ways of knowledge extraction from single-question online polls. With concepts of the Social Web, internet users want to contribute and express their opinion. As a consequence, the popularity of online polls is rapidly increasing; they can be found in news articles of media sites, on blogs etc. It would be desirable to bring intelligence to the application of polls by using technologies of the SemanticWeb and Natural Language Processing as this would allow to build a great knowledge base and to draw conclusions from it. This paper surveys the current landscape of tools and state-of-the-art technologies and analyses them with regard to pre-defined requirements that need to be accomplished, in order to be useful for extracting knowledge from the results generated by online polls.
Backward Modelling and Look-Ahead Fuzzy Energy Management Controller for a Parallel Hybrid Vehicle

This paper focuses on a parallel hybrid electric vehicle. It first develops a model for the vehicle using the backward-looking approach where the flow of energy starts from wheels and spreads towards engine and electric motor. Next, a fuzzy logic-based strategy is developed to control the operation of the vehicle. The objectives of the controller include managing the energy flow from engine and electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines current vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. Then, it determines the best values for continuous variable transmission ratio, speed, and torque. A slope window scheme is also developed to take into account the look-ahead slope information and determine the best vehicle speed for better fuel economy. The developed model and control strategy are simulated. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces fuel consumption.
Chronic implantation of transit-time flow probes on the ascending aorta of rodents

This study describes the implantation of transit-time flow probes on the ascending aorta of rats while minimizing the risk of postoperative complications. Special emphasis is placed on our new method of rat intubation as well as the production of materials necessary for the implantation procedure such as endotracheal tubes and heparin bonded vessel catheters. The effects of these devices on the response to acute hypoxia were studied in rats following a 5–7 day recovery from the implantation procedure. Systemic and microvascular measurements were made on instrumented rats (n = 5) and non-instrumented controls (n = 3) that were ventilated with 21%, 15%, 10%, 8% and 5% oxygen. Arterial pressure, PO2, lactate, and base deficit were not different between the implanted and control animals at any inspired oxygen concentration. Microvascular flow in the primary arterioles of the spinotrapezius muscle was also similar between the two groups at all inspired oxygen concentrations. We conclude that this novel methodology facilitates the measurement of whole body oxygen delivery in resting and haemodynamically-stressed rats.
A combined transcriptome and proteome survey of malaria parasite liver stages

For 50 years since their discovery, the malaria parasite liver stages (LS) have been difficult to analyze, impeding their utilization as a critical target for antiinfection vaccines and drugs. We have undertaken a comprehensive transcriptome analysis in combination with a proteomic survey of LS. Green fluorescent protein-tagged Plasmodium yoelii (PyGFP) was used to efficiently isolate LS-infected hepatocytes from the rodent host. Genome-wide LS gene expression was profiled and compared with other parasite life cycle stages. The analysis revealed ≈2,000 genes active during LS development, and proteomic analysis identified 816 proteins. A subset of proteins appeared to be expressed in LS only. The data revealed exported parasite proteins and LS metabolic pathways including expression of FASII pathway enzymes. The FASII inhibitor hexachlorophene and the antibiotics, tetracycline and rifampicin, that target the apicoplast inhibited LS development, identifying FASII and other pathways localized in the apicoplast as potential drug targets to prevent malaria infection.
Modeling high-level information by using Gaussian mixture correlation for GMM-UBM based speaker recognition

The Gaussian mixture model-universal background model (GMM-UBM) has been dominant in text-independent speaker recognition tasks. However the conventional GMM-UBM method assumes that each Gaussian mixture is independent and ignores the fact that within Gaussian mixtures, there do exist some useful high-level speaker-dependent characteristics, such as word usage or speaking habits. Based on the GMM-UBM method, a method is proposed to use Gaussian mixture correlation to model the high-level information for speaker recognition tasks. In this method, we first cluster the Gaussian mixtures of the UBM into a small number of classes in terms of the mean vectors; in the following step, a universal class transition probability matrix (UCTPM) is learned which is helpful in modeling the high-level speaker's characteristics embedded in Gaussian mixture correlation. During the training phase, a speaker-dependent class transition probability matrix is adapted from the UCTPM. Experiments over two different databases show that an average 20.38% error rate reduction (ERR) can be achieved compared with the conventional GMM-UBM method.
Basic Biological Sciences

A rotating disc method has been used to study simulated white spot formation at a bovine enamel surface in the presence of 1-hydroxy-1-ethane-1, 1-diphosphonic acid (HEDP) at PH = 5.0. The reaction appears to be accompanied by the slow precipitation of a CaHEDP phase within the pores of the "intact" layer.
AHSCAN: Agglomerative Hierarchical Structural Clustering Algorithm for Networks

Many systems in sciences, engineering and nature can be modeled as networks. Examples include the internet, WWW and social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted on real datasets demonstrate promising performance of the new method.
Tretinoin-loaded liposomal formulations: from lab to comparative clinical study in acne patients

Abstract Topical tretinoin is the most commonly used retinoid for acne. However, its irritative potential on the applied area and the barrier properties of the stratum corneum limit its use. The objective of the present study was to formulate tretinoin liposomal gel to obtain a formula with lower skin irritation potential and greater clinical effect. A statistical 24 factorial design was adopted. Sixteen formulae prepared and were properly evaluated. A candidate formula (F13G) prepared with 0.025% tretinoin, phospholipid– cholesterol–dicetylphosphate (9:1:0.01) and incorporated in 1% carbopol gel was selected for skin irritation test. Clinical study was conducted on acne patients and compared to marketed product. All liposomes formulations were spherical in shape. The addition of cholesterol in the film hydration method significantly decreased the vesicle size, and increased the percentage of incorporation efficiency at (p < 0.05). The presence of dicetylphosphate significantly increased drug release but did not affect the percentage of incorporation efficiency and vesicle size. The results of the clinical study in acne patients revealed that F13G showed significantly higher efficacy when compared to marketed product (p < 0.05).
LCC-Demons : a robust and accurate diffeomorphic registration algorithm

Non-linear registration is a key instrument for computational anatomy to study the morphology of organs and tissues. However, in order to be an effective instrument for the clinical practice, registration algorithms must be computationally efficient, accurate and most importantly robust to the multiple biases affecting medical images. In this work we propose a fast and robust registration framework based on the log-Demons diffeomorphic registration algorithm. The transformation is parameterized by stationary velocity fields (SVFs), and the similarity metric implements a symmetric local correlation coefficient (LCC). Moreover, we show how the SVF setting provides a stable and consistent numerical scheme for the computation of the Jacobian determinant and the flux of the deformation across the boundaries of a given region. Thus, it provides a robust evaluation of spatial changes. We tested the LCC-Demons in the inter-subject registration setting, by comparing with state-of-the-art registration algorithms on public available datasets, and in the intra-subject longitudinal registration problem, for the statistically powered measurements of the longitudinal atrophy in Alzheimer’s disease. Experimental results show that LCC-Demons is a generic, flexible, efficient and robust algorithm for the accurate non-linear registration of images, which can find several applications in the field of medical imaging. Without any additional optimization, it solves equally well intra & IData used in preparation of this article were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: ’’http://adni.loni. ucla.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List. pdf’’ IIThis work was partially funded by the European Research Council (ERC advanced Grant MedYMA), ANR blanc Karametria and the EU project Care4Me. ∗Corresponding author Email addresses: marco.lorenzi@inria.fr (M. Lorenzi), nicholas.ayache@inria.fr (N. Ayache), gfrisoni@fatebenefratelli.it (G.B. Frisoni), xavier.pennec@inria.fr (X. Pennec) Preprint submitted to Elsevier April 18, 2013 ha l-0 08 19 89 5, v er si on 1 2 M ay 2 01 3 Author manuscript, published in "NeuroImage 81, 1 (2013) 470-483" DOI : 10.1016/j.neuroimage. 2013.04. 114
A CMOS capacitance sensor that monitors cell viability

We describe a CMOS capacitance sensor for measuring the capacitive behavior of living cells in a culture environment, in the presence of weak electric fields. The underlying physical phenomenon results primarily from polarization of the ionic cloud surrounding the cell in aqueous medium. The measured capacitance depends on a variety of factors including cell morphology, membrane integrity, medium pH and extra cellular ionic concentration and serves as an indicator of cell health. The capacitance sensor uses the principle of charge sharing and maps sensed capacitance values to voltages. The sensor chip has been fabricated in a commercially available 0.5 mum, 2-poly 3-metal CMOS technology. The sensors have been successfully used for long term monitoring of cell viability in vitro