Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposure of Human Lung Cells to Inhalable Substances: A Novel Test Strategy Involving Clean Air Exposure Periods Using Whole Diluted Cigarette Mainstream Smoke

An experimental approach was established for the validation of an in vitro test system for complex environmental test atmospheres consisting of both gaseous substances and particulates. Smoke from two different cigarette types (generated by an automatic cigarette-smoking machine) was employed to assess both the sensitivity and the specificity of the system. The smoke was diluted with synthetic air and used to expose human lung cells grown on microporous membranes. Cells were exposed alternately to diluted cigarette smoke and pure synthetic air. The effect of diluted smoke was assessed without humidification, addition of CO 2, or any other physical or chemical modification of the smoke. The experimental setup included online monitoring of the gas phase (by analysis of CO concentration) and particulate phase (by light-scattering photometry). Replicate experiments confirmed a reproducible generation and dilution of the smoke and a smoke age of about 7 s at the time it came into contact with the cells. Experiments using human lung cells revealed that smoke from the two different cigarette types induced different levels of dose-dependent toxicity. A cell exposure of 6 min using 6 alternating smoke and synthetic air periods was sufficient to cause different effects as measured by intracellular glutathione content. The fact that the system could differentiate between two different types of cigarette smoke demonstrated its high sensitivity and specificity. The system offers new ways to test native complex gaseous and aerosol mixtures in vitro using short exposure times and very small amounts of test substances.
Using hyperprediction to compensate for delayed updates in value predictors

Value prediction has been proposed as a technique to break true data dependences in order to increase the instruction-level parallelism available in programs. Recent work has pointed out, however, that the delay inherent in updating the value prediction table with the actual correct value can introduce a substantial number of wrong value predictions, which can then decrease the overall processor performance. The authors propose and systematically study a technique that they call ‘hyperprediction’ to compensate for the delay in updating the value prediction table. This approach accurately computes and records the number of outstanding instances of an instruction, which is the number of times an instruction has requested a value to be predicted since the last time the corresponding entry in the value prediction table was updated. With this information, the value predictor can provide reliable predictions for the currently requesting instance of an instruction based on both the currently stored value and the number of outstanding instances. They show how the hyperprediction technique can be implemented in a stride value predictor, a context-based predictor and a hybrid predictor. Their simulations using an extension to the SimpleScalar tool set and integer and floating-point programs from the SPEC95 and SPEC2000 benchmark suites indicate that this technique can consistently improve both the value prediction accuracy and the overall processor performance for each of the different types of value predictors.
Response of sago pondweed to combinations of low doses of diquat, cutting, and shade

Sago pondweed (Potamogeton pectinatus L.) is a problem submersed weed of rivers, irrigation and drainage channels throughout temperate and subtropical regions of the world. Responses of this weed to low doses of diquat and cutting in the presence and absence of shade are described. Diquat at conditions concentrations ranging from 0.2 to 0.5 mg/l for 24 to 168 hours provided good control of sago pondweed under glasshouse conditions. Diquat at low doses of 0.1 and 0.2 mg/l and cutting alone, were not sufficient to provide acceptable control for normal management purposes, but low doses of diquat or cutting combined with 50 to 75% shade provided good control.
Detection of Ochratoxin a Using Molecular Beacons and Real-Time PCR Thermal Cycler

We developed a simple and cheap assay for quantitatively detecting ochratoxin A (OTA) in wine. A DNA aptamer available in literature was used as recognition probe in its molecular beacon form, i.e., with a fluorescence-quenching pair at the stem ends. Our aptabeacon could adopt a conformation allowing OTA binding, causing a fluorescence rise due to the increased distance between fluorophore and quencher. We used real-time PCR equipment for capturing the signal. With this assay, under optimized conditions, the entire process can be completed within 1 h. In addition, the proposed system exhibited a good selectivity for OTA against other mycotoxins (ochratoxin B and aflatoxin M1) and limited interference from aflatoxin B1 and patulin. A wide linear detection range (0.2–2000 µM) was achieved, with LOD = 13 nM, r = 0.9952, and R2 = 0.9904. The aptabeacon was also applied to detect OTA in red wine spiked with the same dilution series. A linear correlation with a LOD = 19 nM, r = 0.9843, and R2 = 0.9708 was observed, with recoveries in the range 63%–105%. Intra- and inter-day assays confirmed its reproducibility. The proposed biosensor, although still being finalized, might significantly facilitate the quantitative detection of OTA in wine samples, thus improving their quality control from a food safety perspective.
Tetracycline for the treatment of pityriasis lichenoides

Thirteen cases of pityriasis lichenoides were treated with oral tetracycline. Five of them were completely cured, another seven had to continue to take 1 g/day. Addition of an antihistamine in one patient who did not respond to tetracycline gave a dramatic result. Throat swab cultures yielded coagulase positive staphylococci in four cases and other kinds of bacteria in many other cases. Based on the results of the treatment and histological findings, it is postulated that the pathogenesis of pityriasis lichenoides may be due to a hypersensitivity reaction to infectious agents.
Disposition of the slab‐like modules formed by axon branches originating from single CA1 pyramidal neurons in the rat hippocampus

The hippocampus is thought to be an area where the neuronal circuits for short‐term memory or the cognitive map may reside. In order to advance theoretical studies and neuronal model simulations of such circuits, the projection of the CA1 pyramidal neurons in the rat dorsal hippocampus, especially in the subiculum, was studied by means of intracellular and extracellular HRP injection. The CA1 pyramidal neurons project principally to the subiculum where each forms a slab‐like axonal field 2 mm long along the septotemporal axis, which may be regarded as a module for columnar organization, at a specific rostrocaudal level of the subiculum. The modules of the CA1a pyramidal neurons are disposed in the rostral part of the subiculum, those of the CA1c pyramidal neurons in the caudal part, and those of the CA1b pyramidal neurons in the middle part of the subiculum. The CA1 pyramidal neurons also participate in the construction of the lamellar organization in the hippocampus in that their axon branches run rostrocaudally following the stream of the alvear fibers. The CA1 pyramidal neurons in the dorsal rat hippocampus transfer the topographic map from field CA1 to the subiculum with reversed order in the lamellar direction. The topographical relationship is composed of partially shifted, overlapping slab‐like modules. As a result, information conveyed through a lamella will diverge into the subiculum approximately 2 mm wide, and information through a group of lamellae 2 mm wide will converge upon single subicular neurons.
On the number of independent cultural traits carried by individuals and populations

In species subject to individual and social learning, each individual is likely to express a certain number of different cultural traits acquired during its lifetime. If the process of trait innovation and transmission reaches a steady state in the population, the number of different cultural traits carried by an individual converges to some stationary distribution. We call this the trait-number distribution. In this paper, we derive the trait-number distributions for both individuals and populations when cultural traits are independent of each other. Our results suggest that as the number of cultural traits becomes large, the trait-number distributions approach Poisson distributions so that their means characterize cultural diversity in the population. We then analyse how the mean trait number varies at both the individual and population levels as a function of various demographic features, such as population size and subdivision, and social learning rules, such as conformism and anti-conformism. Diversity at the individual and population levels, as well as at the level of cultural homogeneity within groups, depends critically on the details of population demography and the individual and social learning rules.
Efficient and Robust Independence-Based Markov Network Structure Discovery

In this paper we introduce a novel algorithm for the induction of the Markov network structure of a domain from the outcome of conditional independence tests on data. Such algorithms work by successively restricting the set of possible structures until there is only a single structure consistent with the conditional independence tests executed. Existing independence-based algorithms have wellknown shortcomings, such as rigidly ordering the sequence of tests they perform, resulting in potential inefficiencies in the number of tests required, and committing fully to the test outcomes, resulting in lack of robustness in case of unreliable tests. We address both problems through a Bayesian particle filtering approach, which uses a population of Markov network structures to maintain the posterior probability distribution over them, given the outcomes of the tests performed. Instead of a fixed ordering, our approach greedily selects, at each step, the optimally informative from a pool of candidate tests according to information gain. In addition, it maintains multiple candidate structures weighed by posterior probability, which makes it more robust to errors in the test outcomes. The result is an approximate algorithm (due to the use of particle filtering) that is useful in domains where independence tests are uncertain (such as applications where little data is available) or expensive (such as cases of very large data sets and/or distributed data).
Motility of triton-demembranated sea urchin sperm flagella during digestion by trypsin

The survival curves for a population of reactivated spermatozoa exposed to digestion by trypsin indicate that a large number of trypsin- sensitive targets must be digested before the flagellum disintegrates. Changes in flagellar movement during trypsin digestion can be very small, especially when the spermatozoa are reactivated at 0.25 M KCl. They are not the changes which would be expected if elastic resistance of the trypsin-sensitive structures responsible for maintaining the integrity of the axoneme is a significant determinant of flagellar bend amplitude. By carrying out trypsin digestion under a variety of conditions, at least six distinct effects of trypsin digestion on parameters of flagellar movement have been detected. These include a gradual increase in the rate of sliding between tubules, gradual and abrupt changes in beat frequency accompanied by reciprocal decreases in bend angle, changes in the symmetry and planarity of bending, and selective interference with mechanisms for bend initiation and bend propagation.
ABCB19‐mediated polar auxin transport modulates Arabidopsis hypocotyl elongation and the endoreplication variant of the cell cycle

Summary Elongation of the Arabidopsis hypocotyl pushes the shoot‐producing meristem out of the soil by rapid expansion of cells already present in the embryo. This elongation process is shown here to be impaired by as much as 35% in mutants lacking ABCB19, an ATP‐binding cassette membrane protein required for polar auxin transport, during a limited time of fast growth in dim white light beginning 2.5 days after germination. The discovery of high ectopic expression of a cyclin B1;1‐based reporter of mitosis throughout abcb19 hypocotyls without an equivalent effect on mitosis prompted investigations of the endoreplication variant of the cell cycle. Flow cytometry performed on nuclei isolated from upper (growing) regions of 3‐day‐old hypocotyls showed ploidy levels to be lower in abcb19 mutants compared with wild type. CCS52A2 messenger RNA encoding a nuclear protein that promotes a shift from mitosis to endoreplication was lower in abcb19 hypocotyls, and fluorescence microscopy showed the CCS52A2 protein to be lower in the nuclei of abcb19 hypocotyls compared with wild type. Providing abcb19 seedlings with nanomolar auxin rescued their low CCS52A2 levels, endocycle defects, aberrant cyclin B1;1 expression, and growth rate defect. The abcb19‐like growth rate of ccs52a2 mutants was not rescued by auxin, placing CCS52A2 after ABCB19‐dependent polar auxin transport in a pathway responsible for a component of ploidy‐related hypocotyl growth. A ccs52A2 mutation did not affect the level or pattern of cyclin B1;1 expression, indicating that CCS52A2 does not mediate the effect of auxin on cyclin B1;1.
Underwater localization by combining Time-of-Flight and Direction-of-Arrival

In this paper we present a combined Time-of-Flight (ToF) and Direction-of-Arrival (DoA) localization approach suitable for shallow underwater monitoring applications such as harbor monitoring. Our localization approach combines one-way ranging and DoA estimation to calculate both position and time-synchronization of the blind-node. We will show that using this localization approach, we are able to reduce the number of reference nodes required to perform localization. By combining ToF and DoA, our approach is also capable of tracking and positioning of sound sources under water. We evaluate our approach through both simulation and underwater experiments in a ten meter deep dive-center (which has many similarities with our target application in terms of depth and reflection). Measurements taken at the dive-center show that this environment is highly reflective and resembles a shallow water harbor environment. Positioning results using the measured Time-of-Arrival (ToA) and DoA indicate that the DoA approach outperforms the ToF approach in our setup. Investigation of the DoA and ToF measurement error distributions, however, indicate the ToF-based localization approach has a higher precision. Shown is that both ToF and DoA and the combined approach achieve sub-meter positional accuracy in the test environment. Using the error distributions derived from the measurement in the dive-center, we run simulations of the same setup. Results from the simulation indicate ToF is more accurate than DoA positioning. Also in simulation all approaches achieve sub-meter accuracy.
’ s repository of research publications and other research outputs ’ Something different for the weekend ’-alterity , performance , routine and proficiency at farmers ’ markets in the northeast of England Book Chapter

The Open University's repository of research publications and other research outputs 'Something different for the weekend'-alterity, performance, routine and proficiency at farmers' markets in the northeast of England Book Chapter How to cite: Spiller, Keith (2010). 'Something different for the weekend'-alterity, performance, routine and proficiency at farmers' markets in the northeast of England.
Retroactive interference in neural networks and in humans: The effect of pattern-based learning

Catastrophic interference is addressed as a problem that arises from pattern-based learning algorithms. As such, it is not limited to artificial neural networks but can be demonstrated in human subjects in so far as they use a pattern-based learning strategy. The experiment tests retroactive interference in humans learning lists of consonant-vowel-consonant nonsense syllable pairs. Results show significantly more interference for subjects learning patterned lists than subjects learning arbitrarily paired lists. To examine how different learning strategies depend on the structure of the learning task, a mixture-of-experts neural network model is presented. The results show how these strategies may interact to give rise to the results seen in the human data.
Transliteration as Alignment vs. Transliteration as Generation for Crosslingual Information Retrieval

Crosslingual Information Retrieval (CLIR) usually requires query tran slation and, due to named entities in the case of IR, query translation requires a good tra nsliteration system when writing systems differ. Transliteration can be seen as a problem of ge neration or align- ment. For IR, since we can extract a word list from the corpus being sear ched, it should be seen as an alignment problem. The shift from generation to alignment can lead to h igher translit- eration accuracies and significant improvements in the CLIR results. We w ere able to achieve an increase (over generation) in the CLIR Mean Average Precision by 2 2.66% and 29.08% for
Word re-ordering and dynamic programming based search algorithm for statistical machine translation

In this work, a new search procedure for statistical machine translation (SMT) is proposed that is based on dynamic programming (DP). The starting point is a DP solution to the traveling salesman problem that works by jointly processing tours that visit the same subset of cities. For SMT, the cities correspond to source sentence positions to be translated. Imposing restrictions on the order in which the source positions are translated yields a DP algorithm for carrying out the word re-ordering in SMT efficiently. A simple data-driven search organization allows the algorithm to prune unlikely translation hypotheses. Search restrictions especially useful for the translation directions German-to-English and English-to-German are presented. A generalization of these re-ordering restrictions is given that is applicable to several different translation directions. Translation results are reported with a widely used SMT model.
Early dropout prediction using data mining: a case study with high school students

Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well‐known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4–6 weeks of the course and trustworthy enough to be used in an early warning system.
Influence of Information Technology Competence and Demographic Characteristics on Undergraduate Student ’ s Perception of E-Portal

Nigerian Universities have adopted the use of e-portal to enhance and connect with students, potential students, staff, parents and the global community. However, personal factors such are information technology competencies and demographic characteristics etc of undergraduate students have brought about digital divide and mix feelings about the usefulness, ease of use and reliability of e-portal in their respective institution. This study therefore surveyed undergraduate students' assessment of university portals in selected private universities in South-West Nigeria. The multistage sampling technique was used to randomly select 1244 undergraduate students from the faculties of Social-Sciences and Management and Science and Technology in the selected universities. Data was collected with a self-structured questionnaire and analyzed using descriptive statistics, regression and correlation. The findings show that the major challenge students face in using the portals was unstable internet connection. Age and level of study were discovered to have significant influence on students’ assessment of e-portal while gender and students’ course of study do not significantly influence their assessment of university e-portal. There was also a positive relationship between students’ demographic characteristics and their IT competence at P>0.05 significant level. Furthermore, students’ IT competence has significant influence on their assessment of the portal. The study therefore recommends that the university administration should expose students to more computer base programs that will improve their skills on the use of computer; adequate training should be provided for students on how to effectively use the e-portal.
Learning Deep Temporal Representations for Brain Decoding

Functional magnetic resonance imaging produces high dimensional data, with a less then ideal number of labelled samples for brain decoding tasks (predicting brain states). In this study, we propose a new deep temporal convolutional neural network architecture with spatial pooling for brain decoding which aims to reduce dimensionality of feature space along with improved classification performance. Temporal representations (filters) for each layer of the convolutional model are learned by leveraging unlabelled fMRI data in an unsupervised fashion with regularized autoencoders. Learned temporal representations in multiple levels capture the regularities in the temporal domain and are observed to be a rich bank of activation patterns which also exhibit similarities to the actual hemodynamic responses. Further, spatial pooling layers in the convolutional architecture reduce the dimensionality without losing excessive information. By employing the proposed temporal convolutional architecture with spatial pooling, raw input fMRI data is mapped to a non-linear, highly-expressive and low-dimensional feature space where the final classification is conducted. In addition, we propose a simple heuristic approach for hyper-parameter tuning when no validation data is available. Proposed method is tested on a ten class recognition memory experiment with nine subjects. The results support the efficiency and potential of the proposed model, compared to the baseline multi-voxel pattern analysis techniques.
Meshless Methods in Biomechanics: Bone Tissue Remodelling Analysis

1. Introduction. - 1.1 Meshless Methods. - 1.2 Natural Neighbour Radial Point Interpolation Method. - 1.3 Bone tissue remodelling analysis. - 1.4 Book Purpose. - 1.5 Meshless method software. - 1.6 Book arrangement. - 2. Solid Mechanics Fundamentals. - 2.1 Continuum formulation. - 2.2 Weak form. - 2.3 Discrete system of equations. - 3. Meshless Methods Introduction. - 3.1 Meshless generic procedure. - 3.2 Nodal connectivity. - 3.3 Numerical integration. - 3.4 Numerical implementation. - 4. Shape Functions. - 4.1 Introduction. - 4.2 Support-Domain. - 4.3 Moving Least Squares. - 4.4 Radial Point Interpolators. - 5. Solid Mechanics Problems. - 5.1 Solid Mechanics NNRPIM flow chart. - 5.2 RPI shape function patch test. - 5.3 Elastostatic numerical examples. - 5.4 Elastodynamic numerical examples. - 6. Bone Tissue. - 6.1 Bone biology: Basic concepts. - 6.2 Bone tissue mechanical properties. - 6.3 Bone remodelling algorithms. - 7. Bone Tissue Remodelling Analysis. - 7.1 Bone Patch Analysis. - 7.2 Bone Structures. - 7.3 Implants. - 8. References. - 9. Subject Index.