Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services

Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements.
Structurally Tractable Uncertain Data

Many data management applications must deal with data which is uncertain, incomplete, or noisy. However, on existing uncertain data representations, we cannot tractably perform the important query evaluation tasks of determining query possibility, certainty, or probability: these problems are hard on arbitrary uncertain input instances. We thus ask whether we could restrict the structure of uncertain data so as to guarantee the tractability of exact query evaluation. We present our tractability results for tree and tree-like uncertain data, and a vision for probabilistic rule reasoning. We also study uncertainty about order, proposing a suitable representation, and study uncertain data conditioned by additional observations.
Alternative Development for Data Migration Using Dynamic Query Generation

Abstract This article presents an ETL (Extract, Transform, Load) prototype called Valery as alternative approach to migration process which includes a compiler for dynamic generation of SQL queries. Its main features involve: SQL dynamic generation, set of configuration commands and environment for file uploading. The tests use the Northwind academic database and an individual environment. The model implementation uses flat files and SQL as query language. Finally, there is an analysis of the results obtained.
Neuron fire rates simulations of cold thermal sensations validated by measurements

Thermal comfort, determined by the influence of the indoor environmental parameters on thermal sensation, is regarded as an important indicator of human wellbeing and health. Neurophysiological mechanisms are responsible for thermal sensation. Models of thermal sensation could be very useful in design of new high performance buildings. Humans do not sense temperature directly. Temperature information is coded into the firing rate of temperature sensitive neurons (thermoreceptors). Human skin contains two types of thermoreceptors “cold” or “warm” sensitive. Kingma developed a new model for thermal sensation based on the neurophysiology of thermal reception and integration through neural pathways. In a proof-of-principle experiment we applied the model of Kingma on an personalized hand-heating system using two incandescent reflector heating lamps. Using the model by Kingma and the results of the experiments, the neuron discharge rate was simulated to see if it captured the dynamics of thermal sensation.
Internet-based cognitive–behavioural therapy for insomnia (ICBT-i): a meta-analysis of randomised controlled trials

Objective To evaluate the effectiveness of internet-based cognitive–behavioural therapy for insomnia (ICBT-i) in adults. Design A meta-analysis of ICBT-i. Data sources Systematic searches of randomised controlled trials of ICBT-i were performed in the PubMed, EMBASE, PsycINFO and Cochrane Library databases up to 19 June 2016. Review method 2 reviewers independently performed study selection, quality assessment and data extraction. Outcomes of interest included sleep onset latency (SOL), total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), number of nocturnal awakenings (NWAK), and Insomnia Severity Index (ISI). RevMan 5.2 and Stata 13.0 meta-analysis software were used to perform statistical analysis. Results 14 records for 15 studies (1013 experimental group participants, 591 waiting list group participants) were included. The meta-analysis indicated that, at the post-test time point, SOL decreased by 18.41 min (95% CI 13.60 to 23.21), TST increased by 22.30 min (95% CI 16.38 to 28.23), SE increased by 9.58% (95% CI 7.30% to 11.85%), WASO decreased by 22.31 min (95% CI 13.50 to 31.11), NWAK decreased by 0.52 (95% CI 0.28 to 0.76), and ISI decreased by 5.88 points (95% CI 4.29 to 7.46). Additionally SOL, TST, SE, and WASO exhibited statistically significant improvements at follow-up versus before treatment. Conclusions ICBT-i is an effective treatment for adults with insomnia. This conclusion should be verified in further studies.
Practice of Epidemiology Estimation of Treatment Efficacy With Complier Average Causal Effects ( CACE ) in a Randomized Stepped Wedge Trial

Complier average causal effects (CACE) estimate the impact of an intervention among treatment compliers in randomized trials. Methods used to estimate CACE have been outlined for parallel-arm trials (e.g., using an instrumental variables (IV) estimator) but not for other randomized study designs. Here, we propose a method for estimating CACE in randomized stepped wedge trials, where experimental units cross over from control conditions to intervention conditions in a randomized sequence. We illustrate the approach with a cluster-randomized drinking water trial conducted in rural Mexico from 2009 to 2011. Additionally, we evaluated the plausibility of assumptions required to estimate CACE using the IV approach, which are testable in stepped wedge trials but not in parallel-arm trials. We observed small increases in the magnitude of CACE risk differences compared with intention-to-treat estimates for drinking water contamination (risk difference (RD) = −22% (95% confidence interval (CI): −33, −11) vs. RD = −19% (95% CI: −26, −12)) and diarrhea (RD = −0.8% (95% CI: −2.1, 0.4) vs. RD = −0.1% (95% CI: −1.1, 0.9)). Assumptions required for IV analysis were probably violated. Stepped wedge trials allow investigators to estimate CACE with an approach that avoids the stronger assumptions required for CACE estimation in parallel-arm trials. Inclusion of CACE estimates in stepped wedge trials with imperfect compliance could enhance reporting and interpretation of the results of such trials.
Lineage specification in the mouse preimplantation embryo

During mouse preimplantation embryo development, totipotent blastomeres generate the first three cell lineages of the embryo: trophectoderm, epiblast and primitive endoderm. In recent years, studies have shown that this process appears to be regulated by differences in cell-cell interactions, gene expression and the microenvironment of individual cells, rather than the active partitioning of maternal determinants. Precisely how these differences first emerge and how they dictate subsequent molecular and cellular behaviours are key questions in the field. As we review here, recent advances in live imaging, computational modelling and single-cell transcriptome analyses are providing new insights into these questions. Summary: This Review discusses recent advances in live imaging, computational modelling and single cell analyses that provide insights into how the first three cell lineages of the mouse embryo are generated.
Personality-guided therapy

FOUNDATIONS. A Brief Review of Modern Treatment Modalities. A Perspective on Contemporary Trends. Personality-Guided Synergism. Planning Personality-Guided Treatment. CLINICAL SYNDROMES--AXIS I. Treating Acute, Posttraumatic, and Generalized Anxiety Syndromes. Treating Anxiety-Related Psychological Syndromes: Phobic, Dissociative, and Obsessive-Compulsive Disorders. Treating Anxiety-Related Physical Syndromes: Somatoform and Conversion Disorders. Treating Mood-Related Syndromes: Dysthymic, Major Depressive, and Bipolar Disorders. Treating Cognitive Dysfunction Syndromes: Substance-Related and Schizophrenic Disorders. PERSONALITY DISORDERS--AXIS II. Treating Schizoid Personality Patterns. Treating Avoidant Personality Patterns. Treating Depressive Personality Patterns. Treating Dependent Personality Patterns. Treating Histrionic Personality Patterns. Treating Narcissistic Personality Patterns. Treating Antisocial Personality Patterns. Treating Sadistic Personality Patterns. Treating Compulsive Personality Patterns. Treating Negativistic Personality Patterns. Treating Masochistic Personality Patterns. Treating Schizotypal Personality Patterns. Treating Borderline Personality Patterns. Treating Paranoid Personality Patterns. References. Indexes.
Generation of Multiple Isoforms of Eukaryotic Translation Initiation Factor 4GI by Use of Alternate Translation Initiation Codons

ABSTRACT Eukaryotic translation initiation factor 4GI (eIF4GI) is an essential protein that is the target for translational regulation in many cellular processes and viral systems. It has been shown to function in both cap-dependent and cap-independent translation initiation by recruiting the 40S ribosomal subunit to the mRNA cap structure or internal ribosome entry site (IRES) element, respectively. Interestingly eIF4GI mRNA itself has been reported to contain an IRES element in its 5′ end that facilitates eIF4GI protein synthesis via a cap-independent mechanism. In HeLa cells, eIF4GI exists as several isoforms that differ in their migration in sodium dodecyl sulfate (SDS) gels; however, the nature of these isoforms was unclear. Here, we report a new cDNA clone for eIF4GI that extends the 5′ sequence 340 nucleotides beyond the previously published sequence. The new extended sequence of eIF4GI is located on chromosome 3, within two additional exons immediately upstream of the previously published eIF4GI sequence. When mRNA transcribed from this cDNA clone was translated in vitro, five eIF4GI polypeptides were generated that comigrated in SDS-polyacrylamide gels with the five isoforms of native eIF4GI. Furthermore, translation of eIF4GI-enhanced green fluorescent protein fusion constructs in vitro or in vivo generated five isoforms of fusion polypeptides, suggesting that multiple isoforms of eIF4GI are generated by alternative translation initiation in vitro and in vivo. Mutation of two of the five in-frame AUG residues in the eIF4GI cDNA sequence resulted in loss of corresponding polypeptides after translation in vitro, confirming alternate use of AUGs as the source of the multiple polypeptides. The 5′ untranslated region of eIF4GI mRNA also contains an out-of-frame open reading frame (ORF) that may down-regulate expression of eIF4GI. Further, data are presented to suggest that a proposed IRES embedded in the eIF4GI ORF is able to catalyze synthesis of multiple eIF4GI isoforms as well. Our data suggest that expression of the eIF4GI isoforms is partly controlled by a complex translation strategy involving both cap-dependent and cap-independent mechanisms.
The effect of genomic position on reversion of a lac frameshift mutation (lacIZ33) during non‐lethal selection (adaptive mutation)

In a system described by Cairns and Foster, starvation of a particular leaky lac mutant (lacIZ33) in the presence of lactose appears to direct mutation in non‐growing cells to sites that allow growth (adaptive mutation). This behaviour requires that the lac operon be located on an F′ plasmid. This position effect was investigated by placing the mutant lac operon at many sites in the genome of Salmonella enterica (Typhimurium; LT2) and testing reversion behaviour. Genomic position did not affect reversion during non‐selective growth. When lac was at any of 550 chromosomal sites, starvation caused little or no enhancement of reversion. In the 28 strains with the lac on Salmonella’s conjugative plasmid (pSLT), selection enhanced reversion strongly, just as seen for strains with lac on an F′ plasmid. In 46 strains, the lac operon was inserted within a small chromosomal duplication, and selection stimulated RecA‐dependent partial reversion by simple amplification (about 8×) of the mutant lac region. The position of lac on a conjugative plasmid is important to reversion because it allows more frequent gene duplication and amplification. These events are central to growth and reversion under selection because they increase the number of replicating lac alleles within each developing revertant clone.
Manganese Blocks Intracellular Trafficking of Shiga Toxin and Protects Against Shiga Toxicosis

Shunning Shiga Infection with bacteria harboring Shiga toxin is responsible for more than 1 million deaths annually worldwide. Antidotes for the toxin are not available, and treatment with antibiotics is contraindicated because it increases the risk of toxin release (from dead bacteria) and leads to severe forms of the disease. Manganese is an essential nutrient and its toxicology in humans is well studied; it inhibits the normal trafficking of Shiga toxin in tissue culture cells. Mukhopadhyay and Linstedt (p. 332) found that levels of Mn2+ that caused no deleterious effects to normal cellular processes nevertheless altered intracellular trafficking of Shiga toxin so that it was degraded in lysosomes. This conferred very high levels of protection of cultured cells against toxin-induced death and made mice completely resistant to Shiga toxin–induced paralysis and death. Manganese may represent a low-cost therapeutic agent for the treatment of a type of Escherichia coli food poisoning. Infections with Shiga toxin (STx)–producing bacteria cause more than a million deaths each year and have no definitive treatment. To exert its cytotoxic effect, STx invades cells through retrograde membrane trafficking, escaping the lysosomal degradative pathway. We found that the widely available metal manganese (Mn2+) blocked endosome-to-Golgi trafficking of STx and caused its degradation in lysosomes. Mn2+ targeted the cycling Golgi protein GPP130, which STx bound in control cells during sorting into Golgi-directed endosomal tubules that bypass lysosomes. In tissue culture cells, treatment with Mn2+ yielded a protection factor of 3800 against STx-induced cell death. Furthermore, mice injected with nontoxic doses of Mn2+ were completely resistant to a lethal STx challenge. Thus, Mn2+ may represent a low-cost therapeutic agent for the treatment of STx infections.
HYBRID SYSTEMS OF COMPUTATIONAL INTELLIGENCE EVOLVED FROM SELF- LEARNING SPIKING NEURAL NETWORK

Computational intelligence paradigm covers several approaches for technical problems solving in an intelligence manner, such as artificial neural networks, fuzzy logic systems, evolutionary computation, etc. Each approach provides engineers and researchers with the smart and powerful tools to handle various real-life concerns. Even more powerful tools were designed at the joint of different computational intelligence approaches. Neuro-fuzzy systems, for example, are well-known and advanced intelligent tool that combines capabilities of neural networks and fuzzy systems together in a synergetic way. Among them, one of the prominent hybrid systems type is self-learning fuzzy spiking neural networks. They were evolved from fuzzy logic systems and selflearning spiking neural networks, and revealed considerable computational capabilities. There were proposed several architectures of self-learning fuzzy spiking neural networks, each handling a particular kind of data processing tasks (processing fuzzy data, fuzzy probabilistic and possibilistic clustering, batch and adaptive methods, new clusters detection, irregular form clusters detection, etc). In this paper, known architectures of self-learning hybrid systems based on spiking neural network are reviewed, compared, and summarized. A generalized architecture and learning algorithm for self-learning fuzzy spiking neural networks are proposed.
Synthesis of Bisindolylmethanes and Their Cytotoxicity Properties

Polymer supported dichlorophosphate (PEG-OPOCl2) is an efficient green catalyst for the electrophilic substitution reaction of indole with aromatic aldehydes, in neat condition, to afford an excellent yield of bis(indolyl) methanes with short reaction time, at room temperature. The synthesized compounds and their anti-cancer activity are evaluated.
Camera calibration of long image sequences with the presence of occlusions

Camera calibration is a critical problem in applications such as augmented reality and image based model reconstruction. When constructing a 3D model of an object from an uncalibrated video sequence, large amounts of frames and self occlusions of parts of the object are common and difficult problems. In this paper we present a fast and robust algorithm that uses a divide and conquer strategy to split the video sequence into sub-sequences containing only the most relevant frames. Then a robust stratified linear based algorithm is able to calibrate each of the subsequences to a metric structure and finally the subsequences are merged together and a final nonlinear optimization refines the solution. Examples of real data reconstructions are presented.
Using Bi-clustering Algorithm for Analyzing Online Users Activity in a Virtual Campus

Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users’ activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time. A relevant objective in this case is to study the behavior of online users and feedback the design processes of online applications to provide better usability and adaption to users’ preferences. The context of this work is that of a virtual campus in which thousands of students and tutors carry out the learning and teaching activity using online applications. The information stored in log files of virtual campuses tend to be large, complex and heterogeneous in nature. Hence, their mining requires both efficient and intelligent processing and analysis of user interaction data during long-term learning activities. In this paper, we present abi-clustering algorithm for processing large log data sets from the online daily activity of students in a real virtual campus. Our approach is useful to extract relevant knowledge about user activity such as navigation patterns, activities performed as well as to study time parameters related to such activities. The extracted information can be useful not only to students and tutors to stimulate and improve their experience when interacting with the system but also to the designers and developers of the virtual campus in order to better support the online teaching and learning.
Quantitative analysis for the quality of ad hoc networks

This paper investigates methods to measure the connection-level quality of mobile ad hoc networks. Using an exactly solvable 1-dimensional model, we derive a set of closed formulas that describe the quality of MANETs concerning connectivity, stability, and coverage. These results allow us to predict the minimum number of network nodes for a certain quality level (the "critical mass") without the need for numerical simulations.
Effect of Selected Water Temperatures Used in Mycoplasma gallisepticum Vaccine Reconstitution on Titer at Selected Time Intervals

Abstract Numerous methods are currently used throughout the poultry industry for the administration of vaccines. Each utilizes water for vaccine reconstitution and/or administration, including two of the three commercially available live Mycoplasma gallisepticum (MG) vaccines. Selected water temperatures were used to reconstitute and/or dilute the three commercially available live MG vaccines. Water temperatures included 4 C, 22 C (room temperature), and 32 C, and titer (color change units) was recorded at four time intervals, at point of reconstitution (time 0), 15, 30, and 60 min postreconstitution of the vaccines (time periods 15, 30, and 60, respectively). Results for F strain MG (FMG) vaccine showed significant decreases in titer from time 0 to time 15 for the 22 C and 32 C water temperatures but no significant decrease for any time period for FMG reconstituted with 4 C water. For 6/85 strain MG no significant difference in titer was noted for any of four time periods within any of the three water temperatures. For ts-11 strain MG a significant decrease was observed in titer at each of the four postdilution time periods when diluted with 32 C water. There was no significant decrease in titer at any time period for ts-11 MG vaccine when diluted with either 4 C or 22 C water.
Nutrition Education for Postpartum Women

This pilot study examined the effectiveness of a 4-month clinic-based dietary intervention emphasizing the intake of deep yellow and dark green vegetables versus usual care on improving diet quality in postpartum women. The intervention group (n = 31) received 1 face-to-face nutrition education session with a registered dietitian, 2 follow-up phone calls, and 3 pamphlets distributed by mail. The usual care group (n = 25) received handouts with guidelines on healthy eating. Dietary outcomes were assessed from 3-day food recalls and evaluated using paired and independent t tests. Intervention women exhibited a significant increase in total vegetable intake (P < .001) and in dark green and deep yellow vegetables (P < .001). In comparison, the control group increased the intake of total vegetables (P < .001), but did not increase the consumption of dark green and deep yellow vegetables. When comparing the change in intake between study groups for both types of vegetables, the difference was not significant. Furthermore, 61% of the intervention women met the goals for total vegetable intake compared with 12% for the usual care group (P < .001). The intervention group also had a greater percentage of women (25.8%) that met the goal for deep yellow and dark green vegetable intake when compared with the usual care group (8%; P < .08). These results suggest that postpartum women are receptive to nutrition education and that nutrition education can influence vegetable intake.
Bayesian Integration Using Multivariate

Multivariate Student importance sampling is a commonly used technique for Bayesian integration problems. For many problems, for example, when the inte-grand has ellipsoidal symmetry, it is a feasible and perhaps preferable, integration strategy. It is widely recognized , however, that the methodology suuers from limitations. We examine the methodology in conjunction with various variance reduction techniques; e.g. control variates, stratiied sampling and systematic sampling, to determine what improvements can be achieved.