Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of biologic therapy in Crohn’s disease

ABSTRACT Introduction: Crohn’s disease (CD) is a manifestation of inflammatory bowel disease (IBD), which can result in significant morbidity. Biologic therapy with anti-TNF medication has been effective in treating inflammation and reducing complications in CD. It is important for clinicians to have better knowledge of the various biologic therapies including mechanisms of action and optimization strategies. Areas covered: The review describes optimization of biologic therapy in CD including different mechanisms of loss of response, therapeutic drug monitoring in CD, clinical implications and management strategies which utilize drug monitoring, and areas of future development and research in optimization of biologic therapy. Expert opinion: Achieving adequate levels of the drug (antibody unbound) is one of the most important determinants of attaining clinical remission and mucosal healing. Drug level is also critical in determining if a patient requires combination therapy with an immunomodulator. Certain populations, including those with active perianal disease, may require higher serum levels to achieve healing or closure. Treat to target level is an algorithm that is not universally accepted and more data is need. Additionally, there are numerous assays that don’t always correlate, especially regarding measuring anti-drug antibodies.
Aquatic Plant Diversity in Italy: Distribution, Drivers and Strategic Conservation Actions

Italy is recognized as one of the prominent hot spot areas for plant diversity at regional and global scale, hosting a rich range of ecosystems and habitat types. This is especially true considering aquatic habitats, which represent a major portion of the total water surfaces in the Mediterranean region. Nevertheless, only a scant attention was paid to clarify the species richness of aquatic plant and its contribution to the total diversity at the country scale, despite such plants are seriously threatened at multiple scales. This paper provided the first comprehensive inventory of aquatic plants at the whole country scale, collecting data on species’ distribution, trends, and explanatory determinants of species richness. We confirmed the key contribution of Italy to the regional and global aquatic plant diversity with a total of 279 species recorded since 2005, equal to the 88.5%, 55.9% and ∼10% of the richness estimated at European/Mediterranean, Palearctic and global scale, respectively. Ten species are considered extinct in the wild [among which Aldrovanda vesiculosa L. , Caldesia parnassifolia (Bassi ex L.) Parl. , Helosciadium repens (Jacq.) W.J.D. Koch, and Pilularia globulifera L. ], four were doubt [among which Luronium natans (L.) Raf. , Utricularia intermedia Hayne, and U. ochroleuca R.W. Hartman. ], and eight were erroneously reported in the past, among which Isoëtes lacustris L. , Myosotis rehsteineri Wartm. , and Ranunculus aquatilis L. Only 18 species – mainly helophytes (14) – were present in all the 20 Italian regions, whereas hydrophytes showed most scanty regional frequencies. Temperature, latitude, area and water resources availability are the main drivers of aquatic plant spatial arrangement and diversity. Furthermore, the number of inhabitants per km2 well described the number of “lost species” since 2000. The findings of the present survey call for an urgent elaboration of large-scale strategies to ensure the survival of aquatic plants, stressing on multiple functions played by aquatic plants in supporting national economy and human well-being. In this context, Italy can play a fundamental role guaranteeing temporary refuge for projected or expected species migrations along latitude and longitude gradients. Besides, in hyper-exploited landscapes man-made water bodies can further enhance the achievement of minimum conservation targets.
Cascaded predictive speed control

This work proposes a new control scheme for electrical drives system, named cascaded predictive speed control (PSC). The strategy seeks to maintain the simplicity of the classic predictive control while excluding linear or other controllers. The control strategy has a cascade architecture, similar to the techniques of classical control (FOC or DTC). The outer loop controls the speed of the machine, determining a reference torque through a mechanical dynamic model, which allows tracking the speed reference. The inner loop controls the stator current with a cost function that selects the state of the converter which generates the best tracking references for the stator current synchronous components. Preliminary simulation results confirm the effectiveness of this approach, which produces produces a high quality drive control.
Cognitive Behavioural Therapy Competency: Pilot Data from a Comparison of Multiple Perspectives

Background: Measurement of cognitive behavioural therapy (CBT) competency is often resource intensive. A popular emerging alternative to independent observers’ ratings is using other perspectives for rating competency. Aims: This pilot study compared ratings of CBT competency from four perspectives – patient, therapist, supervisor and independent observer using the Cognitive Therapy Scale (CTS). Method: Patients (n = 12, 75% female, mean age 30.5 years) and therapists (n = 5, female, mean age 26.6 years) completed the CTS after therapy sessions, and clinical supervisor and independent observers rated recordings of the same session. Results: Analyses of variance revealed that therapist average CTS competency ratings were not different from supervisor ratings, and supervisor ratings were not different from independent observer ratings; however, therapist ratings were higher than independent observer ratings and patient ratings were higher than all other raters. Conclusions: Raters differed in competency ratings. Implications for potential use and adaptation of CBT competency measurement methods to enhance training and implementation are discussed.
Felt structure characterised by synchrotron microtomography

This article is devoted to the characterisation of the structure of felts used in paper production. Felt structure has a huge influence on mechanical and physical properties, such as permeability and is of major importance in developments to improve the press section performance. During its fabrication, a felt is needled in order to imbricate some fibres in the porous network. This operation makes it difficult to model the structure by superposition of batts and base; consequently, the fine structure has to be determined. In order to obtain a structural description, X-ray synchrotron radiation from the European Synchrotron Radiation Facility (ESRF), has been used. The pixel size has been chosen equal to 6.6 microns to optimise the efficiency of the experiments. Improving the resolution would require a considerably smaller sample that would not correspond to a representative volume of the felt. The experimental set-up and some examples of three-dimensional (3D) structures are presented in order to illustrate the capability of this technique.
Content‐based image retrieval based on binary signatures cluster graph

In this paper, we approach a method of clustering binary signature of image in order to create a clustering graph structure for building the content‐based image retrieval. First, the paper presents the segmentation method based on low‐level visual features including colour and texture of image. On the basis of segmented image, the paper creates binary signature to describe location, colour, and shape of interest objects. In order to match similar images, the paper presents a similarity measure between the images based on binary signature. From that, the paper proposes the method of clustering binary signature to quickly query similar images. At the same time, the graph data structure is built using the partition cluster technique and the rules of binary signatures' distribution of images. On the basis of data structure, we propose a graph creation algorithm, a cluster splitting/merging algorithm, and a similarity image retrieval algorithm. To illustrate the proposed theory, we build an image retrieval application and assess the experimental results on the image datasets including COREL (1,000 images), CBIR images (1,344 images), WANG (10,800 images), MSRDI (15,720 images), and ImageCLEF (20,000 images).
Strain range fatigue life assessment of lead‐free solder interconnects subject to temperature cycle loading

Purpose – The aim is to present temperature fatigue model constants for lead‐free tin‐silver‐copper solder derived from test data and demonstrate the validity of the model using published experimental results.Design/methodology/approach – Temperature cycle fatigue life data were collected from a controlled set of tests using ceramic leadless chip carriers. Using a regression algorithm, temperature cycle fatigue model constants were derived from fatigue life data. The model was then applied to a variety of package formats including ball grid arrays, quad flatpack and thin small outline packages to determine the validity of the model and constants.Findings – The temperature cycle fatigue life experimental data were found to be in good agreement with the model with the derived model constants for various package types. Using this model, engineers can determine acceleration factors between test and field temperature cycle conditions.Research limitations/implications – The technology has been used to ensure in.
Mapping of a Major QTL for Ceratocystis Wilt Disease in an F1 Population of Theobroma cacao

Cacao is an important crop, its beans are key raw materials for the chocolate and cosmetic industries. Ceratocystis wilt of cacao (CWC) caused by Ceratocystis cacaofunesta is a lethal disease for the crop. Therefore, the selection of resistant cacao varieties is one of the viable ways to minimize losses in cacao production. In this paper, we described the identification of a major QTL associated with CWC in an F1 mapping population from a cross between a resistant, “TSH 1188,” and a susceptible genotype, “CCN 51. ” A set of 266 trees were genotyped using 3,526 single nucleotide polymorphic markers and then multiple QTL mapping analyses were performed. Two QTLs were identified on chromosomes IV and VI. The major QTL was located at 20 cM from the top position of chromosome VI, accounting for more than 60% of the phenotypic variation. The favorable allele T1, with haplotype GTT, came from the “TSH 1188” parent. It was evident that the haplotype combination T1C2 on chromosome VI was the most significant for resistance, since 93% of resistant trees had this haplotype. The major QTL converged to a genomic region of 739.4 kb that harbored nine candidate genes, including two major classes of resistance genes, which would make them the primary candidates involved in the resistance to CWC. The haplotypes detected are now used to improve the efficiency and precision of the selection of resistant trees in cacao breeding.
Bilingual Terminology Extraction in Sketch Engine

We present a method of bilingual terminology extraction from parallel corpora and a few heuristics and experiments with improving the performance of the basic variant of the method. An evaluation is given using a small gold standard manually prepared for English-Czech language pair from DGT translation memory. The bilingual terminology extraction (ABTE) is available for several languages in Sketch Engine—the corpus management tool.
Effects of achievement contexts on the meaning structure of emotion words

ABSTRACT Little is known about the impact of context on the meaning of emotion words. In the present study, we used a semantic profiling instrument (GRID) to investigate features representing five emotion components (appraisal, bodily reaction, expression, action tendencies, and feeling) of 11 emotion words in situational contexts involving success or failure. We compared these to the data from an earlier study in which participants evaluated the typicality of features out of context. Profile analyses identified features for which typicality changed as a function of context for all emotion words, except contentment, with appraisal features being most frequently affected. Those context effects occurred for both hypothesised basic and non-basic emotion words. Moreover, both data sets revealed a four-dimensional structure. The four dimensions were largely similar (valence, power, arousal, and novelty). The results suggest that context may not change the underlying dimensionality but affects facets of the meaning of emotion words.
Boosting Persistence Parallelism in Memory Bus and RDMA Network

Emerging non-volatile memories (NVMs) incorporate the features of fast byte-addressability and data persistence, which are beneficial for data services, such as file systems and databases. To support data persistence, a persistent memory system requires ordering for write requests. We observe that the memory bus and the Remote Direct Memory Access (RDMA) network are severely under-utilized during the process of ensuring data persistence, because the persistence parallelism in the memory bus and the RDMA network is not fully leveraged during ordering. To address this problem, we propose an architecture design to exploit the persistence parallelism in the memory bus and the RDMA network. First, we utilize inter-thread persistence parallelism for barrier epoch management with better bank level parallelism (BLP). Second, we enable intra-thread persistence parallelism with multiple barrier epochs inside one network packet, and propose a fine-grained memory persistence scheme through the network. With these features, our architecture is capable of efficiently supporting transaction-level persistence through RDMA network for file systems or NVM libraries. The experimental results show that our work can achieve 1.3× performance improvement for local applications and about 1.93× for remote applications serviced through the RDMA network.
Learning to Recognize Touch Gestures: Recurrent vs. Convolutional Features and Dynamic Sampling

We propose a fully automatic method for learning gestures on big touch devices in a potentially multi-user context. The goal is to learn general models capable of adapting to different gestures, user styles and hardware variations (e.g. device sizes, sampling frequencies and regularities). Based on deep neural networks, our method features a novel dynamic sampling and temporal normalization component, transforming variable length gestures into fixed length representations while preserving finger/surface contact transitions, that is, the topology of the signal. This sequential representation is then processed with a convolutional model capable, unlike recurrent networks, of learning hierarchical representations with different levels of abstraction. To demonstrate the interest of the proposed method, we introduce a new touch gestures dataset with 6591 gestures performed by 27 people, which is, up to our knowledge, the first of its kind: a publicly available multi-touch gesture dataset for interaction. We also tested our method on a standard dataset of symbolic touch gesture recognition, the MMG dataset, outperforming the state of the art and reporting close to perfect performance.
Hypothesis test of mediation effect in causal mediation model with high‐dimensional continuous mediators

Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through a mediator. However, current methods are not applicable to the setting with a large number of mediators. We propose a testing procedure for mediation effects of high‐dimensional continuous mediators. We characterize the marginal mediation effect, the multivariate component‐wise mediation effects, and the L2 norm of the component‐wise effects, and develop a Monte‐Carlo procedure for evaluating their statistical significance. To accommodate the setting with a large number of mediators and a small sample size, we further propose a transformation model using the spectral decomposition. Under the transformation model, mediation effects can be estimated using a series of regression models with a univariate transformed mediator, and examined by our proposed testing procedure. Extensive simulation studies are conducted to assess the performance of our methods for continuous and dichotomous outcomes. We apply the methods to analyze genomic data investigating the effect of microRNA miR‐223 on a dichotomous survival status of patients with glioblastoma multiforme (GBM). We identify nine gene ontology sets with expression values that significantly mediate the effect of miR‐223 on GBM survival.
A graphical model approach to systematically missing data in meta‐analysis of observational studies

When studies in meta‐analysis include different sets of confounders, simple analyses can cause a bias (omitting confounders that are missing in certain studies) or precision loss (omitting studies with incomplete confounders, i.e. a complete‐case meta‐analysis). To overcome these types of issues, a previous study proposed modelling the high correlation between partially and fully adjusted regression coefficient estimates in a bivariate meta‐analysis. When multiple differently adjusted regression coefficient estimates are available, we propose exploiting such correlations in a graphical model. Compared with a previously suggested bivariate meta‐analysis method, such a graphical model approach is likely to reduce the number of parameters in complex missing data settings by omitting the direct relationships between some of the estimates. We propose a structure‐learning rule whose justification relies on the missingness pattern being monotone. This rule was tested using epidemiological data from a multi‐centre survey. In the analysis of risk factors for early retirement, the method showed a smaller difference from a complete data odds ratio and greater precision than a commonly used complete‐case meta‐analysis. Three real‐world applications with monotone missing patterns are provided, namely, the association between (1) the fibrinogen level and coronary heart disease, (2) the intima media thickness and vascular risk and (3) allergic asthma and depressive episodes. The proposed method allows for the inclusion of published summary data, which makes it particularly suitable for applications involving both microdata and summary data. Copyright © 2016 John Wiley & Sons, Ltd.
Ductile Damage Evolution and Strain Path Dependency

Forming limit diagrams are commonly used in sheet metal industry to define the safe forming regions. These diagrams are built to define the necking strains of sheet metals. However, with the rise in the popularity of advance high strength steels, ductile fracture through damage evolution has also emerged as an important parameter in the determination of limit strains. In this work, damage evolution in two different steels used in the automotive industry is examined to observe the relationship between damage evolution and the strain path that is followed during the forming operation.
A Novel Genome-Information Content-Based Statistic for Genome-Wide Association Analysis Designed for Next-Generation Sequencing Data

The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T(2), collapsing method, multivariate and collapsing (CMC) method, individual χ(2) test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets.
A matter of time:the influence of context-based timing on compliance with well-being triggers

As occupational computer use grows, so do associated risks of negative health consequences. Repetitive strain injury and sedentary behavior are prevalent among computer users and both are associated with serious health risks. These risks can be minimized by healthy working behavior. One way to decrease the health risks associated with both of these conditions is taking short, frequent breaks during work, which are called microbreaks. Encouraging such behavior with persuasive technology (PT) has several advantages. Among the advantages are the availability and scalability of such interventions. Literature suggests that influencing people at the right time is critical to ensure the effectiveness of PT. Additionally, researchers have claimed that if a technology is aware of context, it should be able to identify such opportune moments and thereby increase compliance with the target behavior. An opportune moment to persuade, according to the Fogg Behavior Model, is one where the subject’s motivation and ability to perform the target behavior are at a high level. As such, a technology that tries to persuade knowledge workers to take microbreaks, should be able to determine when the motivation and ability of those workers to take a microbreak are at a high level. However, there appears to be a lack of empirical evidence for the Fogg Behavior Model, as well as for the claims of the importance of timing for PT and the usefulness of context information in determining opportune moments. The current research presents two studies. The first study was performed to assess whether context information (the computer activity of knowledge workers, such as the number of mouse clicks and key presses) can be used to make inferences about the level of motivation and ability to take a microbreak (e.g., overall computer activity is high, therefore the worker is too busy to take a microbreak). Six knowledge workers rated their level of motivation and ability to take a microbreak at different points in time, over the course of seven working days. Simultaneously, their computer activity was recorded. Confirming our expectations, the results show that moments of high and low (perceived) ability to take a microbreak can be partially predicted based on computer activity. More specifically, it can be based on two factors: the time since their last break and the change in their overall computer activity level. The level of motivation, on the other hand, could not be predicted based on computer activity. Next, the second study assessed whether presenting persuasive triggers at times of high ability leads to higher compliance, compared to times of low ability. A within-subjects experiment with 35 knowledge workers was carried out over the course of five working days. Each participant was subject to two conditions: triggers presented at times of high estimated ability and low estimated ability. The conditions were interactive and based on the current computer activity of the participants. Specifically, they were based on the two contextual factors described above.