Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining neural tracking and control to improve rehabilitation of upper limb movements in hemiplegia

This paper aims at introducing a novel approach for assisting and restoring upper arm movements in stroke patients. The presented system integrates advanced markerless motion analysis together with an artificial neural network controller for a biomechanical arm model. The keypoint of the project is to acquire kinematics information from the healthy arm of a stroke patient during planar arm movements and elaborate them in order to obtain a selfrehabilitative stimulation of the plegic arm of the same patient. The first experimental tests show good results and allow to define working direction for the extension of the work and for its application in clinical contexts.
The SCID–HuRAg mouse as a model for rheumatoid arthritis

Abstract Many animal models have been developed for a study of rheumatoid arthritis (RA). However, RA animal models are not always similar to RA patients in their response to antirheumatic drugs. Recently, humanized monoclonal antibody (mAb) has been developed for the treatment of RA, but at present there is no animal model on which to screen this mAb therapy because of problems with cross-reactivity. We therefore considered the development of a novel animal model for the screening of antirheumatic drugs using the severe combined immunodeficiency (SCID) mouse in order to prevent the rejection of human transplant cells. Following subcutaneous implantation of synovial tissue in the SCID mouse, all target cells within the SCID–HuRAg mouse were of human origin, having migrated from the implanted tissue. Moreover, human interlukin-6 and rheumatoid factor were detected in this mouse serum. We therefore propose that this SCID–HuRAg mouse is a novel, useful animal model for the study and development of new drugs for RA patients. This novel RA animal model is reviewed in this chapter.
Weather correlates of Campylobacter prevalence in broilers at slaughter under tropical conditions in Sri Lanka

Abstract Campylobacter is the primary agent of human bacterial gastroenteritis worldwide. In contrast to temperate zones, weather effects on Campylobacter prevalence in broilers under tropical conditions are under-researched. We examined the association between weather and Campylobacter prevalence in slaughtered broilers in Sri Lanka, a tropical country with weather variations led by monsoons. Each month (October 2009–July 2011), 20–30 broiler batches referring to two semi-automated slaughterhouses from five Sri Lankan provinces were tested for Campylobacter contamination and analysed in relation to temperature, humidity and rainfall. Overall prevalence was 63.8% (95% CI 59.6–67.9%, n = 542), peaking in September–November. Each 1 °C increase in monthly mean temperature up to 26 °C increased Campylobacter-positive batches by 16.4% (95% CI 0.4–35.1%). For each 10 mm increase in monthly total rainfall up to 300 mm, Campylobacter-positive batches increased significantly by 0.8% (0.1–1.5%) at 1-month lag. For each 1% increase in relative humidity up to 80% at 1- and 2-month lags, Campylobacter-positive batches increased of respectively 4.2% (1.9–6.7%) and 4.0% (1.5–6.5), and decreased by 3.6% (2.6–4.6%) and 4.0% (2.6–5.4%) for unit increases above 80%. These results suggest that even in tropical countries without marked seasons, there are weather effects possibly reflecting Campylobacter potential to colonise its preferred host and/or survive in the environment.
AIRLINE DISRUPTION RECOVERY USING SYMBIOTIC SIMULATION AND MULTI-FIDELITY MODELLING

The airlines industry is prone to disruption due to various causes. Whilst an airline may not be able to control the causes of disruption, it can reduce the impact of a disruptive event, such as a mechanical failure, with its response by revising the schedule. Potential actions include swapping aircraft, delaying flights and cancellations. This paper will present our research into how symbiotic simulation could potentially be used to improve the response to a disruptive event by evaluating potential revised schedules. Due to the large solution space, exhaustive searches are infeasible. Our research is investigating the use of multi-fidelity models to help guide the search of the optimisation algorithm, leading to good solutions being generated within the time constraints of disruption management.
Comparing a Mobile Decision Support System Versus the Use of Printed Materials for the Implementation of an Evidence-Based Recommendation: Protocol for a Qualitative Evaluation

Background The distribution of printed materials is the most frequently used strategy to disseminate and implement clinical practice guidelines, although several studies have shown that the effectiveness of this approach is modest at best. Nevertheless, there is insufficient evidence to support the use of other strategies. Recent research has shown that the use of computerized decision support presents a promising approach to address some aspects of this problem. Objective The aim of this study is to provide qualitative evidence on the potential effect of mobile decision support systems to facilitate the implementation of evidence-based recommendations included in clinical practice guidelines. Methods We will conduct a qualitative study with two arms to compare the experience of primary care physicians while they try to implement an evidence-based recommendation in their clinical practice. In the first arm, we will provide participants with a printout of the guideline article containing the recommendation, while in the second arm, we will provide participants with a mobile app developed after formalizing the recommendation text into a clinical algorithm. Data will be collected using semistructured and open interviews to explore aspects of behavioral change and technology acceptance involved in the implementation process. The analysis will be comprised of two phases. During the first phase, we will conduct a template analysis to identify barriers and facilitators in each scenario. Then, during the second phase, we will contrast the findings from each arm to propose hypotheses about the potential impact of the system. Results We have formalized the narrative in the recommendation into a clinical algorithm and have developed a mobile app. Data collection is expected to occur during 2018, with the first phase of analysis running in parallel. The second phase is scheduled to conclude in July 2019. Conclusions Our study will further the understanding of the role of mobile decision support systems in the implementation of clinical practice guidelines. Furthermore, we will provide qualitative evidence to aid decisions made by low- and middle-income countries’ ministries of health about investments in these technologies.
Behavior2Vec

Most studies on recommender systems target at increasing the click through rate, and hope that the number of orders will increase as well. We argue that clicking and purchasing an item are different behaviors. Thus, we should probably apply different strategies for different objectives, e.g., increase the click through rate, or increase the order rate. In this article, we propose to generate the distributed representations of users’ viewing and purchasing behaviors on an e-commerce website. By leveraging on the cosine distance between the distributed representations of the behaviors on items under different contexts, we can predict a user’s next clicking or purchasing item more precisely, compared to several baseline methods. Perhaps more importantly, we found that the distributed representations may help discover interesting analogies among the products. We may utilize such analogies to explain how two products are related, and eventually apply different recommendation strategies under different scenarios. We developed the Behavior2Vec library for demonstration. The library can be accessed at https://github.com/ncu-dart/behavior2vec/.
Two modes of participation: A conceptual analysis of 102 cases of Internet and social media participation from 2005 – 2015

In this article we analyze 102 case studies of Internet or social media-enabled participatory projects, technologies, platforms and companies in operation between roughly 2005 – 2015. We assign each case a “ signature ” representing the degree of presence/absence of seven dimensions of participation and then cluster these signatures to look for patterns of the most common ways of “ doing participation ” today. Two main clusters become apparent: 1) a “ radical-direct ” mode that emphasizes direct individual autonomy and in ﬂ uence, commitment to having a voice and setting goals, and individual or collective control over resources thereby produced; and 2) an “ experiential-affective ” mode that emphasizes the experience of being or becoming part of a collective, and the affective, communicational, and educational features of that experience.
A vectorcardiogram-based classification system for the detection of Myocardial infarction

Myocardial infarction (MI), generally known as a heart attack, is one of the top leading causes of mortality in the world. In clinical diagnosis, cardiologists generally utilize 12-lead ECG system to classify patients into MI symptoms: 1. ST segment elevation, 2. ST segment depression or T wave inversion. However unstable ischemic syndromes have rapidly changing supply versus demand characteristics that is one of the several limitations of 12-lead ECG system for MI detection. In addition, the ECG sensor placements of 12-lead system is not easily donned and doffed for tele-healthcare monitoring at home. Vectorcardiogram (VCG) system in clinic is another type of diagnosis plot which represents the magnitude and direction of the electrical potential in the form of a vector loop during cardiac electric activity. The VCG system can easily acquire three ECG waves from X, Y, Z directions to composite vector signal in space and the VCG signals can be transferred to 12-lead ECG signal through Dower transformation and vice versa. Hence, this study attempts to develop a VCG-based classification system for the detection of Myocardial infarction. In the experiment results, the proposed system can select the proper ECG features based on cardiologist's knowledge and proposed principal moments of QRS complex. The classification performance of MI detection can be reached to 99.89% of sensitivity, 92.51% of specificity, 95.35% of positive predictive value, and 96.96% overall accuracy with maximum-likelihood classifier (MLC).
Telomerase-Null Survivor Screening Identifies Novel Telomere Recombination Regulators

Telomeres are protein–DNA structures found at the ends of linear chromosomes and are crucial for genome integrity. Telomeric DNA length is primarily maintained by the enzyme telomerase. Cells lacking telomerase will undergo senescence when telomeres become critically short. In Saccharomyces cerevisiae, a very small percentage of cells lacking telomerase can remain viable by lengthening telomeres via two distinct homologous recombination pathways. These “survivor” cells are classified as either Type I or Type II, with each class of survivor possessing distinct telomeric DNA structures and genetic requirements. To elucidate the regulatory pathways contributing to survivor generation, we knocked out the telomerase RNA gene TLC1 in 280 telomere-length-maintenance (TLM) gene mutants and examined telomere structures in post-senescent survivors. We uncovered new functional roles for 10 genes that affect the emerging ratio of Type I versus Type II survivors and 22 genes that are required for Type II survivor generation. We further verified that Pif1 helicase was required for Type I recombination and that the INO80 chromatin remodeling complex greatly affected the emerging frequency of Type I survivors. Finally, we found the Rad6-mediated ubiquitination pathway and the KEOPS complex were required for Type II recombination. Our data provide an independent line of evidence supporting the idea that these genes play important roles in telomere dynamics.
General-purpose validation and model selection when estimating individual treatment effects

Practitioners in medicine, business, political science, and other fields are increasingly aware that decisions should be personalized to each patient, customer, or voter. A given treatment (e.g. a drug or advertisement) should be administered only to those who will respond most positively, and certainly not to those who will be harmed by it. Individual-level treatment effects (ITEs) can be estimated with tools adapted from machine learning, but different models can yield contradictory estimates. Unlike risk prediction models, however, treatment effect models cannot be easily evaluated against each other using a held-out test set because the true treatment effect itself is never directly observed. Besides outcome prediction accuracy, several approaches that use held-out data to evaluate treatment effects models have been proposed, but they are largely unknown or cloistered within disciplines. We present a review of these approaches and demonstrate theoretical relationships among them. We demonstrate their behavior using simulations of both randomized and observational data. Based on our empirical and theoretical results, we advocate for the standardized use of estimated decision value for individual treatment effect model selection and validation.
An Active Learning Approach for Training the Probabilistic RBF Classification Network

Active learning for classification constitutes a type of learning problem where a classifier is gradually built by iteratively asking for the labels of data points. The method involves a data selection mechanism that queries for the labels of those data points that considers to be mostly beneficial for improving the performance of the current classifier. We present an active learning methodology for training the probabilistic RBF (PRBF) network which is a special case of the RBF network, and constitutes a generalization of the Gaussian mixture model. The method employs a suitable criterion to select an unlabeled observation and query its label. The proposed criterion selects points that lie near the decision boundary. The learning performance of the algorithm is tested with experiments on several data sets.
Is the cardiovascular system a therapeutic target for cannabidiol?

Cannabidiol (CBD) has beneficial effects in disorders as wide ranging as diabetes, Huntington's disease, cancer and colitis. Accumulating evidence now also suggests that CBD is beneficial in the cardiovascular system. CBD has direct actions on isolated arteries, causing both acute and time‐dependent vasorelaxation. In vitro incubation with CBD enhances the vasorelaxant responses in animal models of impaired endothelium‐dependent vasorelaxation. CBD protects against the vascular damage caused by a high glucose environment, inflammation or the induction of type 2 diabetes in animal models and reduces the vascular hyperpermeability associated with such environments. A common theme throughout these studies is the anti‐inflammatory and anti‐oxidant effect of CBD. In the heart, in vivo CBD treatment protects against ischaemia‐reperfusion damage and against cardiomyopathy associated with diabetes. Similarly, in a different model of ischaemia‐reperfusion, CBD has been shown to reduce infarct size and increase blood flow in animal models of stroke, sensitive to 5HT1A receptor antagonism. Although acute or chronic CBD treatment seems to have little effect on haemodynamics, CBD reduces the cardiovascular response to models of stress, applied either systemically or intracranially, inhibited by a 5HT1A receptor antagonist. In blood, CBD influences the survival and death of white blood cells, white blood cell migration and platelet aggregation. Taken together, these preclinical data appear to support a positive role for CBD treatment in the heart, and in peripheral and cerebral vasculature. However, further work is required to strengthen this hypothesis, establish mechanisms of action and whether similar responses to CBD would be observed in humans.
Biomechanical-based image registration for head and neck radiation treatment

Deformable image registration of four head and neck cancer patients has been conducted using a biomechanical-based model. Patient-specific 3D finite element models have been developed using CT and cone-beam CT image data of the planning and a radiation treatment session. The model consists of seven vertebrae (C1 to C7), mandible, larynx, left and right parotid glands, tumor and body. Different combinations of boundary conditions are applied in the model in order to find the configuration with a minimum registration error. Each vertebra in the planning session is individually aligned with its correspondence in the treatment session. Rigid alignment is used for each individual vertebra and the mandible since no deformation is expected in the bones. In addition, the effect of morphological differences in the external body between the two image sessions is investigated. The accuracy of the registration is evaluated using the tumor and both parotid glands by comparing the calculated Dice similarity index of these structures following deformation in relation to their true surface defined in the image of the second session. The registration is improved when the vertebrae and mandible are aligned in the two sessions with the highest average Dice index of 0.86 ± 0.08, 0.84 ± 0.11 and 0.89 ± 0.04 for the tumor, left and right parotid glands, respectively. The accuracy of the center of mass location of tumor and parotid glands is also improved by deformable image registration where the errors in the tumor and parotid glands decrease from 4.0 ± 1.1, 3.4 ± 1.5 and 3.8 ± 0.9 mm using rigid registration to 2.3 ± 1.0, 2.5 ± 0.8 and 2.0 ± 0.9 mm in the deformable image registration when alignment of vertebrae and mandible is conducted in addition to the surface projection of the body.
Migration as a risk factor for schizophrenia: A Danish population-based cohort study

Background A growing body of evidence suggests that migration is a risk factor for the development of schizophrenia, although the putative mechanism remains obscure. Aims To examine immigrant background and history of foreign residence as risk factors for schizophrenia. Method Using data from the Danish Civil Registration System, we established a population-based cohort of 2.14 million persons resident in Denmark by their fifteenth birthday. Schizophrenia in cohort members and parental psychiatric disorder were identified by cross-linkage with the Danish Psychiatric Case Register. Results The relative risk of developing schizophrenia was 2.45 (95% CI 2.25–2.67) and 1.92 (95% CI 1.74–2.12) among first- and second-generation immigrants respectively, and 1.60 (95% CI 1.25–2.05) among Danes with a history of foreign residence. Conclusions Migration confers an increased risk for schizophrenia that is not solely attributable to selection factors and may also be independent of foreign birth.
Pollinator convergence and the nature of species' boundaries in sympatric Sardinian Ophrys (Orchidaceae).

BACKGROUND AND AIMS In the sexually deceptive Ophrys genus, species isolation is generally considered ethological and occurs via different, specific pollinators, but there are cases in which Ophrys species can share a common pollinator and differ in pollen placement on the body of the insect. In that condition, species are expected to be reproductively isolated through a pre-mating mechanical barrier. Here, the relative contribution of pre- vs. post-mating barriers to gene flow among two Ophrys species that share a common pollinator and can occur in sympatry is studied. METHODS A natural hybrid zone on Sardinia between O. iricolor and O. incubacea, sharing Andrena morio as pollinator, was investigated by analysing floral traits involved in pollinator attraction as odour extracts both for non-active and active compounds and for labellum morphology. The genetic architecture of the hybrid zone was also estimated with amplified fragment length polymorphism (AFLP) markers, and pollination fitness and seed set of both parental species and their hybrids in the sympatric zone were estimated by controlled crosses. KEY RESULTS Although hybrids were intermediate between parental species in labellum morphology and non-active odour compounds, both parental species and hybrids produced a similar odour bouquet for active compounds. However, hybrids produced significantly lower fruit and seed set than parental species, and the genetic architecture of the hybrid zone suggests that they were mostly first-generation hybrids. CONCLUSIONS The two parental species hybridize in sympatry as a consequence of pollinator overlap and weak mechanical isolation, but post-zygotic barriers reduce hybrid frequency and fitness, and prevent extensive introgression. These results highlight a significant contribution of late post-mating barriers, such as chromosomal divergence, for maintaining reproductive isolation, in an orchid group for which pre-mating barriers are often considered predominant.
Neural Color Transfer between Images

We propose a new algorithm for color transfer between images that have perceptually similar semantic structures. We aim to achieve a more accurate color transfer that leverages semantically-meaningful dense correspondence between images. To accomplish this, our algorithm uses neural representations for matching. Additionally, the color transfer should be spatially-variant and globally coherent. Therefore, our algorithm optimizes a local linear model for color transfer satisfying both local and global constraints. Our proposed approach jointly optimize matching and color transfer, adopting a coarse-to-fine strategy. The proposed method can be successfully extended from "one-to-one" to "one-to-many" color transfers. The latter further addresses the problem of mismatching elements of the input image. We validate our proposed method by testing it on a large variety of image content.
Speaker Adaptation for Multichannel End-to-End Speech Recognition

Recent work on multichannel end-to-end automatic speech recognition (ASR) has shown that multichannel speech enhancement and speech recognition functions can be integrated into a deep neural network (DNN)-based system, and promising experimental results have been shown using the CHiME-4 and AMI corpora. In other recent DNN-based hidden Markov model (DNN-HMM) hybrid architectures, the effectiveness of speaker adaptation has been well established. Motivated by these results, we propose a multi-path adaptation scheme for end-to-end multichannel ASR, which combines the unprocessed noisy speech features with a speech-enhanced pathway to improve upon previous end-to-end ASR approaches. Experimental results using CHiME-4 show that (1) our proposed multi-path adaptation scheme improves ASR performance and (2) adapting the encoder network is more effective than adapting the neural beamformer, attention mechanism, or decoder network.