Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitioning in hydrocephalus: Current practices in Canadian Paediatric Neurosurgery centres.

Introduction Advances in perinatal care in the developed world have resulted in more children living into adulthood with complex chronic health conditions. Transition is a process to improve and maximize the functional status of adolescents via the provision of adequate and appropriate health services in adulthood. This process is frequently disjointed, fragmented and inconsistent and inadequate transition increases morbidity, hospital admissions and urgent interventions. Ten thousand children are diagnosed with hydrocephalus annually in North America. Most survive to adulthood yet there are few transitioning programs and little research data on successful programs for this population. Methods An email survey of paediatric neurosurgical centres in Canada was carried out to establish current transition practices and attitudes for adolescents with hydrocephalus. Data were analyzed descriptively. Results Eleven out of 12 centres responded. The age of transition ranged from 16 to 18 years. Four centres have access to a dedicated Adult Hydrocephalus Clinic. Referral practices vary between centres and we highlight inconsistencies in care to this cohort of patients in Canada. There is little satisfaction among neurosurgeons with current transition arrangements in Canada. Several suggestions were made on how to improve this process. Conclusion We recommend research into the needs of patients with hydrocephalus in order to formalize appropriate standards for transitioning patients with a view to developing national guidelines to standardize the transition process. This will require input from patients, families and the wider medical and allied health professional groups.
Clinical follow up and the impact of the Paris system in the assessment of patients with atypical urine cytology

Urinary cytology is routinely used in the diagnosis of urothelial neoplasms, with good sensitivity for high‐grade urothelial carcinoma (HGUC) but less so for low‐grade urothelial neoplasm (LGUN). There is significant interobserver and interinstitutional variability, especially for the atypical category. The Paris system for reporting urinary cytology (TPS) was introduced to better define the various categories, especially atypical cytology.
TestNMT: function-to-test neural machine translation

Test generation can have a large impact on the software engineering process by decreasing the amount of time and effort required to maintain a high level of test coverage. This increases the quality of the resultant software while decreasing the associated effort. In this paper, we present TestNMT, an experimental approach to test generation using neural machine translation. TestNMT aims to learn to translate from functions to tests, allowing a developer to generate an approximate test for a given function, which can then be adapted to produce the final desired test. We also present a preliminary quantitative and qualitative evaluation of TestNMT in both cross-project and within-project scenarios. This evaluation shows that TestNMT is potentially useful in the within-project scenario, where it achieves a maximum BLEU score of 21.2, a maximum ROUGE-L score of 38.67, and is shown to be capable of generating approximate tests that are easy to adapt to working tests.
User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes.
Human-Robotic Variable-Stiffness Grasps of Small-Fruit Containers Are Successful Even Under Severely Impaired Sensory Feedback

Application areas of robotic grasping extend to delicate objects like groceries. The intrinsic elasticity offered by variable-stiffness actuators (VSA) appears to be promising in terms of being able to adapt to the object shape, to withstand collisions with the environment during the grasp acquisition, and to resist the weight applied to the fingers by a lifted object during the actual grasp. It is hypothesized that these properties are particularly useful in the absence of high-quality sensory feedback, which would otherwise be able to guide the shape adaptation and collision avoidance, and that in this case, VSA hands perform better than hands with fixed stiffness. This hypothesis is tested in an experiment where small-fruit containers are picked and placed using a newly developed variable-stiffness robotic hand. The grasp performance is measured under different sensory feedback conditions: full or impaired visual feedback, full or impaired force feedback. The hand is switched between a variable-stiffness mode and two fixed-stiffness modes. Strategies for modulating the stiffness and exploiting environmental constraints are observed from human operators that control the robotic hand. The results show consistently successful grasps under all stiffness and feedback conditions. However, the performance is affected by the amount of available visual feedback. Different stiffness modes turn out to be beneficial in different feedback conditions and with respect to different performance criteria, but a general advantage of VSA over fixed stiffness cannot be shown for the present task. Guidance of the fingers along cracks and gaps is observed, which may inspire the programming of autonomously grasping robots.
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-

Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks. For image recognition tasks, many previous studies have reported that, when transfer learning is applied to deep neural networks, performance improves, despite having limited training data. This paper proposes a two-stage feature transfer learning method focusing on the recognition of textural medical images. During the proposed method, a model is successively trained with massive amounts of natural images, some textural images, and the target images. We applied this method to the classification task of textural X-ray computed tomography images of diffuse lung diseases. In our experiment, the two-stage feature transfer achieves the best performance compared to a from-scratch learning and a conventional single-stage feature transfer. We also investigated the robustness of the target dataset, based on size. Two-stage feature transfer shows better robustness than the other two learning methods. Moreover, we analyzed the feature representations obtained from DLDs imagery inputs for each feature transfer models using a visualization method. We showed that the two-stage feature transfer obtains both edge and textural features of DLDs, which does not occur in conventional single-stage feature transfer models.
SpiNNTools: The Execution Engine for the SpiNNaker Platform

SpiNNaker is a massively parallel distributed architecture primarily focused on real time simulation of spiking neural networks. The largest realization of the architecture consists of one million general purpose processors, making it the largest neuromorphic computing platform in the world at the present time. Utilizing these processors efficiently requires expert knowledge of the architecture to generate executable code and to harness the potential of the unique inter-processor communications infra-structure that lies at the heart of the SpiNNaker architecture. This work introduces a software suite called SpiNNTools that can map a computational problem described as a graph into the required set of executables, application data and routing information necessary for simulation on this novel machine. The SpiNNaker architecture is highly scalable, giving rise to unique challenges in mapping the problem to the machines resources, loading the generated files to the machine and subsequently retrieving the results of simulation. In this paper we describe these challenges in detail and the solutions implemented.
National Athletic Trainers' Association Position Statement: Management of Individuals With Patellofemoral Pain.

OBJECTIVE: To present recommendations for athletic trainers and other health care providers regarding the identification of risk factors for and management of individuals with patellofemoral pain (PFP). BACKGROUND: Patellofemoral pain is one of the most common knee diagnoses; however, this condition continues to be one of the most challenging to manage. Recent evidence has suggested that certain risk factors may contribute to the development of PFP. Early identification of risk factors may allow clinicians to develop and implement programs aimed at reducing the incidence of this condition. To date, clinicians have used various treatment strategies that have not necessarily benefitted all patients. Suboptimal outcomes may reflect the need to integrate clinical practice with scientific evidence to facilitate clinical decision making. RECOMMENDATIONS: The recommendations are based on the best available evidence. They are intended to give athletic trainers and other health care professionals a framework for identifying risk factors for and managing patients with PFP.
Adhesins of Yeasts: Protein Structure and Interactions

The ability of yeast cells to adhere to other cells or substrates is crucial for many yeasts. The budding yeast Saccharomyces cerevisiae can switch from a unicellular lifestyle to a multicellular one. A crucial step in multicellular lifestyle adaptation is self-recognition, self-interaction, and adhesion to abiotic surfaces. Infectious yeast diseases such as candidiasis are initiated by the adhesion of the yeast cells to host cells. Adhesion is accomplished by adhesin proteins that are attached to the cell wall and stick out to interact with other cells or substrates. Protein structures give detailed insights into the molecular mechanism of adhesin-ligand interaction. Currently, only the structures of a very limited number of N-terminal adhesion domains of adhesins have been solved. Therefore, this review focuses on these adhesin protein families. The protein architectures, protein structures, and ligand interactions of the flocculation protein family of S. cerevisiae; the epithelial adhesion family of C. glabrata; and the agglutinin-like sequence protein family of C. albicans are reviewed and discussed.
Tape‐stripping alters the microbe‐host correlations in mouse skin

1. Thijs J, Krastev T, Weidinger S, et al. Biomarkers for atopic dermatitis: a systematic review and meta‐analysis. Curr Opin Allergy Clin Immunol. 2015;15:453‐460. 2. Landheer J, de Bruin-Weller M, Boonacker C, Hijnen D, BruijnzeelKoomen C, Rockmann H. Utility of serum thymus and activation‐regulated chemokine as a biomarker for monitoring of atopic dermatitis severity. J Am Acad Dermatol. 2014;71:1160‐1166. 3. Thijs JL, Nierkens S, Herath A, et al. A panel of biomarkers for disease severity in atopic dermatitis. Clin Exp Allergy. 2015;45:698‐701. 4. Thijs JL, Drylewicz J, Fiechter R, et al. EASI p‐EASI: utilizing a combination of serum biomarkers offers an objective measurement tool for disease severity in atopic dermatitis patients. J Allergy Clin Immunol. 2017;140:1703‐1705. 5. Leshem YA, Hajar T, Hanifin JM, Simpson EL. What the Eczema area and severity index score tells us about the severity of atopic dermatitis: an interpretability study. Br J Dermatol. 2015;172:1353‐1357. 6. Ungar B, Garcet S, Gonzalez J, et al. An integrated model of atopic dermatitis biomarkers highlights the systemic nature of the disease. J Invest Dermatol. 2017;137:603‐613.
Integrated Vehicle Scheduling and Vehicle Assignment

The vehicle scheduling problem has been extensively studied in the past decades. Yet, most models and methods given in the literature consider only a theoretical scenario where vehicles just have to service the timetabled trips of the input. However, schedules created this way cannot be used in real life, as they should also consider constraints such as refueling, parking, and maintenance, which are all connected to the vehicle servicing the trips. In this paper, we give a set partitioning model for the multi-depot integrated vehicle scheduling and vehicle assignment problem. This model can also be used as a general framework, which can integrate multiple activities based on the rules or regulation of the different possible input scenarios. We give a column generation-based solution method, and demonstrate its efficiency on randomly generated test instances, which treat the refueling of vehicles with two different fuel types as the vehicle-specific activity.
An ideal e‐health system for pelvic floor muscle training adherence: Systematic review

Nowadays, Pelvic Floor Muscle Training (PFMT) is a first line, level 1 evidence‐based treatment for urinary incontinence (UI), but adherence to PFMT is often problematic. Today, there are several mobile applications (mApps) for PFMT, but many lack specific strategies for enhancing adherence.
Hierarchical Game-Theoretic Planning for Autonomous Vehicles

The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle’s planning and its predictions of other drivers’ behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology. Unfortunately, dynamic games are too computationally demanding to meet the real-time constraints of autonomous driving in its continuous state and action space. In this paper, we introduce a novel game-theoretic trajectory planning algorithm for autonomous driving, that enables real-time performance by hierarchically decomposing the underlying dynamic game into a long-horizon “strategic” game with simplified dynamics and full information structure, and a short-horizon “tactical” game with full dynamics and a simplified information structure. The value of the strategic game is used to guide the tactical planning, implicitly extending the planning horizon, pushing the local trajectory optimization closer to global solutions, and, most importantly, quantitatively accounting for the autonomous vehicle and the human driver’s ability and incentives to influence each other. In addition, our approach admits non-deterministic models of human decision-making, rather than relying on perfectly rational predictions. Our results showcase richer, safer, and more effective autonomous behavior in comparison to existing techniques.
A Search for Disorders of Sex Development among Infertile Men

A retrospective cross-sectional study was performed in a DSD clinic at a tertiary service (University Hospital) to estimate the frequency of disorders of sex development (DSD) among men who seek medical care because of infertility. The sample included 84 men >20 years of age referred from 2010-2017 due to oligozoospermia or nonobstructive azoospermia of unknown etiology. Twelve cases (14%) were diagnosed as DSD, including Klinefelter Syndrome, 46,XX testicular DSD, and mild androgen insensitivity syndrome. Y chromosome microdeletions were detected in 2 patients. Among the remaining 70 cases there were patients with chromosome abnormalities which are not included in the DSD classification as well as rare NR5A1 variants of uncertain significance and hypergonadotropic hypogonadism and microorchidism in 46,XY subjects. In conclusion, the frequency of DSD in this study was 14%, consisting mainly of sex chromosome abnormalities but also 46,XX and 46,XY DSD. However, this figure may increase as further investigations are conducted in idiopathic cases with signs of primary testicular failure, which may present partial gonadal dysgenesis.
Adaptive Hierarchical Siamese Network for Object Tracking

A new object tracking algorithm based on hierarchical siamese network is proposed to solve the challenges of target deformation and background interference in tracking tasks. The introduction of hierarchical features makes up for the lack of features information caused by too small target. A similarity measure mechanism is firstly learned through matching training of extracted hierarchical features on annotated datasets. Online target tracking is achieved by searching candidate images at multiple scales around the target center of the previous frame. The adaptive examplar and the first frame exemplar are used to measure the similarity of the current frame to obtain an accurate score map, which allows the algorithm to adapt to the change of the target. Our algorithm achieves real-time target tracking without online fine-tune and achieves long-term effective tracking on multiple challenging video sequences.
Hypermethylator Phenotype and Ectopic GIP Receptor in GNAS Mutation-Negative Somatotropinomas.

CONTEXT Besides GNAS gene mutations, the molecular pathogenesis of somatotroph adenomas responsible for gigantism and acromegaly remains elusive. OBJECTIVE To investigate alternative driver events in somatotroph tumorigenesis, focusing on a subgroup of acromegalic patients with a paradoxical increase in growth hormone (GH) secretion after oral glucose, resulting from ectopic glucose-dependent insulinotropic polypeptide receptor (GIPR) expression in their somatotropinomas. DESIGN, SETTING, AND PATIENTS We performed combined molecular analyses, including array-comparative genomic hybridization, RNA/DNA fluorescence in situ hybridization, and RRBS DNA methylation analysis on 41 somatotropinoma samples from 38 patients with acromegaly and three sporadic giants. Ten patients displayed paradoxical GH responses to oral glucose. RESULTS GIPR expression was detected in 13 samples (32%), including all 10 samples from patients with paradoxical GH responses. All GIPR-expressing somatotropinomas were negative for GNAS mutations. GIPR expression occurred through transcriptional activation of a single allele of the GIPR gene in all GIPR-expressing samples, except in two tetraploid samples, where expression occurred from two alleles per nucleus. In addition to extensive 19q duplications, we detected in four samples GIPR locus microamplifications in a certain proportion of nuclei. We identified an overall hypermethylator phenotype in GIPR-expressing samples compared with GNAS-mutated adenomas. In particular, we observed hypermethylation in the GIPR gene body, likely driving its ectopic expression. CONCLUSIONS We describe a distinct molecular subclass of somatotropinomas, clinically revealed by a paradoxical increase of GH to oral glucose related to pituitary GIPR expression. This ectopic GIPR expression occurred through hypomorphic transcriptional activation and is likely driven by GIPR gene microamplifications and DNA methylation abnormalities.
LprG and PE_PGRS33 Mycobacterium tuberculosis virulence factors induce differential mitochondrial dynamics in macrophages

The interaction of a pathogen with its host cell takes place at different levels, including the bioenergetics adaptation of both the pathogen and the host cell in the course of an infection. In this regard, Mycobacterium tuberculosis infection of macrophages induces mitochondrial membrane potential (Δψm) changes and cytochrome c release, depending on the bacteria strain's virulence, and the mitochondrial dynamics is modified by pathogens, such as Listeria monocytogenes. Here, we investigated whether two M. tuberculosis virulence factors are able to induce distinguishable bioenergetics traits in human monocyte‐derived macrophages (MDMs). Results showed that Rv1411c (LprG, p27) induced mitochondrial fission, lowered the cell respiratory rate and modified the kinetics of mitochondrial Ca2+ uptake in response to agonist stimulation. In contrast, Rv1818c (PE_PGRS33) induced mitochondrial fusion, but failed to induce any appreciable effect on cell respiratory rate or mitochondrial Ca2+ uptake. Overall, these results suggest that two different virulence factors from the same pathogen (M. tuberculosis) induce differential effects on mitochondrial dynamics, cell respiration and mitochondrial Ca2+ uptake in MDMs. The timing of differential mitochondrial activity could ultimately determine the outcome of host‐pathogen interactions.
Traumatisme fermé de l’abdomen occasionnant une pancréatite aiguë: présentation de cas

La pancréatite aiguë est une affection grave de pronostic réservé, son étiologie est soit biliaire ou alcoolique et l'origine traumatique est extrêmement rare et souvent associée à d'autres lésions intra abdominale. Nous présentons le cas d'un homme 56 ans aux atcds psychiatriques sous neuroleptiques et aux ATCDS de néphrotomie gauche et qui consulte dans les suites d'une chute d'un escalier 3 jours auparavant pour sd occlusif, à l'examen état hémodynamique respiratoire et neurologique correct, sensibilité épigastrique, une TDM pratiqué montre une pancréatite stade E traumatique avec au bilan de nombreuses perturbations: élévation des amylases, de la glycémie, des GB et de la CRP; l'évolution était défavorable avec le décès du patient 72 H plupart par une défaillance multi viscérale suite à un choc septique malgré une réanimation approprié. La pancréatite aiguë traumatique étant une affection grave, pourvue qu'elle est très rare et parfois difficile à diagnostiquer vue la non spécificité des signes cliniques, il faut savoir y penser afin la prendre en charge à temps.
Nonlinear Prediction of Multidimensional Signals via Deep Regression with Applications to Image Coding

Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from input to output. In this light, DCNNs are well suited for the task of sequential prediction of multidimensional signals, such as images, and have the potential of improving the performance of traditional linear predictors. In this research we investigate how far DCNNs can push the envelop in terms of prediction precision. We propose, in a case study, a two-stage deep regression DCNN framework for nonlinear prediction of two-dimensional image signals. In the first-stage regression, the proposed deep prediction network (PredNet) takes the causal context as input and emits a prediction of the present pixel. Three PredNets are trained with the regression objectives of minimizing l1, l2 and l∞ norms of prediction residuals, respectively. The second-stage regression combines the outputs of the three PredNets to generate an even more precise and robust prediction. The proposed deep regression model is applied to lossless predictive image coding, and it outperforms the state-of-the-art linear predictors by appreciable margin.