Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-Campylobacter Guillain Barré Syndrome in the USA: secondary analysis of surveillance data collected during the 2009–2010 novel Influenza A (H1N1) vaccination campaign

Abstract Guillain Barré syndrome (GBS), which is triggered by autoantibodies produced in response to antigenic stimuli such as certain infections and vaccinations, is the most common cause of acute flaccid paralysis worldwide. Campylobacter, the most common bacterial enteric infection in the USA, is reported to be the most commonly diagnosed antecedent of GBS, yet little information is available about the risk of post-Campylobacter GBS. Data collected through active, population-based surveillance in the Emerging Infections Program during the 2009–2010 novel Influenza A (H1N1) vaccination campaign allowed us to compare confirmed and probable GBS cases to non-cases to determine whether antecedent Campylobacter infection (or a diarrhoeal illness consistent with campylobacteriosis) was more common among cases and to assess the risk of GBS following Campylobacter infection. We estimate that 8–12% of GBS cases in the USA are attributable to Campylobacter infection (or a diarrhoeal illness consistent with campylobacteriosis), with 434–650 cases of post-diarrhoeal GBS annually and about 49 cases of GBS per 100 000 Campylobacter infections. These results provide updated estimates for post-Campylobacter GBS incidence in the USA and highlight an important benefit of effective measures to prevent Campylobacter infections.
Binary Coding based Label Distribution Learning

Label Distribution Learning (LDL) is a novel learning paradigm in machine learning, which assumes that an instance is labeled by a distribution over all labels, rather than labeled by a logic label or some logic labels. Thus, LDL can model the description degree of all possible labels to an instance. Although many LDL methods have been put forward to deal with different application tasks, most existing methods suffer from the scalability issue. In this paper, a scalable LDL framework named Binary Coding based Label Distribution Learning (BC-LDL) is proposed for large-scale LDL. The proposed framework includes two parts, i.e., binary coding and label distribution generation. In the binary coding part, the learning objective is to generate the optimal binary codes for the instances. We integrate the label distribution information of the instances into a binary coding procedure, leading to high-quality binary codes. In the label distribution generation part, given an instance, the k nearest training instances in the Hamming space are searched and the mean of the label distributions of all the neighboring instances is calculated as the predicted label distribution. Experiments on five benchmark datasets validate the superiority of BC-LDL over several state-of-the-art LDL methods.
Germline DNA replication timing shapes mammalian genome composition

Mammalian DNA is replicated in a highly organized and regulated manner. Large, Mb-sized regions are replicated at defined times along S phase. DNA Replication Timing (RT) has been suggested to play an important role in shaping the mammalian genome by affecting mutation rates. Previous analyses relied on somatic DNA RT profiles, while to fully understand the influences of RT on the mammalian genome, germ cell RT information is necessary, as only germline mutations are passed to offspring and thus affect genomic composition. Using an improved RT mapping technique that allows mapping the RT from limited amounts of cells, we measured RT from two stages in the mouse germline - primordial germ cells (PGCs) and spermatogonial stem cells (SSCs). The germ cell RT profiles were distinct from those of both somatic and embryonic tissues. The correlations between RT and both mutation rate and recombination hotspots were not only confirmed in the germline tissues, but were shown to be stronger compared to correlations with RT of somatic tissues, emphasizing the importance of using RT profiles from the correct tissue of origin. Expanding the analysis to additional genetic features such as GC content, transposable elements (SINEs and LINEs) and gene density, also revealed a stronger correlation with the germ cell RT maps. GC content stratification along with multiple regression analysis revealed the independent contribution of RT to SINE, gene, mutation and recombination hotspot densities. Taken together, our results point to the centrality of RT in shaping multiple levels of mammalian genome composition.
Introduction to redundancy rules: the continuous wavelet transform comes of age

Redundancy: it is a word heavy with connotations of lacking usefulness. I often hear that the rationale for not using the continuous wavelet transform (CWT)—even when it appears most appropriate for the problem at hand—is that it is ‘redundant’. Sometimes the conversation ends there, as if self-explanatory. However, in the context of the CWT, ‘redundant’ is not a pejorative term, it simply refers to a less compact form used to represent the information within the signal. The benefit of this new form—the CWT—is that it allows for intricate structural characteristics of the signal information to be made manifest within the transform space, where it can be more amenable to study: resolution over redundancy. Once the signal information is in CWT form, a range of powerful analysis methods can then be employed for its extraction, interpretation and/or manipulation. This theme issue is intended to provide the reader with an overview of the current state of the art of CWT analysis methods from across a wide range of numerate disciplines, including fluid dynamics, structural mechanics, geophysics, medicine, astronomy and finance. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.
Virion structure and genome delivery mechanism of sacbrood honeybee virus

Significance Honey bee pollination is required to sustain the biodiversity of wild flora and for agricultural production; however, honey bee populations in Europe and North America are declining due to virus infections. Sacbrood virus (SBV) infection is lethal to honey bee larvae and decreases the fitness of honey bee colonies. Here we present the structure of the SBV particle and show that it contains 60 copies of a minor capsid protein attached to its surface. No similar minor capsid proteins have been previously observed in any of the related viruses. We also present a structural analysis of the genome release of SBV. The possibility of blocking virus genome delivery may provide a tool to prevent the spread of this honey bee pathogen. Infection by sacbrood virus (SBV) from the family Iflaviridae is lethal to honey bee larvae but only rarely causes the collapse of honey bee colonies. Despite the negative effect of SBV on honey bees, the structure of its particles and mechanism of its genome delivery are unknown. Here we present the crystal structure of SBV virion and show that it contains 60 copies of a minor capsid protein (MiCP) attached to the virion surface. No similar MiCPs have been previously reported in any of the related viruses from the order Picornavirales. The location of the MiCP coding sequence within the SBV genome indicates that the MiCP evolved from a C-terminal extension of a major capsid protein by the introduction of a cleavage site for a virus protease. The exposure of SBV to acidic pH, which the virus likely encounters during cell entry, induces the formation of pores at threefold and fivefold axes of the capsid that are 7 Å and 12 Å in diameter, respectively. This is in contrast to vertebrate picornaviruses, in which the pores along twofold icosahedral symmetry axes are currently considered the most likely sites for genome release. SBV virions lack VP4 subunits that facilitate the genome delivery of many related dicistroviruses and picornaviruses. MiCP subunits induce liposome disruption in vitro, indicating that they are functional analogs of VP4 subunits and enable the virus genome to escape across the endosome membrane into the cell cytoplasm.
Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism

Knowledge graph embedding is an essential problem in knowledge extraction. Recently, translation based embedding models (e.g., TransE) have received increasingly attentions. These methods try to interpret the relations among entities as translations from head entity to tail entity and achieve promising performance on knowledge graph completion. Previous researchers attempt to transform the entity embedding concerning the given relation for distinguishability. Also, they naturally think the relation-related transforming should reflect attention mechanism, which means it should focus on only a part of the attributes. However, we found previous methods are failed with creating attention mechanism, and the reason is that they ignore the hierarchical routine of human cognition. When predicting whether a relation holds between two entities, people first check the category of entities, then they focus on fined-grained relation-related attributes to make the decision. In other words, the attention should take effect on entities filtered by the right category. In this paper, we propose a novel knowledge graph embedding method named TransAt to learn the translation based embedding, relation-related categories of entities and relation-related attention simultaneously. Extensive experiments show that our approach outperforms state-of-the-art methods significantly on public datasets, and our method can learn the true attention varying among relations.
Longitudinal changes in neuroimaging and neuropsychiatric status of post-deployment veterans: a CENC pilot study

ABSTRACT Primary objective: The purpose of this study was to evaluate preliminary data on longitudinal changes in psychiatric, neurobehavioural, and neuroimaging findings in Iraq and Afghanistan combat veterans following blast exposure. Research design: Longitudinal observational analysis. Methods and procedures: Participants were invited to participate in two research projects approximately 7 years apart. For each project, veterans completed the Structured Clinical Interview for DSM-IV Disorders and/or the Clinician-Administered PTSD Scale, Neurobehavioral Symptom Inventory, and magnetic resonance imaging (MRI). Main outcomes and results: Chi-squared tests indicated no significant changes in current psychiatric diagnoses, traumatic brain injury (TBI) history, or blast exposure history between assessment visits. Wilcoxon signed-rank tests indicated significant increases in median neurobehavioural symptoms, total number of white matter hyperintensities (WMH), and total WMH volume between assessment visits. Spearman rank correlations indicated no significant associations between change in psychiatric diagnoses, TBI history, blast exposure history, or neurobehavioural symptoms and change in WMH. Conclusion: MRI WMH changes were not associated with changes in psychiatric diagnoses or symptom burden, but were associated with severity of blast exposure. Future, larger studies might further evaluate presence and aetiology of long-term neuropsychiatric symptoms and MRI findings in blast-exposed populations.
Extension of the Multi-TP Model Transformation to Functions with Different Numbers of Variables

The tensor product (TP) model transformation defines and numerically reconstructs the Higher-Order Singular Value Decomposition (HOSVD) of functions. It plays the same role with respect to functions as HOSVD does for tensors (and SVD for matrices). The need for certain advantageous features, such as rank/complexity reduction, trade-offs between complexity and accuracy, and a manipulation power representative of the TP form, has motivated novel concepts in TS fuzzy model based modelling and control. The latest extensions of the TP model transformation, called the multi- and generalised TP model transformations, are applicable to a set functions where the dimensionality of the outputs of the functions may differ, but there is a strict limitation on the dimensionality of their inputs, which must be the same. The paper proposes an extended version that is applicable to a set of functions where both the input and output dimensionalities of the functions may differ. This makes it possible to transform complete multicomponent systems to TS fuzzy models along with the above-mentioned advantages.
A Survey on the Knowledge and Attitudes of Italian Medical Students toward Body Donation: Ethical and Scientific Considerations

Post mortem body donation (PMBD) for medical training and research plays a key role in medical-surgical education. The aim of this study is to evaluate Italian medical students’ awareness and attitudes regarding this practice. A questionnaire was sent to 1781 Italian medical students (MS). A total of 472 MS responded: 406 (92.91%) had a strongly positive attitude to PMBD, while 31 (7.09%) were not in favor. The majority of subjects were Catholic (56.36%), while 185 and 21 subjects, said that they did not hold any religious beliefs, or were of other religions, respectively. Multivariate analysis showed significant associations (p-values < 0.05) between PMBD and religion, as well as perceptions of PMBD as an act of altruism, a tool for learning surgical practices, body mutilation, and an act contrary to faith. Although Italian MS believed cadaver dissection to be an important part of their education, they did not know much about it and had not received training on this altruistic choice. As future doctors, MS can play an important role in raising public awareness of the importance of PMBD for medical education and research. Specific educational programs to improve knowledge of this topic among MS are needed.
Effects of Dry Needling on Post-Stroke Spasticity, Motor Function and Stability Limits: A Randomised Clinical Trial

Objective To determine the effects of inclusion of deep dry needling into a treatment session following the Bobath concept on spasticity, motor function and postural control after a stroke. Methods 26 patients who had suffered a stroke were randomly assigned to one of two treatment groups: Bobath only, or Bobath plus dry needling. Both groups received a session including strengthening, stretching and reconditioning exercises following the principles of the Bobath concept. Patients in the Bobath plus dry needling group also received a single session of ultrasound-guided dry needling of the tibialis posterior. Spasticity (Modified Modified Ashworth Scale), function (Fugl-Meyer Scale) and stability limits (computerised dynamic posturography using the SMART EquiTest System) were collected before and 10 min after treatment by a blinded assessor. The parameters of the stability limits included movement velocity (MVL), maximum excursion (MXE), end-point excursion (EPE) and directional control (DCL). Results A greater number of individuals receiving Bobath plus dry needling exhibited a decrease in spasticity after treatment (P<0.001). Analysis of covariance (ANCOVA) showed that patients receiving Bobath plus dry needling exhibited greater improvements in the balance (0.8, 95% CI 0.2 to 1.4), sensory (1.7, 95% CI 0.7 to 2.7) and range of motion (3.2, 95% CI 2.0 to 4.4) domains of the Fugl-Meyer Scale than those receiving Bobath only. ANCOVA also found that subjects receiving dry needling showed a greater increase in MVL non-affected forward direction, EPE non-affected direction, MXE backward and MXE affected/non-affected, DCL backward and DCL affected backward direction, than those who did not receive it. Conclusions The inclusion of deep dry needling into a treatment session following the Bobath concept was effective at decreasing spasticity and improving balance, range of motion and the accuracy of maintaining stability in patients who had experienced a stroke. Trial registration number NCT02579291.
Show, Observe and Tell: Attribute-driven Attention Model for Image Captioning

Despite the fact that attribute-based approaches and attention-based approaches have been proven to be effective in image captioning, most attribute-based approaches simply predict attributes independently without taking the co-occurrence dependencies among attributes into account. Besides, most attention-based captioning models directly leverage the feature map extracted from CNN, in which many features may be redundant in relation to the image content. In this paper, we focus on training a good attribute-inference model via the recurrent neural network (RNN) for image captioning, where the co-occurrence dependencies among attributes can be maintained. The uniqueness of our inference model lies in the usage of a RNN with the visual attention mechanism to \textit{observe} the image before generating captions. Additionally, it is noticed that compact and attribute-driven features will be more useful for the attention-based captioning model. To this end, we extract the context feature for each attribute, and guide the captioning model adaptively attend to these context features. We verify the effectiveness and superiority of the proposed approach over the other captioning approaches by conducting massive experiments and comparisons on MS COCO image captioning dataset.
Family Physicians' Perspectives on Their Weight Loss Nutrition Counseling in a High Obesity Prevalence Area

Background: Obesity is a serious and highly prevalent health problem. Behavioral modification for weight loss is effective, and physician nutrition counseling is encouraged. Nevertheless, several studies have reported that physicians provide nutrition counseling infrequently. Time constraints and lack of patient compliance are among frequently reported barriers. Aim: In this pilot study, we aimed to examine physician weight loss nutrition counseling among family physicians in Huntington, West Virginia, an area with the highest obesity prevalence in the United States. Methods: We administered an anonymous 13-question online survey designed for this study to all area family physicians in continuity ambulatory practice, asking about how often they provided nutrition counseling to their comorbidly obese patients, their nutrition education background, the counseling resources used, and the barriers they faced. Results: Thirty-eight of the 47 invited physicians completed surveys. The 35 to 55 age group comprised 55% of the respondents. Men comprised 53% of our sample. Two-thirds of the physicians reported that they counseled at a high frequency. Twenty-six of the 38 physicians reported that their nutrition education in medical school was none to minimal. Of the rest, 47.2% viewed their education as clinically relevant. The most frequently-used specific patient education sources were those embedded in electronic health records, the US Department of Agriculture's MyPlate tool, and a variety of smartphone-based apps. Time constraints and lack of patient interest in nutrition topics were the leading barriers cited. Conclusion: Family physicians practicing in the most obese population in the United States tend to be high-frequency obesity nutrition counselors who frequently use specific tools, consider their education lacking and face oft-cited barriers. Studies in other highly endemic areas are needed to confirm these findings.
Risk-Factor Trajectories Preceding Diabetic Polyneuropathy: ADDITION-Denmark

OBJECTIVE To study cardiometabolic risk-factor trajectories (in terms of levels and changes over time) preceding diabetic polyneuropathy (DPN) 13 years after a screen-detected diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS We clinically diagnosed DPN in a nested case-control study of 452 people in the Danish arm of the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care (ADDITION). By linear regression models, we estimated preceding risk-factor trajectories during 13 years. Risk of DPN was estimated by multivariate logistic regression models of each individual’s risk-factor trajectory intercept and slope adjusting for sex, age, diabetes duration, height, and trial randomization group. RESULTS Higher baseline levels of HbA1c (odds ratio [OR] 1.76 [95% CI 1.37; 2.27] and OR 1.68 [95% CI 1.33; 2.12] per 1% and 10 mmol/mol, respectively) and steeper increases in HbA1c over time (OR 1.66 [95% CI 1.21; 2.28] and OR 1.59 [95% CI 1.19; 2.12] per 1% and 10 mmol/mol increase during 10 years, respectively) were associated with DPN. Higher baseline levels of weight, waist circumference, and BMI were associated with DPN (OR 1.20 [95% CI 1.10; 1.31] per 5 kg, OR 1.27 [95% CI 1.13; 1.43] per 5 cm, and OR 1.24 [95% CI 1.12; 1.38] per 2 kg/m2, respectively). CONCLUSIONS Both higher levels and slopes of HbA1c trajectories were associated with DPN after 13 years. Our findings indicate that the rate of HbA1c increase affects the development of DPN over and above the effect of the HbA1c level. Furthermore, this study supports obesity as a risk factor for DPN.
Rhinology Future Debates 2017 by EUFOREA: Novel treatments and surgical solutions in rhinology

The second Rhinology Future Debates organized by EUFOREA (European Forum for Research and Education in Allergy and Airways diseases) was held in Brussels in December 2017. These debates aim at bringing novel developments in the field of Rhinology to the attention of the medical, paramedical and patient community, in a credible and balanced context. Novel developments are presented by key experts from industry and/or key opinion leaders, and are followed by a lively debate on the potential positioning of new developments in care pathways, the strengths and weaknesses of the novel development(s), and comparisons with existing and/or competing products, devices, and/or molecules. Debate topics presented here include: i) Real World Evidence data in AIT - What does it bring to us? , ii) Nasal turbinate as therapeutic target in patients with turbinate-induced nasal symptoms, and iii) Lessons from biologicals in asthma for ENT physicians. As all debates are recorded and distributed on-line with limited editing (www.rhinology-future.com), EUFOREA aims at maximizing the education on novel developments, allowing a critical appraisal of the future and a more rapid implementation of promising novel tools, techniques and/or molecules in clinical practise. The next Rhinology Future Debate will be in held in 2018. This article is protected by copyright. All rights reserved.
Dynamic Interaction of Eukaryotic Initiation Factor 4G1 (eIF4G1) with eIF4E and eIF1 Underlies Scanning-Dependent and -Independent Translation

Translation initiation of most mRNAs involves m7G-cap binding, ribosomal scanning, and AUG selection. Initiation from an m7G-cap-proximal AUG can be bypassed resulting in leaky scanning, except for mRNAs bearing the translation initiator of short 5′ untranslated region (TISU) element. ABSTRACT Translation initiation of most mRNAs involves m7G-cap binding, ribosomal scanning, and AUG selection. Initiation from an m7G-cap-proximal AUG can be bypassed resulting in leaky scanning, except for mRNAs bearing the translation initiator of short 5′ untranslated region (TISU) element. m7G-cap binding is mediated by the eukaryotic initiation factor 4E (eIF4E)-eIF4G1 complex. eIF4G1 also associates with eIF1, and both promote scanning and AUG selection. Understanding of the dynamics and significance of these interactions is lacking. We report that eIF4G1 exists in two complexes, either with eIF4E or with eIF1. Using an eIF1 mutant impaired in eIF4G1 binding, we demonstrate that eIF1-eIF4G1 interaction is important for leaky scanning and for avoiding m7G-cap-proximal initiation. Intriguingly, eIF4E-eIF4G1 antagonizes the scanning promoted by eIF1-eIF4G1 and is required for TISU. In mapping the eIF1-binding site on eIF4G1, we unexpectedly found that eIF4E also binds it indirectly. These findings uncover the RNA features underlying regulation by eIF4E-eIF4G1 and eIF1-eIF4G1 and suggest that 43S ribosome transition from the m7G-cap to scanning involves relocation of eIF4G1 from eIF4E to eIF1.