Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum calprotectin correlates with risk and disease severity of ankylosing spondylitis and its change during first month might predict favorable response to treatment

Abstract Objectives: To investigate the association of serum calprotectin with risk and severity of ankylosing spondylitis (AS), and to study its value for predicting treatment responses. Methods: 262 AS patients and 260 health controls (HCs) were enrolled in this study. 142 active AS patients were treated by pharmaceutical therapy and clinical response was evaluated according to ASAS 40 improvement criteria. 4 ml blood sample was collected from AS patients and HCs at enrollment, and from 142 active AS patients at month 1, 3 and 6 after treatments. And, serum calprotectin, IL-1β, IL-17 and TNF-α expressions were assessed by Enzyme-linked immune sorbent assay (ELISA). Results: The expression of calprotectin in AS patients was remarkably higher compared to HCs, and Receiver Operating Curve (ROC) analysis showed that baseline calprotectin was of great diagnostic value for AS. Calprotectin level was positively correlated with CRP, ESR, PGA, pain VAS, BASDAI and BASFI scores, moreover, patients with higher calprotectin levels were with elevated expressions of IL-1β, IL-17 and TNF-α. Post-treatment, the calprotectin levels of active AS patients notably decreased from baseline to 1, 3 or 6 month. Additionally, patients achieved ASAS 40 were with more considerable change in calprotectin level during first month, and change of calprotectin during first month could predict patients achieving ASAS 40 with AUC of 0.691. Conclusion: Serum calprotectin level could be a promising biomarker for risk and severity of AS, and change of calprotectin during first month post-treatment might predict more favorable treatment response in active AS patients.
Rethink ReLU to Training Better CNNs

Most of convolutional neural networks share the same characteristic: each convolutional layer is followed by a nonlinear activation layer where Rectified Linear Unit (ReLU) is the most widely used. In this paper, we argue that the designed structure with the equal ratio between these two layers may not be the best choice since it could result in the poor generalization ability. Thus, we try to investigate a more suitable method on using ReLU to explore the better network architectures. Specifically, we propose a proportional module to keep the ratio between convolution and ReLU amount to be N:M (N>M). The proportional module can be applied in almost all networks with no extra computational cost to improve the performance. Comprehensive experimental results indicate that the proposed method achieves better performance on different benchmarks with different network architectures, thus verify the superiority of our work.
Noise cancellation for a whole-head magnetometer-based MEG system in hospital environment

We describe a strategy of removing magnetic field interference for a whole-head pediatric magnetoencephalography (MEG) system (‘babyMEG’) installed in a hospital. The 375-channel sensor array of babyMEG consists entirely of magnetometers in two layers to maximize the sensitivity for detecting MEG signals from infants, toddlers, and young children. It is equipped with a continuously operating closed-cycle helium recycler to reduce the operating costs. These two features pose special challenges for noise cancellation. Our strategy uses a combination of several methods. The system is installed in a light-weight, magnetically shielded room (MSR) equipped with an active external shielding. In addition we employ two software-based techniques—a signal space projection (SSP) technique and a synthetic gradiometer (SG) method—for removing the environmental magnetic noise in real time and displaying the output online. The shielding effects are: passive shielding–36 dB, active shielding–12 dB, SSP–40 dB and SG–40 dB, for a combined maximum shielding of about 90 dB at 0.1 Hz. We evaluated the performance of the babyMEG after applying the noise cancellation techniques. The dipole localization errors were <3 mm after averaging 50 epochs with empty room noise in a simulation study for dipoles >10 nAm, which is in the low range of empirically observed dipole moments. In a phantom study with realistic environmental noise, we could clearly recover an evoked cortical magnetic field produced by a 20 nAm dipole after averaging 50 epochs. The localization error was ∼6 mm after averaging 20 epochs. In infants, we could clearly detect a somatic evoked field after averaging ∼20 responses. The unique two-layer sensor design combined with the SSP or SG provides effective noise suppression for a magnetometer-based pediatric MEG system in hospital environment with the closed-cycle helium recycler operating continuously during MEG measurements.
Nano-cameras: a key enabling technology for the internet of multimedia nano-things

Nanotechnology is enabling the development of a new generation of devices which are able to sense, process and communicate, while being in the scale of tens to hundreds of cubic nanometers. Such small, imperceptible devices enhance not only current applications but enable entirely new paradigms. This paper introduces the concept of nano-cameras, which are built upon nanoscale photodetectors, lenses and electronic circuitry. The state-of-the-art in nanoscale photodetectors and lenses is presented and the expected performance of nano-cameras is numerically evaluated through simulation studies. Finally, the open challenges towards integrating nano-cameras in practical applications and ultimately building the Internet of Multimedia Nano-Things are discussed.
Securing Tag-based recommender systems against profile injection attacks: A comparative study

This work addresses challenges related to attacks on social tagging systems, which often comes in a form of malicious annotations or profile injection attacks. In particular, we study various countermeasures against two types of threats for such systems, the Overload and the Piggyback attacks. The studied countermeasures include baseline classifiers such as, Naive Bayes filter and Support Vector Machine, as well as a deep learning-based approach. Our evaluation performed over synthetic spam data, generated from del.icio. us, shows that in most cases, the deep learning-based approach provides the best protection against threats.
Demographic inference in barn swallows using whole‐genome data shows signal for bottleneck and subspecies differentiation during the Holocene

Accounting for historical demographic features is vital for many types of evolutionary inferences, including the estimation of divergence times between closely related populations. In barn swallow, Hirundo rustica, inferring historical population sizes and subspecies divergence times can shed light on the recent co‐evolution of this species with humans. Pairwise sequentially Markovian coalescent uncovered population growth beginning on the order of one million years ago—which may reflect the radiation of the broader Hirundo genus—and a more recent population decline. Additionally, we used approximate Bayesian computation to evaluate hypotheses about recent timescale barn swallow demography, including population growth due to human commensalism, and a potential founder event associated with the onset of nesting on human structures. We found signal for a bottleneck event approximately 7,700 years ago, near the time that humans began building substantial structures, although there was considerable uncertainty associated with this estimate. Subspecies differentiation and subsequent growth occurred after the bottleneck in the best‐supported model, an order of magnitude more recently than previous estimates in this system. We also compared results obtained from whole‐genome sequencing versus reduced representation sequencing, finding many similar results despite substantial allelic dropout in the reduced representation data, which may have affected estimates of some parameters. This study presents the first genetic evidence of a potential barn swallow founder effect and subspecies divergence coinciding with the Holocene, which is an important step in analysing the biogeographical history of a well‐known human commensal species.
Diapause affects cuticular hydrocarbon composition and mating behavior of both sexes in Drosophila montana

Environmental cues, mainly photoperiod and temperature, are known to control female adult reproductive diapause in several insect species. Diapause enhances female survival during adverse conditions and postpones progeny production to the favorable season. Male diapause (a reversible inability to inseminate receptive females) has been studied much less than female diapause. However, if the males maximized their chances to fertilize females while minimizing their energy expenditure, they would be expected to be in diapause at the same time as females. We investigated Drosophila montana male mating behavior under short‐day conditions that induce diapause in females and found the males to be reproductively inactive. We also found that males reared under long‐day conditions (reproducing individuals) court reproducing postdiapause females, but not diapausing ones. The diapausing flies of both sexes had more long‐chain and less short‐chain hydrocarbons on their cuticle than the reproducing ones, which presumably increase their survival under stressful conditions, but at the same time decrease their attractiveness. Our study shows that the mating behavior of females and males is well coordinated during and after overwintering and it also gives support to the dual role of insect cuticular hydrocarbons in adaptation and mate choice.
Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a Multi-Cell architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy. Further, considering the different stages of DR actually progress gradually, which means the labels of different stages are related. To considering the relationships of images with different stages, we propose a Multi-Task learning strategy which predicts the label with both classification and regression. Experimental results on the Kaggle dataset show that our method achieves a Kappa of 0.841 on test set which is the 4th rank of all state-of-the-arts methods. Further, our Multi-Cell Multi-Task Convolutional Neural Networks (M2CNN) solution is a general framework, which can be readily integrated with many other deep neural network architectures.
Content-based feature exploration for transparent music recommendation using self-attentive genre classification

Interpretation of retrieved results is an important issue in music recommender systems, particularly from a user perspective. In this study, we investigate the methods for providing interpretability of content features using self-attention. We extract lyric features with the self-attentive genre classification model trained on 140,000 tracks of lyrics. Likewise, we extract acoustic features using the acoustic model with self-attention trained on 120,000 tracks of acoustic signals. The experimental results show that the proposed methods provide the characteristics that are interpretable in terms of both lyrical and musical contents. We demonstrate this by visualizing the attention weights, and by presenting the most similar songs found using lyric or audio features.
The relationship between motor abilities and quality of life in children with severe multiple disabilities

BACKGROUND This study aimed to determine the relationship between motor abilities and quality of life in children with severe multiple disabilities. METHODS In this cross-sectional study, motor abilities of 29 children (mean age 9.8 years; 45% girls) with severe multiple disabilities [IQ < 25; Gross Motor Function Motor Classification System level V] were measured with the MOtor eVAluation in Kids with Intellectual and Complex disabilities (Movakic) questionnaire (completed by the child's physical therapist). Quality of life was measured with the Quality of Life-Profound Multiple Disabilities (QoL-PMD) questionnaire (completed by the child's parents). RESULTS A significantly moderate to high correlation was found between the total scores on the Movakic and the QoL-PMD (r = 0.40, P = 0.03), indicating that higher scores in motor abilities are associated with a higher level of quality of life. Furthermore, significantly moderate to high correlations were found between the total score on the Movakic and the dimension Physical Well-Being, Development and Activities of the Qol-PMD. In multiple linear regression models, all significant bivariate relationships between the Movakic total scores and QoL-PMD dimensions remained significant after controlling for the Gross Motor Function Motor Classification System level. CONCLUSIONS In these children with severe multiple disabilities, motor abilities (as measured by Movakic) are moderately related to quality of life (as measured by the QoL-PMD).
Capacity of the hierarchical multi-layered cortical microcircuit communication channel

The high level of connections and hierarchy in the cortical microcircuits communication channel characterises a complex system that yet neuroscientists cannot understand it fully. The quantification of the communication channel capacity of cortical microcircuits enables a further understanding of information transfer inside that microscale of the brain. In this paper, we present a cortical microcircuit communication channel capacity using a novel integrated approach to cortical microcircuit data and information theory. We use data from the Digital Reconstruction of Neocortical Microcir-cuitry portal that accounts for detailed neurological characteristics including neuron morphology, connection probabilities, neuronal noise, and cortex connection hierarchy. Our results show a constant significant decrease of system's capacity over time, noise and neuronal stimulation.We also observed an independent hierarchical property between arbitrary layers, meaning that the configuration of the cortical microcircuit is crucial to define its communication performance. Based on these observations, we find the first piece of evidence that the cortical microcircuit channel behaviour resembles a typical fast fading channel.
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

The effect of amplifiers, downtoners, and negations has been studied in general and particularly in the context of sentiment analysis. However, there is only limited work which aims at transferring the results and methods to discrete classes of emotions, e.g., joy, anger, fear, sadness, surprise, and disgust. For instance, it is not straight-forward to interpret which emotion the phrase "not happy" expresses. With this paper, we aim at obtaining a better understanding of such modifiers in the context of emotion-bearing words and their impact on document-level emotion classification, namely, microposts on Twitter. We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification. In addition, we build a term weighting approach based on the different modifiers into a lexical model for the analysis of the semantics of modifiers and their impact on emotion meaning. We show that amplifiers separate emotions expressed with an emotion-bearing word more clearly from other secondary connotations. Downtoners have the opposite effect. In addition, we discuss the meaning of negations of emotion-bearing words. For instance we show empirically that "not happy" is closer to sadness than to anger and that fear words in the scope of downtoners often express surprise.
Clinicopathological characteristics and prognosis of pulmonary pleomorphic carcinoma: a population-based retrospective study using SEER data.

Background Pulmonary pleomorphic carcinoma (PPC) is a rare malignancy; unique clinicopathological characteristics and prognosis have not been described. Methods PPC patient information collected in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2014 were extracted, evaluated, and compared with NSCLC patient data. Overall survival (OS) was evaluated by the Kaplan-Meier method. Univariate analysis (UVA) and multivariate analysis (MVA) by the Cox proportional hazards regression identified risk factors that predicted OS and the results were used to construct a nomogram to predict 1-, 3-, and 5-year OS in PPC patients. Results A total of 309 patients diagnosed with PPC were identified among the records of 320,510 NSCLC patients. The median age was 66 (IQR, 57-75) years, 59.2% were men, 79.3% were White, 63.9% had upper lobe tumors, 77.0% were poorly differentiated, and 38.0% were AJCC stage IV. Median OS was 9 (95% CI: 6.69-11.31) months and 5-year OS was 25.1% (95% CI: 23.6-26.6%). PPC patients had significantly larger and less differentiated tumors, a higher rate of radical surgical resection, less N+ disease, and fewer distant metastases (P<0.001) than NSCLC patients. UVA and MVA identified age, T stage, M stage, surgery, and chemotherapy as independent risk factors. The nomogram had a calibration index of 0.798. Conclusions PPC had distinct clinicopathological characteristics. Age, T stage, M stage, surgery and chemotherapy were independently associated with OS. The nomogram accurately predicted 1-, 3- and 5-year OS.
Effective Antimicrobial Activity of Green ZnO Nano Particles of Catharanthus roseus

In the present study, zinc oxide nanoparticles (ZnO NPs) were synthesized using leaf extract of Catharanthus roseus (C. roseus) under different physical parameters. Biosynthesis of ZnO NPs was confirmed by UV-Visible spectrophotometer and further, characterized by X-Ray Diffraction (XRD), Fourier Transform Infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Energy-Dispersive X-ray spectroscopy (EDX), Atomic Force Microscopy (AFM), Photoluminescence study and Dynamic Light Scattering (DLS). We have also confirmed that several physical parameters such as pH, temperature, concentration of metal ions and reaction time were able to regulate shape and size of synthesized ZnO NPs. XRD and TEM analysis provided the information about the average size and hexagonal morphology of ZnO NPs. FTIR spectra analysis suggested that phenolic compounds played crucial role in the biosynthesis of ZnO NPs. The significant antibacterial activity of ZnO NPs was observed against Staphylococcus aureus MTCC 9760 (S. aureus), Streptococcus pyogenes MTCC 1926 (S. pyogenes), Bacillus cereus MTCC 430 (B. cereus), Pseudomonas aeruginosa MTCC 424 (P. aeruginosa), Proteus mirabilis MTCC 3310 (P. mirabilis) and Escherichia coli MTCC 40 (E. coli). The synthesized ZnO NPs have shown antibacterial efficacy against both Gram-positive and Gram-negative pathogens. Synergistic effects of ZnO NPs and streptomycin showed increased efficacy as indicated by the increased zone of clearance in comparison to their individual effects (either ZnO NPs or streptomycin). Overall, the results elucidated a rapid, cost-effective, environmentally friendly and convenient method for ZnO NPs synthesis, which could be used as a potential antimicrobial agent against drug resistant microbes.
Iterative Recursive Attention Model for Interpretable Sequence Classification

Natural language processing has greatly benefited from the introduction of the attention mechanism. However, standard attention models are of limited interpretability for tasks that involve a series of inference steps. We describe an iterative recursive attention model, which constructs incremental representations of input data through reusing results of previously computed queries. We train our model on sentiment classification datasets and demonstrate its capacity to identify and combine different aspects of the input in an easily interpretable manner, while obtaining performance close to the state of the art.
Adaptation and Robust Learning of Probabilistic Movement Primitives

Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability distribution over trajectories, providing a sensible region of exploration and the ability to adapt to changes in the robot environment. However, to be able to capture variability and correlations between different joints, a probabilistic movement primitive requires the estimation of a larger number of parameters compared to their deterministic counterparts, which focus on modeling only the mean behavior. In this article, we make use of prior distributions over the parameters of a probabilistic movement primitive to make robust estimates of the parameters with few training instances. In addition, we introduce general purpose operators to adapt movement primitives in joint and task space. The proposed training method and adaptation operators are tested in a coffee preparation and in robot table tennis task. In the coffee preparation task we evaluate the generalization performance to changes in the location of the coffee grinder and brewing chamber in a target area, achieving the desired behavior after only two demonstrations. In the table tennis task we evaluate the hit and return rates, outperforming previous approaches while using fewer task specific heuristics.
A high-density EEG study of differentiation between two speeds and directions of simulated optic flow in adults and infants.

A high-density EEG study was carried out to investigate cortical activity in response to forward and backward visual motion at two different driving speeds, simulated through optic flow. Participants were prelocomotor infants at the age of 4-5 months and infants with at least 3 weeks of crawling experience at the age of 8-11 months, and adults. Adults displayed shorter N2 latencies in response to forward as opposed to backward visual motion and differentiated significantly between low and high speeds, with shorter latencies for low speeds. Only infants at 8-11 months displayed similar latency differences between motion directions, and exclusively in response to low speed. The developmental differences in latency between infant groups are interpreted in terms of a combination of increased experience with self-produced locomotion and neurobiological development. Analyses of temporal spectral evolution (TSE, time-dependent amplitude changes) were also performed to investigate nonphase-locked changes at lower frequencies in underlying neuronal networks. TSE showed event-related desynchronization activity in response to visual motion for infants compared to adults. The poorer responses in infants are probably related to immaturity of the dorsal visual stream specialized in the processing of visual motion and could explain the observed problems in infants with differentiating high speeds of up to 50 km/h.