Miracle-dz/UniFAR-FTUs huggingface dataset:
Probiotics in the Management of Atopic Dermatitis for Children: A Case-Based Review

Background Atopic dermatitis or eczema is one of the most common dermatologic problems, especially in children. Several studies have hypothesized that alteration of gut-colonizing microbes might have induced and conditioned the development of the disease. Thus, modulation of microbial diversity and abundance might help alleviate symptoms and conditions for patients. Given the ability of commensal and symbiotic microorganisms in modulating the immune system, probiotics administration has been studied in previous research in the management of eczema. However, until today, there are conflicting results between studies making inconclusive recommendations towards probiotics supplementation in the management of atopic dermatitis. This case-based review was done to assess and evaluate the therapeutic efficacy of probiotics supplementation in the management of eczema in children. Method An electronic database search was conducted in PubMed-NCBI, Cochrane, EBSCO, ProQuest, and SCOPUS in March 2020. Individual studies and reviews were then gathered for screening using predetermined inclusion and exclusion criteria. The included studies were then critically appraised for their validity and importance. Result A total of 5 studies, all of which were RCTs, were included in this review. Out of all the studies included, 4 showed no clinically significant improvements in using probiotics in the management of eczema in children as they did not pass the minimal clinically important difference (MCID) of eczema severity as determined by SCORAD (SCORing Atopic Dermatitis). Conclusion Supplementation of probiotics in the management of eczema in children does not show a clinically relevant difference vs. standard treatment in reducing eczema severity.
Glycosides from Muscaricomosum. 5. Structure of muscaroside B

A further component of the glycoside mixture extracted from the bulbs of Muscaricomosum has been completely characterized by chemical degradative study and spectral investigation. This glycoside, 2, termed muscaroside B, possesses an α-L-arabinofuranosyl(1 → 2)-α-L-arabinopyranosyl(1 → 3)[α-L-rhamnopyranosyl(1 → 2)]-β-D-glucopyranosyl(1 → 2)-α-L-arabinopyranosyl(1 → 6)-β-D-glucopyranosyl chain linked at the 3-position of eucosterol.
Oncolytic effects of the recombinant Newcastle disease virus, rAF-IL12, against colon cancer cells in vitro and in tumor-challenged NCr-Foxn1nu nude mice

Colon cancer remains one of the main cancers causing death in men and women worldwide as certain colon cancer subtypes are resistant to conventional treatments and the development of new cancer therapies remains elusive. Alternative modalities such as the use of viral-based therapeutic cancer vaccine is still limited, with only the herpes simplex virus (HSV) expressing granulocyte-macrophage colony- stimulating factor (GM-CSF) or talimogene laherparepvec (T-Vec) being approved in the USA and Europe so far. Therefore, it is imperative to continue the search for a new treatment modality. This current study evaluates a combinatorial therapy between the oncolytic Newcastle disease virus (NDV) and interleukin-12 (IL-12) cytokine as a potential therapeutic vaccine to the current anti-cancer drugs. Several in vitro analyses such as MTT assay, Annexin V/FITC flow cytometry, and cell cycle assay were performed to evaluate the cytotoxicity effect of recombinant NDV, rAF-IL12. Meanwhile, serum cytokine, serum biochemical, histopathology of organs and TUNEL assay were carried out to assess the anti-tumoral effects of rAF-IL12 in HT29 tumor-challenged nude mice. The apoptosis mechanism underlying the effect of rAF-IL12 treatment was also investigated using NanoString Gene expression analysis. The recombinant NDV, rAF-IL12 replicated in HT29 colon cancer cells as did its parental virus, AF2240-i. The rAF-IL12 treatment had slightly better cytotoxicity effects towards HT29 cancer cells when compared to the AF2240-i as revealed by the MTT, Annexin V FITC and cell cycle assay. Meanwhile, the 28-day treatment with rAF-IL12 had significantly (p < 0.05) perturbed the growth and progression of HT29 tumor in NCr-Foxn1nu nude mice when compared to the untreated and parental wild-type NDV strain AF2240-i. The rAF-IL12 also modulated the immune system in nude mice by significantly (p < 0.05) increased the level of IL-2, IL-12, and IFN-γ cytokines. Treatment with rAF-IL12 had also significantly (p < 0.05) increased the expression level of apoptosis-related genes such as Fas, caspase-8, BID, BAX, Smad3 and granzyme B in vitro and in vivo. Besides, rAF-IL12 intra-tumoral delivery was considered safe and was not hazardous to the host as evidenced in pathophysiology of the normal tissues and organs of the mice as well as from the serum biochemistry profile of liver and kidney. Therefore, this study proves that rAF-IL12 had better cytotoxicity effects than its parental AF2240-i and could potentially be an ideal treatment for colon cancer in the near future.
Associations between cola consumption and bone mineral density in Korean adolescents and young adults: a cross-sectional study using data from the Korea National Health and Nutrition Examination Survey, 2008–2011

The present study aimed to elucidate the relationship between cola consumption and bone mineral density (BMD) in Korean adolescents and young adults. We used data from the Korea National Health and Nutrition Examination Survey 2008–2011. A total of 2499 adolescents and young adults aged 12–25 years were included. The study participants were classified as cola drinkers and non-cola drinkers according to 24-h dietary recall data. BMD was measured using dual X-ray absorptiometry. In the male population, whole body, whole femur and femoral neck BMD in cola drinkers were lower than that of non-cola drinkers by 4% (95% CI −0⋅071, −0⋅007), 5% (−0⋅092, −0⋅012) and 5% (−0⋅090, −0⋅001), respectively. In both sex groups, cola drinkers had less frequent milk consumption than non-cola drinkers. However, there were no significant differences in cola consumption according to calcium intake in both sexes. In conclusion, cola intake and BMD were inversely associated with Korean male adolescents and young adults. Considering the importance of peak bone mass attainment at adolescents and the increasing trend in carbonated beverage consumption in South Korea, further studies are needed to elucidate the causality between cola intake and lower BMD.
Clinical Observation of COVID-19 in a Patient With an Acquired Humoral Deficiency Secondary to Chemotherapeutic Agents

Introduction The coronavirus disease 2019 (COVID-19) pandemic due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes worldwide devastation. We describe the course of a patient with COVID-19 in the setting of an acquired humoral deficiency as a result of chemotherapeutic treatment for rheumatologic conditions. Case Report A 49-year-old Caucasian male presented with non-relieving fever, hypoxemia, and persistent diarrhea after seven days following a positive SARS-CoV-2 polymerase chain reaction (PCR) assay. The patient’s past medical history was significant for mixed connective tissue disease, rheumatoid arthritis, and systemic lupus erythematosus treated with methotrexate and rituximab since 2008. He was diagnosed with acquired humoral deficiency in 2017 managed by intravenous immunoglobulin (IVIG) infusion every three weeks. The patient’s course of hospitalization was complicated by acute respiratory distress which necessitated intensive unit care and required up to 20 L/min oxygen supplementation via a humidified high flow generator. He was treated with hydroxychloroquine and azithromycin and received an IVIG transfusion. The patient was discharged to home after forty-two days of hospitalization with oxygen supplementation only during ambulation and a complete resolution of diarrhea. Discussion According to current limited data, patients with immunodeficiency have longer length of hospitalization compared to immunocompetent individuals. Our patient demonstrated a form of immunodeficiency as the result of a chemotherapeutic agent, and his clinical course appeared to be more severe. Conclusion More studies are necessary to shed light on the immunological response to SARS-CoV-2 and its impact on immunocompromised and immunocompetent and individuals. We describe the course of a patient with COVID-19 in the setting of an acquired humoral deficiency.
Does Sexual Orientation Influence Trajectories of Change in Health? A 20-Year Follow-Up Study

Abstract We examined the differences in physical health outcomes over a 20-year period between lesbian, gay, and bisexual (LGB) and heterosexual middle-aged and older adults. We also examined whether the associations were moderated by social support and affect. The analytical sample included 168 LGB adults and 336 propensity-matched heterosexual adults from the Midlife in the United States (MIDUS) study, ranging in age from 25 to 74 years (mean age=42.83) at baseline. Using negative binomial generalized estimating equations and mixed-effects analyses, data from three waves of MIDUS, spanning approximately 20 years from 1995 to 2014, were used to examine the associations between sexual orientation and the health outcomes (number of chronic conditions and functional limitations). Social support and affect were added to the models to test for moderation. The results found that LGB participants reported one more chronic condition at baseline and scored significantly higher for functional limitations. However, LGB participants increased less over time for number of chronic conditions than heterosexual participants, and there were no significant differences in terms of changes in functional limitation over time. Positive affect reduced the strength of the relationship between sexual orientation and functional limitations for LGB participants. No other moderating effects were significant. The results of this study suggest that LGB individuals may become resilient to the negative health effects of minority stressors over time. Interventions should focus on improving the health of LGB individuals when they are younger and more at risk of negative health outcomes.
Exploring the neural correlates of self-related names in healthy subjects

Abstract Objectives: This study aimed to clarify the neural correlates and underlying mechanisms of the subject's own name (SON) and the unique name derived from the SON (SDN). Methods: A name that was most familiar to the subject (SFN) was added as a self-related reference. We used 4 auditory stimuli—pure tone (1000 Hz), SON, SDN, and SFN—to evaluate the corresponding activated brain areas in 19 healthy subjects by using functional magnetic resonance imaging. Results: Our results demonstrated that pure tone activated the fewest brain regions. Although SFN was a very strong self-related stimulus, it failed to activate many midline structures. The brain regions activated by SON and SDN were very similar. SFN as a self-related stimulus was less self-related compared with SDN. What's more, the additionally activated fusiform gyrus and parahippocampal gyrus of SDN might revealed its processing path. Conclusions: SDN, which has created by us, is a new and self-related stimulus similar to SON. They might provide a useful reference for consciousness assessment with SON and SDN.
An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation.
Sim-to-Real for High-Resolution Optical Tactile Sensing: From Images to Three-Dimensional Contact Force Distributions

The images captured by vision-based tactile sensors carry information about high-resolution tactile fields, such as the distribution of the contact forces applied to their soft sensing surface. However, extracting the information encoded in the images is challenging and often addressed with learning-based approaches, which generally require a large amount of training data. This article proposes a strategy to generate tactile images in simulation for a vision-based tactile sensor based on an internal camera that tracks the motion of spherical particles within a soft material. The deformation of the material is simulated in a finite element environment under a diverse set of contact conditions, and spherical particles are projected to a simulated image. Features extracted from the images are mapped to the three-dimensional contact force distribution, with the ground truth also obtained using finite-element simulations, with an artificial neural network that is therefore entirely trained on synthetic data avoiding the need for real-world data collection. The resulting model exhibits high accuracy when evaluated on real-world tactile images, is transferable across multiple tactile sensors without further training, and is suitable for efficient real-time inference.
Minimax Active Learning

Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either rely on model uncertainty to select the most uncertain samples or use clustering or reconstruction to choose the most diverse set of unlabeled examples. While uncertainty-based strategies are susceptible to outliers, solely relying on sample diversity does not capture the information available on the main task. In this work, we develop a semi-supervised minimax entropy-based active learning algorithm that leverages both uncertainty and diversity in an adversarial manner. Our model consists of an entropy minimizing feature encoding network followed by an entropy maximizing classification layer. This minimax formulation reduces the distribution gap between the labeled/unlabeled data, while a discriminator is simultaneously trained to distinguish the labeled/unlabeled data. The highest entropy samples from the classifier that the discriminator predicts as unlabeled are selected for labeling. We extensively evaluate our method on various image classification and semantic segmentation benchmark datasets and show superior performance over the state-of-the-art methods.
Multi-stream Convolutional Neural Network with Frequency Selection for Robust Speaker Verification

Speaker verification aims to verify whether an input speech corresponds to the claimed speaker, and conventionally, this kind of system is deployed based on single-stream scenario, wherein the feature extractor operates in full frequency range. In this paper, we hypothesize that machine can learn enough knowledge to do classification task when listening to partial frequency range instead of full frequency range, which is so called frequency selection technique, and further propose a novel framework of multi-stream Convolutional Neural Network (CNN) with this technique for speaker verification tasks. The proposed framework accommodates diverse temporal embeddings generated from multiple streams to enhance the robustness of acoustic modeling. For the diversity of temporal embeddings, we consider feature augmentation with frequency selection, which is to manually segment the full-band of frequency into several sub-bands, and the feature extractor of each stream can select which sub-bands to use as target frequency domain. Different from conventional single-stream solution wherein each utterance would only be processed for one time, in this framework, there are multiple streams processing it in parallel. The input utterance for each stream is pre-processed by a frequency selector within specified frequency range, and post-processed by mean normalization. The normalized temporal embeddings of each stream will flow into a pooling layer to generate fused embeddings. We conduct extensive experiments on VoxCeleb dataset, and the experimental results demonstrate that multi-stream CNN significantly outperforms single-stream baseline with 20.53 % of relative improvement in minimum Decision Cost Function (minDCF).
Anisotropic time-domain electronic response in cuprates driven by midinfrared pulses

1 Department of Physics, Università degli Studi di Trieste, 34127 Trieste, Italy 2 Elettra Sincrotrone Trieste S.C. p.A. , 34127 Basovizza Trieste, Italy 3 Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA Quantum Matter Institute, University of British Columbia, Vancouver, BC V6T 1Z4, Canada Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada 6 Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec J3X 1S2, Canada 7 Nanoelectronics Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8568, Japan 8School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA 9 Dipartimento di Fisica “E.R. Caianiello,” Universita` degli Studi di Salerno, I-84084 Fisciano (SA), Italy 10 CNR-SPIN, UOS di Salerno, I-84084 Fisciano (SA), Italy
PEFC Stack Operating in Anodic Dead End Mode

The polymer electrolyte fuel cell (PEFC) system is considered as an alternative power source and is particularly promising for mobile applications. This paper reports some experimental results performed on a PEFC stack, operating in anodic dead end mode. The flush frequency of the anode is investigated. In order to reduce hydrogen consumption in an embedded application, the flush frequency could be calculated from system modelling, according to the operating conditions. The first step of such a model is presented. It simulates the water exchanges between the anode and cathode channels in the anode dead end case. It has been implemented in a MATLAB® environment.
Single-level Optimization For Differential Architecture Search

In this paper, we point out that differential architecture search (DARTS) makes gradient of architecture parameters biased for network weights and architecture parameters are updated in different datasets alternatively in the bi-level optimization framework. The bias causes the architecture parameters of non-learnable operations to surpass that of learnable operations. Moreover, using softmax as architecture parameters' activation function and inappropriate learning rate would exacerbate the bias. As a result, it's frequently observed that non-learnable operations are dominated in the search phase. To reduce the bias, we propose to use single-level to replace bi-level optimization and non-competitive activation function like sigmoid to replace softmax. As a result, we could search high-performance architectures steadily. Experiments on NAS Benchmark 201 validate our hypothesis and stably find out nearly the optimal architecture. On DARTS space, we search the state-of-the-art architecture with 77.0% top1 accuracy (training setting follows PDARTS and without any additional module) on ImageNet-1K and steadily search architectures up-to 76.5% top1 accuracy (but not select the best from the searched architectures) which is comparable with current reported best result.
Multilevel Approximation for Generalized Method of Moments Estimators

Generalized Method of Moments (GMM) estimators in their various forms, including the popular Maximum Likelihood (ML) estimator, are frequently applied for the evaluation of complex econometric models with not analytically computable moment or likelihood functions. As the objective functions of GMMand ML-estimators themselves constitute the approximation of an integral, more precisely of the expected value over the real world data space, the question arises whether the approximation of the moment function and the simulation of the entire objective function can be combined. Motivated by the popular Probit and Mixed Logit models, we consider double integrals with a linking function which stems from the considered estimator, e.g. the logarithm for Maximum Likelihood, and apply a sparse tensor product quadrature to reduce the computational effort for the approximation of the combined integral. Given Hölder continuity of the linking function, we prove that this approach can improve the order of the convergence rate of the classical GMMand ML-estimator by a factor of two, even for integrands of low regularity or high dimensionality. This result is illustrated by numerical simulations of Mixed Logit and Multinomial Probit integrals which are estimated by MLand GMM-estimators, respectively.
ResizeMix: Mixing Data with Preserved Object Information and True Labels

Data augmentation is a powerful technique to increase the diversity of data, which can effectively improve the generalization ability of neural networks in image recognition tasks. Recent data mixing based augmentation strategies have achieved great success. Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image. To further promote the performance of CutMix, a series of works explore to use the saliency information of the image to guide the mixing. We systematically study the importance of the saliency information for mixing data, and find that the saliency information is not so necessary for promoting the augmentation performance. Furthermore, we find that the cutting based data mixing methods carry two problems of label misallocation and object information missing, which cannot be resolved simultaneously. We propose a more effective but very easily implemented method, namely ResizeMix. We mix the data by directly resizing the source image to a small patch and paste it on another image. The obtained patch preserves more substantial object information compared with conventional cut-based methods. ResizeMix shows evident advantages over CutMix and the saliency-guided methods on both image classification and object detection tasks without additional computation cost, which even outperforms most costly search-based automatic augmentation methods.