Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in pathological understanding of high-grade B cell lymphomas

ABSTRACT Introduction: The designation high-grade B-cell lymphoma (HGBL) has been incorporated into the 2016 Revision of the WHO classification of lymphoid neoplasms and includes two types: (1) HGBL, not otherwise specified; and (2) HGBL with MYC and BCL2 and/or BCL6 rearrangements, also known as double or triple hit lymphoma (DHL/THL). These categories of lymphomas represent 1–2% of non-Hodgkin lymphomas and a considerable portion of DLBCL patients who are primary refractory to R-CHOP therapy. It corresponds to the designation ‘B-cell lymphoma, unclassifiable, with features intermediate between DLBCL and Burkitt lymphoma’ in the 2008 WHO classification. Areas covered: This paper provides an update of HGBL, focusing on their pathologic features, prognosis, and diagnostic workup. It highlights advances in our understanding of DHL/THL. Expert commentary: The diagnosis relies on FISH testing and the major controversial question is when to perform it to diagnose virtually all DHL/THL cases, but also being cost effective. Currently there is no consensus. Considering the high refractory rate of these patients to standard R-CHOP induction, the authors recommend FISH testing in all newly diagnosed large B-cell lymphoma by using our stepwise test strategy. With the progress of molecular genetics, the prognosis will be further stratified and HGBL-NOS maybe further evolve too.
De-biasing Covariance-Regularized Discriminant Analysis

Fisher's Linear Discriminant Analysis (FLD) is a well-known technique for linear classification, feature extraction and dimension reduction. The empirical FLD relies on two key estimations from the data -- the mean vector for each class and the (inverse) covariance matrix. To improve the accuracy of FLD under the High Dimension Low Sample Size (HDLSS) settings, Covariance-Regularized FLD (CRLD) has been proposed to use shrunken covariance estimators, such as Graphical Lasso, to strike a balance between biases and variances. Though CRLD could obtain better classification accuracy, it usually incurs bias and converges to the optimal result with a slower asymptotic rate. Inspired by the recent progress in de-biased Lasso, we propose a novel FLD classifier, DBLD, which improves classification accuracy of CRLD through de-biasing. Theoretical analysis shows that DBLD possesses better asymptotic properties than CRLD. We conduct experiments on both synthetic datasets and real application datasets to confirm the correctness of our theoretical analysis and demonstrate the superiority of DBLD over classical FLD, CRLD and other downstream competitors under HDLSS settings.
ANRL: Attributed Network Representation Learning via Deep Neural Networks

Network representation learning (RL) aims to transform the nodes in a network into low-dimensional vector spaces while preserving the inherent properties of the network. Though network RL has been intensively studied, most existing works focus on either network structure or node attribute information. In this paper, we propose a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way. Specifically, we propose a neighbor enhancement autoencoder to model the node attribute information, which reconstructs its target neighbors instead of itself. To capture the network structure, attribute-aware skip-gram model is designed based on the attribute encoder to formulate the correlations between each node and its direct or indirect neighbors. We conduct extensive experiments on six real-world networks, including two social networks, two citation networks and two user behavior networks. The results empirically show that ANRL can achieve relatively significant gains in node classification and link prediction tasks.
Intranasal ovalbumin immunotherapy with mycobacterial adjuvant promotes regulatory T cell accumulation in lung tissues

Allergen‐specific immunotherapy to induce T regulatory cells in the periphery has been used to treat allergic diseases. Mycobacteria can be used as an adjuvant for inducing T regulatory cells. However, it is unclear whether intranasal immunotherapy in combination with Mycobacteria adjuvant induces regulatory T cell differentiation and attenuates allergic responses in vivo. To investigate the role of intranasal ovalbumin (OVA) treatment alone and in combination with Mycobacteria vaccae, proportions of FoxP3+ regulatory T cells and anti‐inflammatory responses were evaluated in a murine model of asthma that was established in three groups of bicistronic Foxp3EGFP reporter BALB/c mice. Before establishment of the asthma model, two groups of mice received intranasal OVA immunotherapy and one also received simultaneous s.c. M. vaccae. Expression of CD4+CD25+Foxp3+EGFP+ T cells in the lung and spleen was analyzed by flow cytometry and the cytokine profiles of allergen‐stimulated lung and spleen lymphocytes assessed. The intranasal OVA immunotherapy group showed greater expression of CD4+CD25+Foxp3+EGFP+ T cells in the spleen whereas in the group that also received M. vaccae such greater expression was demonstrated in the lung. Additionally, the proportion of IL‐10 and IFN‐γ‐secreting splenocytes was greater in the intranasal OVA + M. vaccae group. CD25 neutralization decreased CD4+Foxp3+ cells more than other groups. In parallel with this finding, production of IL‐10 and IFN‐γ was down‐regulated. Mucosal administration of OVA antigen results in a greater proportion of CD4+Foxp3+ T cells in the spleen. IL‐10 and IFN‐γ induced by intranasal OVA immunotherapy and M. vaccae administration is down‐regulated after CD25 neutralization.
Residents’ Disaster Preparedness after the Meinong Taiwan Earthquake: A Test of Protection Motivation Theory

Because effective preparations are required to mitigate disaster impacts before implementing effective interventions, it is important to understand why people do or do not act on disaster preparedness. This study explores factors influencing residents’ intentions and actual behaviors following the 2016 Kaohsiung Meinong earthquake in southern Taiwan. Protection Motivation Theory was used to develop a hypothesized model to test hypotheses regarding residents’ disaster preparedness, and structural equation modeling (SEM) was used to test the model. Data were comprised of 286 valid responses from seven major administration areas in Tainan, Taiwan. Self-efficacy, response-efficacy, and obstacles were significantly correlated with behavioral intentions and actual disaster preparedness behaviors. SEM results revealed that (a) the model fit the data well, (b) the relationship between risk perception and response-efficacy was fully mediated by behavioral intention, and (c) self-efficacy and obstacles were partially mediated by behavioral intention. Behavioral intent and actual disaster preparedness behavior are related but not equal. The main factors affecting actual disaster preparedness behavior are self-efficacy and obstacles. Therefore, strategies like drills or workshops can improve disaster-preparedness knowledge and capabilities and reduce difficulties of implementing disaster preparedness. To improve health and well-being, healthcare providers should promote disaster preparedness by interventions to increase self-efficacy during disasters.
Towards Generalized and Efficient Metric Learning on Riemannian Manifold

Modeling data as points on non-linear Riemannian manifold has attracted increasing attentions in many computer vision tasks, especially visual recognition. Learning an appropriate metric on Riemannian manifold plays a key role in achieving promising performance. For widely used symmetric positive definite (SPD) manifold and Grassmann manifold, most of existing metric learning methods are designed for one manifold, and are not straightforward for the other one. Furthermore, optimizations in previous methods usually rely on computationally expensive iterations. To address above limitations, this paper makes an attempt to propose a generalized and efficient Riemannian manifold metric learning (RMML) method, which can be flexibly adopted to both SPD and Grassmann manifolds. By minimizing the geodesic distance of similar pairs and the interpoint geodesic distance of dissimilar ones on nonlinear manifolds, the proposed RMML is optimized by computing the geodesic mean between inverse of similarity matrix and dissimilarity matrix, benefiting a global closed-form solution and high efficiency. The experiments are conducted on various visual recognition tasks, and the results demonstrate our RMML performs favorably against its counterparts in terms of both accuracy and efficiency.
Serum Potassium and Cardiovascular Events in Heart Failure With Preserved Left Ventricular Ejection Fraction Patients

BACKGROUND Although serum potassium (sK) levels are closely associated with the prognosis of chronic heart failure patients, the clinical significance of sK levels in cardiovascular outcomes of heart failure with preserved ejection fraction (HFpEF) patients is not fully understood. METHODS This study was a retrospective, single-center, observational study. We enrolled 506 consecutive HFpEF patients admitted to Kumamoto University Hospital and divided them into four groups according to the quartiles of the sK levels at discharge (Q1: sK < 4.1 mEq/l, Q2: 4.1 ≤ sK < 4.4 mEq/l, Q3: 4.4 ≤ sK < 4.7 mEq/l, and Q4: sK ≥ 4.7 mEq/l). RESULTS No significant differences were observed in the use of all drugs (loop diuretics, mineralocorticoid receptor antagonists, renin-angiotensin-aldosterone system inhibitors, calcium channel blockers, β-blockers, and statins) among the four groups. Hemoglobin, the estimated glomerular filtration rate, and pulse wave velocity levels were lower, and the serum sodium levels were higher in the Q4 group compared with those in the Q2 group. Kaplan-Meier analysis revealed significantly higher probabilities of both cardiovascular and HF-related events in the Q1, Q3, and Q4 groups than those in the Q2 group. Multivariate Cox proportional hazard analysis revealed that the Q1, Q3, and Q4 groups had significantly and independently higher probabilities of cardiovascular events compared with those in the Q2 group, indicating a J-shaped association between sK levels and cardiovascular events. CONCLUSIONS sK levels at discharge could provide important prognostic information in regard to HFpEF. Further evaluation in a larger number of patients might be needed. Clinical Trials Registration UMIN-CTR (http://www.umin.ac.jp/ctr/). Identifier UMIN000029600. Public Access Information Opt-out materials are available at the website: http://www.kumadai-junnai.com/home/wp-content/uploads/houkatsu.pdf.
A Question Type Driven Framework to Diversify Visual Question Generation

Visual question generation aims at asking questions about an image automatically. Existing research works on this topic usually generate a single question for each given image without considering the issue of diversity. In this paper, we propose a question type driven framework to produce multiple questions for a given image with different focuses. In our framework, each question is constructed following the guidance of a sampled question type in a sequence-to-sequence fashion. To diversify the generated questions, a novel conditional variational auto-encoder is introduced to generate multiple questions with a specific question type. Moreover, we design a strategy to conduct the question type distribution learning for each image to select the final questions. Experimental results on three benchmark datasets show that our framework outperforms the state-of-the-art approaches in terms of both relevance and diversity.
Progressive Blockwise Knowledge Distillation for Neural Network Acceleration

As an important and challenging problem in machine learning and computer vision, neural network acceleration essentially aims to enhance the computational efficiency without sacrificing the model accuracy too much. In this paper, we propose a progressive blockwise learning scheme for teacher-student model distillation at the subnetwork block level. The proposed scheme is able to distill the knowledge of the entire teacher network by locally extracting the knowledge of each block in terms of progressive blockwise function approximation. Furthermore, we propose a structure design criterion for the student subnetwork block, which is able to effectively preserve the original receptive field from the teacher network. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches.
Association Between Dialysis Modalities and Risk of Coronary Artery Disease: A Population‐Based Cohort Study in Taiwan

The hemodynamic effects of hemodialysis (HD) and peritoneal dialysis (PD) on end‐stage renal disease (ESRD) patients differ. The influence of dialysis modalities on the cardiovascular system has not been well investigated. We aimed to evaluate the association between dialysis modalities and risk of coronary artery disease (CAD) by using the claim data of Taiwan's Longitudinal Health Insurance Database. This study followed up a cohort of 1624 new onset ESRD patients (≥18 years old), who had started renal replacement therapy during 2000 to 2010; and was followed until 2012. After adjusting for potential confounders, patients who underwent HD had significantly higher risks of incidence of CAD, in comparison with patients who underwent PD (adjusted hazard ratio = 1.47; 95% confidence interval = 1.01–2.11). An increased risk of incident CAD was distinguished in patients receiving HD, compared with those on PD. Further studies are warranted to explore the underlying mechanism and improve dialysis outcomes.
Psychiatric Comorbidity in Children and Adults with Gluten-Related Disorders: A Narrative Review

Gluten-related disorders are characterized by both intestinal and extraintestinal manifestations. Previous studies have suggested an association between gluten-related disorder and psychiatric comorbidities. The objective of our current review is to provide a comprehensive review of this association in children and adults. A systematic literature search using MEDLINE, Embase and PsycINFO from inception to 2018 using terms of ‘celiac disease’ or ‘gluten-sensitivity-related disorders’ combined with terms of ‘mental disorders’ was conducted. A total of 47 articles were included in our review, of which 28 studies were conducted in adults, 11 studies in children and eight studies included both children and adults. The majority of studies were conducted in celiac disease, two studies in non-celiac gluten sensitivity and none in wheat allergy. Enough evidence is currently available supporting the association of celiac disease with depression and, to a lesser extent, with eating disorders. Further investigation is warranted to evaluate the association suggested with other psychiatric disorders. In conclusion, routine surveillance of potential psychiatric manifestations in children and adults with gluten-related disorders should be carried out by the attending physician.
Multifractal-Multiscale Analysis of Cardiovascular Signals: A DFA-Based Characterization of Blood Pressure and Heart-Rate Complexity by Gender

Detrended Fluctuation Analysis (DFA) is a popular method for assessing the fractal characteristics of biosignals, recently adapted for evaluating the heart-rate multifractal and/or multiscale characteristics. However, the existing methods do not consider the beat-by-beat sampling of heart rate and have relatively low scale resolutions and were not applied to cardiovascular signals other than heart rate. Therefore, aim of this work is to present a DFA-based method for joint multifractal/multiscale analysis designed to address the above critical points and to provide the first description of the multifractal/multiscale structure of interbeat intervals (IBI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) in male and female volunteers separately. The method optimizes data splitting in blocks to reduce the DFA estimation variance and to evaluate scale coefficients with Taylor’s expansion formulas and maps the scales from beat domains to temporal domains. Applied to cardiovascular signals recorded in 42 female and 42 male volunteers, it showed that scale coefficients and degree of multifractality depend on the temporal scale, with marked differences between IBI, SBP, and DBP and with significant sex differences. Results may be interpreted considering the distinct physiological mechanisms regulating heart-rate and blood-pressure dynamics and the different autonomic profile of males and females.
Deterministic Binary Filters for Convolutional Neural Networks

We propose Deterministic Binary Filters, an approach to Convolutional Neural Networks that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional filters. This approach results in model architectures with significantly fewer parameters (4x to 16x) and smaller model sizes (32x due to the use of binary rather than floating point precision). We show our deterministic filter design can be integrated into well-known network architectures (such as ResNet and SqueezeNet) with as little as 2% loss of accuracy (under datasets like CIFAR-10). Under ImageNet, they result in 3x model size reduction compared to sub-megabyte binary networks while reaching comparable accuracy levels.
Differentiated Attentive Representation Learning for Sentence Classification

Attention-based models have shown to be effective in learning representations for sentence classification. They are typically equipped with multi-hop attention mechanism. However, existing multi-hop models still suffer from the problem of paying much attention to the most frequently noticed words, which might not be important to classify the current sentence. And there is a lack of explicitly effective way that helps the attention to be shifted out of a wrong part in the sentence. In this paper, we alleviate this problem by proposing a differentiated attentive learning model. It is composed of two branches of attention subnets and an example discriminator. An explicit signal with the loss information of the first attention subnet is passed on to the second one to drive them to learn different attentive preference. The example discriminator then selects the suitable attention subnet for sentence classification. Experimental results on real and synthetic datasets demonstrate the effectiveness of our model.
Teaching Machines to Ask Questions

We propose a novel neural network model that aims to generate diverse and human-like natural language questions. Our model not only directly captures the variability in possible questions by using a latent variable, but also generates certain types of questions by introducing an additional observed variable. We deploy our model in the generative adversarial network (GAN) framework and modify the discriminator which not only allows evaluating the question authenticity, but predicts the question type. Our model is trained and evaluated on a question-answering dataset SQuAD, and the experimental results shown the proposed model is able to generate diverse and readable questions with the specific attribute.
Effect of Nutritional Interventions on Micronutrient Status in Pregnant Malawian Women with Moderate Malnutrition: A Randomized, Controlled Trial

Micronutrient deficiencies during pregnancy are common in Africa and can cause adverse outcomes. The objective was to measure micronutrient status and change in moderately malnourished pregnant Malawian women randomized to one of three nutritional interventions. Serum vitamin B12, 25-hydroxyvitamin D, folate, retinol, ferritin, zinc, albumin and C-reactive protein were measured in pregnant women with MUAC ≥20.6 cm and ≤23.0 cm at enrollment (n = 343) and after 10 weeks (n = 229) of receiving: (1) ready-to-use supplementary food (RUSF); (2) fortified corn-soy blend (CSB+) with multiple-micronutrient supplement (CSB+UNIMMAP); or (3) CSB+ with iron and folic acid (CSB+IFA). Each provided 100–300% Recommended Dietary Allowance of most micronutrients and 900 kcal/day. Birth length was measured in 272 infants. Enrollment measurements indicated deficiencies in vitamin B12 (20.9%) and zinc (22.3%), low values of ferritin (25.1%) and albumin (33.7%), and elevated CRP (46.0%). Vitamin B12 is known to decrease in the third trimester; the RUSF group had the smallest decrease from enrollment to week 10 (3%), compared to 20% decrease in the CSB+IFA group and 8% decrease in the CSB+UNIMMAP group (p = 0.001). Mean serum 25-hydroxyvitamin D increased most in the RUSF group (+6.4 ng/mL), compared to CSB+IFA (+1.7 ng/mL) and CSB+UNIMMAP (+2.7 ng/mL) (p < 0.001). Micronutrient deficiencies and inflammation are common among moderately malnourished pregnant women and had little improvement despite supplementation above the RDA, with the exception of vitamins B12 and D.
LC-RNN: A Deep Learning Model for Traffic Speed Prediction

Traffic speed prediction is known as an important but challenging problem. In this paper, we propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction than existing solutions. It takes advantage of both RNN and CNN models by a rational integration of them, so as to learn more meaningful time-series patterns that can adapt to the traffic dynamics of surrounding areas. Furthermore, since traffic evolution is restricted by the underlying road network, a network embedded convolution structure is proposed to capture topology aware features. The fusion with other information, including periodicity and context factors, is also considered to further improve accuracy. Extensive experiments on two real datasets demonstrate that our proposed LC-RNN outperforms six well-known existing methods.
Current global trends in the incidence of pediatric-onset inflammatory bowel disease

AIM To perform a comprehensive review and provide an up-to-date synopsis of the incidence and trends of inflammatory bowel disease (IBD). METHODS We systematically searched the MEDLINE (source PubMed), EMBASE and Cochrane Library databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (period: 1985-2018) to identify studies reporting population-based data on the incidence of pediatric-onset (< 19 years at diagnosis) IBD in full manuscripts. Two authors carried out screening and data extraction. Choropleth interactive maps and temporal trends were used to illustrate the international differences and incidences of and changes in IBD and subtypes. RESULTS In total, one hundred forty studies reporting data from 38 countries were considered in this review. The highest annual pediatric incidences of IBD were 23/100000 person-years in Europe, 15.2/100000 in North America, and 11.4/100000 in Asia/the Middle East and Oceania. The highest annual incidences of Crohn’s disease (CD) were 13.9/100000 in North America and 12.3/100000 in Europe. The highest annual incidences of ulcerative colitis (UC) were 15.0/100000 in Europe and 10.6/100000 in North America. The highest annual incidences of IBD-unclassified (IBD-U) were 3.6/100000 in Europe and 2.1/100000 in North America. In the time-trend analyses, 67% of CD, 46% of UC and 11% of IBD-U studies reported an increasing incidence (P < 0.05). The risk of IBD is increasing among first-generation of migrant populations. CONCLUSION Globally, the incidence of IBD varies greatly by geographical areas. The steadily increasing incidence of pediatric IBD over time indicates its emergence as a global disease, suggesting that studies should investigate the environmental risk factors among pediatric cohorts.