Miracle-dz/UniFAR-FTUs huggingface dataset:
How to assess the best immunohistochemical panel in the diagnosis of malignant pleural mesothelioma in a pathology lab

ABSTRACT Background: malignant pleural mesothelioma (MPM) is a rare tumor with a challenging diagnosis. Even if, clinical data are mandatory to suspect the diagnosis, the positive diagnosis is based on microscopic features. Morphologic features are still the port of call of the diagnosis but their non specific character and the multiplicity of differential diagnoses made the immunohistochemical markers mandatory for the diagnosis. Many antibodies with a positive diagnostic value including claretinin, mesothelin, WT1 and antibodies with a negative diagnostic value including TTF1, EMA, CD15 are recommended by the scientific societies. This is due to the diagnostic limits of every antibody which necessitate the association of multiple antibodies. In the diagnostic demarch, pathologists deal with different antibodies and clones. Even if many recommendations are available, every pathology lab has to experiment its own antibodies in order to optimize the routine diagnostic demarch especially in low-income country. Our aim was to assess the diagnostic value of different antibodies available in our lab and to recommend a decisional flowchart. Patients and methods: we conducted a retrospective study about 30 MPM diagnosed over a 20-year-period. The different techniques were realized manually. The different antibodies used were anti-calretinin, anti-Epithelial Membrane Antigen (EMA), anti-mesothelin, anti-Thyroid Transcription Factor 1 (TTF1), anti-ACE, anti-cytokeratin, anti-vimentin, anti-CD15, anti-cytokeratin 5/6, anti-bcl2, and anti-CD99 and anti-CD34 antibodies. The sensitivity and specificity of these antibodies were assessed. Results: the microscopic exam concluded to an epithelioid mesothelioma (EM) in 17 cases, sarcomatoid mesothelioma (SM) in four cases and biphasic mesothelioma (BM) in nine cases. The immunohistochemical study was performed in all cases. A mean of eight antibodies was used in every case, average 4 to 20 antibodies. The immunohistochemical study was repeated from 2 to 5 times in 15 cases and concerned a mean of 3 antibodies per case. In EM and BM, the antibodies with positive predictive value and highest sensitivity were calretinin, EMA, cytokeratin, and vimentin reaching respectively a sensitivity of 86.2%, 89.7%, 92.9% and 89.3%. The most valuable antibodies with negative predictive value were TTF1, CD15 and ACE that presented a specificity reaching respectively 100%. In sarcomatoid mesothelima, the most sensitive antibody was the cytokeratin antibody. Conclusion: these results yielded to a diagnostic flowchart that we can use in routine practice and that is in accordance with the literature findings. Many diagnostic and technical pitfalls have to be known by pathologists when dealing with MPM.
Consumption of carotenoids not increased by bacterial infection in brown trout embryos (Salmo trutta)

Carotenoids are organic pigment molecules that play important roles in signalling, control of oxidative stress, and immunity. Fish allocate carotenoids to their eggs, which gives them the typical yellow to red colouration and supports their resistance against microbial infections. However, it is still unclear whether carotenoids act mainly as a shield against infection or are used up during the embryos’ immune defence. We investigated this question with experimental families produced from wild-caught brown trout (Salmo trutta). Singly raised embryos were either exposed to the bacterial pathogen Pseudomonas fluorescens or sham-treated at one of two stages during their development. A previous study on these experimental families reported positive effects of egg carotenoids on embryo growth and resistance against the infection. Here, we quantified carotenoid consumption in these infected and sham-infected maternal sib groups. We found that carotenoid contents mostly decreased during embryogenesis. However, these decreases were neither linked to the virulence induced by the pathogen nor dependent on the time point of infection. We conclude that egg carotenoids are not significantly used up by the embryos’ immune defence.
Demand Heterogeneity in Platform Markets: Implications for Complementors

While two-sided platforms e.g., video game consoles depend on complements e.g., games for their success, the success of complements is also influenced by platform-level dynamics. Research suggests that greater platform adoption benefits complements by providing more potential users, but this assumes that platform adopters are homogeneous. We build on extensive research exploring the heterogeneity between early and late platform adopters to identify counterintuitive dynamics for complements. Complements launched early in a platform's life cycle face an audience entirely of early platform adopters, whereas later-launching complements face a mixed audience of both early and late adopters, and we argue that differences in preferences and behavior between early and late adopters affect whether complements will succeed and which types will be most successful. We explore these dynamics in the context of the console video game industry using a unique data set of 2,918 video games released in the United Kingdom from 2000 to 2007. We show that despite the increase in the potential user pool as the platform evolves, video games launched later in the platform life cycle realize lower sales than those launched earlier. While increased competition explains part of this effect, we show substantial evidence consistent with our theory of preference differences between early and late adopters. This includes the finding that the negative effect is stronger for novel games and that the gap between popular and less popular complements widens as later adopters move into the platform, consistent with late adopters being risk averse and seeking to avoid purchasing mistakes. The e-companion is available at https://doi.org/10.1287/orsc.2017. 1183 .
Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation

BACKGROUND: Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment. OBJECTIVE: The main challenges for skin lesion segmentation are numerous variations in viewpoint and scale of skin lesion region. METHODS: To handle these challenges, we propose a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images. Specifically, the deep dense layer and generic multi-path Deep RefineNet are combined to improve the segmentation performance. The deep representation of all available layers is aggregated to form the global feature maps using skip connection. Also, the dense deconvolution layer is leveraged to capture diverse appearance features via the contextual information. Finally, we apply the dense deconvolution layer to smooth segmentation maps and obtain final high-resolution output. RESULTS: Our proposed method shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesion challenge dataset and achieves the accuracy of 96.0% and 93.9%, which obtained a 6.0% and 1.2% increase over the traditional method, respectively. CONCLUSIONS: By utilizing Dense Deconvolution Net, the average time for processing one testing images with our proposed framework was 0.253 s.
The non-contact biometric identified bio signal measurement sensor and algorithms

In these days, wearable devices have been developed for effectively measuring biological data. However, these devices have tissue allege and noise problem. To solve these problems, biometric measurement based on a non-contact method, such as face image sequencing is developed. This makes it possible to measure biometric data without any operation and side effects. However, it is impossible for a remote center to identify the person whose data are measured by the novel methods. In this paper, we propose the novel non-contact heart rate and blood pressure imaging system, Deep Health Eye. This system has authentication process at the same time as measuring bio signals, through non-contact method. In the future, this system can be convenient home bio signal monitoring system by combined with smart mirror.
Attitudes and perceptions about cigarette smoking among nonsmoking high school students

Purpose: To explore the association between cigarette smoking intention and attitude, subjective norm, and perceived behavioral control as identified by the Theory of Planned Behavior (TPB) in a cohort of nonsmoking teenagers. Methods: A convenience sample of 76 nonsmoking students from a rural Northeastern Pennsylvania high school completed a self-administered questionnaire based on the TPB, which included cigarette smoking behavior, intention, and the direct measures of attitude, subjective norm, and perceived behavioral control. Conclusions: Regression analyses demonstrated that all three independent variables (attitude, subjective norm, and perceived behavioral control) contributed significantly to the respondents' intention to not smoke cigarettes, accounting for 64% of the variance. These findings may have implications for promoting nonsmoking behavior. Implications for practice: This is the first study to examine the efficacy of the TPB in predicting the intention to not smoke in a cohort of all nonsmokers. The intention to not smoke was significantly correlated with the respondents' negative general attitudes toward cigarette smoking, belief that significant others would disapprove of their cigarette smoking, and positive tobacco refusal self-efficacy. Understanding the factors associated with nonsmoking behavior in teenagers is important information for nurse practitioners, as we develop strategies to encourage and reinforce cigarette smoking abstinence.
Participation in inter-organisational collaborative open foresight A matter of culture

ABSTRACT Foresight at the fuzzy front end of the innovation process has become a widespread managerial practice to increase innovation capacity. Nevertheless, conducting foresight by focusing exclusively on internal know-how and capabilities might inhibit companies from maximising the full potential of this managerial practice. Therefore, opening up companies’ boundaries to look into the long term future jointly with other organisations and to share knowledge on possible future developments, is proposed as a promising approach against these limitations. Before committing to inter-organisational collaborations like collaborative open foresight, it is important to analyse whether the company possesses the necessary prerequisites. Findings from adjacent areas indicate that organisational culture and its influence on a company’s openness to engage in open approaches is an important precondition. So far, no clear evidence of a culture fostering openness for open foresight has yet been presented. Based on two in-depth case studies applying the Competing Values Framework for (organisational) culture analysis we identify cultural values that foster or inhibit openness: (1) A corporate culture characterised by a clan culture and adhocracy culture is fostering openness, whereas (2) a market and hierarchy culture inhibits openness for open foresight participation.
General Edgeworth expansions with applications to profiles of random trees

We prove an asymptotic Edgeworth expansion for the profiles of certain random trees including binary search trees, random recursive trees and plane-oriented random trees, as the size of the tree goes to infinity. All these models can be seen as special cases of the one-split branching random walk for which we also provide an Edgeworth expansion. These expansions lead to new results on mode, width and occupation numbers of the trees, settling several open problems raised in Devroye and Hwang [Ann. Appl. Probab. 16(2): 886--918, 2006], Fuchs, Hwang and Neininger [Algorithmica, 46 (3--4): 367--407, 2006], and Drmota and Hwang [Adv. in Appl. Probab. , 37 (2): 321--341, 2005]. The aforementioned results are special cases and corollaries of a general theorem: an Edgeworth expansion for an arbitrary sequence of random or deterministic functions $\mathbb L_n:\mathbb Z\to\mathbb R$ which converges in the mod-$\phi$-sense. Applications to Stirling numbers of the first kind will be given in a separate paper.
Assessing the heterogeneity of autism spectrum symptoms in a school population

The aim of the present study was to assess whether the nature of the main autistic features (i.e., social communication problems and repetitive and restrictive patterns) are better conceptualized as dimensional or categorical in a school population. The study was based on the teacher ratings of two different age groups: 2,585 children between the ages of 10 and 12 (Primary Education; PE) and 2,502 children between the ages of 3 and 5 (Nursery Education; NE) from 60 mainstream schools. The analyses were based on Factor Mixture Analysis, a novel approach that combines dimensional and categorical features and prevents spurious latent classes from appearing. The results provided evidence of the dimensionality of autism spectrum symptoms in a school age population. The distribution of the symptoms was strongly and positively skewed but continuous; and the prevalence of high‐risk symptoms for autism spectrum disorders (ASD) and social‐pragmatic communication disorder (SCD) was 7.55% of NE children and 8.74% in PE. A categorical separation between SCD and ASD was not supported by our sample. In view of the results, it is necessary to establish clear cut points for detecting and diagnosing autism and to develop specific and reliable tools capable of assessing symptom severity and functional consequences in children with ASD. Autism Res 2018, 11: 979–988. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
Image-Based Food Calorie Estimation Using Recipe Information

SUMMARY Recently, mobile applications for recording everyday meals draw much attention for self dietary. However, most of the applications return food calorie values simply associated with the estimated food categories, or need for users to indicate the rough amount of foods manually. In fact, it has not been achieved to estimate food calorie from a food photo with practical accuracy, and it remains an unsolved problem. Then, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In the experiments, we trained both multi-task and single-task CNNs, and compared them. As a result, a multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coe ﬃ cient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN. In addition, we showed that the proposed multi-task CNN based method outperformed search-based meth-ods proposed before.
Solving high-dimensional partial differential equations using deep learning

Significance Partial differential equations (PDEs) are among the most ubiquitous tools used in modeling problems in nature. However, solving high-dimensional PDEs has been notoriously difficult due to the “curse of dimensionality. ” This paper introduces a practical algorithm for solving nonlinear PDEs in very high (hundreds and potentially thousands of) dimensions. Numerical results suggest that the proposed algorithm is quite effective for a wide variety of problems, in terms of both accuracy and speed. We believe that this opens up a host of possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their interrelationships. Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the “curse of dimensionality. ” This paper introduces a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black–Scholes equation, the Hamilton–Jacobi–Bellman equation, and the Allen–Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and cost. This opens up possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their interrelationships.
MRI visualization of pathological forms by suppression of normal tissue signals

To improve the visualization and 3D-reconstruction of some pathological formations of the brain, it is offered to use a new method of processing of MR images with suppression of signals from normal tissues. The special attention is offered to be given suppression of signals of fatty tissue, free water and partially bound water of mucous membranes. For such way realization, it is offered to lead two scans with simultaneous suppression of two normal components and to multiply the obtained images. Simultaneous suppression of signals from two normal tissues is realized with the help of pulse sequence twice using inversion-recovery effect. Delays in pulse sequence are selected in accordance with the times of longitudinal relaxation of fat, free water and partially bound water. In comparison with earlier described technique of simultaneous suppression of signals of water and fat, the new method is especially useful at research of pathological formations when the zone of defeat is placed in a zone of nose bosoms. Besides allocation of a zone of defeat, MIP reconstruction becomes simpler. The offered technique well proves at research of tumors and hemorrhages.
Establishment of the European meningococcal strain collection genome library (EMSC-GL) for the 2011 to 2012 epidemiological year

Invasive meningococcal disease surveillance in Europe combines isolate characterisation and epidemiological data to support public health intervention. A representative European Meningococcal Strain Collection (EMSC) of IMD isolates was obtained, and whole genome sequenced to characterise 799 EMSC isolates from the epidemiological year July 2011–June 2012. To establish a genome library (GL), the isolate information was deposited in the pubMLST.org/neisseria database. Genomes were curated and annotated at 2,429 meningococcal loci, including those defining clonal complex, capsule, antigens, and antimicrobial resistance. Most genomes contained genes encoding B (n = 525; 65.7%) or C (n = 163; 20.4%) capsules; isolates were genetically highly diverse, with >20 genomic lineages, five of which comprising 60.7% (n = 485) of isolates. There were >350 antigenic fine-types: 307 were present once, the most frequent (P1.7-2,4:F5-1) comprised 8% (n = 64) of isolates. Each genome was characterised for Bexsero Antigen Sequence Typing (BAST): 25.5% (n = 204) of isolates contained alleles encoding the fHbp and/or the PorA VR1 vaccine component, but most genomes (n = 513; 64.2%) did not contain the NadA component. EMSC-GL will support an integrated surveillance of disease-associated genotypes in Europe, enabling the monitoring of hyperinvasive lineages, outbreak identification, and supporting vaccine programme implementation.
Adaptive Aggregation of Flow Records

This paper explores the problem of processing the immense volume of measurement data arising during network traﬃc monitoring. Due to the ever-increasing demands of current networks, observing accurate information about every single ﬂow is virtually infeasible. In many cases the existing methods for the reduction of ﬂow records are still not suﬃcient enough. Since the accurate knowledge of ﬂows termed as “heavy-hitters” suﬃces to fulﬁll most of the monitoring purposes, we decided to aggregate the ﬂow records pertaining to non-heavy-hitters. However, due to the ever-changing nature of traﬃc, their identiﬁcation is a challenge. To overcome this challenge, our proposed approach – the adaptive aggregation of ﬂow records – automatically adjusts its operation to the actual traﬃc load and to the monitoring requirements. Preliminary experiments in existing network topologies showed that adaptive aggregation eﬃciently reduces the number of ﬂow records, while a signiﬁcant proportion of traﬃc details is preserved.
A Hybrid Field Model for Open-Circuit Field Prediction in Surface-Mounted PM Machines Considering Saturation

This paper presents a nonlinear hybrid field model to predict the open-circuit magnetic field distribution in surface-mounted permanent-magnet machines. It combines the complex permeance model (CPM) with the magnetic equivalent circuit (MEC). The saturation effect is accounted for in the CPM by considering the magnetic potential distribution on the stator bore, which is calculated by the MEC and can be transformed to the virtual current on the slot. The proposed model significantly improves the calculation accuracy for saturated machines, which is verified by the finite-element analysis and the experimental results.
Body composition , energy expenditure and physical activity Is misreporting of dietary intake by weighed food records or 24-hour recalls food speci fi c ?

Background/objectives Healthy eating advice is informed, in part, by dietary surveys that rely on self-reported data. Misreporting of food intake may distort relationships between diet and health outcomes. This study directly quantified the food groups that were under-reported or over-reported in common dietary assessment techniques. Subjects/methods Food and drink consumption of 59 adults, with ad libitum access to a range of familiar foods, was objectively and covertly measured by investigators, and validated against independent measures of energy balance, while participants were resident in the Human Nutrition Unit of the Rowett Institute. Participants self-reported their diets using weighed dietary records (WDR) and multiple-pass 24-hr recalls over two periods of 3 days using a cross-over design. Foods and drinks were aggregated into 41 food groups. Results The mean daily weight of food and drinks reported was significantly lower than actually consumed; 3.3 kg (p= 0.004, 95% confidence interval (CI)= 3.07–3.55 kg) and 3.0 kg (p < 0.001, CI= 2.80–3.15 kg) for the WDR and 24-hr recall respectively, compared with 3.6 kg for the objective measure. Reported intakes were significantly lower than the objective measure for four and eight food groups (WDR and 24 h recall, respectively), and not significantly different for the remaining food groups. Conclusions Although under-reporting was greater for some food groups than for others, ‘healthy’ foods were not overreported and ‘unhealthy’ foods were not consistently under-reported. A better understanding of which foods tend to be misreported could lead to improvements in the methods of self-reported dietary intakes.