Miracle-dz/UniFAR-FTUs huggingface dataset:
CD 8 + Cytotoxic Immune Infiltrate in Non-Muscle Invasive Bladder Cancer : A Standardized Methodology to Study Association with Clinico-Pathological Features and Prognosis

Background: Major interest lies in the evaluation of immune infiltrate in bladder cancer. CD8+ cytotoxic lymphocytes are key effectors of adaptive immune response. Objectives: The aims of the study were to set up a standardized methodology for CD8+ lymphocytes estimation in NMIBC and investigate how intra-tumoral heterogeneity influences CD8+ immune infiltrate. Methods: We considered 995 NMIBC included in the Spanish Bladder Cancer (SBC)/EPICURO Study. Duplicate 0.6 mm TMA spots and paired full sections (FS) for 50 selected cases were double stained with anti-pan cytokeratin antibody and antiCD8 antibody. Slides were digitalized and CD8+ cells were automatically counted after tissue recognition (tumor vs stroma). Spatial heterogeneity was assessed and a resampling strategy was applied to estimate the proper number of 0.6 mm TMA spots providing an adequate CD8+ cell estimate. Association between CD8+ count and expression of urothelial differentiation markers was estimated. Cox regression models were performed to assess association between CD8+ cell count and risk of recurrence and progression. ∗Correspondence to: Dr Alexandra Masson-Lecomte, Department of Urology Saint Louis Academic Hospital, 1 avenue Claude Vellefaux, 75010 Paris, France. Tel. : +33 01 42 49 96 15; E-mail: alexandra.massonlecomte@aphp.fr. ISSN 2352-3727/19/$35.00 © 2019 – IOS Press and the authors. All rights reserved This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0). 160 A. Masson-Lecomte et al. / CD8 in Non-Muscle Invasive Bladder Cancer Results: Microscopic examination of full sections showed spatial heterogeneity for CD8+ infiltrates. Simulation analyses demonstrated that 5 TMA regions provided a correct sampling of tumor and stromal compartments in Ta while 2 and 6 TMA regions were necessary in T1, respectively. CD8+ cells infiltration was associated with stage, regardless of the histological compartment analyzed (median CD8 + /mm2 were 25/mm2 and 129/mm2 in tumor and stroma respectively in Ta and 111/mm2 and 344/mm2 in T1; p-value = 0.006). CD8+ infiltration in tumor compartment was significantly associated with low FGFR3 expression. CD8 + /mm2 count in the tumor compartment was not associated with prognosis. Conclusion: Differences identified between Ta and T1 tumours supported the hypothesis that rigorous efforts should be placed in proper study design. These results provide a new framework to investigate microenvironment complexity in bladder
Ferrimagnetism and anisotropic phase tunability by magnetic fields in 
Na2Co2TeO6

Na$_2$Co$_2$TeO$_6$ has recently been proposed to be a Kitaev-like honeycomb magnet. To assess how close it is to realizing Kitaev quantum spin liquids, we have measured magnetization and specific heat on high-quality single crystals in magnetic fields applied along high-symmetry directions. Small training fields reveal a weak but canonical ferrimagnetic behavior below 27 K, which cannot be explained by the zigzag antiferromagnetic order alone and suggests coexisting Neel-type order of moments canted away from the zigzag chains. Moderate fields in the honeycomb plane suppress the thermal transition at 27 K, and seem to partly reverse the moment-canting when applied perpendicular to the zigzag chains. In contrast, out-of-plane fields leave the transition largely unaffected, but promotes another transition below 10 K, possibly also related to canting reversal. The magnetism in Na$_2$Co$_2$TeO$_6$ is highly anisotropic and close to tipping points between competing phases.
Aedes Control Amongst Residents of Dengue Hotspot Areas in Selangor : A Cross-Sectional Study

Dengue fever is endemic and remains a public health concern in Malaysia. The highest number of dengue cases occurred in Selangor in 2015. The majority of hotspot and outbreak areas in Malaysia were located in residential areas. Despite the high number of dengue prevalence in Malaysia, evidence for the level of community knowledge and practice of methods for Aedes control and prevention in the country remains scant. This cross-sectional study aimed to assess the demographic factors associated with the knowledge and practice of Aedes control and prevention methods. It also aimed to assess the associations and relationships between the public knowledge and practice of Aedes control and preventive activities involving Aedes larvae ovitraps (ALOTs), Mousticide, Denguard and other methods. Two residential areas that were considered as dengue hotspots were selected for the introduction of Aedes control and prevention programs. A sample size was calculated using G*power and a total of 152 residents who attended the dengue awareness events held at Section 3 and Section 8, Bandar Baru Bangi, Selangor, were recruited. A purposive sampling approach was adopted, and data were collected by using a pretested questionnaire with three subsections on sociodemographic information and on the knowledge and practice of Aedes control and prevention methods. No statistically significant (p>0.05) relationship between demographic factors and knowledge and practice scores was found. The results of χ2 square test showed a strong (φ-coefficient = 0.605) and significant (p<0.001) association between knowledge and practice scores. In addition, a strong and significant positive correlation (r=0.648, p<0.001) existed between knowledge and practice scores. In conclusion, health promotion programs that focus on educating the public regarding the use of Aedes larvae ovitraps (ALOTs), Mousticide, Denguard and other methods to prevent and control Aedes mosquitoes and that involve multiple stakeholders, such as government officials and responsible authorities, and the active participation of communities are crucial in the war against Aedes.
LASSO GUARANTEES FOR β-MIXING HEAVY TAILED TIME SERIES ∗ By

Many theoretical results for lasso require the samples to be iid. Recent work has provided guarantees for lasso assuming that the time series is generated by a sparse Vector Auto-Regressive (VAR) model with Gaussian innovations. Proofs of these results rely critically on the fact that the true data generating mechanism (DGM) is a finite-order Gaussian VAR. This assumption is quite brittle: linear transformations, including selecting a subset of variables, can lead to the violation of this assumption. In order to break free from such assumptions, we derive non-asymptotic inequalities for estimation error and prediction error of lasso estimate of the best linear predictor without assuming any special parametric form of the DGM. Instead, we rely only on (strict) stationarity and geometrically decaying βmixing coefficients to establish error bounds for lasso for subweibull random vectors. The class of subweibull random variables that we introduce includes subgaussian and subexponential random variables but also includes random variables with tails heavier than an exponential. We also show that, for Gaussian processes, the β-mixing condition can be relaxed to summability of the α-mixing coefficients. Our work provides an alternative proof of the consistency of lasso for sparse Gaussian VAR models. But the applicability of our results extends to non-Gaussian and non-linear times series models as the examples we provide demonstrate.
Wood Recruitment Processes and Wood Budgeting

—Wood is recruited to rivers by a diversity of processes, including chronic mortality, windstorms, wildfires, bank erosion, landslides, and ice storms. Recruitment, storage, and transport of large wood in streams can be understood in terms of a mass balance, or quantitative wood budget, similar to the study of other material fluxes in watersheds. A wood budgeting framework is presented that includes numerical expressions for punctuated forest mortality by fire, chronic mortality and tree fall, bank erosion, mass wasting, decay, and stream transport. When used with appropriate parameter values derived for specific conditions or regions, the wood budget equations can be used to make predictions on the importance of various landscape processes on wood abundance in streams in any locale. For example, wood budgets can be used to predict how variations in climate (wet – dry), topography (steep – gentle), basin size (small – large), and land management could affect abundance and distribution of large wood in streams. Wood budgets also can be integrated into numerical simulation models for estimating the natural range of variability, specifically temporal fluctuations of wood supply driven by large storms, floods, fires, and mass wasting, and spatial variability driven by topographic heterogeneity and variations in wood transport. Field studies of wood in streams may be enhanced by the use of a wood budget framework. This includes specifying what measurements are required over what length of stream for estimating recruitment rates of all relevant inputs processes, wood loss by decay, and stream transport of wood. Finally, wood budgets can be used to estimate rates of bank erosion, forest mortality, and landsliding, given appropriate field measurements of wood in streams and riparian conditions.
In Vehicle Resource Orchestration for Multi-V2X Services

Transmit Rate Control (TRC) for V2X networks is critical for distributed channel resource allocation, limiting the channel usage per vehicle while preventing channel saturation. Similarly, efficiently distributing the limited transmit opportunities among multiple services of a vehicle is necessary, but has not been sufficiently studied. In this paper, we present a multi-V2X service resource orchestrator composed of two complementary mechanisms: (i) a multi-factor prioritization function, (ii) a budgetary scheduler allowing smooth resource earning/spending. Simulation-based evaluations showed improved access time for various V2X services under restricted resources and enhanced control on resource balancing between V2X services.
Single-Particle Tracking of Membrane Protein Diffusion in a Potential : Simulation , Detection , and Application to Confined Diffusion of CFTR Cl 2 Channels

Confined diffusion of membrane receptors and lipids can result from intramembrane barriers, skeletal interactions, rafts, and other phenomena. We simulated single-particle diffusion in two dimensions in an arbitrary potential, V(r), based on summation of random and potential gradient-driven motions. Algorithms were applied and verified for detection of potential-driven diffusion, and for determination of V(r) from radial particle density distributions, taking into account experimental uncertainties in particle position and finite trajectory recording. Single-particle tracking (SPT) analysis of the diffusion of cystic fibrosis transmembrane conductance regulator (CFTR) Cl channels in mammalian cells revealed confined diffusion with diffusion coefficient ;0.004mm/s. SPT data fitted closely to a springlike attractive potential, V(r)1⁄4 kr, but not to other V(r) forms such as hard-wall or viscoelastic-like potentials. The ‘‘spring constant’’, k, determined fromSPT datawas 2.66 0.8 pN/mm, and not altered significantly by modulation of skeletal protein architecture by jasplakinolide. However, k was reduced by a low concentration of latrunculin, supporting the involvement of actin in the springlike tethering of CFTR. Confined diffusion of membrane proteins is likely a general phenomenon suitable for noninvasive V(r) analysis of force-producing mechanisms. Our data provide the first measurement of actin elasticity, to the best of our knowledge, that does not involve application of an external force.
Kernel Approximation

In this project, I explore the kernel approximation techniques based on randomization. In the classification tasks when data is not linearly separable in the current feature space, kernel methods project input data points into high dimensional feature space (infinite-dimensional in cases of kernels like Gaussian kernel) and find the optimal hyperplane in that feature space. But if number of data points are large, size of the kernel matrix is going to be large and it could affect the efficiency of the algorithms. Using kernel approximation technique such as random Fourier features, random feature maps, Nystrom approximation, data points are projected into some approximated low dimensional space (compared to infinite or very high dimensional space in kernel trick). I explore some of these kernel approximation techniques and compare their results.
ff ect of Iron Supplementation on the Outcome of Non-Progressive Pulmonary Mycobacterium tuberculosis Infection

The human response to Mycobacterium tuberculosis (Mtb) infection is affected by the availability of iron (Fe), which is necessary for proper immune cell function and is essential for the growth and virulence of bacteria. Increase in host Fe levels promotes Mtb growth and tuberculosis (TB) pathogenesis, while Fe-supplementation to latently infected, asymptomatic individuals is a significant risk factor for disease reactivation. However, the effect of Fe-supplementation on the host immunity during latent Mtb infection remains unclear, due partly to the paucity in availability of animal models that recapitulate key pathophysiological features seen in humans. We have demonstrated that rabbits can develop non-progressive latency similar to infected humans. In this study, using this model we have evaluated the effect of Fe-supplementation on the bacterial growth, disease pathology, and immune response. Systemic and lung Fe parameters, gene expression profile, lung bacterial burden, and disease pathology were determined in the Mtb-infected/Feor placebo-supplemented rabbits. Results show that Fe-supplementation to Mtb-infected rabbits did not significantly change the hematocrit and Hb levels, although it elevated total Fe in the lungs. Expression of selected host ironand immune-response genes in the blood and lungs was perturbed in Mtb-infected/Fe-supplemented rabbits. Iron-supplementation during acute or chronic stages of Mtb infection did not significantly affect the bacterial burden or disease pathology in the lungs. Data presented in this study is of significant relevance for current public health policies on Fe-supplementation therapy given to anemic patients with latent Mtb infection.
Air pollutants exposure and frequency of micronuclei (MN) among primary school children nearby industrial area

Introduction: Air pollutants that possessed genotoxic properties have the potential to induce genetic damage. Micronuclei (MN) frequency is used as an indicator for identifying potential genotoxic exposures. A comparative cross-sectional study was carried out among primary school children in a petrochemical industrial area (N=111, Kemaman) and a rural (N=65, Dungun) area in Terengganu. Methods: Validated questionnaires were distributed to obtain the respondents’ socio-demographic data, previous exposure and reported respiratory illness. The frequency of micronuclei was assessed in collected buccal mucosa samples of children. The air monitoring was also carried out at 6 selected schools. Results: Results from the statistical analysis carried out showed significant differences with p=0.001 for all parameters assessed between areas, which included ultrafine particles, UFP (z = -4.842), PM2.5 (z = -10.392), PM10 (z= -11.074) NO2 (z = -11.868), SO2 (z = -5.667), relative humidity (z = -5.587). The MN frequency was statistically significant with PM2.5 (χ2= 17.78, p=0.001) and PM10 (χ2= 15.429, p =0.001). The statistical analysis also showed a significant association between UFP and coughing (PR=2.965, 95% CI=1.069-8.225). The multiple logistic regression analysis showed that the main pollutants influencing MN frequencies were UFP and NO2 with UFP (PR=1.877, 95%CI= 1.174-3.002) and NO2 (PR=1.008, 95%CI= 1.001-1.015). Conclusion: This study demonstrated that exposure to air pollutants may increase the risk of respiratory illness and may induce MN formation among children.
Emergent Cosmological Constant from a Holographic Mass/Energy Distribution

A new methodology is introduced suggesting that an exact cosmological constant is theoretically and numerically derived and described as the squared ratio of Planck length and the particle horizon radius. Additionally, equations relating the sterile neutrino mass, Planck mass and mass of the universe are established. Furthermore, the mass of the universe can be derived as encoded information located on the cosmic horizon. Finally, a relationship of the Hubble radius and comoving radius is reviewed. This hypothesis is tested for convergence for an overall flat curvature using the Friedmann equations.
Intergroup Emotions Effects of Intergroup Contact and Political Predispositions on Prejudice : Role of

can be found at: Group Processes & Intergroup Relations Additional services and information for http://gpi.sagepub. com/cgi/alerts Email Alerts: http://gpi.sagepub. com/subscriptions Subscriptions: http://www.sagepub. com/journalsReprints.nav Reprints: http://www.sagepub. com/journalsPermissions.nav Permissions: http://gpi.sagepub. com/cgi/content/abstract/7/3/221#BIBL SAGE Journals Online and HighWire Press platforms): (this article cites 17 articles hosted on the Citations
Disassociation for electronic health record

The dissemination of Electronic Health Record (EHR) data, beyond the originating healthcare institutions, can enable large-scale, low-cost medical studies that have the potential to improve public health. Thus, funding bodies, such as the National Institutes of Health (NIH) in the U.S., encourage or require the dissemination of EHR data, and a growing number of innovative medical investigations are being performed using such data. However, simply disseminating EHR data, after removing identifying information, may risk privacy, as patients can still be linked with their record, based on diagnosis codes. This paper proposes the first approach that prevents this type of data linkage using disassociation, an operation that transforms records by splitting them into carefully selected subsets. Our approach preserves privacy with significantly lower data utility loss than existing methods and does not require data owners to specify diagnosis codes that may lead to identity disclosure, as these methods do. Consequently, it can be employed when data need to be shared broadly and be used in studies, beyond the intended ones. Through extensive experiments using EHR data, we demonstrate that our method can construct data that are highly useful for supporting various types of clinical case count studies and general medical analysis tasks. 2014 Published by Elsevier Inc.
The Effect Of Hyperparameters In The Activation Layers Of Deep Neural Networks

Deep neural networks (DNNs), and artificial neural networks (ANNs) in general, have recently received a great amount of attention from both the media and the machine learning community at large. DNNs have been used to produce world-class results in a variety of domains, including image recognition, speech recognition, sequence modeling, and natural language processing. Many of most exciting recent deep neural network studies have made improvements by hardcoding less about the network and giving the neural network more control over its own parameters, allowing flexibility and control within the network. Although much research has been done to introduce trainable hyperparameters into transformation layers (GRU [7], LSTM [13], etc), the introduction of hyperparameters into the activation layers have been largely ignored. This paper serves several purposes: to (1) equip the reader with the background knowledge, including theory and best practices for DNNs, which help contextualize the contributions of this paper, (2) to describe and verify the effectiveness of current techniques in the literature that utilize hyperparameters in the activation layer, and (3) to introduce some new activation layers that introduce hyperparameters into the model, including activation pools (APs) and parametric activation pools (PAPs), and study the effectiveness of these new constructs on popular image recognition datasets.
Impairment losses: causes and impacts

Purpose – To analyze recognition of impairment losses in tangible and intangible assets, and their relevance to investors in companies listed in the Lisbon and Madrid Stock Exchange (2007-2011). Methodology – Quantitative analysis of a panel data sample of 80 companies listed in the Lisbon and Madrid Stock Exchange (20072011) was carried out. Panel data linear and non-linear regression models were estimated. Findings – We found that the amount of impairment losses showed an upward trend, and that these losses are most significant among intangibles, especially goodwill (GW). We also found that the probability of recognition of impairment losses is positively influenced by the dimension of entities and negatively by market value (p < 0.10). Portuguese export-oriented companies have a higher probability of not recognizing impairments. However, Portuguese companies with higher market values have greater probability of recognizing impairment losses, contrary to the sample as a whole, in which the relationship is negative (p < 0.10). The results also suggest that there is a smoothing effect on results because of impairments, especially in IBEX35 companies. As to the relevance of impairment losses to market value, we confirm a significant negative relationship, in line with conclusions from previous studies. Originality/value – This study contributes to the introduction of the cultural factor in this analysis, highlighting the differentiated behaviors between Portuguese and Spanish companies.
1 MUTUAL FUND PERFORMANCE AND CHANGES IN FACTORS EXPOSURES

This paper proposes a measure to assess the change in mutual funds’ exposures to risk factors and examines the relationship with subsequent performance. The proposed measure is based upon the factors contributions to the variability in mutual fund returns, and has the main advantage of not requiring data related to funds’ portfolio holdings. Investigating a large sample of US active mutual funds over the period from 1990 to 2016, we analyze the relationship between changes in mutual fund factor exposures and subsequent performance. We provide evidence that mutual fund managers that actively change their risk exposures regularly generate greater and significant alphas. These findings are not explainable using other variables such as the funds’ tracking error, the fund size or its investment style. Moreover, the evidence suggests that the outperformance is attributable to the skills of the fund managers rather than to luck. Hence, our results provide further evidence supporting the importance and benefits of active management in the mutual fund industry, and highlight the skills of some portfolio managers in generating at least short-term performance persistence.
Log-file data as indicators for problem-solving processes

This chapter describes a theoretical rationale and an empirical methodology for analysing log-file data recorded from students’ interactions with computer-based problem-solving assessment tasks. These analyses can play an important role in the development and refinement of rules for automatically scoring complicated sequences of process data which describe procedural aspects of problem solving. It presents examples of log files and analyses from an interactive problem-solving task. In complex domains where a variety of skills and dispositions may influence performance, interpreting log-file data to infer how students solve problems could provide valid information to help meet their instructional needs.