Miracle-dz/UniFAR-FTUs huggingface dataset:
DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition

We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully convolutional neural network that estimates absolute albedo and shading jointly. Our solution relies on a single end-to-end deep sequence of residual blocks and a perceptually-motivated metric formed by two adversarially trained discriminators. As opposed to classical intrinsic image decomposition work, it is fully data-driven, hence does not require any physical priors like shading smoothness or albedo sparsity, nor does it rely on geometric information such as depth. Compared to recent deep learning techniques, we simplify the architecture, making it easier to build and train, and constrain it to generate a valid and reversible decomposition. We rediscuss and augment the set of quantitative metrics so as to account for the more challenging recovery of non scale-invariant quantities. We train and demonstrate our architecture on the publicly available MPI Sintel dataset and its intrinsic image decomposition, show attenuated overfitting issues and discuss generalizability to other data. Results show that our work outperforms the state of the art deep algorithms both on the qualitative and quantitative aspect.
Investigating the benefits of molecular profiling of advanced non-small cell lung cancer tumors to guide treatments

In this study we utilized data on patient responses to guided treatments, and we evaluated their benefit for a non-small cell lung cancer cohort. The recommended therapies used were predicted using tumor molecular profiles that involved a range of biomarkers but primarily used immunohistochemistry markers. A dataset describing 91 lung non-small cell lung cancer patients was retrospectively split into two. The first group's drugs were consistent with a treatment plan whereby all drugs received agreed with their tumor's molecular profile. The second group each received one or more drug that was expected to lack benefit. We found that there was no significant difference in overall survival or mortality between the two groups. Patients whose treatments were predicted to be of benefit survived for an average of 402 days, compared to 382 days for those that did not (P = 0.7934). In the matched treatment group, 48% of patients were deceased by the time monitoring had finished compared to 53% in the unmatched group (P = 0.6094). The immunohistochemistry biomarker for the ERCC1 receptor was found to be a marker that could be used to predict future survival; ERCC1 loss was found to be predictive of poor survival.
Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.
Factors Influencing Participation in Clinical Trials: Emergency Medicine vs. Other Specialties

Introduction This study investigated factors that influence emergency medicine (EM) patients’ decisions to participate in clinical trials and whether the impact of these factors differs from those of other medical specialties. Methods A survey was distributed in EM, family medicine (FM), infectious disease (ID), and obstetrics/gynecology (OB/GYN) outpatient waiting areas. Eligibility criteria included those who were 18 years of age or older, active patients on the day of the survey, and able to complete the survey without assistance. We used the Kruskal-Wallis test and ordinal logistic regression analyses to identify differences in participants’ responses. Results A total of 2,893 eligible subjects were approached, and we included 1,841 surveys in the final analysis. Statistically significant differences (p≤0.009) were found for eight of the ten motivating factors between EM and one or more of the other specialties. Regardless of a patient’s gender, race, and education, the relationship with their doctor was more motivating to patients seen in other specialties than to EM patients (FM [odds ratio {OR}:1.752, 95% confidence interval {CI}{1.285–2.389}], ID [OR:3.281, 95% CI{2.293–4.695}], and OB/GYN [OR:2.408, 95% CI{1.741–3.330}]). EM’s rankings of “how well the research was explained” and whether “the knowledge learned would benefit others” as their top two motivating factors were similar across other specialties. All nine barriers showed statistically significant differences (p≤0.008) between EM and one or more other specialties. Participants from all specialties indicated “risk of unknown side effects” as their strongest barrier. Regardless of the patients’ race, “time commitment” was considered to be more of a barrier to other specialties when compared to EM (FM [OR:1.613, 95% CI{1.218–2.136}], ID [OR:1.340, 95% CI{1.006–1.784}], or OB/GYN [OR:1.901, 95% CI{1.431–2.526}]). Among the six resources assessed that help patients decide whether to participate in a clinical trial, only one scored statistically significantly different for EM (p<0.001). EM patients ranked “having all material provided in my own language” as the most helpful resource. Conclusion There are significant differences between EM patients and those of other specialties in the factors that influence their participation in clinical trials. Providing material in the patient’s own language, explaining the study well, and elucidating how their participation might benefit others in the future may help to improve enrollment in EM-based clinical trials.
Leishmaniasis–HIV coinfection: current challenges

Leishmaniasis – human immunodeficiency virus (HIV) coinfection can manifest itself as tegumentary or visceral leishmaniasis. Almost 35 countries have reported autochthonous coinfections. Visceral leishmaniasis is more frequently described. However, usual and unusual manifestations of tegumentary leishmaniasis have been reported mainly in the Americas, but the real prevalence of Leishmania infection in HIV-infected patients is not clear. Regarding the clinical manifestations, there are some reports showing unusual manifestations in visceral leishmaniasis and tegumentary leishmaniasis in HIV-infected patients; yet, the usual manifestations are more frequent. Leishmaniasis diagnosis relies on clinical methods, but serological tests are used to diagnose visceral leishmaniasis despite them having a low sensitivity to tegumentary leishmaniasis. The search for the parasite is used to diagnose both visceral leishmaniasis and tegumentary leishmaniasis. Nevertheless, in HIV-infected patients, the sensitivity of serology is very low. Drugs available to treat leishmaniasis are more restricted and cause severe side effects. Furthermore, in HIV-infected patients, these side effects are more prominent and relapses and lethality are more recurrent. In this article, we discuss the current challenges of tegumentary leishmaniasis and visceral leishmaniasis–HIV infection, focusing mainly on the clinical manifestations, diagnosis, and treatment of leishmaniasis.
Asthma and influenza vaccination in elderly hospitalized patients: Matched case-control study in Spain

ABSTRACT Objectives: Influenza infection is an exacerbating factor for asthma, and its prevention is critical in managing asthmatic patients. We investigated the effect of influenza vaccination on asthmatic and non-asthmatic patients hospitalized with laboratory-confirmed influenza in Spain. Methods: We made a matched case-control study to assess the frequency of hospitalization for influenza in people aged ≥65 years. Hospitalized patients with unplanned hospital admissions were recruited from 20 hospitals representing seven Spanish regions. Cases were defined as those hospitalized due to a laboratory-confirmed influenza infection and controls were matched by age, sex, and hospital. Data were obtained from clinical records, and patients stratified by clinical asthma history. Vaccination status and asthma due to influenza infection were analyzed according to sociodemographic variables and medical risk conditions. Multivariable analysis was made using conditional logistic regression models. Results: 582 hospitalized patients with influenza (15.8% asthmatic) and 1,570 hospitalized patients without influenza (7.9% asthmatic) were included. In the multivariable conditional logistic regression using unvaccinated and non-asthmatic patients as the reference group, vaccination significantly prevented influenza in non-asthmatic patients (aOR = 0.63; 95% CI: 0.45, 0.88) and also showed a trend for a possibly protective effect in asthmatic patients (aOR = 0.79; 95% CI: 0.34, 1.81). Conclusion: Our results suggest that influenza vaccination could be a protective factor for asthmatic patients, although the results are inconclusive and further research is required. Practically, given the better clinical evolution of vaccinated asthma cases, and the lack of better evidence, the emphasis on vaccination of this group should continue.
Evaluation and improvement of thermo-physiological comfort properties of firefighters’ protective clothing containing super absorbent materials

In this research, super absorbent materials were incorporated into the internal layer of the firefighters’ protective clothing with the aim of increasing absorption of sweat to improve the thermo-physiological comfort properties. The performance properties were evaluated following the standard test methods (ISO 6942:2002 and ISO 9151: 1995(E)) and the thermo-physiological comfort-related properties were evaluated by measuring the transport properties such as air permeability, water sorption and evaporation, thermal resistance and water vapour resistance of the fabric assemblies with super absorbent materials. The results indicated that it is possible to improve the comfort properties of the protective clothing by the incorporation of super absorbent materials into the internal layer. The use of super absorbent materials is likely to help in the absorption of sweat in higher amount and keeping the skin and internal microclimate dry, which in turn improves the comfort level. The performance properties of all the combinations satisfied the requirements for firefighter’s clothing as mentioned in AS/NZS 4967-2009.
Adversarial Defense based on Structure-to-Signal Autoencoders

Adversarial attacks have exposed the intricacies of the complex loss surfaces approximated by neural networks. In this paper, we present a defense strategy against gradientbased attacks, on the premise that input gradients need to expose information about the semantic manifold for attacks to be successful. We propose an architecture based on compressive autoencoders (AEs) with a two-stage training scheme, creating not only an architectural bottleneck but also a representational bottleneck. We show that the proposed mechanism yields robust results against a collection of gradient-based attacks under challenging white-box conditions. This defense is attack-agnostic and can, therefore, be used for arbitrary pre-trained models, while not compromising the original performance. These claims are supported by experiments conducted with state-of-the-art image classifiers (ResNet50 and Inception v3), on the full ImageNet validation set. Experiments, including counterfactual analysis, empirically show that the robustness stems from a shift in the distribution of input gradients, which mitigates the effect of tested adversarial attack methods. Gradients propagated through the proposed AEs represent less semantic information and instead point to low-level structural features.
Interactions between Snow Chemistry, Mercury Inputs and Microbial Population Dynamics in an Arctic Snowpack

We investigated the interactions between snowpack chemistry, mercury (Hg) contamination and microbial community structure and function in Arctic snow. Snowpack chemistry (inorganic and organic ions) including mercury (Hg) speciation was studied in samples collected during a two-month field study in a high Arctic site, Svalbard, Norway (79°N). Shifts in microbial community structure were determined by using a 16S rRNA gene phylogenetic microarray. We linked snowpack and meltwater chemistry to changes in microbial community structure by using co-inertia analyses (CIA) and explored changes in community function due to Hg contamination by q-PCR quantification of Hg-resistance genes in metagenomic samples. Based on the CIA, chemical and microbial data were linked (p = 0.006) with bioavailable Hg (BioHg) and methylmercury (MeHg) contributing significantly to the ordination of samples. Mercury was shown to influence community function with increases in merA gene copy numbers at low BioHg levels. Our results show that snowpacks can be considered as dynamic habitats with microbial and chemical components responding rapidly to environmental changes.
Adenovirus Vector Vaccination Impacts NK Cell Rheostat Function following Lymphocytic Choriomeningitis Virus Infection

ABSTRACT Natural killer (NK) cells respond rapidly as a first line of defense against infectious pathogens. In addition, NK cells may provide a “rheostat” function and have been shown to reduce the magnitude of antigen-specific T cell responses following infection to avoid immunopathology. However, it remains unknown whether NK cells similarly modulate vaccine-elicited T cell responses following virus challenge. We used the lymphocytic choriomeningitis virus (LCMV) clone 13 infection model to address whether NK cells regulate T cell responses in adenovirus vector-vaccinated mice following challenge. As expected, NK cell depletion in unvaccinated mice resulted in increased virus-specific CD4+ and CD8+ T cell responses and immunopathology following LCMV challenge. In contrast, NK cell depletion had minimal to no impact on antigen-specific T cell responses in mice that were vaccinated with an adenovirus serotype 5 (Ad5)-GP vector prior to LCMV challenge. Moreover, NK cell depletion in vaccinated mice prior to challenge did not result in immunopathology and did not compromise protective efficacy. These data suggest that adenovirus vaccine-elicited T cells may be less sensitive to NK cell rheostat regulation than T cells primed by LCMV infection. IMPORTANCE Recent data have shown that NK cell depletion leads to enhanced virus-elicited T cell responses that can result in severe immunopathology following LCMV infection in mice. In this study, we observed that NK cells exerted minimal to no impact on vaccine-elicited T cells following LCMV challenge, suggesting that adenovirus vaccine-elicited T cells may be less subject to NK cell regulation. These data contribute to our understanding of NK cell regulatory functions and T cell-based vaccines.
The Bitonic Filter: Linear Filtering in an Edge-Preserving Morphological Framework

A new filter is presented which has better edge and detail preserving properties than a median, noise reduction capability similar to a Gaussian, and is applicable to many signal and noise types. It is built on a definition of signal as bitonic, i.e., containing only one local maxima or minima within the filter range. This definition is based on data ranking rather than value; hence, the bitonic filter comprises a combination of non-linear morphological and linear operators. It has no data-level-sensitive parameters and can locally adapt to the signal and noise levels in an image, precisely preserving both smooth and discontinuous signals of any level when there is no noise, but also reducing noise in other areas without creating additional artifactual noise. Both the basis and the performance of the filter are examined in detail, and it is shown to be a significant improvement on the Gaussian and median. It is also compared over various noisy images to the image-guided filter, anisotropic diffusion, non-local means, the grain filter, and self-dual forms of leveling and rank filters. In terms of signal-to-noise, the bitonic filter outperforms all these except non-local means, and sometimes anisotropic diffusion. However, it gives good visual results in all circumstances, with characteristics which make it appropriate particularly for signals or images with varying noise, or features at varying levels. The bitonic has very few parameters, does not require optimization nor prior knowledge of noise levels, does not have any problems with stability, and is reasonably fast to implement. Despite its non-linearity, it hence represents a very practical operation with general applicability.
Reducing the Computational Complexity of Information Theoretic Approaches for Reconstructing Gene Regulatory Networks

Information theoretic approaches are increasingly being used for reconstructing regulatory networks from microarray data. These approaches start by computing the pairwise mutual information (MI) between all gene pairs. The resulting MI matrix is then manipulated to identify regulatory relationships. A barrier to these approaches is the time-consuming step of computing the MI matrix. We present a method to reduce this computation time. We apply spectral analysis to re-order the genes, so that genes that share regulatory relationships are more likely to be placed close to each other. Then, using a "sliding window" approach with appropriate window size and step size, we compute the MI for the genes within the sliding window, and the remainder is assumed to be zero. Using both simulated data and microarray data, we demonstrate that our method does not incur performance loss in regions of high-precision and low-recall, while the computational time is significantly lowered. The proposed method can be used with any method that relies on the mutual information to reconstruct networks.
β-amyloid expression in age-related cataract lens epithelia and the effect of β-amyloid on oxidative damage in human lens epithelial cells

Purpose To evaluate the changes in β-amyloid (Aβ) expression in age-related cataract (ARC) lens epithelia and the effect of Aβ on oxidative damage in human lens epithelial cells (HLECs). Methods Specimens of lens epithelia and aqueous humor were obtained from 255 cataract surgery patients and 48 healthy donor eyes. The ARC samples were divided into four groups according to the Lens Opacities Classification System III, with increasing severity from Group I to Group IV. The HLECs were cultured under healthy or oxidative conditions with or without Aβ pretreatment. Western blot, immunofluorescence, real-time PCR, and enzyme-linked immunosorbent assay were performed to detect Aβ and β-amyloid precursor protein (APP) expression. β-secretase activity was analyzed in lens epithelia and HLECs. The effect of Aβ on the viability of HLECs under oxidative conditions was investigated using a cell viability assay. Results Compared with the healthy group, the Aβ 1–42 expression levels in lens epithelia and Aβ 1–40 expression levels in aqueous humor decreased in Groups I, II, and III (p<0.05) but were unchanged in Group IV. In contrast, APP expression levels increased in Groups I, II, and III (p<0.05) compared with those in the healthy group but were unchanged in Group IV. H2O2-treated HLECs exhibited decreased amounts of Aβ 1–42 and increased amounts of APP. β-secretase activity decreased in the lens epithelia of all four subgroups of ARCs compared with that in the lens epithelia of healthy subjects and decreased in H2O2-treated HLECs. Furthermore, treatment with nanomolar concentrations (0.2 nM to 10 nM) of Aβ could protect cell viability from oxidative damage. Conclusions Aβ and APP expression levels exhibited differential changes during the development of ARC, indicating active feedback of this protein processing. Decreased expression of physiologically generated Aβ in the early and mid-stages of ARC development might be one of the potential mechanisms accelerating oxidative stress in HLECs during cataractogenesis.
Soil Fertilization Study on Zaghloul Date Palm Grown in Calcareous Soil and Irrigated with Drainage Water

The present study was conducted during 2008/2009 and 2009/2010 seasons on Zaghloul date palm grown in calcareous loamy sand soil and irrigated with drainage water. Palms were fertilized with 1000g/palm of actual nitrogen as ammonium sulphate (N1), ammonium nitrate (N2) or urea (N3) as well as two levels (500 and 1000g/palm) of potassium (K) and elemental sulfur (S) in order to study their effect on the nutritional status, yield and fruit quality. Data showed that the N1 increased yield compared to N2 and N3. Also, the N1 gave better results of fruit weight, length and color than the N3 one. In general, fruit chemical characteristic were improved by the N1 and N2 compared to N3. Fruit TSS, sugars, anthocyanin and dry matter content were increased while, fruit acidity and tannins were decreased by the N1. Furthermore, the K2 and S2 rates greatly enhanced the fruit physico-chemical characters. Leaf N, K, Ca, Fe and Zn content increased by N1, K2 and S2 applications. Fruit N, P, K, Fe and Zn content increased by N1 whereas, fruit Cd, Pb and NO was the highest 3 with N2 form. The high level of both K and S increased fruit N, Fe, Mn, Cd, Pb and NO . 3
Use of the Moon to support on-orbit sensor calibration for climate change measurements

Production of reliable climate datasets from multiple observational measurements acquired by remote sensing satellite systems available now and in the future places stringent requirements on the stability of sensors and consistency among the instruments and platforms. Detecting trends in environmental parameters measured at solar reflectance wavelengths (0.3 to 2.5 microns) requires on-orbit instrument stability at a level of 1% over a decade. This benchmark can be attained using the Moon as a radiometric reference. The lunar calibration program at the U.S. Geological Survey has an operational model to predict the lunar spectral irradiance with precision ~1%, explicitly accounting for the effects of phase, lunar librations, and the lunar surface photometric function. A system for utilization of the Moon by on-orbit instruments has been established. With multiple lunar views taken by a spacecraft instrument, sensor response characterization with sub-percent precision over several years has been achieved. Meteorological satellites in geostationary orbit (GEO) capture the Moon in operational images; applying lunar calibration to GEO visible-channel image archives has the potential to develop a climate record extending decades into the past. The USGS model and system can provide reliable transfer of calibration among instruments that have viewed the Moon as a common source. This capability will be enhanced with improvements to the USGS model absolute scale. Lunar calibration may prove essential to the critical calibration needs to cover a potential gap in observational capabilities prior to deployment of NPP/NPOESS. A key requirement is that current and future instruments observe the Moon.