Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of the mitochondrial and cellular uptake of MPP+, as probed by the fluorescent mimic, 4'I-MPP+

The discovery that 1-methyl-4-phenylpyridinium (MPP+) selectively destroys dopaminergic neurons and causes Parkinson’s disease (PD) symptoms in mammals has strengthened the environmental hypothesis of PD. The current model for the dopaminergic toxicity of MPP+ is centered on the uptake into dopaminergic neurons, accumulation into the mitochondria, inhibition of the complex-I leading to ATP depletion, increased reactive oxygen species (ROS) production, and apoptotic cell death. However, some aspects of this mechanism and the details of the cellular and mitochondrial accumulation of MPP+ are still poorly understood. The aim of this study was to characterize a structural and functional MPP+ mimic which is suitable to study the cellular distribution and mitochondrial uptake of MPP+ in live cells and use it to identify the molecular details of these processes to advance the understanding of the mechanism of the selective dopaminergic toxicity of MPP+. Here we report the characterization of the fluorescent MPP+ derivative, 1-methyl-4-(4’-iodophenyl)pyridinium (4’I-MPP+), as a suitable candidate for this purpose. Using this novel probe, we show that cytosolic/mitochondrial Ca2+ play a critical role through sodium-calcium exchanger (NCX) in the mitochondrial and cellular accumulation of MPP+ suggesting for the first time that MPP+ and related mitochondrial toxins may also exert their toxic effects through the perturbation of Ca2+ homeostasis in dopaminergic cells. We also found that the specific mitochondrial NCX (mNCX) inhibitors protect dopaminergic cells from the MPP+ and 4’I-MPP+ toxicity, most likely through the inhibition of the mitochondrial uptake, which could potentially be exploited for the development of pharmacological agents to protect the central nervous system (CNS) dopaminergic neurons from PD-causing environmental toxins.
Inhibitive Effects of FGF2/FGFR1 Pathway on Astrocyte-Mediated Inflammation in vivo and in vitro After Infrasound Exposure

Infrasound, a kind of ambient noise, can cause severe disorders to various human organs, specially to central nervous system (CNS). Our previous studies have shown that infrasound-induced CNS injury was closely related with astrocytes activation and astrocytes-mediated neuroinflammation, but the underlying molecular mechanisms are still largely unclear. FGF2/FGFR1 (Fibroblast growth factor 2/Fibroblast growth factor receptor 1) pathway was reported to play an important role in anti-inflammation in CNS disorders. To further study the possible roles of FGF2/FGFR1 pathway in infrasound-induced CNS injury, here we exposed Sprague-Dawley rats or cultured astrocytes to 16 Hz, 150 dB infrasound, and explored the effects of FGF2 on infrasound-induced astrocytes activation and neuroinflammation. Western blotting, immunofluorescence and liquid chip method were used in this experiment. Our results showed that after 3- or 7-day exposure (2 h/day) of rats as well as 2 h exposure of cultured astrocytes to 16 Hz, 150 dB infrasound, astrocyte-expressed FGFR1 was downregulated in vivo and in vitro. FGF2 pretreatment not only inhibited infrasound-induced astrocyte activation in rat hippocampal CA1 region, but also reduced the levels of pro-inflammatory cytokines, such as TNF-α, IL-1β, IL-18, IL-6, and IFN-γ in vitro and in vivo. However, FGF2 significantly upregulated the expression of FGFR1. Furthermore, we showed that FGF2 could attenuate IκBα phosphorylation, NF-κB p65 translocation, pro-inflammatory cytokines levels, and neuronal loss in the CA1 region induced by infrasound. On the contrary, PD173074, a special antagonist of FGFR1, could reverse the effects above in vitro and in vivo. Taken together, our findings showed that FGF2/FGFR1 pathway may exert inhibitive effects on astrocyte-mediated neuroinflammation in vitro and in vivo after infrasound exposure.
Learning to Importance Sample in Primary Sample Space

Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. We propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene‐dependent training phase, we learn to generate samples with a desired density in the primary sample space of the renderer using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real‐valued non‐volume preserving (“Real NVP”) transformations in high dimensional spaces. We use Real NVP to non‐linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with an existing rendering technique by treating it as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.
EFFECTS OF POLLINATION INTENSITY IN CAMPSIS RADICANS.

I examined effects of pollination intensity on fruit, seed and seedling characteristics in Campsis radicans, and joint effects of pollen donor and pollination intensity on fruit production. Large pollen loads were more likely to initiate fruit production than small pollen loads, and the former fruits contained more seeds and a greater total seed mass. No further increases in seed number or mass occurred for pollen loads above 4,000 grains. The weight of individual seeds was unaffected by pollen load. Effects of pollen donor were generally larger than effects of pollen load, and fruit production from small loads of pollen from one donor were sometimes equal to fruit production from larger pollen loads from another donor. The ratio of pollen grains deposited to resultant seeds increased with pollen load, and several explanations are proposed. Seeds from heavy pollinations emerged better than seeds from light pollinations, but did not differ in speed of germination or in the performance of seedlings up to 126 days. The emergence differences are probably due to differing intensities of pollen tube competition.
Molecular Characterization of African Swine Fever Virus, China, 2018

On August 3, 2018, an outbreak of African swine fever in pigs was reported in China. We subjected a virus from an African swine fever–positive pig sample to phylogenetic analysis. This analysis showed that the causative strain belonged to the p72 genotype II and CD2v serogroup 8.
FORMATION OF HAPLOID EMBRYOIDS OF LARIX DECIDUA: EARLY EMBRYOGENESIS.

Callus initiation of sections of megagametophytes of Larix decidua occurs just below the cut surface and is followed by the formation of one or more long protruding cells. The long cells then divide transversely at their tips to yield small cytoplasmically-dense terminal cells. The latter divide, forming loose aggregates of dense cells, microcalli, which develop long cells that radiate in all directions and divide terminally to produce aggregates of small cells. This long/short cell alternation is repeated a few times. Eventually the aggregates divide in a polarized manner producing files of long cells predominantly in one direction. These loosely bundled long cells form a suspensor-like structure. The meristematic small cells continue dividing forming a mass of embryonal cells. This early embryoid eventually turns green and produces both a root and shoot. Haploid embryoids are also derived from long cells in which karyokinesis but not cytokinesis occurs, resulting in a four-nucleate coenocyte. The nuclei migrate to one pole and become surrounded by cell walls. The cells thus formed are the originators of a new embryoid.
XPCA: Extending PCA for a Combination of Discrete and Continuous Variables

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. In this paper, we consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we propose a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We present the new model, its induced likelihood function, and a fitting algorithm which can be applied in the presence of missing data. We demonstrate how to use XPCA to produce an estimated full conditional distribution for each data point, and use this to produce to provide estimates for missing data that are automatically range respecting. We compare the methods as applied to simulated and real-world data sets that have a mixture of discrete and continuous variables.
Awareness, knowledge, social norms, and vaccination intentions among Khmer mother–daughter pairs

ABSTRACT Cervical cancer is caused by human papillomavirus (HPV) infection, which can be prevented by vaccination. Mothers play an important role in promoting vaccination and health education. However, Cambodian American mothers reported to have challenges to play a role as primary health educators due to lack of health knowledge and language and cultural gaps. Therefore, this study aims to understand the Cambodian American daughters’ and mothers’ awareness, knowledge and social norms of HPV vaccination and their health communication and vaccination decision-making. We conducted a pilot randomized clinical trial to promote HPV vaccination. In this study, we have only reported findings from baseline data examining individual, interpersonal and social determinants of HPV vaccination behavior among 19 dyads of Cambodian American mothers and daughters. Both mothers and daughters demonstrated low levels of awareness and knowledge. A significant relationship was found between the daughters’ HPV vaccine decisions and their perception of their mothers’ intention on HPV vaccination for them. Culturally and linguistically appropriate communication strategies such as storytelling or visual presentation approaches may be more effective than the current practice of using information-based written materials to promote HPV vaccination and health education among Cambodian Americans.
Assessment of Preventive Behavior for Cervical Cancer with the Health Belief Model

Introduction: Cervical cancer is a leading cause of death in developing countries. However, taking regular Pap smears screening, one of the most effective screening tests, can reduce chance of cervical cancer remarkably. The first step in health education is choosing the right model, one of the best known being the Health Belief Model (HBM). Here, we evaluated different HBM factors with regard to cervical cancer preventive behavior in Fasa, Iran. Materials and methods: This cross-sectional study included 200 married women between the ages of 17 and 64 in Fasa during 2013. Participants were selected through stratified sampling from urban health centers. The questionnaire of the HBM included four sections and was filled out by interview. Data analysis was with SPSS 21, ANOVA and t-tests and Internal correlations between components of model were analyzed in terms of the Spearman Pearson correlation coefficient. Results: The mean age of the participants was 35.6±9.89 years. Some 52% had undergone a Pap test. The percentage values for participants’ knowledge and perceived susceptibility were 49.5% and 46%, respectively, considered as moderate. Perceived severity and benefits were good at 56.5% and 73%, respectively, while perceived barriers was moderate (46.5%). Also, 57.1% demonstrated a good self-efficacy and 61% good behavior. Discussion: We found that the most predictable factors for knowledge and behavior were age, income level, perceived benefits, perceived severity and self-efficacy according to the HBM. These factors should be taken into account for achieving acceptable preventive behaviors in health programs.
PLANTING DENSITY INFLUENCES THE EXPRESSION OF GENETIC VARIATION IN SEED MASS IN WILD RADISH (RAPHANUS SATIVUS L.: BRASSICACEAE).

To determine the effects of density, genotype, and their interaction on individual seed mass in Raphanus sativus L. , we replicated maternal and paternal families of seed across two planting densities in an experimental garden. Seeds were produced by a nested breeding design performed in the greenhouse. Among garden-raised plants, density had a strong negative effect on the mass of seeds produced. At low density, the identity of the greenhouse-grown maternal plants had a strong effect on F2 seed mass, while in high-density plots, there were no significant parental effects on mean seed mass. Significant parental genotype density interactions contributed to variation in F2 seed mass. Norms of reaction for each of the 15 paternal sibships illustrate paternal family density interactions. Three sibships exhibited significant declines in mean seed mass with increasing density; 12 sibships showed no change. Maternal family density interaction effects on seed mass were also detected; among maternal sibships, mean seed mass at low density was negatively correlated with mean seed mass at high density. These results demonstrate: a) planting density has a strong effect on mean individual seed mass produced by adults; b) density influences the magnitude of maternal effects on progeny phenotype; and c) genotype density interactions influence seed mass, potentially contributing to the maintenance of maternal genetic variation in seed mass in natural populations of wild radish.
Scientific Data Analysis Using Data-Intensive Scalable Computing: The SciDISC Project

Data-intensive science requires the integration of two fairly different paradigms: high-performance computing (HPC) and data-intensive scalable computing (DISC), as exemplified by frameworks such as Hadoop and Spark. In this context, the SciDISC project addresses the grand challenge of scientific data analysis using DISC, by developing architectures and methods to combine simulation and data analysis. SciDISC is an ongoing project between Inria, several research institutions in Rio de Janeiro and NYU. This paper introduces the motivations and objectives of the project, and reports on the first results achieved so far.
An Approach to Extracting Thematic Views from Highly Heterogeneous Sources of a Data Lake

In the last years, data lakes are emerging as an eﬀective and eﬃcient support for information and knowledge extraction from a huge amount of highly heterogeneous and quickly changing data sources. Data lake management requires the deﬁnition of new techniques, very diﬀerent from the ones adopted for data warehouses in the past. One of the main issues to address in this scenario consists in the extraction of thematic views from the (very heterogeneous and generally unstructured) data sources of a data lake. In this paper, we propose a new network-based model to uniformly represent structured, semi-structured and unstructured sources of a data lake. Then, we present a new approach to, at least partially, “structure” unstructured data. Finally, we deﬁne a technique to extract thematic views from the sources of a data lake, based on similarity and other semantic relations among the metadata of data sources.
Dependency-based Query Result Approximation (Extended Abstract)

Failing queries are database queries returning few o no results. It might be useful reformulating them in order to retrieve results that are close to those intended with original queries. In this paper, we introduce an approach for rewriting failing queries that are in the disjunctive normal form. In particular, the approach prescribes to replace some of the attributes of the failing queries with attributes semantically related to them by means of Relaxed Functional Dependencies (rfds), which can be automatically discovered from data. The semantics of automatically discovered rfds allow us to rank them in a way to provide an application order during the query rewriting process. Experiments show that such application order of rfds yields a ranking of the approximate query answers meeting the expectations of the user.
Insulin-Like Peptides Regulate Feeding Preference and Metabolism in Drosophila

Fruit flies have eight identified Drosophila insulin-like peptides (DILPs) involved in regulation of carbohydrate concentrations in hemolymph as well as accumulation of storage metabolites. In the present study, we investigated diet-dependent roles of DILPs encoded by genes dilp1–5, and dilp7 in regulation of insect appetite, food choice, accumulation of triglycerides, glycogen, glucose, and trehalose in fruit fly body and carbohydrates in hemolymph. We found that dilp2 gene predominantly influences body glycogen level, dilp3 – trehalose level in hemolymph, while dilp5 and dilp7 affect triglyceride level. Fruit fly appetite was found to be regulated by dilp3 and dilp7 genes. Our data contribute to the understanding of Drosophila as a model for further studies of metabolic diseases and may serve as a guide for uncovering the evolution of metabolic regulatory pathways. HIGHLIGHTS Different Drosophila insulin-like peptides play distinctive roles in metabolism, physiology and appetite regulation. Lack of Dilp2 and Dilp5 abrogates glycogen accumulation on high carbohydrate diets Lack of Dilp3 leads to build-up of trehalose in haemolymph on high-carbohydrate-low-protein diets Lack of Dilp3 and Dilp7 leads to increased consumption of protein on low-carbohydrate-high-protein diets GRAPHICAL ABSTRACT
Human visual pattern recognition of medical images

The output of most medical imaging systems is a display for interpretation by human observers. This paper provides a general summary of recent work on shape recognition by humans. Two broad modes of visual image processing executed by different cortical loci can be distinguished: a) a mode for motor interaction which is sensitive to quantitative variation in image parameters and b) a mode for basic-level object recognition which is based on a small set of qualitative contrasts in viewpoint invariant properties of images edges. Many medical image classifications pose inherently difficult problems for the recognition system in that they are based on quantitative and surface patch variations--rather than qualitative--variations. But when recognition can be achieved quickly and accurately it is possible that a small viewpoint invariant contrast has been discovered and is being exploited by the interpreter.
Rethinking Monocular Depth Estimation with Adversarial Training

Monocular depth estimation is an extensively studied computer vision problem with a vast variety of applications. Deep learning-based methods have demonstrated promise for both supervised and unsupervised depth estimation from monocular images. Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function. In this work, we innovate beyond existing approaches by using adversarial training to learn a context-aware, non-local loss function. Such an approach penalizes the joint configuration of predicted depth values at the patch-level instead of the pixel-level, which allows networks to incorporate more global information. In this framework, the generator learns a mapping between RGB images and its corresponding depth map, while the discriminator learns to distinguish depth map and RGB pairs from ground truth. This conditional GAN depth estimation framework is stabilized using spectral normalization to prevent mode collapse when learning from diverse datasets. We test this approach using a diverse set of generators that include U-Net and joint CNN-CRF. We benchmark this approach on the NYUv2, Make3D and KITTI datasets, and observe that adversarial training reduces relative error by several fold, achieving state-of-the-art performance.
Risk Factors for Prolonged Intensive Care Unit Stay After Open Heart Surgery in Adults.

Objective Prolonged intensive care unit (ICU) stay prevents the use of ICU equipment by other patients and increases hospital cost. This retrospective study aimed to investigate the risk factors for prolonged ICU stay in patients undergoing open heart surgery. Methods The medical records of 513 patients who underwent coronary artery bypass grafting and valvular heart surgery were retrospectively evaluated. Patients were divided into two groups based on their ICU stay: groups I (<48 h) and II (≥48 h). The effect of patient variables on the ICU stay duration was investigated using logistic regression analysis. Results The mean age of the patients was 61.5±10 years, and 69% were males. The ICU stay of ≥48 h was observed in 20.1% of the patients. Diabetes mellitus and low ejection fraction (pre-operative variables); long aortic cross clamp, cardiopulmonary bypass time and intra-aortic balloon pump requirement (intra-operative variables); arrhythmia, myocardial infarction, renal dysfunction and need for haemodialysis, use of ≥2 inotropic agents, infection, sepsis and respiratory complication (post-operative variables) were found to prolong the ICU stay. In multivariate logistic regression analysis, intra-aortic balloon pump requirement, use of ≥2 inotropic agents, post-operative myocardial infarction and need for haemodialysis were found to be independent risk factors for prolonged ICU stay (p<0.05). Early mortality was 0.97% (5 patients). Conclusion Intra-aortic balloon pump requirement, use of ≥2 inotropic agents, post-operative myocardial infarction and need for post-operative haemodialysis are independent risk factors for patients undergoing open heart surgery. Selection of methods for protecting the myocardium and renal functions during the intra-operative period would reduce the duration of ICU stay.
From Hypertree Width to Submodular Width and Data-dependent Structural Decompositions

Identifying the frontier of tractability for conjunctive q ueries is a longstanding question in database theory. Recent advances in st ructural decomposition methods provide an important answer regarding the fixed -parameter tractability of conjunctive queries, and efficient algorithms based o n ecomposition methods have been implemented in database management systems. T he paper shows that these techniques are useful not only for long and comple x qu ries, but even for short and simple ones. Moreover, it sheds some light on so me subtle issues that are not completely understood yet, and discuss open pro blems, such as the gap remaining between the theoretical fixed-parameter poly nomial-time upperbound and the running times required by actual algorithms us ed in practice. 1