Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive map-based model: Toward a developmental framework for self-driving cars

End-to-end learning and multi-sensor fusion-based methods are two major frameworks used for self-driving cars. To enable these intelligence vehicles to acquire driving skills at a level comparable to that of human drivers, long short-term memory of previous self-driving processes is necessary, but is difficult to introduce into the above-mentioned frameworks. In this paper, we propose a model for self-driving cars called the cognitive map-based neural network (CMNN). Our framework consists of three parts: a convolutional neural network that can perceive the environment in the manner that the human visual cortex does, a cognitive map to describe the locations of objects in a complex traffic scene and the relationships among them, and a recurrent neural network to process long short-term memory from the cognitive map, which is updated in real time. The proposed model is built to simultaneously handle three tasks: i) detecting free space and lane boundaries, ii) estimating vehicle pose and obstacle distance, and iii) learning to plan and control based on the behaviors of a human driver. More significantly, our approach introduces external instructions during an end-to-end driving process. To test it, we created a large-scale road vehicle dataset (RVD) containing more than 50,000 labeled road images captured by three cameras. We implemented the proposed model on an embedded system.
Transport Properties and Novel Sensing Applications of Organic Semiconducting Crystals

The present thesis is focused on the study of Organic Semiconducting Single Crystals (OSSCs) and crystalline thin films. In particular solution-grown OSSC, e.g. 4-hdroxycyanobenzene (4HCB) have been characterized in view of their applications as novel sensors of X-rays, gamma-rays, alpha particles radiations and chemical sensors. In the field of ionizing radiation detection, organic semiconductors have been proposed so far mainly as indirect detectors, i.e. as scintillators or as photodiodes. I first study the performance of 4HCB single crystals as direct X-ray detector i.e. the direct photon conversion into an electrical signal, assessing that they can operate at room temperature and in atmosphere, showing a stable and linear response with increasing dose rate. A dedicated study of the collecting electrodes geometry, crystal thickness and interaction volume allowed us to maximize the charge collection efficiency and sensitivity, thus assessing how OSSCs perform at low operating voltages and offer a great potential in the development of novel ionizing radiation sensors. To better understand the processes generating the observed X-ray signal, a comparative study is presented on OSSCs based on several small-molecules: 1,5-dinitronaphthalene (DNN), 1,8-naphthaleneimide (NTI), Rubrene and TIPS-pentacene. In addition, the proof of principle of gamma-rays and alpha particles has been assessed for 4HCB single crystals. I have also carried out an investigation of the electrical response of OSSCs exposed to vapour of volatile molecules, polar and non-polar. The last chapter deals with rubrene, the highest performing molecular crystals for electronic applications. We present an investigation on high quality, millimeter-sized, crystalline thin films (10 – 100 nm thick) realized by exploiting organic molecular beam epitaxy on water-soluble substrates. Space-Charge-Limited Current (SCLC) and photocurrent spectroscopy measurements have been carried out. A thin film transistor was fabricated onto a Cytop® dielectric layer. The FET mobility exceeding 2 cm2/Vs, definitely assess the quality of RUB films.
Integration of GPS and satellite images for detection and classification of fleet hotspots

Hotspot detection and classification for a fleet of vehicles is usually performed based on GPS data sampled from the vehicles. In this paper, we explore how the integration of satellite images can improve GPS-based hotspot classification. We propose a system composed of a deep Convolutional Neural Network (CNN) for image classification and a Random Forest classifier that combines GPS-based features with the CNN output for hotspot classification. We introduce also a novel metric for scoring place detection and classification systems, able to account for both detection and classification errors. The new metric is used to assess experimentally the effectiveness of our system in combining the two sources of information.
CHARACTERIZATION OF GYMNODINIUM MIKIMOTOI (DINOPHYCEAE) NUCLEI AND IDENTIFICATION OF THE MAJOR HISTONE‐LIKE PROTEIN, HGm

A simple and rapid method for isolation of nuclei from Gymnodinium mikimotoi Miyake et Kominami ex Oda is described along with chemical characterization of the nuclei. The isolated nuclei were completely free of whole cells, 99.96% free of cytoplasmic contamination, and were collected with a yield of 40% from harvested whole cells. Each nucleus contained 47 pg of DNA and the ratio of DNA to acid‐soluble proteins to acid‐insoluble proteins was 1:0.25:1.21, respectively. SDS electrophoresis of acid‐extracted proteins showed one histone‐like protein, which we termed HGm, with an apparent molecular mass of 12 kDa. V8 protease digestion analysis of HGm, the histone‐like protein from Crypthecodinium cohnii (HCc), and two histone‐like proteins from Gymnodinium dorsum, showed that the HGm digestion pattern was more similar to that of HCc than to that of either of the G. dorsum histone‐like proteins.
Enhancing Smoking Risk Communications: The Influence of Health Literacy and Message Content.

Background Efforts are needed to ensure that smokers with lower health literacy are provided with understandable and impactful information about the health consequences of smoking and benefits of quitting. Purpose To test the influence of health literacy on smokers' responses to health risk messages manipulated on framing (gain vs. loss) and emotionality (factual vs. emotional). Methods Participants (N = 402) were randomized to evaluate one of four sets of smoking risk messages (factual gain-framed, factual loss-framed, emotional gain-framed, or emotional loss-framed). Multiple linear regressions examined main effects of health literacy, message emotionality, and message framing on: (a) risk perceptions, (b) behavioral expectations (i.e. cut down, limit, quit), and (c) risk knowledge. Two-way interactions of health literacy with emotionality and framing were examined for these outcomes. Analyses were based on theory-driven, a priori hypotheses. Results As hypothesized, main effects emerged such that smokers with higher health literacy reported stronger risk perceptions and knowledge retention regardless of message type. Additionally, emotional (vs. factual) and gain- (vs. loss-) framed messages were associated with certain lower risk perceptions regardless of health literacy level. Consistent with hypotheses, two-way crossover interactions emerged between health literacy and emotionality. Among smokers with higher health literacy, factual messages produced higher perceived risk and stronger expectations for quitting. Among smokers with lower health literacy, emotional messages produced higher perceived risk and stronger expectations for quitting. Conclusions Health literacy plays an important role in influencing how smokers respond to different risk messages. One's health literacy should be considered when determining whether risk communications emphasize factual or emotional content.
Sensitivity and specificity of a quantitative point of care D-dimer assay using heparinized whole blood, in patients with clinically suspected deep vein thrombosis

Summary D-dimer assays are efficient in the exclusion diagnostics of deep vein thrombosis (DVT) in patients without severe concomitant diseases. We have determined diagnostic sensitivity and specificity of a new point-of-care rapid assay for quantitative determination of D-dimer in heparinized whole blood in outpatients with suspected DVT. In 19 participating centers, 637 patients were included in the study, of which 77 were excluded, the majority because of inadequate documentation of analytical quality control measures. DVT was diagnosed in 223 of the remaining 560 patients by duplex ultrasound examination. The POC D-dimer assay showed a high sensitivity of 96.9% for the diagnosis of DVT and a high specificity of 60.8% at a pre-specified cutoff of 0.5 µg/ml. For Tina-quant D-dimer, sensitivity was slightly lower at 94.9%, with a specificity of 64.8%. The VIDAS D-dimer assay showed a sensitivity of 98.2%, but specificity was 40.7%. The area under the curve (AUC ± standard error, 95% confidence interval) was 0.879 ± 0.019 (0.845–0.909) for POC D-dimer, 0.908 ± 0.016 (0.877–0.934) for Tina-quant D-dimer, and 0.895± 0.018 (0.862–0.922) forVIDAS D-dimer. Differences were not statistically significant. The new whole blood POC D-dimer assay isa reliable tool for exclusion of DVT in symptomatic outpatients, displaying a comparable diagnostic performance as VIDAS D-dimer and Tina-quant D-dimer assays. Roche CARDIAC and TINA-QUANT are tradenames of Roche.
Identification of ALK Rearrangements in Malignant Peritoneal Mesothelioma

Importance Malignant peritoneal mesothelioma is a rare, aggressive tumor arising from the peritoneal lining, induced by asbestos, therapeutic radiation, or germline mutations. Nevertheless, the molecular features remain largely unknown. Objective To investigate anaplastic lymphoma kinase (ALK) rearrangements in a large series of peritoneal mesothelioma and characterize the mutational landscape of these tumors. Design, Setting, and Participants We studied 88 consecutive patients (39 men, 49 women; median age 61, range 17-84 years) with peritoneal mesotheliomas diagnosed at a single institution between 2005 and 2015. We identified ALK-positive mesotheliomas by immunohistochemistry and confirmed ALK rearrangement by fluorescence in situ hybridization (FISH). In ALK-rearranged cases, we characterized the fusion partners using targeted next-generation sequencing of both tumor DNA and RNA. In select cases, we quantified asbestos fibers by combined scanning electron microscopy and x-ray spectroscopy. We also explored ALK rearrangement in a separate series of 205 patients with pleural mesothelioma. Main Outcomes and Measures Identification and characterization of novel ALK rearrangements and correlations with clinicopathologic characteristics. Results Anaplastic lymphoma kinase was positive by immunohistochemistry in 11 (13%) peritoneal mesotheliomas (focal weak in 8, diffuse strong in 3). In focal weak ALK-positive cases, no ALK rearrangement was detected by FISH or next-generation sequencing. In strong diffuse ALK-positive cases, FISH confirmed ALK rearrangements, and next-generation sequencing identified novel fusion partners ATG16L1, STRN, and TPM1. Patients with ALK-rearranged peritoneal mesotheliomas were women and younger than patients without ALK rearrangement (median age 36 vs 62; Mann-Whitney test, P = .02), but all other clinicopathologic characteristics (size of tumor nodules, histology, treatment, and survival) were not different. No asbestos fibers were detected in ALK-rearranged cases. Furthermore, loss of chromosomal region 9p or 22q or genetic alterations in BAP1, SETD2, or NF2 typically present in peritoneal mesothelioma were absent in the ALK-rearranged cases. All pleural mesotheliomas were ALK-negative by immunohistochemistry. Conclusions and Relevance We identified unique ALK rearrangements in a subset of patients with peritoneal mesothelioma, each lacking asbestos fibers, therapeutic radiation, and cytogenetic and molecular alterations typically found in these tumors. Identification of clinically actionable ALK rearrangements may represent a novel pathogenetic mechanism of malignant peritoneal mesothelioma with promise for targeted therapy.
Enriching Frame Representations with Distributionally Induced Senses

We introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora. These features are extracted from distributionally induced sense inventories and subsequently linked to the manually-constructed frame representations to boost the performance of frame disambiguation in context. Since Framester is a frame-based knowledge graph, which enables full-fledged OWL querying and reasoning, our resource paves the way for the development of novel, deeper semantic-aware applications that could benefit from the combination of knowledge from text and complex symbolic representations of events and participants. Together with the resource we also provide the software we developed for the evaluation in the task of Word Frame Disambiguation (WFD).
A convolutional neural network for traffic information sensing from social media text

Mining social media data to obtain traffic relevant information is an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific issue in social media mining that concerns to extract traffic relevant microblogs from the Sina Weibo platform, which is the first and essential step to further extract detailed traffic information, such as the location of a traffic incident. It is transformed into a machine learning problem of short text classification. We employ deep neural networks to classify microblogs into traffic relevant and traffic irrelevant ones. More specifically, we firstly adopt the continuous bag-of-word (CBOW) model to learn word embedding representations based on the dataset of three billion unlabeled microblogs. Next we use a convolutional neural network (CNN) to learn the abstract features of traffic relevant and traffic irrelevant microblogs. The key advances in this paper are: use of semantics of words and deployment of deep neural networks to extract traffic information from social media text. Experiments show that the proposed deep learning method has superior performance over support vector machine (SVM) based method and multi-layer perceptron (MLP) based method.
Preliminary Insights into the Phylogeography of Six Aquatic Hyphomycete Species

Aquatic hyphomycetes occur worldwide on a wide range of plant substrates decomposing in freshwaters, and are known to play a key role in organic matter turnover. The presumed worldwide distribution of many aquatic hyphomycete species has been based on morphology-based taxonomy and identification, which may overlook cryptic species, and mask global-scale biogeographical patterns. This might be circumvented by using DNA sequence data. The internal transcribed spacer (ITS) region from rDNA was recently designated as the most suitable barcode for fungal identification. In this study, we generated ITS barcodes of 130 isolates belonging to 6 aquatic hyphomycete species (Anguillospora filiformis, Flagellospora penicillioides, Geniculospora grandis, Lunulospora curvula, Tetrachaetum elegans and Tricladium chaetocladium), and collected from streams of Southwest Europe (86 isolates) and East Australia (44 isolates). European and Australian populations of 4 species (A. filiformis, F. penicillioides, G. grandis and T. elegans) grouped into different clades, and molecular diversity indices supported significant differentiation. Continents did not share haplotypes, except for T. chaetocladium. Overall results show substantial population diversity for all tested species and suggests that the biogeography of aquatic hyphomycetes may be species-specific.
Conditioned Media From Adipose Tissue Derived Mesenchymal Stem Cells Reverse Insulin Resistance in Cellular Models

The link between insulin resistance (IR) and type 2 diabetes has been recognized for a long time. Type 2 diabetes is often associated with basal hyperinsulinemia, reduced sensitivity to insulin, and disturbances in insulin release. There are evidences showing the reversal of IR by mesenchymal stem cells. However, the effect of conditioned media from adipose derived mesenchymal stem cells (ADSCs‐CM) in reversal of IR has not been established. We established an insulin resistant model of 3T3L1 and C2C12 cells and treated with ADSCs‐CM. 2‐NBDG (2‐[N‐[7‐Nitrobenz‐2‐oxa‐1,3‐diazol‐4‐yl]Amino]‐2‐Deoxyglucose) uptake was performed to assess improvement in glucose uptake. Genes involved in glucose transport and in inflammation were also analysed. Western blot for glucose transporter‐4 and Akt was performed to evaluate translocation of Glut4 and insulin signaling respectively. We found that the ADSCs‐CM treated cells restored insulin, stimulated glucose uptake as compared to the untreated control indicating the insulin sensitizing effect of the CM. The treated cells also showed inhibition adipogenesis in 3T3L1 cells and significant reduction of intramuscular triglyceride accumulation in C2C12 cells. Gene expressions studies revealed the drastic upregulation of GLUT4 gene and significant reduction in IL6 and PAI1 gene in both 3T3L1 and C2C12 cells, indicating possible mechanism of glucose uptake with concomitant decrease in inflammation. Enhancement of GLUT4 and phospho Akt protein expression seems to be responsible for the increment in glucose uptake and enhanced insulin signaling, respectively. Our study revealed for the first time that ADSCs‐CM acts as an alternative insulin sensitizer providing stem cell solution to IR. J. Cell. Biochem. 118: 2037–2043,2017. © 2016 Wiley Periodicals, Inc.
Novel Susceptibility Loci for Moyamoya Disease Revealed by a Genome-Wide Association Study

Background and Purpose— Moyamoya disease (MMD) is a rare cerebral vasculopathy characterized by bilateral internal carotid artery stenosis and often leads to stroke in children or young adults. Although familial inheritance is well recognized, the genetic basis of MMD remains poorly understood. Methods— A 2-stage genome-wide association study was conducted involving 1492 cases and 5084 controls. In the discovery stage, logistic regression was used to test associations, and imputation was conducted based on genotyped single-nucleotide polymorphisms (SNPs). In the validation stage, the top significant SNPs were again genotyped in an independent cohort. Fixed-effects inverse variance-weighted meta-analysis was used in the combined discovery and validation samples. Furthermore, association analysis was conducted in subgroups using patient clinical data. Results— The study identified 10 novel risk loci with genome-wide significance (P<5×10−8) and confirmed a previously reported locus on 17q25. No significant SNP showed evidence of heterogeneity between the 2 stages. Cumulatively, these SNPs explained 14.76% of disease risk variance—a substantial proportion of the 39.02% of risk variance explained by all genome-wide genotyped SNPs. One SNP, rs9916351 in RNF213 (Pcombined=4.57×10−54; odds ratio, 1.96), showed a stronger genetic effect on early-onset than late-onset MMD (P=0.003). Two novel SNPs in genes regulating homocysteine metabolism, rs9651118 in MTHFR (Pcombined=2.49×10−19; odds ratio, 0.65) and rs117353193 in TCN2 (Pcombined=6.15×10−13; odds ratio, 1.43), were associated with high-serum homocysteine in MMD cases. Additionally, another SNP associated with MMD (rs2107595 in HDAC9; Pcombined=1.49×10−29; odds ratio, 1.64) was previously implicated in large-vessel disease. Tissue enrichment analysis showed that the genes of associated loci were highly expressed in the immune system (false discovery rate, <0.05). Conclusions— This study identifies several novel susceptibility genes for MMD. The association with homocysteine metabolism and the immune system enrichment of susceptibility gene expression suggest that therapeutic interventions targeting these pathways may be effective approaches for MMD treatment.
Embedded system for driver behavior analysis based on GMM

The work presented describes a tool for driver behavior analysis. The proposal offers a contribution based on Gaussian Mixture Model (GMM) modeling technique. GMM is a powerful tool for the statistical modeling, which allows to identify specific patterns of behavior in comparison with available data. Data are obtained by the in-vehicle sensors using Controller Area Network bus (CAN bus), an Inertial Measurement Unit (IMU) and a GPS. These data allow to provide driver behavior analysis and aggressive behavior identification. This development has been tested in real-traffic situations with different drivers.
The segmental diffusivity profile of amyotrophic lateral sclerosis associated white matter degeneration

Magnetic resonance diffusivity indices have been repeatedly proposed as biomarkers of neurodegeneration in amyotrophic lateral sclerosis (ALS), but no consensus exists as to which diffusivity parameter is the most sensitive to identify early degenerative changes. Despite numerous studies, surprisingly little is known of the segmental vulnerability of the corticospinal tracts and corpus callosum. Our objective was to characterize the core three‐dimensional white matter signature of ALS, to describe phenotype‐specific patterns of white matter degeneration and to evaluate the diffusivity profile of individual patients and controls in specific white matter segments.
Fabrication of Micro Three Dimensional Structures by Two Photon Polymerization with SiO/Resin

In this paper we presented the synthesis of TEOS with photoresist in order to use it like a hybrid material for 3D printer on the micrometer scale by means of the two-photon polymerization process, in which two photon are absorbed simultaneously by the material using an ultrafast laser causing its polymerization. We analyzed the mix of TEOS and photoresist with UV-VIS and FTIR spectrometers, checking that complies with two important conditions: has an optical transmission at 780 nm and absorbs at 390 nm. Finally we fabricated micro-structures with a new hybrid material; TEOS does not absorb the laser in this system and does not interfere with the formation of a three-dimensional structure. After formation the 3D microstructure, samples were heated to form the SiO. These samples of microstructures were observed under digital microscope and SEM.
Structural and biochemical characterization of Siw14: A protein-tyrosine phosphatase fold that metabolizes inositol pyrophosphates

Inositol pyrophosphates (PP-InsPs) are “energetic” intracellular signals that are ubiquitous in animals, plants, and fungi; structural and biochemical characterization of PP-InsP metabolic enzymes provides insight into their evolution, reaction mechanisms, and regulation. Here, we describe the 2.35-Å-resolution structure of the catalytic core of Siw14, a 5-PP-InsP phosphatase from Saccharomyces cerevisiae and a member of the protein tyrosine-phosphatase (PTP) superfamily. Conclusions that we derive from structural data are supported by extensive site-directed mutagenesis and kinetic analyses, thereby attributing new functional significance to several key residues. We demonstrate the high activity and exquisite specificity of Siw14 for the 5-diphosphate group of PP-InsPs. The three structural elements that demarcate a 9.2-Å-deep substrate-binding pocket each have spatial equivalents in PTPs, but we identify how these are specialized for Siw14 to bind and hydrolyze the intensely negatively charged PP-InsPs. (a) The catalytic P-loop with the CX5R(S/T) PTP motif contains additional, positively charged residues. (b) A loop between the α5 and α6 helices, corresponding to the Q-loop in PTPs, contains a lysine and an arginine that extend into the catalytic pocket due to displacement of the α5 helix orientation through intramolecular crowding caused by three bulky, hydrophobic residues. (c) The general-acid loop in PTPs is replaced in Siw14 with a flexible loop that does not use an aspartate or glutamate as a general acid. We propose that an acidic residue is not required for phosphoanhydride hydrolysis.