Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in‐betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence‐free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re‐sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700× faster than re‐simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300×.
Toxic and genotoxic effects of graphene and multi-walled carbon nanotubes

ABSTRACT Graphene and multi-walled carbon nanotubes (MWCNT) are widely used in nanomedicine, and other fields, due to their unique physicochemical properties including high tensile strength, ultra-light weight, thermal and chemical stability, and reliable semi-conductive electronic properties. Although extensive amount of data exist describing their adverse effects including potential genotoxicity, few studies using gene mutation detection approaches in mammalian cells are available, which represents an important gap for risk estimations. The aim of the present study was to determine the effects of graphene or MWCNT [as pure, carboxyl (COOH) functionalized, and amide (NH2) functionalized] on cytotoxicity, intracellular levels of reactive oxygen species, apoptosis, gene expression changes, and gene mutation induction in L5178Y/Tk+/–3.7. 2C mouse lymphoma cell line. Although some adverse effects were observed at concentrations of 350 and 450 µg/ml, which are excessive and not environmentally relevant levels, no marked effects were detected at concentrations of 250 µg/ml and lower. This is the first study reporting cytotoxicity, mutagenicity, and gene expression findings in the mouse lymphoma cell line for graphene and different MWCNT forms at high concentrations; however, the biological relevance of these observations needs to be assessed following chronic in vivo exposure.
MR-guided Cardiac Interventions

Abstract Diagnostic and interventional cardiac catheterization is routinely used in the diagnosis and treatment of congenital heart disease. There are well-established concerns regarding the risk of radiation exposure to patients and staff, particularly in children given the cumulative effects of repeat exposure. Magnetic resonance imaging (MRI) offers the advantage of being able to provide better soft tissue visualization, tissue characterization, and quantification of ventricular volumes and vascular flow. Initial work using MRI catheterization employed fusion of x-ray and MRI techniques, with x-ray fluoroscopy to guide catheter placement and subsequent MRI assessment for anatomical and hemodynamic assessment. Image overlay of 3D previously acquired MRI datasets with live fluoroscopic imaging has also been used to guide catheter procedures. Hybrid x-ray and MRI-guided catheterization paved the way for clinical application and validation of this technique in the assessment of pulmonary vascular resistance and pharmacological stress studies. Purely MRI-guided catheterization also proved possible with passive catheter tracking. First-in-man MRI-guided cardiac catheter interventions were possible due to the development of MRI-compatible guidewires, but halted due to guidewire limitations. More recent developments in passive and active catheter tracking have led to improved visualization of catheters for MRI-guided catheterization. Improvements in hardware and software have also increased image quality and scanning times with better interactive tools for the operator in the MRI catheter suite to navigate through the anatomy as required in real time. This has expanded to MRI-guided electrophysiology studies and radiofrequency ablation in humans. Animal studies show promise for the utility of MRI-guided interventional catheterization. Ongoing investment and development of MRI-compatible guidewires will pave the way for MRI-guided diagnostic and interventional catheterization coming into the mainstream.
fMRI of Working Memory in Military Traumatic Brain Injury

J. Graner, H. Pan, P-H. Yeh, B. Wang, T. R. Oakes, W. Liu, L. M. French, F. Munter, and G. Riedy TBI Image Analysis Lab, Uniformed Services University of the Health Sciences / HJF, Bethesda, MD, United States, National Capital Neuroimaging Consortium, Walter Reed Army Medical Center, Washington, DC, United States, Defense and Veterans Brain Injury Center, Walter Reed Army Medical Center, Washington, DC, United States, National Intrepid Center of Excellence, Bethesda, MD, United States
Peer and Vicarious Framing, Problematization, and Situated Learning in Online Professional Masters Courses

This paper presents a socio-cognitive view of collaborative knowledge-building in situated learning. We explore the modes of interaction encountered in an online, professionally-oriented graduate Project Management course, to understand how we can provide the conditions for vicarious and peer knowledge-building that situates learning within the context, cultural identity, and practices of real-world, professional work environments.
Using mHealth to Increase Treatment Utilization Among Recently Incarcerated Homeless Adults (Link2Care): Protocol for a Randomized Controlled Trial

Background There is a significant revolving door of incarceration among homeless adults. Homeless adults who receive professional coordination of individualized care (ie, case management) during the period following their release from jail experience fewer mental health and substance use problems, are more likely to obtain stable housing, and are less likely to be reincarcerated. This is because case managers work to meet the various needs of their clients by helping them to overcome barriers to needed services (eg, food, clothing, housing, job training, substance abuse and mental health treatment, medical care, medication, social support, proof of identification, and legal aid). Many barriers (eg, limited transportation, inability to schedule appointments, and limited knowledge of available services) prevent homeless adults who were recently released from incarceration from obtaining available case management, crisis management, substance abuse, and mental health services. Objective The aim of the Link2Care study is to assess the effectiveness of a smartphone app for increasing case management and treatment service utilization, and in turn reduce homelessness and rearrest. The goals of this research are to (1) assess the impact of an innovative smartphone app that will prompt and directly link recently incarcerated homeless adults to community-based case management services and resources and (2) utilize in-person and smartphone-based assessments to identify key variables (eg, alcohol or drug use, social support, psychological distress, and quality of life) that predict continued homelessness and rearrest. Methods Homeless adults (N=432) who enroll in a shelter-based Homeless Recovery Program after release from the Dallas County Jail will be randomly assigned to one of the three treatment groups: (1) usual case management, (2) usual case management plus smartphone, and (3) usual case management with a study-provided smartphone that is preloaded with an innovative case management app (smartphone-based case management). Those assigned to smartphone-based case management will receive smartphones that prompt (twice weekly) connections to shelter-based case managers. The app will also offer direct links to case managers (available during normal business hours) and crisis interventionists (available 24 hours a day, 7 days a week) with the touch of a button. Results Recruitment began in the spring of 2018, and data collection will conclude in 2021. Conclusions This research represents an important step toward integrated service connection and health care service provision for one of the most underserved, high need, and understudied populations in the United States. Trial Registration ClinicalTrials.gov NCT03399500; https://clinicaltrials.gov/ct2/show/NCT03399500 (Archived by WebCite at http://www.webcitation.org/6zSJwdgUS) Registered Report Identifier RR1-10.2196/9868
A Cloud Computing Fault Detection Method Based on Deep Learning

In the cloud computing, in order to provide reliable and continuous service, the need for accurate and timely fault detection is necessary. However, cloud failure data, especially cloud fault feature data acquisition is difficult and the amount of data is too small, with large data training methods to solve a certain degree of difficulty. Therefore, a fault detection method based on depth learning is proposed. An auto-encoder with sparse denoising is used to construct a parallel structure network. It can automatically learn and extract the fault data characteristics and realize fault detection through deep learning. The experiment shows that this method can detect the cloud computing abnormality and determine the fault more effectively and accurately than the traditional method in the case of the small amount of cloud fault feature data.
Development of the variant calling algorithm, ADIScan, and its use to estimate discordant sequences between monozygotic twins

Abstract Calling variants from next-generation sequencing (NGS) data or discovering discordant sequences between two NGS data sets is challenging. We developed a computer algorithm, ADIScan1, to call variants by comparing the fractions of allelic reads in a tester to the universal reference genome. We then created ADIScan2 by modifying the algorithm to directly compare two sets of NGS data and predict discordant sequences between two testers. ADIScan1 detected >99.7% of variants called by GATK with an additional 724 393 SNVs. ADIScan2 identified ∼500 candidates of discordant sequences in each of two pairs of the monozygotic twins. About 200 of these candidates were included in the ∼2800 predicted by VarScan2. We verified 66 true discordant sequences among the candidates that ADIScan2 and VarScan2 exclusively predicted. ADIScan2 detected many discordant sequences overlooked by VarScan2 and Mutect, which specialize in detecting low frequency mutations in genetically heterogeneous cancerous tissues. Numbers of verified sequences alone were >5 times more than expected based on recently estimated mutation rates from whole genome sequences. Estimated post-zygotic mutation rates were 1.68 × 10−7 in this study. ADIScan1 and 2 would complement existing tools in screening causative mutations of diverse genetic diseases and comparing two sets of genome sequences, respectively.
Image Recognition of Disease-Carrying Insects: A System for Combating Infectious Diseases Using Image Classification Techniques and Citizen Science

We propose a system that assists infectious disease experts in the rapid identification of potential outbreaks resulting from arboviruses (mosquito, ticks, and other arthropodborne viruses). The proposed system currently identifies mosquito larvae in images received from citizen scientists. Mosquito-borne viruses, such as the recent outbreak of Zika virus, can have devastating consequences in affected communities. We describe the first implemented prototype of our system, which includes modules for image collection, training of image classifiers, specimen recognition, and expert validation and analytics. The results of the recognition of specimens in images provided by citizen scientists can be used to generate visualizations of geographical regions of interest where the threat of an arbovirus may be imminent. Our system uses state-of-the-art image classification algorithms and a combination of mobile and desktop applications to ensure that crucial information is shared appropriately and accordingly among its users.
Receptor-Like Cytoplasmic Kinases Directly Link Diverse Pattern Recognition Receptors to the Activation of Mitogen-Activated Protein Kinase Cascades in Arabidopsis[OPEN]

Multiple pattern-recognition receptors activate mitogen-activated protein kinase cascades and trigger disease resistance in plants via direct phosphorylation and positive feedback regulation. Plants deploy numerous cell surface-localized pattern-recognition receptors (PRRs) to perceive host- and microbe-derived molecular patterns that are specifically released during infection and activate defense responses. The activation of the mitogen-activated protein kinases MPK3, MPK4, and MPK6 (MPK3/4/6) is a hallmark of immune system activation by all known PRRs and is crucial for establishing disease resistance. The MAP kinase kinase kinase (MAPKKK) MEKK1 controls MPK4 activation, but the MAPKKKs responsible for MPK3/6 activation downstream of diverse PRRs and how the perception of diverse molecular patterns leads to the activation of MAPKKKs remain elusive. Here, we show that two highly related MAPKKKs, MAPKKK3 and MAPKKK5, mediate MPK3/6 activation by at least four PRRs and confer resistance to bacterial and fungal pathogens in Arabidopsis thaliana. The receptor-like cytoplasmic kinases VII (RLCK VII), which act downstream of PRRs, directly phosphorylate MAPKKK5 Ser-599, which is required for pattern-triggered MPK3/6 activation, defense gene expression, and disease resistance. Surprisingly, MPK6 further phosphorylates MAPKKK5 Ser-682 and Ser-692 to enhance MPK3/6 activation and disease resistance, pointing to a positive feedback mechanism. Finally, MEKK1 Ser-603 is phosphorylated by both RLCK VII and MPK4, which is required for pattern-triggered MPK4 activation. These findings illustrate central mechanisms by which multiple PRRs activate MAPK cascades and disease resistance.
A new fast real‐time PCR method for the identification of three sibling Apodemus species (A. sylvaticus, A. flavicollis, and A. alpicola) in Italy

Abstract The identification of field mice Apodemus flavicollis, Apodemus sylvaticus, and Apodemus alpicola represents a challenge for field scientists due to their highly overlapping morphological traits and habitats. Here, we propose a new fast real‐time PCR method to discriminate the three species by species‐specific TaqMan assays. Primers and probes were designed based on the alignment of 54 cyt‐b partial sequences from 25 different European countries retrieved from GenBank. TaqMan assays were then tested on 133 samples from three different areas of Italy. Real‐time PCR analysis showed 92 samples classified as A. flavicollis, 13 as A. sylvaticus, and 28 as A. alpicola. We did not observe any double amplification and DNA sequencing confirmed species assignment obtained by the TaqMan assays. The method is implementable on different matrices (ear tissues, tail, and blood). It can be used on dead specimens or on alive animals with minimally invasive sampling, and given the high sensitivity, the assay may be also suitable for degraded or low‐DNA samples. The method proved to work well to discriminate between the species analyzed. Furthermore, it gives clear results (amplified or not) and it does not require any postamplification handling of PCR product, reducing the time needed for the analyses and the risk of carryover contamination. It therefore represents a valuable tool for field ecologists, conservationists, and epidemiologists.
Diminished gallbladder filling, increased fecal bile acids, and promotion of colon epithelial cell proliferation and neoplasia in fibroblast growth factor 15-deficient mice

Fibroblast growth factor-19 (human FGF19; murine FGF15) suppresses bile acid synthesis. In FGF19 deficiency, diarrhea resulting from bile acid spillage into the colon mimics irritable bowel syndrome. To seek other consequences of FGF19/15 deficiency, we used Fgf15-/- and wild-type (WT) mice to assess gallbladder filling, the bile acid pool, fecal bile acid levels, and colon neoplasia. We fasted mice for six hours before assessing gallbladder size by magnetic resonance imaging (MRI). We measured bile acid levels in different compartments by enzymatic assay, and induced colon neoplasia with azoxymethane (AOM)/dextran sodium sulfate (DSS) and quantified epithelial Ki67 immunostaining and colon tumors 20 weeks later. In vivo MRI confirmed the gross finding of tubular gallbladders in FGF15-deficient compared to WT mice, but fasting gallbladder volumes overlapped. After gavage with a bile acid analogue, ex vivo MRI revealed diminished gallbladder filling in FGF15-deficient mice (P = 0.0399). In FGF15-deficient mice, the total bile acid pool was expanded 45% (P <0.05) and fecal bile acid levels were increased 2.26-fold (P <0.001). After AOM/DSS treatment, colons from FGF15-deficient mice had more epithelial cell Ki67 staining and tumors (7.33 ± 1.32 vs. 4.57 ± 0.72 tumors/mouse; P = 0.003 compared to WT mice); carcinomas were more common in FGF15-deficient mice (P = 0.01). These findings confirm FGF15, the murine homolog of FGF19, plays a key role in modulating gallbladder filling and bile acid homeostasis. In a well-characterized animal model of colon cancer, increased fecal bile acid levels in FGF15-deficient mice promoted epithelial proliferation and advanced neoplasia.
A single chip multi-function sensor system for wood inspection

Image processing is used in the forest products industry to detect various defects on wood surfaces. Normally, several different sensors are needed, which makes the systems complicated and expensive. In this paper we present a highly integrated sensor system for wood defect detection based on a single MAPP2200 smart sensor. Five different measuring principles are simultaneously utilized to detect surface grayscale, surface roughness, deviating grain direction, 3D-profile and surface density. Using a pixel resolution of 1/spl times/0.5 mm, the sensor permits scanning of boards at the speed of several meters per second.
Music Recommender Systems Challenges and Opportunities for Non-Superstar Artists

Music Recommender Systems (MRS) are important drivers in music industry and are widely adopted by music platforms. Other than most MRS research exploring MRS from a technical or from a consumers’ perspective, this work focuses on the impact, value generation, challenges and opportunities for those, who contribute the core value, i.e. the artists. We outline the non-superstar artist’s perspective on MRS, and explore the question if and how non-superstar artists may benefit from MRS to foster their professional advancement. Thereby, we explain several techniques how MRS generate recommendations and discuss their impact on non-superstar artists.
TopRank: A practical algorithm for online stochastic ranking

Online learning to rank is a sequential decision-making problem where in each round the learning agent chooses a list of items and receives feedback in the form of clicks from the user. Many sample-efficient algorithms have been proposed for this problem that assume a specific click model connecting rankings and user behavior. We propose a generalized click model that encompasses many existing models, including the position-based and cascade models. Our generalization motivates a novel online learning algorithm based on topological sort, which we call TopRank. TopRank is (a) more natural than existing algorithms, (b) has stronger regret guarantees than existing algorithms with comparable generality, (c) has a more insightful proof that leaves the door open to many generalizations, (d) outperforms existing algorithms empirically.
A new scheme for massively parallel image analysis

This paper presents a new reconfigurable architecture for image analysis: the associative mesh. This architecture provides powerful computational primitives that can apply an associative operator over the connex sets of a graph. These primitives can be easily and efficiently realised in hardware by means of asynchronous operations and are adapted to a large number of image analysis primitives. As an example, the implementation of distance transforms is described.
Computational development of a molecular-based approach to improve risk stratification of endometrial cancer patients

Histological classification and staging are the gold standard for the prognosis of endometrial cancer (EC). However, in morphologically intermediate and doubtful cases this approach results largely insufficient, defining the need for better classification criteria. In this work we developed an algorithm that based on EC genetic alterations and in combination with the current histological classification, improves EC patients prognostic stratification, in particular in doubtful cases. A panel of 26 cancer related genes was analyzed in 89 EC patients and somatic functional mutations were investigated in association with different histology and outcome. An unsupervised hierarchical clustering analysis revealed that two groups of patients with different tumor grade and different prognosis can be distinguished by mutational profile. In particular, the mutational status of APC, CTNNB1, PIK3CA, PTEN, SMAD4 and TP53 resulted to be principal drivers of prognostic clustering. Consistently, a decisional tree generated by a data mining approach summarizes the consequential molecular criteria for patients prognostic stratification. The model proposed by this work provides the clinician with a tool able to support the prognosis of EC patients and consequently drives the choice of the most appropriated therapeutic strategy and follow up.
From Anticipation to the Experience of Pain: The Importance of Visceral Versus Somatic Pain Modality in Neural and Behavioral Responses to Pain-Predictive Cues

Objective The aim of this study was to compare behavioral and neural anticipatory responses to cues predicting either somatic or visceral pain in an associative learning paradigm. Methods Healthy women (N = 22) underwent functional magnetic resonance imaging. During an acquisition phase, two different visual cues repeatedly signalled either experimental visceral or somatic pain. In a subsequent extinction phase, identical cues were presented without pain. Before and after each phase, cue valence and contingency awareness were assessed on visual analog scales. Results Visceral compared to somatic pain–predictive cues were rated as more unpleasant after acquisition (visceral, 32.18 ± 13.03 mm; somatic, −18.36 ± 10.36 mm; p = .021) with similarly accurate cue-pain contingencies. After extinction, cue valence returned to baseline for both modalities (visceral, 1.55 ± 9.81 mm; somatic, −18.45 ± 7.12; p = .41). During acquisition, analyses of cue-induced neural responses revealed joint neural activation engaging areas associated with attention processing and cognitive control. Enhanced deactivation of posterior insula to visceral cues was observed, which correlated with enhanced responses within the salience network (anterior cingulate cortex, anterior insula) during visceral compared to somatic pain stimulation. During extinction, both pain modalities induced anticipatory neural activation in the extinction and salience network (all pFWE values < .05). Conclusions Conditioned emotional responses to pain-predictive cues are modality specific and enhanced for the visceral modality, suggesting that pain anticipation is shaped by the salience of painful stimuli. Common but also modality-specific neural mechanisms are involved during cue-pain learning, whereas extinction of cued responses seems unaffected by modality. Future research should examine potential implications for the pathophysiology of chronic pain conditions, especially chronic visceral pain.