ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
UAVM: Towards Unifying Audio and Visual Models

Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). The UAVM achieves a new state-of-the-art audio-visual event classification accuracy of 65.8% on VGGSound. More interestingly, we also find a few intriguing properties of UAVM that the modality-independent counterparts do not have. Code at github.com/yuangongnd/uavm.
Exploring the Potential for Steel Slags Valorisation in an Industrial Symbiosis Perspective at Meso-scale Level

A greater reuse of steel slags would bring considerable benefits both from an environmental and economic point of view. The development of tools and strategies to monitor at different scales resources and waste flows would allow for better resource planning and a more sustainable management on territory. The aim of this study is to investigate and analyse the supply chain that deals with the management of steel slags at meso-level, in order to investigate the state of implementation of industrial symbiosis (IS), its potential and its improvement. A Mass Flow Analysis (MFA) has been implemented, through big data analysis coming from the integration of regional and provincial databases with a careful data processing from questionnaires. This integrated methodology has proved to be a valid tool to monitor the recovery and reuse, the implementation of industrial symbiosis and to plan improvement actions. This paper reports a representation of the current situation regarding the production, recovery and reuse of these materials in production processes for which they are suitable, with a view to their full exploitation, following the principles of circular economy and an analysis of the mutual exchange that occur among steelmaking plants and other business partners in a network of industrial companies. The results showed that most of the steel slags managed at meso-level (Province of Brescia, Italy) is still unfortunately destined for landfill with low percentage of them classified as by-product highlighting as the IS is not adequately applied. Of the slag destined for treatments and recovery processes, almost all of them are Electric Arc Furnace slag, which are mainly reused for hydraulically bound base layers and road sub-bases (about 85% of the total recovered) and as aggregates for the production of cement and bituminous mixes (about 15% of the total recovered). Results shows as further effort should be made in term of policies and strategies to incentivize IS and to increase the recovery.
CNN-Based Real-Time Dense Face Reconstruction with Inverse-Rendered Photo-Realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data.(1) With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.
Targeting the nitric oxide/cGMP signaling pathway to treat chronic pain

Nitric oxide (NO)/cyclic guanosine 3',5'-monophosphate (cGMP) signaling has been shown to act as a mediator involved in pain transmission and processing. In this review, we summarize and discuss the mechanisms of the NO/cGMP signaling pathway involved in chronic pain, including neuropathic pain, bone cancer pain, inflammatory pain, and morphine tolerance. The main process in the NO/cGMP signaling pathway in cells involves NO activating soluble guanylate cyclase, which leads to subsequent production of cGMP. cGMP then activates cGMP-dependent protein kinase (PKG), resulting in the activation of multiple targets such as the opening of ATP-sensitive K+ channels. The activation of NO/cGMP signaling in the spinal cord evidently induces upregulation of downstream molecules, as well as reactive astrogliosis and microglial polarization which participate in the process of chronic pain. In dorsal root ganglion neurons, natriuretic peptide binds to particulate guanylyl cyclase, generating and further activating the cGMP/PKG pathway, and it also contributes to the development of chronic pain. Upregulation of multiple receptors is involved in activation of the NO/cGMP signaling pathway in various pain models. Notably the NO/cGMP signaling pathway induces expression of downstream effectors, exerting both algesic and analgesic effects in neuropathic pain and inflammatory pain. These findings suggest that activation of NO/cGMP signaling plays a constituent role in the development of chronic pain, and this signaling pathway with dual effects is an interesting and promising target for chronic pain therapy.
iCVI-ARTMAP: Using Incremental Cluster Validity Indices and Adaptive Resonance Theory Reset Mechanism to Accelerate Validation and Achieve Multiprototype Unsupervised Representations

This article presents an adaptive resonance theory predictive mapping (ARTMAP) model, which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning, namely, iCVI-ARTMAP. Incorporating iCVIs to the decision-making and many-to-one mapping capabilities of this adaptive resonance theory (ART)-based model can improve the choices of clusters to which samples are incrementally assigned. These improvements are accomplished by intelligently performing the operations of swapping sample assignments between clusters, splitting and merging clusters, and caching the values of variables when iCVI values need to be recomputed. Using recursive formulations enables iCVI-ARTMAP to considerably reduce the computational burden associated with cluster validity index (CVI)-based offline clustering. In this work, six iCVI-ARTMAP variants were realized via the integration of one information-theoretic and five sum-of-squares-based iCVIs into fuzzy ARTMAP. With proper choice of iCVI, iCVI-ARTMAP either outperformed or performed comparably to three ART-based and four non-ART-based clustering algorithms in experiments using benchmark datasets of different natures. Naturally, the performance of iCVI-ARTMAP is subject to the selected iCVI and its suitability to the data at hand; fortunately, it is a general model in which other iCVIs can be easily embedded.
Monitoring Changes in Intracellular Reactive Oxygen Species Levels in Response to Histone Deacetylase Inhibitors

Reactive oxygen species (ROS) are induced by several chemotherapeutics. In this protocol, we describe a flow cytometry-based method for the analysis of the intracellular levels of ROS in vital leukemic cells in response to the histone deacetylase inhibitor vorinostat. This measurement of ROS using the cell-permeable dye CM-H2DCFDA indicates intracellular oxidative stress.
Epigenetic targeting of transposon relics: beating the dead horses of the genome?

Transposable elements (TEs) have been seen as selfish genetic elements that can propagate in a host genome. Their propagation success is however hindered by a combination of mechanisms such as mutations, selection, and their epigenetic silencing by the host genome. As a result, most copies of TEs in a given genome are dead relics: their sequence is too degenerated to allow any transposition. Nevertheless, these TE relics often, but not always, remain epigenetically silenced, and if not to prevent transposition anymore, one can wonder the reason for this phenomenon. The mere self-perpetuating loop inherent to epigenetic silencing could alone explain that even when inactive, TE copies remain silenced. Beyond this process, nevertheless, antagonistic selective forces are likely to act on TE relic silencing. Especially, without the benefit of preventing transposition, TE relic silencing may prove deleterious to the host fitness, suggesting that the maintenance of TE relic silencing is the result of a fine, and perhaps case-by-case, evolutionary trade-off between beneficial and deleterious effects. Ultimately, the release of TE relics silencing may provide a 'safe' ground for adaptive epimutations to arise. In this review, we provide an overview of these questions in both plants and animals.
Exploiting Hardware Multicast and GPUDirect RDMA for Efficient Broadcast

Broadcast is a widely used operation in many streaming and deep learning applications to disseminate large amounts of data on emerging heterogeneous High-Performance Computing (HPC) systems. However, traditional broadcast schemes do not fully utilize hardware features for Graphics Processing Unit (GPU)-based applications. In this paper, a model-oriented analysis is presented to identify performance bottlenecks of existing broadcast schemes on GPU clusters. Next, streaming-based broadcast schemes are proposed to exploit InfiniBand hardware multicast (IB-MCAST) and NVIDIA GPUDirect technology for efficient message transmission. The proposed designs are evaluated in the context of using Message Passing Interface (MPI) based benchmarks and applications. The experimental results indicate improved scalability and up to 82 percent reduction of latency compared to the state-of-the-art solutions in the benchmark-level evaluation. Furthermore, compared to the state-of-the-art, the proposed design yields stable higher throughput for a synthetic streaming workload, and 1.3x faster training time for a deep learning framework.
Neural progenitor cells derived from fibroblasts induced by small molecule compounds under hypoxia for treatment of Parkinson's disease in rats

Neural progenitor cells (NPCs) capable of self-renewal and differentiation into neural cell lineages offer broad prospects for cell therapy for neurodegenerative diseases. However, cell therapy based on NPC transplantation is limited by the inability to acquire sufficient quantities of NPCs. Previous studies have found that a chemical cocktail of valproic acid, CHIR99021, and Repsox (VCR) promotes mouse fibroblasts to differentiate into NPCs under hypoxic conditions. Therefore, we used VCR (0.5 mM valproic acid, 3 μM CHIR99021, and 1 μM Repsox) to induce the reprogramming of rat embryonic fibroblasts into NPCs under a hypoxic condition (5%). These NPCs exhibited typical neurosphere-like structures that can express NPC markers, such as Nestin, SRY-box transcription factor 2, and paired box 6 (Pax6), and could also differentiate into multiple types of functional neurons and astrocytes in vitro. They had similar gene expression profiles to those of rat brain-derived neural stem cells. Subsequently, the chemically-induced NPCs (ciNPCs) were stereotactically transplanted into the substantia nigra of 6-hydroxydopamine-lesioned parkinsonian rats. We found that the ciNPCs exhibited long-term survival, migrated long distances, and differentiated into multiple types of functional neurons and glial cells in vivo. Moreover, the parkinsonian behavioral defects of the parkinsonian model rats grafted with ciNPCs showed remarkable functional recovery. These findings suggest that rat fibroblasts can be directly transformed into NPCs using a chemical cocktail of VCR without introducing exogenous factors, which may be an attractive donor material for transplantation therapy for Parkinson's disease.
A Latency-Optimized Reconfigurable NoC for In-Memory Acceleration of DNNs

In-memory computing reduces latency and energy consumption of Deep Neural Networks (DNNs) by reducing the number of off-chip memory accesses. However, crossbar-based in-memory computing may significantly increase the volume of on-chip communication since the weights and activations are on-chip. State-of-the-art interconnect methodologies for in-memory computing deploy a bus-based network or mesh-based Network-on-Chip (NoC). Our experiments show that up to 90% of the total inference latency of a DNN hardware is spent on on-chip communication when the bus-based network is used. To reduce the communication latency, we propose a methodology to generate an NoC architecture along with a scheduling technique customized for different DNNs. We prove mathematically that the generated NoC architecture and corresponding schedules achieve the minimum possible communication latency for a given DNN. Furthermore, we generalize the proposed solution for edge computing and cloud computing. Experimental evaluations on a wide range of DNNs show that the proposed NoC architecture enables 20%-80% reduction in communication latency with respect to state-of-the-art interconnect solutions.
Security for Grids

Securing a Grid environment presents a distinctive set of challenges. This paper groups the activities that need to be secured into four categories: naming and authentication; secure communication; trust, policy, and authorization; and enforcement of access control. It examines the current state of the art in securing these activities and introduces new technologies that promise to meet the security requirements of Grids more completely.
An adaptive rejection sampler for sampling from the Wiener diffusion model

The Wiener diffusion model with two absorbing boundaries is one of the most frequently applied models for jointly modeling responses and response latencies in psychological research. We consider four methods for sampling from the model with and without variability in drift rate, starting point, and non-decision time: Inverse transform sampling, rejection sampling, and two new methods based on adaptive rejection sampling (ARS). We implement these four methods in an R package, validate the methods, and compare their sampling speed in different settings. All four implemented methods provide samples that follow the intended distributions. The ARS-based methods, however, outperform the other methods in sampling speed as the requested sample size increases. We provide guidelines for when using ARS is more efficient than using traditional methods and vice versa.
A rapid bacterial pathogen and antimicrobial resistance diagnosis workflow using Oxford nanopore adaptive sequencing method

Metagenomic sequencing analysis (mNGS) has been implemented as an alternative approach for pathogen diagnosis in recent years, which is independent of cultivation and is able to identify all potential antibiotic resistance genes (ARGs). However, current mNGS methods have to deal with low amounts of prokaryotic deoxyribonucleic acid (DNA) and high amounts of host DNA in clinical samples, which significantly decrease the overall microbial detection resolution. The recently released nanopore adaptive sampling (NAS) technology facilitates immediate mapping of individual nucleotides to a given reference as each molecule is sequenced. User-defined thresholds allow for the retention or rejection of specific molecules, informed by the real-time reference mapping results, as they are physically passing through a given sequencing nanopore. We developed a metagenomics workflow for ultra-sensitive diagnosis of bacterial pathogens and ARGs from clinical samples, which is based on the efficient selective 'human host depletion' NAS sequencing, real-time species identification and species-specific resistance gene prediction. Our method increased the microbial sequence yield at least 8-fold in all 21 sequenced clinical Bronchoalveolar Lavage Fluid (BALF) samples (4.5 h from sample to result) and accurately detected the ARGs at species level. The species-level positive percent agreement between metagenomic sequencing and laboratory culturing was 100% (16/16) and negative percent agreement was 100% (5/5) in our approach. Further work is required for a more robust validation of our approach with large sample size to allow its application to other infection types.
DCNS: An Adaptable High Throughput RFID Reader-to-Reader Anticollision Protocol

The reader-to-reader collision problem represents a research topic of great recent interest for the radio frequency identification (RFID) technology. Among the state-of-the-art anticollision protocols, the ones that provide high throughput often have special requirements, such as extra hardware. This study investigates new high throughput solutions for static RFID networks without additional requirements. In this paper, two contributions are presented: a new configuration, called Killer, and a new protocol, called distributed color noncooperative selection (DCNS). The proposed configuration generates selfish behavior, thereby increasing channel utilization and throughput. DCNS fully exploits the Killer configuration and provides new features, such as dynamic priority management, which modifies the performance of the RFID readers when it is requested. Simulations have been conducted in order to analyze the effects of the innovations proposed. The proposed approach is especially suitable for low-cost applications with a priority not uniformly distributed among readers. The experimental analysis has shown that DCNS provides a greater throughput than the state-of-the-art protocols, even those with additional requirements (e. g., 16 percent better than NFRA).
GM-CSF-activated human dendritic cells promote type 1 T follicular helper cell polarization in a CD40-dependent manner

T follicular helper (Tfh) cells regulate humoral responses and present a marked phenotypic and functional diversity. Type 1 Tfh (Tfh1) cells were recently identified and associated with disease severity in infection and autoimmune diseases. The cellular and molecular requirements to induce human Tfh1 differentiation are not known. Here, using single-cell RNA sequencing (scRNAseq) and protein validation, we report that human blood CD1c+ dendritic cells (DCs) activated by GM-CSF (also known as CSF2) drive the differentiation of naive CD4+ T cells into Tfh1 cells. These Tfh1 cells displayed typical Tfh molecular features, including high levels of PD-1 (encoded by PDCD1), CXCR5 and ICOS. They co-expressed BCL6 and TBET (encoded by TBX21), and secreted large amounts of IL-21 and IFN-γ (encoded by IFNG). Mechanistically, GM-CSF triggered the emergence of two DC sub-populations defined by their expression of CD40 and ICOS ligand (ICOS-L), presenting distinct phenotypes, morphologies, transcriptomic signatures and functions. CD40High ICOS-LLow DCs efficiently induced Tfh1 differentiation in a CD40-dependent manner. In patients with mild COVID-19 or latent Mycobacterium tuberculosis infection, Tfh1 cells were positively correlated with a CD40High ICOS-LLow DC signature in scRNAseq of peripheral blood mononuclear cells or blood transcriptomics, respectively. Our study uncovered a novel CD40-dependent Tfh1 axis with potential physiopathological relevance to infection. This article has an associated First Person interview with the first author of the paper.
Attention and counter-framing in the Black Lives Matter movement on Twitter

The social media platform Twitter platform has played a crucial role in the Black Lives Matter (BLM) movement. The immediate, flexible nature of tweets plays a crucial role both in spreading information about the movement's aims and in organizing individual protests. Twitter has also played an important role in the right-wing reaction to BLM, providing a means to reframe and recontextualize activists' claims in a more sinister light. The ability to bring about social change depends on the balance of these two forces, and in particular which side can capture and maintain sustained attention. The present study examines 2 years worth of tweets about BLM (about 118 million in total). Timeseries analysis reveals that activists are better at mobilizing rapid attention, whereas right-wing accounts show a pattern of moderate but more sustained activity driven by reaction to political opponents. Topic modeling reveals differences in how different political groups talk about BLM. Most notably, the murder of George Floyd appears to have solidified a right-wing counter-framing of protests as arising from dangerous "terrorist" actors. The study thus sheds light on the complex network and rhetorical effects that drive the struggle for online attention to the BLM movement.
Statistical comparison of InSAR tropospheric correction techniques

Correcting for tropospheric delays is one of the largest challenges facing the interferometric synthetic aperture radar (InSAR) community. Spatial and temporal variations in temperature, pressure, and relative humidity create tropospheric signals in InSAR data, masking smaller surface displacements due to tectonic or volcanic deformation. Correction methods using weather model data, GNSS and/or spectrometer data have been applied in the past, but are often limited by the spatial and temporal resolution of the auxiliary data. Alternatively a correction can be estimated from the interferometric phase by assuming a linear or a power-law relationship between the phase and topography. Typically the challenge lies in separating deformation from tropospheric phase signals. In this study we performed a statistical comparison of the state-of-the-art tropospheric corrections estimated from the MERIS and MODIS spectrometers, a low and high spatial-resolution weather model (ERA-I and WRF), and both the conventional linear and new power-law empirical methods. Our test-regions include Southern Mexico, Italy, and El Hierro. We find spectrometers give the largest reduction in tropospheric signal, but are limited to cloud-free and daylight acquisitions. We find a similar to 10-20% RMSE increase with increasing cloud cover consistent across methods. None of the other tropospheric correction methods consistently reduced tropospheric signals over different regions and times. We have released a new software package called TRAIN (Toolbox for Reducing Atmospheric InSAR Noise), which includes all these state-of-the-art correction methods. We recommend future developments should aim towards combining the different correction methods in an optimal manner. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
Left common pulmonary venous trunk-better be careful than sorry

Pulmonary vein abnormalities are very commonly encountered in general thoracic surgical practice. While performing a lobectomy, ideally all the pulmonary veins should be identified before ligating the corresponding vein. Failing to recognize a common pulmonary vein may lead to an unwarranted pneumonectomy which may end up morbid for the patient. In this report, we present a patient with left lower lobe bronchiectasis who underwent a left lower lobectomy and was identified to have a common left pulmonary venous trunk intra-operatively.