Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization of the Ballast Long Guiding Sheath for Neuroendovascular Procedures: Institutional Experience in 68 Cases

Background: The rise of neurointerventional devices has created a demand for guide systems capable of navigating to the carotid artery consistently regardless of tortuosity. The shift toward large distal access catheters (DACs) and desire for greater trackability have inspired the creation of flexible, supportive, large-lumen long guiding sheaths. Recently, the Ballast long guiding sheath was introduced to provide stability and flexibility while navigating neurointerventional devices through tortuous intracranial anatomy. Objective: To report our experience using the Ballast long guiding sheath in a series of patients undergoing a variety of neuroendovascular procedures. Methods: We retrospectively identified all patients who underwent neuroendovascular treatment with a long guiding sheath were selected from a prospectively maintained endovascular database from January 2019 to November 2019. Baseline clinical characteristics and procedural details were collected. Results: A total of 68 patients were included, mean patient age 67.6 ± 13.6 years. Of the patients treated, 52.9% (36/68) presented with stenosis, 25% (17/68) with aneurysms, 13.2% (9/68) with stroke or emboli, 1.5% (1/68) with a tumor, 1.5% (1/68) with an arteriovenous fistula (AVF), and 4.4% (3/68) with a carotid web. Of the patients with stenosis, 20/36 (55.6%) were extracranial, and 16/36 (44.4%) were intracranial. The Ballast long guiding sheath was used to deliver treatment devices for stenting (37/68, 54.4%), flow diversion (12/68, 17.6%), mechanical thrombectomy (8/68, 11.8%), endovascular coiling (5/68, 7.4%), liquid embolization (3/68, 4.4%), balloon angioplasty (2/68, 2.9%), and balloon angioplasty with stenting (1/68, 1.5%). No Ballast-related complications or adverse events were encountered. Conclusions: We demonstrate the feasibility of the Ballast long guiding sheath to successfully deliver modern neurointerventional treatment devices through tortuous anatomy.
Effect of Water Chemistry, Land Use Patterns, and Geographic Distances on the Spatial Distribution of Bacterioplankton Communities in an Anthropogenically Disturbed Riverine Ecosystem

The spatial distribution of bacterioplankton communities in rivers is driven by multiple environmental factors, including local and regional factors. Local environmental condition is associated with effect of river water chemistry (through species sorting); ecological process in region is associated with effects of land use and geography. Here, we investigated variation in bacterioplankton communities (free-living, between 0.22 and 5 μm) in an anthropogenically disturbed river using high-throughput DNA sequencing of community 16S rRNA genes in order to investigate the importance of water chemistry, land use patterns, and geographic distance. Among environmental factors, sulfate (SO42–), manganese (Mn), and iron (Fe) concentrations were the water chemistry parameters that best explained bacterioplankton community variation. In addition, forest and freshwater areas were the land use patterns that best explained bacterioplankton community variation. Furthermore, cumulative dendritic distance was the geographic distance parameter that best explained bacterial community variation. Variation partitioning analysis revealed that water chemistry, land use patterns, and geographic distances strongly shaped bacterioplankton communities. In particular, the direct influence of land use was prominent, which alone contributed to the highest proportion of variation (26.2% in wet season communities and 36.5% in dry season communities). These results suggest that the mechanisms of species sorting and mass effects together control bacterioplankton communities, although mass effects exhibited higher contributions to community variation than species sorting. Given the importance of allochthonous bacteria input from various land use activities (i.e., mass effects), these results provide new insights into the environmental factors and determinant mechanisms that shape riverine ecosystem communities.
Review of Current COVID-19 Diagnostics and Opportunities for Further Development

Diagnostic testing plays a critical role in addressing the coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Rapid and accurate diagnostic tests are imperative for identifying and managing infected individuals, contact tracing, epidemiologic characterization, and public health decision making. Laboratory testing may be performed based on symptomatic presentation or for screening of asymptomatic people. Confirmation of SARS-CoV-2 infection is typically by nucleic acid amplification tests (NAAT), which requires specialized equipment and training and may be particularly challenging in resource-limited settings. NAAT may give false-negative results due to timing of sample collection relative to infection, improper sampling of respiratory specimens, inadequate preservation of samples, and technical limitations; false-positives may occur due to technical errors, particularly contamination during the manual real-time polymerase chain reaction (RT-PCR) process. Thus, clinical presentation, contact history and contemporary phyloepidemiology must be considered when interpreting results. Several sample-to-answer platforms, including high-throughput systems and Point of Care (PoC) assays, have been developed to increase testing capacity and decrease technical errors. Alternatives to RT-PCR assay, such as other RNA detection methods and antigen tests may be appropriate for certain situations, such as resource-limited settings. While sequencing is important to monitor on-going evolution of the SARS-CoV-2 genome, antibody assays are useful for epidemiologic purposes. The ever-expanding assortment of tests, with varying clinical utility, performance requirements, and limitations, merits comparative evaluation. We herein provide a comprehensive review of currently available COVID-19 diagnostics, exploring their pros and cons as well as appropriate indications. Strategies to further optimize safety, speed, and ease of SARS-CoV-2 testing without compromising accuracy are suggested. Access to scalable diagnostic tools and continued technologic advances, including machine learning and smartphone integration, will facilitate control of the current pandemic as well as preparedness for the next one.
Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes

Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases.
Proactive Control Mediates the Relationship Between Working Memory and Math Ability in Early Childhood

Based on the dual mechanisms of control (DMC) theory, there are two distinct mechanisms of cognitive control, proactive and reactive control. Importantly, accumulating evidence indicates that there is a developmental shift from predominantly using reactive control to proactive control during childhood, and the engagement of proactive control emerges as early as 5–7 years old. However, less is known about whether and how proactive control at this early age stage is associated with children’s other cognitive abilities such as working memory and math ability. To address this issue, the current study recruited 98 Chinese children under 5–7 years old. Among them, a total of 81 children (mean age = 6.29 years) contributed useable data for the assessments of cognitive control, working memory, and math ability. The results revealed that children at this age period predominantly employed a pattern of proactive control during an AX-Continuous Performance Task (AX-CPT). Moreover, the proactive control index estimated by this task was positively associated with both working memory and math performance. Further regression analysis showed that proactive control accounted for significant additional variance in predicting math performance after controlling for working memory. Most interestingly, mediation analysis showed that proactive control significantly mediated the association between working memory and math performance. This suggests that as working memory increases so does proactive control, which may in turn improve math ability in early childhood. Our findings may have important implications for educational practice.
A New Technique for Century-Scale Wind Component Indices

Advancing the understanding of how variations in the climate over the ocean influences the weather over the United States can be aided by developing marine climatic indices. Herein, wind component indices are developed using nearly 125 years of wind observations from ships. A new technique using probability density functions for the values of meridional and zonal wind components is developed to create indices for a user-selected region and accumulation interval (e.g., annual or seasonal) over a climatological period. The index is a measure of the shift in the likelihood of values above or below a threshold for a given season or year as compared to the long-term (e.g., 125 year) probability distribution. The new index method is demonstrated using ship-based wind observations for select regions of the Atlantic Ocean. Ship observations are extracted from release 3.0. 0 of the International Comprehensive Ocean-Atmosphere Data Set. Prior to index creation, an assessment of wind data quality is completed, and suspect observations are removed. The method to create a probabilistic wind component index is described along with a metric of the uncertainty in the calculated index. Two wind component indices, for regions in the north Atlantic and eastern Gulf of Mexico, are presented to demonstrate the technique. Using the Gulf of Mexico index as a case study, we compare the wind component indices to precipitation measured over the Gulf coastal states and identify several relationships between multi-year changes in winds in the eastern Gulf of Mexico and precipitation on a seasonal basis. Exploring the spatiotemporal patterns of the onshore/offshore component wind indices derived from seasonal wind forecasts could provide a metric for future prediction of seasonal or annual precipitation to support the agricultural sector. The index method demonstrated can be applied to other spatiotemporal regions for different parameters and using other source datasets.
A Cross-Linguistic Study of Individual Differences in Speech Planning

Although previous research has shown that there exist individual and cross-linguistic differences in planning strategies during language production, little is known about how such individual differences might vary depending on which language a speaker is planning. The present series of studies examines individual differences in planning strategies exhibited by speakers of American English, French, and German. Participants were asked to describe images on a computer monitor while their eye movements were monitored. In addition, we measured participants' working memory capacity and speed of processing. The results indicate that in the present study, English and German were planned less incrementally (further in advance) prior to speech onset compared to French, which was planned more incrementally (not as far in advance). Crucially, speed of processing predicted the scope of planning for French speakers, but not for English or German speakers. These results suggest that the different planning strategies that are invoked by syntactic choices available in different languages are associated with the tendency for speakers to rely on different cognitive support systems as they plan sentences.
Collaborative Video Cache Management Strategy in Mobile Edge Computing

With the rapid development of multimedia services in wireless mobile networks, the video data traffic has increased exponentially. Traditional video traffic service based on cloud computing caused a large amount of traffic load and longer access delay, which severely reduces the quality of service (QoS) of users. Mobile edge caching as one of the use cases of mobile edge computing (MEC), can directly serve user requests so as to greatly reduce the traffic load and shorten access delay. At present, distributed caching is widely used in the caching deployment of base stations (BSs). However, the caching capacity of a single BS is generally particularly limited, which will degrade the performance of wireless mobile network. In this paper, a collaborative caching strategy in the heterogeneous MEC networks is designed, and the edge caching of macro base station (MBS) and small base stations (SBSs) is utilized to bring storage resources closer to users. In addition, an optimization problem of content caching is formulated to minimize the total delay cost of all users requesting content in the MEC networks. In order to solve this problem, cache management strategy (GCS) is proposed, which consists of greedy cache placement strategy and greedy cache update strategy. Finally, numerical simulations demonstrate that the GCS scheme effectively improves the cache hit rate, and significantly reduces the average delay and backhaul traffic load.
V2MEC: Low-Latency MEC for Vehicular Networks in 5G Disaggregated Architectures

5G redefines the network architecture, through the introduction of base station disaggregation, adding up to the overall flexibility for deployments of network elements based on the existing demand. Moreover, through the wide application of Multi-access Edge Computing (MEC), the latency for accessing services offered on top of the infrastructure can be drastically minimized, thus supporting several applications that exchange critical data over the network. Vehicular communications are a type of applications that can highly benefit from the MEC approach, as they need to exchange critical data with roadside infrastructure (V2I). Nevertheless, the technology through which the vehicles exchange data with the infrastructure may cause fluctuations to the network performance observed by the end-users. In this work, we blend the concept of disaggregated 5G base stations with integrated access for non-3GPP technologies, and the MEC concept, by realizing a novel placement for services that is not yet suggested in existing literature. The setup is applied to a vehicular environment, providing the ability to dynamically steer the traffic over multiple technologies serving each user (vehicle) of the network. We implement, deploy and evaluate our solution in a testbed environment, providing proof on reduced latency for accessing the MEC services as well as the role that the technology selection plays for the overall performance observed by the end-users.
LSR: Link-aware Spatial Reuse in IEEE 802.11ax WLANs

The upsurge of mobile traffic volume has led to dense deployment of IEEE 802.11 wireless local area networks (WLANs). To enhance spectral efficiency in dense WLAN deployments, a new 802.11 amendment, namely 802.11ax, introduces overlapping basic service set packet detection-based spatial reuse (OBSS PD-based SR) operation. Although the OBSS PD-based SR operation enables more simultaneous transmissions among OBSSs, whether it improves network performance is highly uncertain and depends on the mutual interference among the simultaneous transmission links. In this paper, we propose a link-aware SR scheme, named LSR, that facilitates simultaneous transmissions among OBSSs considering the mutual interference. LSR efficiently exploits SR transmission opportunities by selecting the appropriate link for the SR transmission while sufficiently protecting the ongoing transmission. Moreover, the proposed method allows BSSs to coordinate with each other in a distributed manner without incurring additional control message exchange. Through extensive ns-3 simulation, we demonstrate that LSR significantly improves the network performance compared with legacy 802.11 and the OBSS PD-based SR operation.
Morphologies and surface properties of cellulose-based activated carbon nanoplates

In this study, cellulose nanoplates (CNPs) were fabricated using cellulose nanocrystals obtained from commercial microcrystalline cellulose (MCC). Their pyrolysis behavior and the characteristics of the product carbonaceous materials were investigated. CNPs showed a relatively high char yield when compared with MCC due to sulfate functional groups in-troduced during the manufacturing process. In addition, pyrolyzed CNPs (CCNPs) showed more effective chemical activation behavior compared with MCC-induced carbonaceous materials. The activated CCNPs exhibited a microporous carbon structure with a high surface area of 1310.6 m 2 /g and numerous oxygen heteroatoms. The results of this study show the effects of morphology and the surface properties of cellulose-based nanomaterials on pyrolysis and the activation process.
Swarm Intelligence based Power Allocation in Hybrid Millimeter-Wave Massive MIMO Systems

This work proposes a novel swarm intelligence based power allocation (PA) technique for multi-user massive multiple-input multiple-output (MU-mMIMO) systems. For the downlink transmission, we consider the geometry-based millimeter-wave (mmWave) channel model. The base station (BS) employs a three-dimensional angular-based hybrid precoding (3D-AB-HP) technique requiring low channel state information (CSI) overhead. The 3D-AB-HP architecture consists of three stages: (i) radio frequency (RF) precoder, (ii) baseband (BB) precoder, (iii) multi-user PA block. First, the RF precoder is built via the slow time-varying angle-of-departure information to reduce the CSI overhead size as well as the number of RF chains. It is designed via low cost phase-shifters, which induces the constant modulus constraint at the RF-stage design. Second, the BB precoder utilizes the regularized zero-forcing technique for mitigating the inter-user interference. Third, at the multi-user PA block, we develop a novel particle swarm optimization based PA (PSO-PA) algorithm to maximize the spectral/energy efficiency. Both the BB precoder and the multi-user PA block are constructed via the reduced-size effective channel seen from the BB-stage. Illustrative results reveal that the 3D-AB-HP with PSO-PA can remarkably improve the spectral/energy efficiency compared to the equal PA (e.g., up to 88% at the low/medium transmit power regime). Also, it is shown that the proposed 3D-AB-HP significantly decreases the number of RF chains (e.g., 94.2%) and the CSI overhead size (e.g., 87.1%), while providing higher energy efficiency than the conventional single-stage fully-digital precoding.
WiAgent: Link Selection for CSI-Based Activity Recognition in Densely Deployed Wi-Fi Environments

In this work, we address the issue of Wi-Fi-based human activity recognition (HAR) system in densely deployed Wi-Fi environments. With the benefit of sufficient information provided by Wi-Fi channel state information (CSI), HAR based on Wi-Fi has become an active research area in recent years. Traditional Wi-Fi CSI-based HAR applications usually focus on utilizing one Wi-Fi transmitter and one or several Wi-Fi receivers to extract the activity-related features, ignoring the communication among multiple Wi-Fi devices in the real world. In this paper, we present a novel Wi-Fi link selection model on the basis of continuous state decision-making process in which CSI is modeled as a part of the state. The model, referred to as WiAgent, takes an action of selecting one Wi-Fi link according to current state, and then updates the state for the choice of the next action. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi transmitters exist.
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control

Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents to adversarial attacks is studied. In particular, we consider multiple DRL agents that perform both dynamic channel access and power control in wireless interference channels. For these victim DRL agents, we design a jammer, which is also a DRL agent. We Propose an adversarial jamming attack scheme that utilizes a listening phase and significantly degrades the users’ sum rate. Subsequently, we develop an ensemble policy defense strategy against such a jamming atta.ck. er by reloa.di. ng models (saved during retraining) that have minimum transition correlation.
Airtime Aware Dynamic Network Slicing for Heterogeneous IoT Services in IEEE 802.11ah

Assuring an efficient Quality of Service (QoS) for heterogeneous Internet of Things services is a challenge, mainly due to spectrum scarcity and bandwidth limitations on the radio access network. To solve these problems, efficient management of airtime per station (STA) is necessary. In this work, we propose a scheduler to perform dynamic network slicing in IEEE 802.11ah Networks. The proposed scheduler is based on virtualization technologies to assure QoS restrictions per slice and can be deployed in the Access Point (AP) or in a virtual machine connected to the AP. Network metrics are used to verify QoS violations per slice over time. Once a QoS restriction is detected, the scheduler performs a reallocation of resources re-configuring the Restricted Access Windows parameters that compose a slice, adjusting airtime per STA. To evaluate the proposed scheduler, we consider a dynamic policy. Simulation results in a smart city scenario show that the dynamic policy is able to scale the network and satisfies QoS slice requirements.