Miracle-dz/UniFAR-FTUs huggingface dataset:
Divide in Vaccine Belief in COVID-19 Conversations: Implications for Immunization Plans

The development of a viable COVID-19 vaccine is a work in progress, but the success of the immunization campaign will depend upon public acceptance. In this paper, we classify Twitter users in COVID-19 discussion into vaccine refusers (anti-vaxxers) and vaccine adherers (vaxxers) communities. We study the divide between anti-vaxxers and vaxxers in the context of whom they follow. More specifically, we look at followership of 1) the U.S. Congress members, 2) four major religions (Christianity, Hinduism, Judaism and Islam), 3) accounts related to the healthcare community, and 4) news media accounts. Our results indicate that there is a partisan divide between vaxxers and anti-vaxxers. We find a religious community with a higher than expected fraction of anti-vaxxers. Further, we find that the variance of vaccine belief within the news media accounts operated by Russian and Iranian governments is higher compared to news media accounts operated by other governments. Finally, we provide messaging and policy implications to inform the COVID-19 vaccine and future vaccination plans.
Construction de lexiques pour l’extraction des mentions de maladies dans les forums de santé (Building lexica for extraction of mentions of diseases from healthcare fora)

Les forums de discussion et les réseaux sociaux sont des sources potentielles de différents types d’information, qui ne sont en général pas accessibles par ailleurs. Par exemple, dans les forums de santé, il est possible de trouver les informations sur les habitudes et le mode de vie des personnes. Ces informations sont rarement partagées avec les médecins. Il est donc possible de se fonder sur ces informations pour évaluer les pratiques réelles des patients. Il s’agit cependant d’une source d’information difficile à traiter, essentiellement à cause des spécificités linguistiques qu’elle présente. Si une première étape pour l’exploration des forums consiste à indexer les termes médicaux présents dans les messages avec des concepts issus de terminologies médicales, cela s’avère extrêmement compliqué car les formulations des patients sont très différentes des terminologies officielles. Nous proposons une méthode permettant de créer et enrichir des lexiques de termes et expressions désignant une maladie ou un trouble, avec un intérêt particulier pour les troubles de l’humeur. Nous utilisons des ressources existantes ainsi que des méthodes non supervisées. Les ressources construites dans le cadre du travail nous permettent d’améliorer la détection de messages pertinents.
Adults vs. neonates: Differentiation of functional connectivity between the basolateral amygdala and occipitotemporal cortex

The amygdala, a subcortical structure known for social and emotional processing, consists of multiple subnuclei with unique functions and connectivity patterns. Tracer studies in adult macaques have shown that the basolateral subnuclei differentially connect to parts of visual cortex, with stronger connections to anterior regions and weaker connections to posterior regions; infant macaques show robust connectivity even with posterior visual regions. Do these developmental differences also exist in the human amygdala, and are there specific functional regions that undergo the most pronounced developmental changes in their connections with the amygdala? To address these questions, we explored the functional connectivity (from resting-state fMRI data) of the basolateral amygdala to occipitotemporal cortex in human neonates scanned within one week of life and compared the connectivity patterns to those observed in young adults. Specifically, we calculated amygdala connectivity to anterior-posterior gradients of the anatomically-defined occipitotemporal cortex, and also to putative occipitotemporal functional parcels, including primary and high-level visual and auditory cortices (V1, A1, face, scene, object, body, high-level auditory regions). Results showed a decreasing gradient of functional connectivity to the occipitotemporal cortex in adults – similar to the gradient seen in macaque tracer studies – but no such gradient was observed in neonates. Further, adults had stronger connections to high-level functional regions associated with face, body, and object processing, and weaker connections to primary sensory regions (i.e., A1, V1), whereas neonates showed the same amount of connectivity to primary and high-level sensory regions. Overall, these results show that functional connectivity between the amygdala and occipitotemporal cortex is not yet differentiated in neonates, suggesting a role of maturation and experience in shaping these connections later in life.
Fast Whole-Genome Phylogeny of the COVID-19 Virus SARS-CoV-2 by Compression

We analyze the whole genome phylogeny and taxonomy of the SARS-CoV-2 virus using compression. This is a new fast alignment-free method called the “normalized compression distance” (NCD) method. It discovers all effective similarities based on Kolmogorov complexity. The latter being incomputable we approximate it by a good compressor such as the modern zpaq. The results comprise that the SARS-CoV-2 virus is closest to the RaTG13 virus and similar to two bat SARS-like coronaviruses bat-SL-CoVZXC21 and bat-SL-CoVZC4. The similarity is quantified and compared with the same quantified similarities among the mtDNA of certain species. We treat the question whether Pangolins are involved in the SARS-CoV-2 virus. The compression method is simpler and possibly faster than any other whole genome method, which makes it the ideal tool to explore phylogeny.
Neuroplasticity and Epilepsy Surgery in Brain Eloquent Areas: Case Report

Introduction: Neuronal plasticity includes changes in any component of the central nervous system in response to intrinsic or extrinsic stimuli. Brain functions that depend on the epileptogenic cortex pose a challenge in epilepsy surgery because many patients are excluded from pre-surgical evaluation for fear of the possible sequelae. Some of these patients may be rescued by enhancing neuronal plasticity with brain neuromodulation techniques. Case Report: We describe a 6-year-old child with refractory focal motor seizures symptomatic to a neuroepithelial dysembryoblastic tumor in the left temporo-parietal region. He underwent limited resection of the lesion in order to avoid sequelae in his language function. A functional study at age of 17 years revealed an overlap of Wernicke's area with the tumor and areas of incipient language reorganization in the contralateral hemisphere. An invasive neuromodulation procedure was designed to enhance neuroplasticity. After craniotomy, he underwent language training and simultaneous electrical inhibition of language using an electrode grid placed over the lesion. The intensity of the language inhibitory stimulus was increased every day to force the use of accessory language areas in the right hemisphere by neuroplasticity. Results: The language of the patient improved for six consecutive days until he was able to speak and understand while undergoing maximum electrical inhibition. The tumor was resected using a cortical mapping guide. Discussion: Application of direct cortical stimulation techniques and language pre-habilitation before epilepsy surgery can be useful to rescue patients excluded from resective surgery, especially young patients with long-term lesions.
Assessment of Autism Zebrafish Mutant Models Using a High-Throughput Larval Phenotyping Platform

In recent years zebrafish have become commonly used as a model for studying human traits and disorders. Their small size, high fecundity, and rapid development allow for more high-throughput experiments compared to other vertebrate models. Given that zebrafish share >70% gene homologs with humans and their genomes can be readily edited using highly efficient CRISPR methods, we are now able to rapidly generate mutations impacting practically any gene of interest. Unfortunately, our ability to phenotype mutant larvae has not kept pace. To address this challenge, we have developed a protocol that obtains multiple phenotypic measurements from individual zebrafish larvae in an automated and parallel fashion, including morphological features (i.e., body length, eye area, and head size) and movement/behavior. By assaying wild-type zebrafish in a variety of conditions, we determined optimal parameters that avoid significant developmental defects or physical damage; these include morphological imaging of larvae at two time points (3 days post fertilization (dpf) and 5 dpf) coupled with motion tracking of behavior at 5 dpf. As a proof-of-principle, we tested our approach on two novel CRISPR-generated mutant zebrafish lines carrying predicted null-alleles of syngap1b and slc7a5, orthologs to two human genes implicated in autism-spectrum disorder, intellectual disability, and epilepsy. Using our optimized high-throughput phenotyping protocol, we recapitulated previously published results from mouse and zebrafish models of these candidate genes. In summary, we describe a rapid parallel pipeline to characterize morphological and behavioral features of individual larvae in a robust and consistent fashion, thereby improving our ability to better identify genes important in human traits and disorders. AUTHOR SUMMARY Zebrafish (Danio rerio) are a well-established model organism for the study of neurodevelopmental disorders. Due to their small size, fast reproduction, and genetic homology with humans, zebrafish have been widely used for characterizing and screening candidate genes for many disorders, including autism-spectrum disorder, intellectual disability, and epilepsy. Although several studies have described the use of high-throughput morphological and behavioral assays, few combine multiple assays in a single zebrafish larva. Here, we optimized a platform to characterize morphometric features at two developmental time points in addition to behavioral traits of zebrafish larvae. We then used this approach to characterize two autism candidate genes (SYNGAP1 and SLC7A5) in two CRISPR-generated zebrafish null mutant models we developed in house. These data recapitulate previously published results related to enhanced seizure activity, while identifying additional defects not previously reported. We propose that our phenotyping platform represents a feasible method for maximizing the use of single zebrafish larvae in the characterization of additional mutants relevant to neurodevelopmental disorders.
Novel rabies virus variant for bi-directional optical control reveals modulatory influence of the pulvinar on visual cortex in rat

Optogenetic tools have become of great utility in the causal analysis of systems in the brain. However, current optogenetic techniques do not reliably support both excitation and suppression of the same cells in vivo, limiting analysis and slowing research. Here we developed a novel glycoprotein-deleted rabies virus expressing two channelrhodopsin proteins, GtACR2 and Chrimson, in order to independently manipulate excitatory and inhibitory transmembrane potentials, respectively. Using this approach, we demonstrated that rodent pulvinar neurons modulate cortical size tuning and suppress flash responses, but do not drive activity in visual cortex. While our goal was primarily to develop this novel method to study the structure-function organization of thalamocortical circuits, this technique is readily applicable to study any brain region.
Multitube Helical-Waveguide Gyrotron Traveling-Wave Amplifier: Device Concept and Electron-Optical System Modeling

A multitube configuration for a gyrotron traveling-wave amplifier (gyro-TWA) that is based on the use of one multibeam electron gun and several parallel helically corrugated interaction waveguides operating with axis-encircling-like electron beamlets at the second cyclotron harmonic is proposed. This new gyro-TWA concept, similar to that for multibeam or clustered linear-beam klystrons, can provide a multifold decrease in the thermal loading and increase in the RF breakdown strength for each tube at the same average or pulse output powers or a significant increase in the output power at a moderate level of the mentioned factors. It is shown that the multibeam electron flow appropriate for such a multitube gyro-TWA can be formed by a single magnetron-injection gun (MIG) using selectively emitting regions on the cathode. In some cases, such a gun can be less sensitive to the influence of space-charge forces and other disturbing factors and generate a beam of better quality than a single-beam cusp gun. In particular, a gun for the ten-tube ${W}$ -band gyro-TWA, which ensures generation of a beam with a total power of about 1.3 MW, a pitch factor from 1.2 to 1.5, and a root-mean-square (rms) transverse velocity spread of about 8%, was designed and numerically modeled.
Resource Management of Maritime Edge Nodes for Collected Data Feedback

With the development of marine economy, more marine applications emerge leading to more deployed sensors and more generated data. However, current marine Internet only provides low transmission rate constrained by limited communication resources and bad transmission environment. How to efficiently use these resources and guarantee quality-of-service (QoS) of applications need to be addressed. This paper proposes an online optimization resource management algorithm to improve the communication resource efficiency with guaranteed QoSs for different applications. This algorithm needs neither of the resource cost function nor QoS constraint function at the resource scheduling node. Furthermore, the gradient information, which is usually needed in learning strategies, is not required either. Instead, this algorithm performs resource management of both computation and communication resources in each time slot only based on the observation of the last time slot. With slot-by-slot resource allocation, the communication cost can be minimized to be suitable for maritime scenarios. In the meantime, a long-term delay constraint can be satisfied. Results show that the proposed algorithm achieves the goal of reducing communication cost while guaranteeing the delay-constraint of different applications. Although more computation resources will improve its performance, this algorithm still can obtain the minimized cost given a low computation resource.
Direct-Splitting-Based CN-FDTD for Modeling 2D Material Nanostructure Problems

Incorporating a truncation of the complex-frequency-shifted perfectly matched layer (CFS-PML), the direct-splitting- based Crank-Nicolson finite-difference time-domain (CNDS-FDTD) is developed and applied to the infrared two-dimensional layered material (2DLM) black phosphorous (BP) metasurface implementations on the all-dielectric nanostructure. To improve extremely low efficiencies in solving infrared terahertz (THz) problems with the few-atomic-layer thickness of 2DLMs, the CFS-CNDS-FDTD is proposed in demand due to the fact that it possesses capabilities of implicit FDTD method and unsplit-field CFS-PML truncation, respectively, in completely conquering the Courant-Friedrich-Levy condition (CFL) limit and holding good performance. The temporal incremental in the CFS-CNDS-FDTD can reach 1000 times larger than that in the regular FDTD for infrared nanoscale problems centered at the 2.5 THz and then keep accurate. Three-dimensional (3D) numerical cases have been carried out to corroborate the proposed method. The CFS-CNDS- FDTD can not only achieve high accuracies and then saves several dozen times of CPU time as compared to the regular FDTD, but also pave the way for designing all-dielectric nanostructures with other 2DLM metasurfaces.
An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons

One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods.
Which Is Better for Liver SBRT: Dosimetric Comparison Between DCAT and VMAT for Liver Tumors

Stereotactic body radiotherapy (SBRT) is currently well-adopted as a curative treatment for primary and metastatic liver tumors. Among SBRT methods, dynamic conformal arc therapy (DCAT) and volumetric-modulated arc therapy (VMAT) are the most preferred methods. In this study, we report a comparison study measuring the dose distribution and delivery efficiency differences between DCAT and VMAT for liver SBRT. All patients who were treated with SBRT for primary or metastatic liver tumors with a curative aim between January 2016 and December 2017 at DIRAMS were enrolled in the study. For all patients, SBRT plans were designed using the Monte Carlo (MC) algorithm in Monaco treatment planning system (version 5.1). The planning goals were set according to the RTOG 0813, RTOG 0915, and RTOG 1112 protocols. A plan comparison was made on the metrics of dose volume histogram, planning and delivery efficiency, monitor unit (MU), and dosimetric indices. PTV coverage was evaluated using the following: Dmean, D95%, D98%, D2%, D50%, Dmax, V95%, heterogeneity index (HI), and conformality index (CI). For DCAT and VMAT, respectively, the Dmean was 5942.8 ± 409.3 cGy and 5890.6 ± 438.8 cGy, D50% was 5968.8 ± 413.1 cGy and 5954.3 ± 405.2 cGy, and CI was 1.05 ± 0.05 and 1.03 ± 0.04. The D98% and V95% were 5580.0 ± 465.3 cGy and 20.4 ± 12.0 mL for DCAT, and 5596.0 ± 478.7 cGy and 20.5 ± 12.0 mL for VMAT, respectively. For normal liver, V40, V30, V20, V17, V5, Dmean, Dmax were evaluated for comparison. The V30, V20, and V10 were significantly higher in DCAT; other parameters of normal livers showed no statistically significant differences. For evaluation of intermediate dose spillage, D2cm(%) and R50% of DCAT and VMAT were 45.8 ± 7.9 and 5.6 ± 0.9 and 45.1 ± 6.7 and 5.5 ± 1.2, respectively. Planning and delivery efficiency were evaluated using MU, Calculation time, and Delivery time. DCAT had shorter Calculation time and Delivery time with smaller MU. MU was smaller in DCAT and the average difference was 300.1 MU. For liver SBRT, DCAT is an effective alternative to VMAT plans that could meet the planning goals proposed by the RTOG SBRT protocol and increases plan and delivery effectiveness, while also ignoring the interplay effect.
Optimizing Heap Memory Object Placement in the Hybrid Memory System With Energy Constraints

Main memory significantly impacts the power and energy utilization of the overall server system. Non-Volatile Memory (NVM) devices, are suitable candidates for the main memory to reduce static energy consumption. But unlike DRAM, the access latencies and the dynamic energy consumption of write operation of the NVM devices are higher. Thus, Hybrid Main Memory Systems (HMMS) employing DRAM and NVM have been proposed to reduce the overall energy depletion of main memory while optimizing the performance of application. However, memory object placement is crucial for optimal performance and energy efficiency in HMMS due to high write latency and energy consumption of NVM devices. This paper proposes <italic>eMap</italic>, an optimal heap memory object placement planner for HMMS. <italic>eMap</italic> takes into account the object-level access patterns and energy consumption to provide an ideal placement policy for objects to mitigate performance and energy consumption. In particular, <italic>eMap</italic> is equipped with two modules, <italic>eMPlan</italic> and <italic>eMDyn</italic>. <italic>eMPlan</italic> is a static placement planner which provides one-time placement policies for memory objects to meet the energy budget. <italic>eMDyn</italic> is a runtime model to consider the requests of changes in the energy constraint during the application execution. Both modules are based in Integer Linear Programming(ILP) and consider three major constraints, namely decision, capacity and energy constraints to optimally placing the memory objects in HMMS. We evaluate the proposed solution with two scientific application benchmarks, NAS Parallel Benchmark (NPB) and Problem-based Benchmark Suit (PBBS), on two testbeds by emulating the NVM using QUARTZ <xref ref-type="bibr" rid="ref32">[32]</xref>. Our extensive experiments in comparison with Memory Object Classification and Allocation (MOCA) framework showed that our solution is <inline-formula> <tex-math notation="LaTeX">$4.17\times $ </tex-math></inline-formula> less costly in terms of the memory object profiling and reduce the energy consumption up to 14% with the same performance. On the other hand, <italic>eMDyn</italic> module also meets the performance and energy requirement during the application execution by considering the migration cost in terms of time and energy.
Development and Analysis of a Quantitative Mathematical Model of Bistability in the Cross Repression System Between APT and SLBO Within the JAK/STAT Signaling Pathway

Cell migration is a key component in development, homeostasis, immune function, and pathology. It is important to understand the molecular activity that allows some cells to migrate. Drosophila melanogaster is a useful model system because its genes are largely conserved with humans and it is straightforward to study biologically. The well-conserved transcriptional regulator Signal Transducer and Activator of Transcription (STAT) promotes cell migration, but its signaling is modulated by downstream targets Apontic (APT) and Slow Border Cells (SLBO). Inhibition of STAT activity by APT and cross-repression of APT and SLBO determines whether an epithelial cell in the Drosophila egg chamber becomes motile or remains stationary. Through mathematical modeling and analysis, we examine how the interaction of STAT, APT, and SLBO creates bistability in the Janus Kinase (JAK)/STAT signaling pathway. In this paper, we update and analyze earlier models to represent mechanistically the processes of the JAK/STAT pathway. We utilize parameter, bifurcation, and phase portrait analyses, and make reductions to the system to produce a minimal three-variable quantitative model. We analyze the manifold between migratory and stationary steady states in this minimal model and show that when the initial conditions of our model are near this manifold, cell migration can be delayed.