Miracle-dz/UniFAR-FTUs huggingface dataset:
Truncated Channel Inversion Power Control for the Uplink of mmWave Cellular Networks

In this paper, using the stochastic geometry, we develop a tractable uplink modeling paradigm for the outage probability of millimeter wave (mmWave) cellular networks. Our model takes account of the maximum power limitation and the per-user equipment (UE) power control as well as the effect of blockages. More specifically, each UE, which could be in line-of-sight (LOS) or non-LOS to its serving base station (BS), controls its transmit power such that the received signal power at its serving BS is equal to a predefined threshold. Hence, a truncated channel inversion power control is implemented for the uplink of the mmWave cellular network. We derive expressions for the truncated outage probability and the signal-to-interference-and-noise-ratio (SINR) outage probability for the uplink of mmWave cellular networks. Our results show that contrary to the conventional ultra-high-frequency (UHF) networks there exists a slow growth region for the truncated outage probability.
Respiratory Health Status of Workers in a Bottling Factory in Benin City, Nigeria

Introduction: There is a paucity of data on the respiratory health status of workers in bottling factories in Benin City, Nigeria. Such data will help to drive future studies and influence policy development on occupational health and safety in the country. This study assesses the respiratory symptoms and spirometric indices of exposed workers and controls. Methods: Respiratory symptoms and spirometric parameters of 18 workers on routine mandatory annual lung screening were assessed using the modified MRC (Medical Research Council) questionnaire and spirometer respectively, according to the European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines. Results: The mean age of workers was 35.1 ± 6.7 years. Workers and controls were similar in age, sex, BMI (Body Mass Index) and health status (p > 0.05). Respiratory symptoms were significantly higher among workers compared to controls. Overall, the result was statistically significant in the variables of wheeze in a smoky or dusty environment, presence of at least one respiratory symptom, better symptoms at weekends and better symptoms during holidays (p < 0.05). In particular, 6 (33.3%) exposed workers had wheeze in a smoky or dusty environment, 9 (50.0%) exposed workers reported at least one respiratory symptom compared with 2 (11.1%) controls, 5 (27.8%) had better symptoms at weekends, and 7 (38.9%) had better symptoms at holidays (p < 0.05). Generally, the reported frequency of respiratory symptoms among exposed workers were: cough (22.2%), sputum production (5.6%), breathlessness (11.1%) and wheeze (44.4%). Similarly, workers had significantly lower spirometric indices than controls, particularly in forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity (FVC) ratio and forced expiratory flow between 25% and 75% of FVC (FEF 25–75%) measurements. Conclusions: This study provides evidence of adverse respiratory health effects among bottling factory workers which requires further investigation.
Cancer-driving H3G34V/R/D mutations block H3K36 methylation and H3K36me3–MutSα interaction

Significance Somatic mutations converting glycine 34 of histone H3 (H3G34) to a large side chain-containing residue (e.g., arginine, valine) cause pediatric gliomas; however, the mechanism of this is unknown. Because H3K36me3 is involved in mismatch repair (MMR) by recruiting MMR protein MutSα to chromatin, we hypothesized that H3G34R/V mutations block H3K36’s interactions with both MutSα and H3K36-specific methyltransferases, leading to MMR deficiency. We show here that this is indeed the case. Structural analysis revealed that H3G34 resides in a narrow substrate channel of the H3K36 trimethyltransferase SETD2. Thus, H3G34R/V mutations impair the catalytic activity of SETD2. H3G34R/V mutations also block the H3K36me3–MutSα interaction. Cells harboring H3G34R/V mutations display a mutator phenotype. This study reveals the molecular basis of how H3G34 mutations cause pediatric gliomas. Somatic mutations on glycine 34 of histone H3 (H3G34) cause pediatric cancers, but the underlying oncogenic mechanism remains unknown. We demonstrate that substituting H3G34 with arginine, valine, or aspartate (H3G34R/V/D), which converts the non-side chain glycine to a large side chain-containing residue, blocks H3 lysine 36 (H3K36) dimethylation and trimethylation by histone methyltransferases, including SETD2, an H3K36-specific trimethyltransferase. Our structural analysis reveals that the H3 “G33-G34” motif is recognized by a narrow substrate channel, and that H3G34/R/V/D mutations impair the catalytic activity of SETD2 due to steric clashes that impede optimal SETD2–H3K36 interaction. H3G34R/V/D mutations also block H3K36me3 from interacting with mismatch repair (MMR) protein MutSα, preventing the recruitment of the MMR machinery to chromatin. Cells harboring H3G34R/V/D mutations display a mutator phenotype similar to that observed in MMR-defective cells. Therefore, H3G34R/V/D mutations promote genome instability and tumorigenesis by inhibiting MMR activity.
Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations

Cerebral cavernous malformations (CCMs) are vascular lesions in the central nervous system causing strokes and seizures which currently can only be treated through neurosurgery. The disease arises through changes in the regulatory networks of endothelial cells that must be comprehensively understood to develop alternative, non‐invasive pharmacological therapies. Here, we present the results of several unbiased small‐molecule suppression screens in which we applied a total of 5,268 unique substances to CCM mutant worm, zebrafish, mouse, or human endothelial cells. We used a systems biology‐based target prediction tool to integrate the results with the whole‐transcriptome profile of zebrafish CCM2 mutants, revealing signaling pathways relevant to the disease and potential targets for small‐molecule‐based therapies. We found indirubin‐3‐monoxime to alleviate the lesion burden in murine preclinical models of CCM2 and CCM3 and suppress the loss‐of‐CCM phenotypes in human endothelial cells. Our multi‐organism‐based approach reveals new components of the CCM regulatory network and foreshadows novel small‐molecule‐based therapeutic applications for suppressing this devastating disease in patients.
Anatomical position of the mandibular canal in relation to the buccal cortical bone: relevance to sagittal split osteotomy

Objectives Classification of the degree of postoperative nerve damage according to contact with the mandibular canal and buccal cortical bone has been studied, but there is a lack of research on the difference in postoperative courses according to contact with buccal cortical bone. In this study, we divided patients into groups according to contact between the mandibular canal and the buccal cortical bone, and we compared the position of the mandibular canal in the second and first molar areas. Materials and Methods Class III patients who visited the Dankook University Dental Hospital were included in this study. The following measurements were made at the second and first molar positions: (1) length between the outer margin of the mandibular canal and the buccal cortical margin (a); (2) mandibular thickness at the same level (b); (3) Buccolingual ratio=(a)/(b)×100; and (4) length between the inferior margin of the mandibular canal and the inferior cortical margin. Results The distances from the canal to the buccal bone and from the canal to the inferior bone and mandibular thickness were significantly larger in Group II than in Group I. The buccolingual ratio of the canal was larger in Group II in the second molar region. Conclusion If mandibular canal is in contact with the buccal cortical bone, the canal will run closer to the buccal bone and the inferior border of the mandible in the second and first molar regions.
Exoskeleton gait training after spinal cord injury: An exploratory study on secondary health conditions.

OBJECTIVE To explore changes in pain, spasticity, range of motion, activities of daily living, bowel and lower urinary tract function and quality of life of individuals with spinal cord injury following robotic exoskeleton gait training. DESIGN Prospective, observational, open-label multicentre study. METHODS Three training sessions per week for 8 weeks using an Ekso™ GT robotic exoskeleton (EKSO Bionics). Included were individuals with recent (<1 year) or chronic (>1 year) injury, paraplegia and tetraplegia, complete and incomplete injury, men and women. RESULTS Fifty-two participants completed the training protocol. Pain was reported by 52% of participants during the week prior to training and 17% during training, but no change occurred longitudinally. Spasticity decreased after a training session compared with before the training session (p <0.001), but not longitudinally. Chronically injured participants increased Spinal Cord Independence Measure (SCIM III) from 73 to 74 (p = 0.008) and improved life satisfaction (p = 0.036) over 8 weeks of training. Recently injured participants increased SCIM III from 62 to 70 (p < 0.001), but no significant change occurred in life satisfaction. Range of motion, bowel and lower urinary function did not change over time. CONCLUSION Training seemed not to provoke new pain. Spasticity decreased after a single training session. SCIM III and quality of life increased longitudinally for subsets of participants.
Morphometric characteristics and time to hatch as efficacious indicators for potential nanotoxicity assay in zebrafish

Although the effects of nano‐sized titania (nTiO2) on hatching events (change in hatching time and total hatching) in zebrafish have been reported, additional consequences of nTiO2 exposure (i.e., the effects of nTiO2‐induced changes in hatching events and morphometric parameters on embryo‐larvae development and survivability) have not been reported. To address this knowledge gap, embryos 4 h postfertilization were exposed to nTiO2 (0, 0.01, 10, and 1000 μg/mL) for 220 h. Hatching rate (58, 82, and 106 h postexposure [hpe]), survival rate (8 times from 34 to 202 hpe), and 21 morphometric characteristics (8 times from 34 to 202 hpe) were recorded. Total hatching (rate at 106 hpe) was significantly and positively correlated to survival rate, but there was no direct association between nTiO2‐induced change in hatching time (hatching rate at 58 and 82 hpe) and survival rate. At 58, 82, and 106 hpe, morphometric characteristics were significantly correlated to hatching rate, suggesting that the nTiO2‐induced change in hatching time can affect larval development. The morphometric characteristics that were associated with change in hatching time were also significantly correlated to survival rate, suggesting an indirect significant influence of the nTiO2‐induced change in hatching time on survivability. These results show a significant influence of nTiO2‐induced change in hatching events on zebrafish embryo‐larvae development and survivability. They also show that morphometric maldevelopments can predict later‐in‐life consequences (survivability) of an embryonic exposure to nTiO2. This suggests that zebrafish can be sensitive biological predictors of nTiO2 acute toxicity. Environ Toxicol Chem 2018;37:3063–3076. © 2018 SETAC
Spatial–Spectral Fusion by Combining Deep Learning and Variational Model

In the field of spatial–spectral fusion, the variational model-based methods and the deep learning (DL)-based methods are state-of-the-art approaches. This paper presents a fusion method that combines the deep neural network with a variational model for the most common case of spatial–spectral fusion: panchromatic (PAN)/multispectral (MS) fusion. Specifically, a deep residual convolutional neural network (CNN) is first trained to learn the gradient features of the high spatial resolution multispectral image (HR-MS). The image observation variational models are then formulated to describe the relationships of the ideal fused image, the observed low spatial resolution multispectral image (LR-MS) image, and the gradient priors learned before. Then, fusion result can then be obtained by solving the fusion variational model. Both quantitative and visual assessments on high-quality images from various sources demonstrate that the proposed fusion method is superior to all the mainstream algorithms included in the comparison, in terms of overall fusion accuracy.
Fabrication of nanofiber coated with l-arginine via electrospinning technique: a novel nanomatrix to counter oxidative stress under crosstalk of co-cultured fibroblasts and satellite cells

Abstract The objective of this study was to synthesize and characterize novel polyurethane (PU)-nanofiber coated with l-arginine by electrospinning technique. This study determined whether l-arginine conjugated with PU-nanofiber could stimulate cell proliferation and prevent H2O2-induced cell death in satellite cells co-cultured with fibroblasts isolated from Hanwoo (Korean native cattle). Our results showed that l-arginine conjugated with PU nanofiber could reduce cytotoxicity of co-cultured satellite cells. Protein expression levels of bcl-2 were significantly upregulated whereas those of caspase-3 and caspase-7 were significantly downregulated in co-culture of satellite cells compared to those of monoculture cells after treatment with PU-nanofiber coated with l-arginine and which confirmed by Confocal microscope. These results suggest that co-culture of satellite cells with fibroblasts might be able to counter oxidative stress through translocation/penetration of antioxidant, collagen, and molecules secreted to satellite cells. Therefore, this nanofiber might be useful as a wound dressing in animals to counter oxidative stresses.
Combinations of Abiotic Factors Differentially Alter Production of Plant Secondary Metabolites in Five Woody Plant Species in the Boreal-Temperate Transition Zone

Plant secondary metabolites (PSMs) are a key mechanism by which plants defend themselves against potential threats, and changes in the abiotic environment can alter the diversity and abundance of PSMs. While the number of studies investigating the effects of abiotic factors on PSM production is growing, we currently have a limited understanding of how combinations of factors may influence PSM production. The objective of this study was to determine how warming influences PSM production and how the addition of other factors may modulate this effect. We used untargeted metabolomics to evaluate how PSM production in five different woody plant species in northern Minnesota, USA are influenced by varying combinations of temperature, moisture, and light in both experimental and natural conditions. We also analysed changes to the abundances of two compounds from two different species – two resin acids in Abies balsamea and catechin and a terpene acid in Betula papyrifera. We used perMANOVA to compare PSM profiles and phytochemical turnover across treatments and NMDS to visualize treatment-specific changes in PSM profiles. We used linear mixed-effects models to examine changes in phytochemical richness and changes in the abundances of our example compounds. Under closed-canopy, experimental warming led to distinct PSM profiles and induced phytochemical turnover in B. papyrifera. In open-canopy sites, warming had no influence on PSM production. In samples collected across northeastern Minnesota, regional temperature differences had no influence on PSM profiles or phytochemical richness but did induce phytochemical turnover in B. papyrifera and Populus tremuloides. Throughout northeast Minnesota, warmer temperatures combined with open canopy resulted in distinct PSM profiles for all species and induced phytochemical turnover in all but Corylus cornuta. Although neither example compound in A. balsamea was influenced by any of the abiotic conditions, both compounds in B. papyrifera exhibited significant changes in response to warming and canopy. Our results demonstrate that the metabolic response of woody plants to combinations of abiotic factors cannot be extrapolated from that of a single factor. This heterogeneous phytochemical response directly affects interactions between plants and other organisms and may yield unexpected results as plant communities adapt to novel environmental conditions.
Optimal Design of High-Strength Ti‒Al‒V‒Zr Alloys through a Combinatorial Approach

The influence of various Zr contents (0–45 wt.%) on the microstructure and mechanical properties of Ti6Al4V alloy was investigated through a combinatorial approach. The diffusion multiples of Ti6Al4V–Ti6Al4V20Fe–Ti6Al4V20Cr–Ti6Al4V20Mo–Ti6Al4V45Zr were manufactured and diffusion-annealed to obtain a large composition space. Scanning electron microscopy, electron probe micro-analysis, and a microhardness system were combined to determine the relationships among the composition, microstructure, and hardness of these alloys. The Ti–6Al–4V–30Zr alloy was found to contain the thinnest α lath and showed peak hardness. X-ray diffraction and transmission electron microscope results indicated that after quenching from the β-field, the metastable α″-phase formed; moreover, at the secondary aging stage, the metastable α″-phase acted as precursor nucleation sites for the stable α-phase. The bulk Ti6Al4V30Zr alloy was manufactured. After aging at 550 °C, the alloy showed excellent balance of strength and ductility, and the tensile strength was 1464 MPa with a moderate elongation (8.3%). As the aging temperature increased, the tensile strength and yield strength of the alloys rose, but the total elongation decreased. The lamella thickness and volume fraction of the α-phase were the major factors that had great impacts on the mechanical properties.
NOD2 Expression in Streptococcus pneumoniae Meningitis and Its Influence on the Blood-Brain Barrier

Streptococcus pneumoniae meningitis is one of the most common disorders seen in clinical practice. It is believed that the brain tissue immune injury is caused by the expression of pattern-recognition receptors (PRR) which can further induce the release of other cytokines and inflammatory cascades. The aim of this study is to investigate the expression of nucleotide-binding oligomerization domain 2 (NOD2) and inflammatory factors in rat brain tissues infected with Streptococcus pneumoniae and its influence on the blood-brain barrier (BBB) permeability. Rats were given an intracranial injection of Streptococcus pneumoniae to construct the Streptococcus pneumoniae meningitis rat models. The expression change curves of NOD2 and inflammatory factors at different time points (0 h, 12 h, 24 h, 48 h, and 7 d) after Streptococcus pneumoniae were evaluated by enzyme-linked immunosorbent assay (ELISA). Western blotting analysis and quantitative real-time polymerase chain reaction (qRT-PCR) were engaged to examine the expression of NOD2. Furthermore, the changing processes of pathological characteristics, nervous system score, cerebral oedema, and BBB permeability were observed. Our results showed that NOD2 expression began to increase in the 12 h after Streptococcus pneumoniae infection group, while the remaining inflammatory factors were not obviously increased. Meanwhile, the levels of NOD2, as well as inflammatory factors IL-1β, TNF-α, and IL-6 were markedly elevated in 24 h and 48 h infection groups, which were consistent with the increases in BBB permeability and BWC, and the positive expression of NOD2 in the infected rat brain tissues was observed using immunohistochemistry (IHC). This study suggests that NOD2 might be related to the activation of inflammation pathways and the damage to the blood-brain barrier. NOD2 and inflammatory factors have played vital roles in the pathogenesis of Streptococcus pneumoniae meningitis.
Analysis of Set-Valued Stochastic Approximations: Applications to Noisy Approximate Value and Fixed point Iterations

The main aim of this paper is the development of Lyapunov function based sufficient conditions for stability (almost sure boundedness) and convergence of stochastic approximation algorithms (SAAs) with set-valued mean-fields, a class of model-free algorithms that have become important in recent times. In this paper we provide a complete analysis of such algorithms under three different, yet related set of sufficient conditions, based on the existence of an associated global/local Lyapunov function. Unlike previous Lyapunov function based approaches, we provide a simple recipe for explicitly constructing the Lyapunov function needed for analysis. Our work builds on the works of Abounadi, Bertsekas and Borkar (2002), Munos (2005) and Ramaswamy and Bhatnagar (2016). An important motivation to the flavor of our assumptions comes from the need to understand approximate dynamic programming and reinforcement learning algorithms, that use deep neural networks (DNNs) for function approximations and parameterizations. These algorithms are popularly known as deep reinforcement learning algorithms. As an important application of our theory we provide a complete analysis of the stochastic approximation counterpart of approximate value iteration (AVI), an important dynamic programming method designed to tackle Bellman's curse of dimensionality. Although motivated by the need to understand deep reinforcement learning algorithms our theory is more generally applicable. It is further used to develop the first SAA for finding fixed points of contractive set-valued maps and provide a comprehensive analysis of the same.
Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction

Attention mechanism is often used in deep neural networks for distantly supervised relation extraction (DS-RE) to distinguish valid from noisy instances. However, traditional 1-D vector attention model is insufficient for learning of different contexts in the selection of valid instances to predict the relationship for an entity pair. To alleviate this issue, we propose a novel multi-level structured (2-D matrix) self-attention mechanism for DS-RE in a multi-instance learning (MIL) framework using bidirectional recurrent neural networks (BiRNN). In the proposed method, a structured word-level self-attention learns a 2-D matrix where each row vector represents a weight distribution for different aspects of an instance regarding two entities. Targeting the MIL issue, the structured sentence-level attention learns a 2-D matrix where each row vector represents a weight distribution on selection of different valid instances. Experiments conducted on two publicly available DS-RE datasets show that the proposed framework with multi-level structured self-attention mechanism significantly outperform baselines in terms of PR curves, P@N and F1 measures.
Cultural literacy in the empire of emoji signs: Who is crying with joy?

Unicode emoji, originating in Japan but expanded through worldwide usage, is a means to assess how the globalization of Japanese popular culture promotes cultural literacy and awareness of multiculturalism. Emoji reveal discrimination and diversity within cultures, but emoji alone are “unreadable. ” They need to be used together with established modes of expression to avoid misunderstandings. Literature, as one of the most intentional and nuanced forms of language, provides insight into this lesson. I propose a semiotic reading of emoji inspired by Roland Barthes and posit his Empire of signs (L’Empire des signes) as an analogy for the incorporation of Japanese emoji into world languages and literatures. To test this theoretical application of emoji, I analyze the findings of “Emoji Literature Translation Contests” held in my university courses.