Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavioral Immunity and Social Distancing in the Wild: The Same as in Humans?

The COVID-19 pandemic imposed new norms on human interactions, perhaps best reflected in the widespread application of social distancing. But social distancing is not a human invention and has evolved independently in species as dissimilar as apes and lobsters. Epidemics are common in the wild, where their spread is enhanced by animal movement and sociality while curtailed by population fragmentation, host behavior, and the immune systems of hosts. In the present article, we explore the phenomenon of behavioral immunity in wild animals as compared with humans and its relevance to the control of disease in nature. We start by explaining the evolutionary benefits and risks of sociality, look at how pathogens have shaped animal evolution, and provide examples of pandemics in wild animal populations. Then we review the known occurrences of social distancing in wild animals, the cues used to enforce it, and its efficacy in controlling the spread of diseases in nature.
Review of Structural Equation Modeling with lavaan, Kamel Gana and Guillaume Broc

Structural equation modeling (SEM) is a common methodology used in the social and behavioral sciences (Kline, 2015). This book focuses on both theoretical and practical content in an attempt to facilitate the reader’s in depth understanding of SEM. While the level of mathematics presented in the book may be moderately difficult for readers who do not have a background in quantitative methods, it is methodically designed for those who have at least an intermediate knowledge of regression, factor analysis, and SEM. Therefore, based on the reader’s statistical background, the pace set by the authors in the theoretical sections of the book may intermittently seem overwhelming. Despite these potential caveats, overall this is a very thorough textbook for readers who want to learn how to perform SEM analyses because it painstakingly explains how to use R and the lavaan package, step by step, accompanied with the appropriate syntax.
INTESTINAL ABSORPTION OF EPROSARTAN MESYLATE FROM SELF EMULSIFYING SYSTEM AND CYCLODEXTRIN COMPLEX

Objective: The aim of this work was to determine the intestinal membrane transport parameters of eprosartan mesylate (EM) and to investigate self-nano emulsifying drug delivery systems (SNEDDS) and inclusion complexation with hydroxypropyl b cyclodextrin (HP b CD) for enhanced intestinal absorption of eprosartan mesylate. Methods: The intestinal absorption was monitored using the in situ rabbit intestinal perfusion technique. SNEDDS was developed using labrafil, Lauroglycol with a tween in the presence of ethanol. Inclusion complexation was achieved by construction of phase solubility diagram in the presence of HP b CD. The prepared complex was evaluated using Fourier Transform Infrared Spectroscopy (FTIR) and differential scanning calorimetry (DSC). Results: The drug was found to be poorly absorbed from the jejuno-ileum and the colon with the absorption being mainly through paracellular pathway. An inclusion complex was developed between the drug and HPβCD. Perfusion of the drug in the nanoemulsion formulation or as an inclusion complex resulted in a significant increase in the intestinal absorption of the drug compared with the control . Conclusion: SNEDDS and inclusion complexation are promising strategies for enhanced intestinal absorption of eprosartan mesylate.
Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation.
Enhanced Autonomous Navigation of Robots by Deep Reinforcement Learning Algorithm with Multistep Method

In this paper, we propose a new method to improve the autonomous navigation of mobile robots. The new method combines a multistep update method with a double deep Q-network (MS-DDQN) to realize reinforcement learning (RL) to enhance the navigation ability of mobile robots. The proposed MS-DDQN gives two types of rewards for taking actions: terminal and non-terminal rewards. These rewards are subdivided into several different ones including rewards for arrival and collision (terminal rewards) and rewards for distance, orientation, and danger (non-terminal rewards). With the subdivided reward system, the new method trains mobile robots more effectively to increase their autonomous navigation ability. In the experimental process of this study, a laser range finder was used as the sensor for the mobile robot to perceive the distance information of the obstacle. Experiment results validated the new method’s enhanced ability, showing higher success rates (97% on average) than those of other methods such as the double deep Q-network (DDQN), prioritized deep Q-network (DQN), and prioritized DDQN. The higher success rates originated from the sophisticated reward system as the total reward of the proposed method was 7–94% higher than those of the other methods in simulations in five different environments. The learning speed was also improved, reducing the learning time, as the new method completed the learning in fewer episodes. The results of the new model suggest that MS-DDQN enables mobile robots to have higher learning efficiency and generalization ability than conventional deep reinforcement (DRL)-based methods and allows them to navigate autonomously even in unknown complex environments.
Affordances, movement dynamics, and a centralized digital communication platform in a networked movement

ABSTRACT Much contemporary social mobilization is digitally enabled. Digital media may provide the communication platforms on which supporters deliberate movement goals, share information, discuss tactics, and generate discourses in response to ongoing happenings. Yet digital media’s capability to serve these functions should depend on platform-specific affordances and movement dynamics. Based on such premises, this article examines how the online forum LIHKG became the central communication platform in the Anti-Extradition Bill Movement in Hong Kong. Empirically, digital media and content analysis data help establish the forum’s prominence during the first few months of the movement, while analyses of protest onsite survey data show how the use of LIHKG systematically related to several movement-related attitudes among the protesters. The article highlights the affordances and movement dynamics that allow the forum to play the role. It contributes to understanding the factors that shape the role and impact of digital media platforms in social mobilization.
Modelling earthquake rates and associated uncertainties in the
Marmara Region, Turkey

Abstract. Modelling the seismic potential of active faults and the associated epistemic uncertainty is a fundamental step of probabilistic seismic hazard assessment (PSHA). We use SHERIFS (Seismic Hazard and Earthquake Rate In Fault Systems), an open-source code allowing to build hazard models including earthquake ruptures involving several faults, to model the seismicity rates on the North Anatolian Fault (NAF) system in the Marmara region. Through an iterative approach, SHERIFS converts the slip-rate on the faults into earthquake rates that follow a Magnitude Frequency Distribution (MFD) defined at the fault system level, allowing to model complex multi-fault ruptures and off-fault seismicity while exploring the underlying epistemic uncertainties. In a logic tree, we explore uncertainties concerning the locking state of the NAF in the Marmara Sea, the maximum possible rupture in the system, the shape of the MFD and the ratio of off-fault seismicity. The branches of the logic tree are weighted according to the match between the modelled earthquake rate and the earthquake rates calculated from the local data, earthquake catalogue and paleoseismicity. In addition, we use the result of the physics-based earthquake simulator RSQSim to inform the logic tree and increase the weight on the hypotheses that are compatible with the result of the simulator. Using both the local data and the simulator to weight the logic tree branches, we are able to reduce the uncertainties affecting the earthquake rates in the Marmara region. The weighted logic tree of models built in this study is used in a companion article to calculate the probability of collapse of a building in Istanbul.
Daily and Landscape Influences of Species Visitation to Toxic Bait Sites for Wild Pigs

Toxic baiting of wild pigs (Sus scrofa) is a potential new tool for population control and damage reduction in the United States. Use of toxic bait sites by non‐target species is concerning because of the risks posed from exposure to a toxic bait. A 2018 field trial in northern Texas, USA, examining the efficacy of a prototype toxic bait (HOGGONE®, containing 10% sodium nitrite) revealed unexpected hazards to non‐target species, primarily passerine birds, from consuming toxic bait spilled outside of bait stations by wild pigs. The hazards jeopardize the ability to register HOGGONE as a tool for controlling wild pigs. We conducted a post hoc analysis from that 2018 trial to identify how daily and landscape factors influenced the use of bait sites by non‐target species, and subsequent hazards from consuming the toxic bait. We discovered that no single strategy can eliminate visitations by all non‐target species, but offering the toxic bait at night, in wild‐pig specific bait stations, and in more homogeneous landscapes appeared to be the best strategy for minimizing visits by passerine birds. We also found that use of bait sites by wild pigs were temporally clustered, and more pigs visited bait sites along linear features of the landscape (e.g., agricultural edges or riparian corridors). We recommend a baiting strategy where applicators do not place bait stations directly at sites where remnant particles of grain (from prebaiting) are available on the ground. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.
Won't You Be My Neighbor? Uncovering ties between Social Capital and COVID-19 Outcomes at Local Levels

Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure - including social ties that enable trust, mutual aid, and collective action - tend to respond to and recover better from crisis. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (2019) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, and Illinois. We found that social capital and social vulnerability predicted as much as 95% of the variation in COVID outbreaks, highlighting their power as diagnostic and predictive tools for combating the spread of COVID.
STRUCTURE–ANTIMICROBIAL PROPERTIES STUDY OF SOME DIBASIC PHENYLCARBAMIC ACID ESTERS

Due to worldwide phenomenon of microbial resistance to commonly used therapeutic agents, antibiotics and antifungals, dibasic di‑/trimethylphenylcarbamic acid esters 1 – 3 , a non-traditional series of potential antimicrobials, has been in vitro evaluated against chosen Gram-positive ( Staphylococcus aureus ) and Gram-negative ( Escherichia coli ) bacterial strains as well as against yeast ( C. albicans ) by the minimum inhibitory concentration (MIC) assay. Taking into consideration chemical structure of tested derivatives, the incorporation of more than one protonated atom of nitrogen into salt forming fragment positively influenced the activity against E. coli . On the contrary, the presence of one or more methyl groups instead of 3-alkoxy side chain attached to lipophilic aromatic moiety has not found to be favorable structural feature. In entire set of inspected compounds, the most promising results have been found for the compound 3 , chemically1-[3-piperidinium-1-yl-2-({[(2,4,6-trimethylphenyl)amino]carbonyl}oxy)propyl] piperidinium dichloride, against E. coli with the MIC=1.56 mg/mL. Key words : Dibasic phenylcarbamic acid esters, Escherichia coli , hydrogen bonding, lipophilicity
AWN-similarity: Towards developing free open-source frameworks for measuring Arabic semantic similarity under Windows / Linux operating systems

Arabic is a highly systematic language where its words exhibit elegant and rigorous logic. The field of Arabic word semantic similarity becomes more challenging due to its higher complexity and subtlety. This research is concerned with investigating the development of free open-source frameworks containing packages to calculate the semantic similarity between two Arabic words or concepts. These packages are known as AWN-ConceptSimilarity and AWN-WordSimilarity. The developed packages implement seven semantic similarity algorithms. One of these algorithms was proposed for Arabic and the rest were proposed for English where successfully adapted to Arabic using an Arabic lexical database, Arabic wordnet. The functionality of the developed packages is validated using two-word similarity benchmarks datasets previously produced for Arabic. The results of the validation process indicate that the developed frameworks represent an important contribution to the Arabic semantic similarity field. Moreover, the developed packages are reliable to use and embed them with Arabic researchers' projects for improving or comparing their methodologies.
Wedge strategies in Russia-Japan relations

Abstract This article analyses wedge strategies in the context of Russo-Japanese relations. In particular, it looks at how both countries have sought to generate a dis-alignment in the opposing side, preventing further steps toward the consolidation of potentially threatening partnerships: the US-Japan alliance for Russia, and the Russia-China entente for Japan. After identifying the respective goals of Russia and Japan, the article examines the conditions that enable the success of wedge strategies. Looking at the case of Russia-Japan relations from 2012 to 2020, the article argues that a strategy constituted of a mix of positive economic and political incentives and a limited amount of coercion can succeed in producing a degree of dis-alignment in the opposing camp. Russia’s strategy seems to be more productive than Japan’s since Moscow has managed to minimize the effect of sanctions imposed by Japan and it has reduced political differences to mostly bilateral and regional issues. A greater level of cooperation with established partners for both Russia and Japan, and limited costs of wedging strategies, have resulted in dis-alignment but not a reversal of existing alignments. The article also indicates that in this case credibility issues do not fundamentally affect the degree of success of wedge strategies.
Gratitude and life satisfaction: the mediating role of spirituality among Filipinos

ABSTRACT This cross-sectional study aimed to determine the relationships between gratitude and life satisfaction; gratitude and spirituality; spirituality and life satisfaction, and the mediating role of spirituality between gratitude and life satisfaction. We utilised the Gratitude Questionnaire (GQ-6), Satisfaction with Life Scale (SWLS), and Daily Spiritual Experience Scale as measures. The study enjoined 415 participants whose age was 13 to 28 years old from the Philippines; more than the majority were Roman Catholics and from Christian denominations. Positive relationships were revealed between gratitude and life satisfaction, between gratitude and spirituality, and between spirituality and life satisfaction. Spirituality was also evidenced to mediate the relationship between gratitude and life satisfaction. Gratitude positively affects spirituality which in turn positively affects life satisfaction. The results provided clarity to the mechanism of the relationship between gratitude and life satisfaction. The present study also supported the transcendental view of gratitude. It also provided empirical evidence to the relationship of the variables in the Philippine context. Counselling implications for the improvement of life satisfaction were also offered.
Evaluating the Drought Endurance of Landscaping Ground Cover Plants in a Roof Top Model

Vegetative ground covers are commonly used in urban, tropical roadside gardens. Such landscaping ground covers usually encounter extreme water-deficits and high temperatures from vehicles and urban infrastructures. However, information about the plant species that are appropriate for low maintenance gardens is not available, especially in tropical areas. This study aimed to investigate potential indicators for evaluating plant tolerance to water-deficit situations. A non-irrigated rooftop model was used to test 25 commercial ground cover species in a greenhouse at Mahidol University, Nakhon Pathom Province, Thailand. Each of these 25 species was potted and subjected to one of two conditions: with or without irrigation for 7 days. Physiological responses relevant to plant endurance during water-deficits were monitored, including changes in leaf relative water content (RWC), percent stomatal opening, leaf surface temperature, leaf total chlorophyll content, leaf greenness, maximum quantum yield, and light quantum yield. Moreover, an additional indicator of landscape utility was evaluated, where each species was judged by trained panelists for their esthetic appeal. Diverse responses were observed based on the type of physiological parameter measured, plant species, and duration of drought conditions. Water withdrawal for three days was deemed an appropriate time to determine plant tolerance to water-deficit conditions, as signs of stress were clearly observed in three parameters, i.e., changes in leaf RWC, percent stomatal opening, and esthetic score. Lastly, cluster analysis revealed that seven plant species were appropriate for tropical, urban ground covers, as they had high endurance under water-deficit conditions, namely, Allium schoenoprasum,Liriope muscari, Aloe sp. , Sedum x rubrotinctum, Alternanthera ficoidea, Pilea libanensis and Plectranthus scutellarioides.
SLAMF7 regulates the inflammatory response in macrophages during polymicrobial sepsis

Uncontrolled inflammation occurred in sepsis results in multiple organ injuries and shock, which contributes to the death of patients with sepsis. However, the regulatory mechanisms that restrict excessive inflammation are still elusive. Here, we identified an Ig-like receptor called signaling lymphocyte activation molecular family 7 (SLAMF7) as a key suppressor of inflammation during sepsis. We found that the expression of SLAMF7 on monocytes/macrophages was significantly elevated in patients with sepsis and in septic mice. SLAMF7 attenuated TLR-dependent MAPK and NF-κB signaling activation in macrophages by cooperating with Src homology 2–containing inositol-5′‑phosphatase 1 (SHIP1). Furthermore, SLAMF7 interacted with SHIP1 and TNF receptor–associated factor 6 (TRAF6) to inhibit K63 ubiquitination of TRAF6. In addition, we found that tyrosine phosphorylation sites within the intracellular domain of SLAMF7 and the phosphatase domain of SHIP1 were indispensable for the interaction between SLAMF7, SHIP1, and TRAF6 and SLAMF7-mediated modulation of cytokine production. Finally, we demonstrated that SLAMF7 protected against lethal sepsis and endotoxemia by downregulating macrophage proinflammatory cytokines and suppressing inflammation-induced organ damage. Taken together, our findings reveal a negative regulatory role of SLAMF7 in polymicrobial sepsis, thus providing sights into the treatment of sepsis.
The evolution and competition patterns of shipping networks under the influence of an Arctic route system

ABSTRACT To study the impact of Arctic routes on shipping networks, this paper uses an Arctic route system (ARS) model. Based on the evolution of the ARS, an improved weighted network evolution model (the BBV model) is used to predict the evolution of shipping networks from 2017 to 2035. Then, the multiple linkage analysis method is applied to extract the significant flows in the networks, and a competition network is built. The main results are as follows: (1) Significant flows in the maritime network under the influence of the ARS show an increasing trend and then a decreasing trend during evolution process. (2) According to multiple linkage analysis, Shanghai, Busan, and Tokyo have higher positions. (3) The overall network competition decreases. (4) In terms of the competitive landscape of China’s container ports, Guangzhou has the strongest domestic competitive intensity. In addition, Chinese ports that face greater competition from foreign ports are always Shanghai and Shenzhen. One implication of analysis is that due to gradual influence of the ARS, port policy makers should implement different policies at different times. The paper also shows that some ports (such as Shanghai and Ningbo) should formulate cooperation agreements to promote ports development.
Characteristics of HER2 FISH-Equivocal Breast Cancers and the Impacts on HER2 Status of 2018 ASCO/CAP Guideline

Background: According to 2018 ASCO/CAP guideline, HER2 FISH-equivocal breast cancers will be categorized as HER2 negative except those with HER2 IHC 3+. However, whether or not HER2 FISH-equivocal breast cancers was a heterogeneous group has not been well illustrated.Methods: 195 HER2 FISH-equivocal breast cancers were collected between 2014 and 2018. The molecular subtype was determined according to 2013 St Gallen consensus, and HER2 status was also redefined following 2018 ASCO/CAP guideline. All cases were classified into 4 groups according to the average HER2 copy number (4.0-4.4, 4.5-4.9, 5.0-5.4, 5.5-5.9 signals/cell). The relationship between HER2 copy number and clinicopathological parameters was analyzed. Results: 183 (93.8%) of 195 FISH-equivocal cases were of luminal subtype, while the other 12 (6.2%) were undetermined. Using 2018 ASCO/CAP guideline, all FISH-equivocal cases were recategorized to HER2 negative. Therefore, 31(15.9%) cases were luminal A-like, 152 (77.9%) were luminal B-like (HER2 negative) and 12 (6.2%) were triple negative. The average HER2 copy number showed positive correlation with chromosome 17 polysomy, but had no significant association with other clinicopathological parameters as well as prognosis. 17 (8.7%) cases were treated with trastuzumab, but showed no difference in prognosis with patients who didn’t receive targeted therapy.Conclusions: In this study, all HER2 FISH-equivocal breast cancers were reclassified to be HER2 negative according to 2018 ASCO/CAP guideline. Most of these patients were luminal B-like (HER2 negative). The average HER2 copy number had no significant association with clinicopathological parameters, as well as prognosis.