bluuebunny/crossref_metadata_2025_split huggingface dataset:
A distributed adaptive scheme for multiagent systems
Authors: Imran Imil Hamda , Chen Zhiyong , Zhu Lijun , Fu Minyue 
Published: November 2020

AbstractIn traditional adaptive control, the certainty equivalence principle suggests a two‐step design scheme. A controller is first designed for the ideal situation assuming the uncertain parameter was known, and it renders a Lyapunov function. Then, the uncertain parameter in the controller is replaced by its estimation that is updated by an adaptive law along the gradient of Lyapunov function. This principle does not generally work for a multiagent system as an adaptive law based on the gradient of (centrally constructed) Lyapunov function cannot be implemented in a distributed fashion, except for limited situations. In this paper, we propose a novel distributed adaptive scheme, not relying on gradient of Lyapunov function, for general multiagent systems. In this scheme, asymptotic consensus of a second‐order uncertain multiagent system is achieved in a network of directed graph.
Distributed consensus control for networked linear agents via event‐triggered communications
Authors: Rong Lina , Hua Yuteng , Jiang Guo‐Ping 
Published: November 2020

AbstractThis paper deals with the distributed consensus of networked linear agents via event‐driven communications by exploring relative‐state and relative‐output feedbacks. First, an alternative event‐triggered consensus design is proposed, consisting of a distributed controller with the relative‐state feedback and an event‐based communication policy, both of which can avoid employing the eigenvalues of the Laplacian matrix. Under the proposed scheme, it is proved that the residual consensus error is bounded, which is dependent on the agent number, the deviation constant in the threshold function, and the associated algebraic Riccati equation. Next, the design is extended to the case of the output event‐driven consensus control by using local observers.
Neurological complications of pandemic A(H1N1)2009pdm, postpandemic A(H1N1)v, and seasonal influenza A
Authors: Radzišauskienė Daiva , Vitkauskaitė Monika , Žvinytė Karolina , Mameniškienė Rūta 
Published: November 2020

AbstractObjectivesNot much is known about influenza‐associated neurological complications. We aimed to describe the case series of hospitalized patients who were confirmed with influenza A and presented with neurological symptoms in order to capture the broad spectrum of influenza clinical manifestation and suggest including influenza diagnostic in some neurological conditions.Materials and methodsThe inclusion criteria were age ≥ 18 and laboratory‐confirmed influenza presenting with neurological symptoms. Influenza‐associated neurological complication was described as a development of neurological symptom with no other origin. The outcomes were classified into 5 categories: 1. recovery with no significant disability; 2. minor disability (able to manage on their own); 3. moderate disability (requiring some help but able to walk without assistance); 4. severe disability (unable to walk without assistance and perform daily activities); 5. death.ResultsIn total, 12 patients (five women and seven men) were enrolled, with age range 18–71 years old. Neurological complications of pandemic A(H1N1)2009pdm influenza developed in seven out of 69 (10.1%) hospitalized patients. The most common neurological complication was encephalopathy. Neurological complications developed in two out of 24 (8.3%) hospitalized patients during postpandemic (H1N1)V period. One patient presented with encephalopathy, another with meningoencephalitis. During the 2018 influenza season, there was one patient who has developed influenza A neurological complications. Overall, two out of 104 (1.9%) influenza A patients developed influenza‐associated neurological complications in 2019.ConclusionsEvery patient with unexplained neurological symptoms and signs similar to aseptic and septic meningitis/encephalitis has to be tested for influenza virus during epidemics and pandemics.
Intergroup helping during the coronavirus crisis: Effects of group identification, ingroup blame and third party outgroup blame
Authors: Zagefka Hanna 
Published: November 2020

AbstractTwo studies tested predictors of helping across national boundaries. British participants reported blame attributions for the coronavirus crisis, either to the British government (ingroup blame), or to the Chinese government (third party outgroup blame), and it was tested whether this was associated with intentions to donate money to help outgroup members suffering from effects of the coronavirus crisis in the world's poorer countries. It was hypothesized that strength of identification with the national ingroup would be negatively associated with blame attributions to the ingroup, and that it would be positively associated with blame attributions to a third party outgroup. Blame attributions were predicted in turn to be related to outgroup helping, with ingroup blame being positively associated with helping intentions, and third party outgroup blame being negatively associated with helping intentions. Support for these predictions were found in one exploratory (N = 100) and one confirmatory (N = 250) study.
Novel nucleolar localization of clusterin and its associated functions in human oral cancers: An in vitro and in silico analysis
Authors: Kadam Rajashree , Harish Mahalakshmi , Dalvi Kajal , Teni Tanuja 
Published: November 2020

Clusterin (CLU), a multifunctional chaperonic glycoprotein associated with diverse cellular functions has been shown to act as an oncogene or tumour suppressor gene in different cancers, implying a dual role in tumorigenesis. Here, we investigated the expression of CLU isoforms, their subcellular localization and functional significance in oral cancer cells. Significant downregulation of secretory CLU (sCLU) transcripts was observed in oral cancer cell lines and tumours versus normal cells while the nuclear CLU (nCLU) transcripts were undetectable. We demonstrated for the first time the nucleolar localization of sCLU, its response to different nucleolar stresses and association with cajal bodies post nucleolar stress. Functionally, knockdown of CLU revealed its negative association with ribosome biogenesis implying a possible tumour suppressor like role in oral cancers. Further, loss of sCLU in these cells also resulted in altered nuclear morphology and shrunken tubulin filaments. In addition, the levels of nucleolar Nucleophosmin 1(NPM1) and Fibrillarin, known to regulate nuclear morphology were downregulated indicating a possible role of sCLU in their stabilization. Further, an in silico docking approach to gain insights into the interaction of sCLU with nucleolar proteins NPM1, Fibrillarin, UBF and Nucleolin, revealed the involvement of a conserved region comprising of amino acid residues 140‐155 of sCLU β‐chain, specifically via the Phe152 residue in hydrophobic interactions with these client nucleolar proteins indicating a possible stabilizing or regulatory role of sCLU.Significance of the studyThis is the first study to demonstrate the nucleolar localization of sCLU and its associated functions in oral cancer cells. Downregulation of sCLU in oral cancer tissues and cell lines, and its negative association with ribogenesis suggest its tumour suppressor like role in oral cancers. The possible role of sCLU in stabilization or regulation of different nucleolar proteins thereby impacting their functions is also implicated.
Metastatic follicular thyroid cancer in a patient with Birt‐Hogg‐Dubé syndrome
Authors: Bongetti Elisa K. , Raj Rajesh , Cooke Duncan R. , Mathew Mathew C. 
Published: November 2020

AbstractBirt‐Hogg‐Dubé syndrome (BHDS) is an extremely rare genetic condition that predisposes to renal cell carcinoma. This case describes a novel case of a patient with BHDS who also develops follicular thyroid cancer.
Granulocyte‐colony stimulating factor‐producing uterine cervical cancer treated with chemoradiotherapy: A case report with mutation analysis and literature review
Authors: Shiba Shintaro , Oike Takahiro , Ando Ken , Yoshimoto Yuya , Takayama Yoshiyasu , Ohno Tatsuya 
Published: November 2020

AbstractGranulocyte‐colony stimulating factor‐producing uterine cervical cancer is a rare aggressive disease, which may be genetically distinct from other uterine cervical cancers.
Generalized simultaneous component analysis of binary and quantitative data
Authors: Song Yipeng , Westerhuis Johan A. , Aben Nanne , Wessels Lodewyk F. A. , Groenen Patrick J. F. , Smilde Age K. 
Published: November 2020

AbstractIn the current era of systems biology research, there is a need for the integrative analysis of binary and quantitative genomics data sets measured on the same objects. One standard tool of exploring the underlying dependence structure present in multiple quantitative data sets is the simultaneous component analysis (SCA) model. However, it does not have any provisions when a part of the data are binary. To this end, we propose the generalized SCA (GSCA) model, which takes into account the distinct mathematical properties of binary and quantitative measurements in the maximum likelihood framework. Like in the SCA model, a common low‐dimensional subspace is assumed to represent the shared information between these two distinct types of measurements. To achieve a low rank solution, we propose to use a concave variant of the nuclear norm penalty. An efficient majorization algorithm is developed to fit this model with different concave penalties. Realistic simulations (low signal‐to‐noise ratio and highly imbalanced binary data) are used to evaluate the performance of the proposed model in recovering the underlying structure. Also, a missing value based cross‐validation procedure is implemented for model selection. We illustrate the usefulness of the GSCA model for exploratory data analysis of quantitative gene expression and binary copy number aberration measurements obtained from the GDSC1000 data sets.
On the Structure of Intermediates in Enyne Gold(I)‐Catalyzed Cyclizations: Formation of trans‐Fused Bicyclo[5.1.0]octanes as a Case Study
Authors: Escofet Imma , Armengol‐Relats Helena , Bruss Hanna , Besora Maria , Echavarren Antonio M. 
Published: November 2020

AbstractThe nature of cyclopropyl gold(I) carbene‐type intermediates has been reexamined as part of a mechanistic study on the formation of cis‐ or trans‐fused bicyclo[5.1.0]octanes in a gold(I)‐catalyzed cascade reaction. Benchmark of DFT methods together with QTAIM theory and NBO analysis confirms the formation of distinct intermediates with carbenic or carbocationic structures in the cycloisomerizations of enynes.
Late‐Stage C(sp2)−H Functionalization: A Powerful Toolkit To Arm Natural Products for In Situ Proteome Profiling?
Authors: Lim Ying‐Jie , Kuang Yulong , Wu Jie , Yao Shao Q. 
Published: November 2020

AbstractThe comprehensive investigation of target interactions from native cellular environments is of paramount importance for natural products and related bioactive compounds in drug discovery and chemical biology. Current chemoproteomic tools, such as in situ proteome profiling can do so effectively, but rely heavily on “tagged” probes that are accessible through traditional organic synthesis at the reactive sites of a compound, which may often be required for target binding. Late‐stage functionalization may resolve such limitations by tagging compounds in a single step at biologically inert C−H bonds. Herein, recent advances in late‐stage C(sp2)−H functionalization of (hetero)arenes, which are present in many natural products, are summarized, and new toolkits for more widespread use of such strategies to install natural products with next‐generation “minimalist” linkers for in situ proteome profiling are suggested.
Evaluation of Water‐Soluble Mannich Base Prodrugs of 2,3,4,5‐Tetrahydroazepino[4,3‐b]indol‐1(6H)‐one as Multitarget‐Directed Agents for Alzheimer's Disease
Authors: Purgatorio Rosa , de Candia Modesto , Catto Marco , Rullo Mariagrazia , Pisani Leonardo , Denora Nunzio , Carrieri Antonio , Nevskaya Alisa A. , Voskressensky Leonid G. , Altomare Cosimo D. 
Published: November 2020

AbstractDifferent Mannich base derivatives have been studied with the aim of addressing the poor aqueous solubility of the recently disclosed 6‐phenethyl‐2,3,4,5‐tetrahydroazepino[4,3‐b]indol‐1(6H)‐one (1), a human butyrylcholinesterase inhibitor (hBChE, IC5013 nM) and protective agent in NMDA‐induced neurotoxicity, in in vivo assays. The N‐(4‐methylpiperazin‐1‐yl)methyl derivative 2 c showed a 50‐fold increase in solubility in pH 7.4‐buffered solution, high stability in serum and (half‐life >24 h) and rapid (<3 min) conversion to 1 at acidic pH. Although less active than 1, 2 c retained moderate hBChE inhibition (IC50=3.35 μM) and a significant protective effect against NMDA‐induced neurotoxicity at 0.1 μM. Moreover, 2 c resulted a weaker serum albumin binder than 1, could pass the blood–brain barrier, and exerted negligible cytotoxicity on HepG2 cells. These findings suggest that 2 c could be a water‐soluble prodrug candidate of 1 for oral administration or a slow‐release injectable derivative in in vivoAlzheimer's disease models.
Minimum water requirements of Japanese lawngrass for survival during prolonged drought
Authors: Hong Mu , Bremer Dale J. 
Published: November 2020

AbstractWater restrictions on irrigation are rarely science based and may cause irreversible damage to turfgrass or inadvertently waste water. Our objectives were to evaluate effects of minimum water applications to ‘Meyer’ Japanese lawngrass (Zoysia japonica Steud.) on (a) canopy performance during prolonged drought and (b) survival and recovery thereafter. Meyer was watered weekly with 0–30% reference evapotranspiration (ETo) replacement for ≥56 d in two consecutive summers under a rainout shelter near Manhattan, KS. Soil moisture and canopy performance (normalized difference vegetation index [NDVI], percentage green cover [PGC], and turfgrass quality) were measured weekly. Results indicated 20–30% ETo slowed the decline in canopy performance compared with no water inputs. Irrigation at 30% ETo maintained Meyer at >75% PGC throughout the first year, but even Meyer at 0 and 5% ETo recovered after full irrigation resumed. In the second year, 30% ETo maintained Meyer at >25% PGC throughout the drought and recovered thereafter, but plots with 0 and 5% ETo only recovered to 30–42% PGC after 50 d of full irrigation. Depending on the weather, reducing irrigation from the recommended 60% ETo for warm‐season turfgrasses to 30% ETo saved 9–11 cm of water; further reductions to 20% ETo saved another 3–4 cm during the droughts of ≥56 d with insignificant additional PGC reductions (<18%). Water restrictions during severe droughts, such as limiting weekly irrigation of Meyer Japanese lawngrass to 20–30% ETo, could reduce turfgrass damage and conserve water.
A priori estimation of sequencing effort in complex microbial metatranscriptomes
Authors: Monleon‐Getino Toni , Frias‐Lopez Jorge 
Published: November 2020

AbstractMetatranscriptome analysis or the analysis of the expression profiles of whole microbial communities has the additional challenge of dealing with a complex system with dozens of different organisms expressing genes simultaneously. An underlying issue for virtually all metatranscriptomic sequencing experiments is how to allocate the limited sequencing budget while guaranteeing that the libraries have sufficient depth to cover the breadth of expression of the community. Estimating the required sequencing depth to effectively sample the target metatranscriptome using RNA‐seq is an essential first step to obtain robust results in subsequent analysis and to avoid overexpansion, once the information contained in the library reaches saturation. Here, we present a method to calculate the sequencing effort using a simulated series of metatranscriptomic/metagenomic matrices. This method is based on an extrapolation rarefaction curve using a Weibull growth model to estimate the maximum number of observed genes as a function of sequencing depth. This approach allowed us to compute the effort at different confidence intervals and to obtain an approximate a priori effort based on an initial fraction of sequences. The analytical pipeline presented here may be successfully used for the in‐depth and time‐effective characterization of complex microbial communities, representing a useful tool for the microbiome research community.
A time‐frequency dependent coherence model for seismic ground motions
Authors: Cui X.Z. , Hong H.P. 
Published: November 2020

AbstractThere are several well‐known empirical lagged spatial coherence models for seismic ground motions proposed in the literature. The models are often developed based on the ordinary Fourier transform. None of the parametric models depend on time and frequency. The present study is focused on the development of the time‐frequency dependent (TF‐dependent) lagged coherence model for the seismic ground motions. The estimation of the TF‐dependent lagged coherence is carried out using the records obtained from dense arrays in Taiwan by applying the S‐transform—a TF‐dependent windowed Fourier transform. The spectral analysis results show that the TF‐dependent lagged coherence decreases with increasing separation or increasing frequency. Most importantly, it is shown that the TF‐dependent lagged coherence varies with the time‐varying intensity within the duration of the records; a higher normalized intensity corresponds to a higher lagged coherence. This feature is included in the developed empirical parametric TF‐dependent lagged coherence model, which is a function of the frequency, the separation between recording sites, and the normalized intensity. A numerical example illustrating its application to simulate nonstationary ground motions at multiple points is presented by using the time‐frequency spectral representation method that was developed based on the S‐transform and discrete orthonormal S‐transform.
Hybrid machine learning approach for community and overlapping community detection in social network
Authors: M Divyapushpalakshmi , R Ramalakshmi 
Published: November 2020

AbstractSocial Network Analysis (SNA) is one of the significant fields of sociology, which probes many researchers toward it. In SNA, overall social network (such as Facebook, Twitter, and so on) structure is learned by community detection process. However, dynamic nature and involvement of numerous users in social network limits the accuracy of community detection process. In this research work, we propose a novel dual examining and hybrid machine learning approaches for community and overlapping community detection. We collect recent tweets from twitter social network in real time as training dataset. An undirected graph is constructed over collected twitter dataset to enhance the community detection process. Upon constructed graph, informative examining and structural examining processes are applied. Informative examining results with informative factor whereas structural examining identifies influencing node. Structural examining is performed using Particle Swarm Optimization algorithm based on centrality factor and node strength factor. By utilizing results from dual examining processes, community detection is performed by a hybrid machine learning approach Naïve Bayes with Firefly Algorithm. Overlapping community detection is followed up to the community detection using Fuzzy with neural network classifier. Here, Jaccard Similarity factor, overlapping coefficient, and modularity are considered in fuzzy with neural network. Extensive simulation results show better performance than single examining methods in terms of accuracy of mutual information, ratio of community, run time, and average degree in proposed hybrid machine learning approach‐based community detection.
Impacts of land use, population, and climate change on global food security
Authors: Molotoks Amy , Smith Pete , Dawson Terence P. 
Published: November 2020

AbstractIn recent years, global hunger has begun to rise, returning to levels from a decade ago. Climate change is a key driver behind these recent rises and is one of the leading causes of severe food crises. When coupled with population growth and land use change, future climate variability is predicted to have profound impacts on global food security. We examine future global impacts of climate variability, population, and land use change on food security to 2050, using the modeling framework FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation). The model uses national food balance sheets (FBS) to determine mean per capita calories, hence incorporating an assumption that minimum dietary energy requirements (MDER) remain constant. To account for climate variability, we use two Representative Concentration Pathway (RCP) scenarios from the Intergovernmental Panel on Climate Change (IPCC), alongside three Shared Socio‐economic Pathway (SSP) scenarios incorporating land use and population change within the model. Our results indicate that SSP scenarios have a larger impact on future food insecurity, in particular because of projected changes in population. Countries with a projected decrease in population growth had higher food security, while those with a projected rapid population growth tended to experience the worst impacts on food security. Although climate change scenarios had an effect on future crop yields, population growth appeared to be the dominant driver of change in undernourishment prevalence. Therefore, strategies to mitigate the consequences of projected population growth, including improved maternal health care, increasing equality of access to food at the national level, closing the yield gap, and changes in trade patterns, are essential to ensuring severe future food insecurity is avoided.
Effective connectivity between bed nucleus of the stria terminalis and amygdala: Reproducibility and relation to anxiety
Authors: Hofmann David , Straube Thomas 
Published: November 2020

AbstractIn a previous study, we investigated the resting‐state fMRI effective connectivity (EC) between the bed nucleus of the stria terminalis (BNST) and the laterobasal (LB), centromedial (CM), and superficial (SF) amygdala. We found strong negative EC from all amygdala nuclei to the BNST, while the BNST showed positive EC to the amygdala. However, the validity of these findings remains unclear, since a reproduction in different samples has not been done. Moreover, the association of EC with measures of anxiety offers deeper insight, due to the known role of the BNST and amygdala in fear and anxiety. Here, we aimed to reproduce our previous results in three additional samples. We used spectral Dynamic Causal Modeling to estimate the EC between the BNST, the LB, CM, and SF, and its association with two measures of self‐reported anxiety. Our results revealed consistency over samples with regard to the negative EC from the amygdala nuclei to the BNST, while the positive EC from BNST to the amygdala was also found, but weaker and more heterogenic. Moreover, we found the BNST‐BNST EC showing a positive and the CM‐BNST EC, showing a negative association with anxiety. Our study suggests a reproducible pattern of negative EC from the amygdala to the BNST along with weaker positive EC from the BNST to the amygdala. Moreover, less BNST self‐inhibition and more inhibitory influence from the CM to the BNST seems to be a pattern of EC that is related to higher anxiety.