bluuebunny/crossref_metadata_2025_split huggingface dataset:
Förster resonance energy transfer biosensors for fluorescence and time-gated luminescence analysis of rac1 activity
Authors: Pham Ha , Hoseini Soflaee Mona , Karginov Andrei V. , Miller Lawrence W. 
Published: March 2022

AbstractGenetically encoded, Förster resonance energy transfer (FRET) biosensors enable live-cell optical imaging of signaling molecules. Small conformational changes often limit the dynamic range of biosensors that combine fluorescent proteins (FPs) and sensing domains into a single polypeptide. To address this, we developed FRET and lanthanide-based FRET (LRET) biosensors of Rac1 activation with two key features that enhance sensitivity and dynamic range. For one, alpha helical linker domains separate FRET partners and ensure a large conformational change and FRET increase when activated Rac1 at the biosensor C-terminus interacts with an amino-terminal Rac binding domain. Incorporation of a luminescent Tb(III) complex with long (~ ms) excited state lifetime as a LRET donor enabled time-gated luminescence measurements of Rac1 activity in cell lysates. The LRET dynamic range increased with ER/K linker length up to 1100% and enabled robust detection of Rac1 inhibition in 96-well plates. The ER/K linkers had a less pronounced, but still significant, effect on conventional FRET biosensors (with FP donors and acceptors), and we were able to dynamically image Rac1 activation at cell edges using fluorescence microscopy. The results herein highlight the potential of FRET and LRET biosensors with ER/K linkers for cell-based imaging and screening of protein activities.
Selective multiple analyte detection using multi-mode excitation of a MEMS resonator
Authors: Yaqoob Usman , Jaber Nizar , Alcheikh Nouha , Younis Mohammad I. 
Published: March 2022

AbstractThis work reports highly selective multiple analyte detection by exploiting two different mechanisms; absorption and thermal conductivity using a single MEMS device. To illustrate the concept, we utilize a resonator composed of a clamped-guided arch beam connected to a flexural beam and a T-shaped moveable mass. A finite element model is used to study the mode shapes and mechanical behavior of the device with good agreement reported with the experimental data. The resonator displays two distinct out-of-plane modes of vibration. For humidity detection, we utilize physisorption by functionalizing the surface with graphene oxide (GO), which has strong affinity toward water vapors. The GO solution is prepared and drop-casted over the mass surface using an inkjet printer. On the other hand, cooling the heated flexural beams is used for helium (He) detection (thermal-conductivity-based sensor). The sensor characteristics are extensively studied when the modes are individually and simultaneously actuated. Results affirm the successful utilization of each mode for selective detection of relative humidity and He. This novel mode-dependent selective detection of multiple analytes can be a promising building block for the development of miniature, low-powered, and selective smart sensors for modern portable electronic devices.
Dimension of visual information interacts with working memory in monkeys and humans
Authors: Fehring Daniel J. , Pascoe Alexander J. , Haque Zakia Z. , Samandra Ranshikha , Yokoo Seiichirou , Abe Hiroshi , Rosa Marcello G. P. , Tanaka Keiji , Yamamori Tetsuo , Mansouri Farshad A. 
Published: March 2022

AbstractHumans demonstrate behavioural advantages (biases) towards particular dimensions (colour or shape of visual objects), but such biases are significantly altered in neuropsychological disorders. Recent studies have shown that lesions in the prefrontal cortex do not abolish dimensional biases, and therefore suggest that such biases might not depend on top-down prefrontal-mediated attention and instead emerge as bottom-up processing advantages. We hypothesised that if dimensional biases merely emerge from an enhancement of object features, the presence of visual objects would be necessary for the manifestation of dimensional biases. In a specifically-designed working memory task, in which macaque monkeys and humans performed matching based on the object memory rather than the actual object, we found significant dimensional biases in both species, which appeared as a shorter response time and higher accuracy in the preferred dimension (colour and shape dimension in humans and monkeys, respectively). Moreover, the mnemonic demands of the task influenced the magnitude of dimensional bias. Our findings in two primate species indicate that the dichotomy of top-down and bottom-up processing does not fully explain the emergence of dimensional biases. Instead, dimensional biases may emerge when processed information regarding visual object features interact with mnemonic and executive functions to guide goal-directed behaviour.
Spatial ecology, activity patterns, and habitat use by giant pythons (Simalia amethistina) in tropical Australia
Authors: Natusch Daniel , Lyons Jessica , Shine Richard 
Published: March 2022

AbstractAlthough giant snakes are abundant in some tropical forests, their ecology is far less well-known than for smaller species of snakes in cooler climates. Information on spatial ecology can clarify management issues such as the sizes and types of habitats needed for conservation. We radio-tracked 27 scrub pythons (Simalia amethistina; snout-vent lengths 2.02 to 3.70 m) in Cape York, near the northeastern tip of Australia, for a mean period of 426 days (up to 1001 days) per snake. Home ranges were larger in males than females (means 0.60 vs. 0.28 km2) and overlapped considerably among individuals. All snakes used rainforest habitat, but seasonal shifts into open woodland were common. Snakes were active primarily by night, with larger snakes hunting less of the time overall, and more often by day. Hunting behaviour was seen more often during the wet season than the dry season. Average daily displacement was < 10 m, typically involving a shift from diurnal refuge to nocturnal ambush-site. A reliance on sit-and-wait predation results in small home ranges and limited movements, despite the large body size of this species.
Qubits made by advanced semiconductor manufacturing
Authors: Zwerver A. M. J. , Krähenmann T. , Watson T. F. , Lampert L. , George H. C. , Pillarisetty R. , Bojarski S. A. , Amin P. , Amitonov S. V. , Boter J. M. , Caudillo R. , Correas-Serrano D. , Dehollain J. P. , Droulers G. , Henry E. M. , Kotlyar R. , Lodari M. , Lüthi F. , Michalak D. J. , Mueller B. K. , Neyens S. , Roberts J. , Samkharadze N. , Zheng G. , Zietz O. K. , Scappucci G. , Veldhorst M. , Vandersypen L. M. K. , Clarke J. S. 
Published: March 2022

AbstractFull-scale quantum computers require the integration of millions of qubits, and the potential of using industrial semiconductor manufacturing to meet this need has driven the development of quantum computing in silicon quantum dots. However, fabrication has so far relied on electron-beam lithography and, with a few exceptions, conventional lift-off processes that suffer from low yield and poor uniformity. Here we report quantum dots that are hosted at a 28Si/28SiO2 interface and fabricated in a 300 mm semiconductor manufacturing facility using all-optical lithography and fully industrial processing. With this approach, we achieve nanoscale gate patterns with excellent yield. In the multi-electron regime, the quantum dots allow good tunnel barrier control—a crucial feature for fault-tolerant two-qubit gates. Single-spin qubit operation using magnetic resonance in the few-electron regime reveals relaxation times of over 1 s at 1 T and coherence times of over 3 ms.
Statistical modeling of SARS-CoV-2 substitution processes: predicting the next variant
Authors: Levinstein Hallak Keren , Rosset Saharon 
Published: March 2022

AbstractWe build statistical models to describe the substitution process in the SARS-CoV-2 as a function of explanatory factors describing the sequence, its function, and more. These models serve two different purposes: first, to gain knowledge about the evolutionary biology of the virus; and second, to predict future mutations in the virus, in particular, non-synonymous amino acid substitutions creating new variants. We use tens of thousands of publicly available SARS-CoV-2 sequences and consider tens of thousands of candidate models. Through a careful validation process, we confirm that our chosen models are indeed able to predict new amino acid substitutions: candidates ranked high by our model are eight times more likely to occur than random amino acid changes. We also show that named variants were highly ranked by our models before their appearance, emphasizing the value of our models for identifying likely variants and potentially utilizing this knowledge in vaccine design and other aspects of the ongoing battle against COVID-19.
EBI2 is a negative modulator of brown adipose tissue energy expenditure in mice and human brown adipocytes
Authors: Copperi Francesca , Schleis Inna , Roumain Martin , Muccioli Giulio G. , Casola Stefano , Klingenspor Martin , Pfeifer Alexander , Gnad Thorsten 
Published: March 2022

AbstractPharmacological activation of brown adipose tissue (BAT) is an attractive approach for increasing energy expenditure to counteract obesity. Given the side-effects of known activators of BAT, we studied inhibitors of BAT as a novel, alternative concept to regulate energy expenditure. We focused on G-protein-coupled receptors that are one of the major targets of clinically used drugs. Here, we identify GPR183, also known as EBI2, as the most highly expressed inhibitory G-protein-coupled receptor in BAT among the receptors examined. Activation of EBI2 using its endogenous ligand 7α,25-dihydroxycholesterol significantly decreases BAT-mediated energy expenditure in mice. In contrast, mice deficient for EBI2 show increased energy dissipation in response to cold. Interestingly, only thermogenic adipose tissue depots — BAT and subcutaneous white adipose tissue —respond to 7α,25-dihydroxycholesterol treatment/EBI2 activation but not gonadal white fat, which has the lowest thermogenic capacity. EBI2 activation in brown adipocytes significantly reduces norepinephrine-induced cAMP production, whereas pharmacological inhibition or genetic ablation of EBI2 results in an increased response. Importantly, EBI2 significantly inhibits norepinephrine-induced activation of human brown adipocytes. Our data identify the 7α,25-dihydroxycholesterol/EBI2 signaling pathway as a so far unknown BAT inhibitor. Understanding the inhibitory regulation of BAT might lead to novel pharmacological approaches to increase the activity of thermogenic adipose tissue and whole body energy expenditure in humans.
Repurposing FDA-approved drugs as FXR agonists: a structure basedin silicopharmacological study
Authors: Jose Sandra , Devi Sreevidya S. , Sajeev Anjana , Girisa Sosmitha , Alqahtani Mohammed S. , Abbas Mohamed , Alshammari Abdulrahman , Sethi Gautam , Kunnumakkara Ajaikumar B. 
Published: March 2022

AbstractFarnesoid X receptor (FXR) modulates the expression of genes involved in lipid and carbohydrate homeostasis and inflammatory processes. This nuclear receptor is likely a tumor suppressor in several cancers, but its molecular mechanism of suppression is still under study. Several studies reported that FXR agonism increases the survival of colorectal, biliary tract, and liver cancer patients. In addition, FXR expression was shown to be down-regulated in many diseases such as obesity, irritable bowel syndrome, glomerular inflammation, diabetes, proteinuria, and ulcerative colitis. Therefore, development of novel FXR agonists may have significant potential in the prevention and treatment of these diseases. In this scenario, computer-aided drug design procedures can be resourcefully applied for the rapid identification of promising drug candidates. In the present study, we applied the molecular docking method in conjunction with molecular dynamics (MD) simulations to find out potential agonists for FXR based on structural similarity with the drug that is currently used as FXR agonist, obeticholic acid. Our results showed that alvimopan and montelukast could be used as potent FXR activators and outperform the binding affinity of obeticholic acid by forming stable conformation with the protein in silico. However, further investigational studies and validations of the selected drugs are essential to figure out their suitability for preclinical and clinical trials.
View graph construction for scenes with duplicate structures via graph convolutional network
Authors: Peng Yang , Yan Shen , Liu Yuxiang , Liu Yu , Zhang Maojun 
Published: March 2022

AbstractView graph construction aims to effectively organise disordered image dataset through image retrieval technique before structure from motion (SfM). Existing view graph construction methods usually fail to handle scenes with duplicate structure, because these methods solely treat the construction of view graph as a process of image‐pair‐wise matching and lack in exploiting images' topological details in dataset. In this paper, we handle this problem from a novel perspective to construct view graph in a global paradigm by introducing an end‐to‐end graph convolutional network (GCN). First, a location‐aware embedding module is introduced to encode images into a feature space that takes into account the feature's location by using Vision Transformer architecture, improving the distinction between features of duplicate structure. Second, graph convolutional network that consists of topological relationship preserving module and feature metric learning module is proposed. Topological relationship preserving network is proposed to help nodes maintain their connected neighbourhood features. By merging the topological connected information into images' embedding, our method can process image matching in a global mode, thus improving the disambiguation ability for images with duplicate scenes. Then a feature metric learning network is embedded into GCN to dynamically compute the linkage prediction among nodes based on their features. Finally, our method combines these three parts to jointly optimise nodes' features and linkage prediction in an end‐to‐end paradigm. We make qualitative and quantitative comparisons based on three public benchmark datasets and demonstrate that our proposed method performs favourably against other state‐of‐the‐art methods.
Semantic‐meshed and content‐guided transformer for image captioning
Authors: Li Xuan , Zhang Wenkai , Sun Xian , Gao Xin 
Published: March 2022

AbstractThe transformer architecture has been the dominant framework for today's image captioning tasks because of its superior performance. However, existing methods based on transformer often lack the integrated use of multi‐level semantic information and are weak in maintaining the relevance of captions to the image. In this paper, a semantic‐meshed and content‐guided transformer network is introduced for image captioning to solve these problems. The semantic‐meshed mechanism allows the model to generate words by selecting semantic information of multiple interaction levels adaptively through attention‐based reconstruction. And the content‐guided module guides the words generation by using attribute features that represent the image content, which aims to keep the generated caption consistent with the main content of the image. Experiments on dataset on the MSCOCO captioning dataset are conducted to validate the authors’ model and achieve superior results compared to other state‐of‐the‐art method approaches.
Adaptive sequential reclosing strategy for hybrid HVDC circuit breakers in MMC‐based DC grids
Authors: Yu Jingqiu , Zhang Zheren , Xu Zheng 
Published: March 2022

AbstractDC circuit breaker (DCCB) is one of the most promising solutions for handling DC fault in half‐bridge modular multilevel converter (MMC)‐based DC grid. Generally after fault isolation, DCCBs are required to have the ability to quickly reclose so as to restore power transmission. However, the traditional simultaneous reclosing scheme may make the whole system suffer from secondary strikes such as overvoltage and overcurrent in the event of permanent faults. To solve this problem, many adaptive reclosing schemes have been proposed. Among them, the sequential reclosing scheme can achieve rapid recovery without laying any burden on the sampling and protection system. Unfortunately, secondary strikes under a reclosing failure still exist though they can be suppressed. Due to this, this paper illustrates that the overcurrent protection‐based fault identification method is not able to identify the fault in time, and thus the secondary strikes are generated during the second tripping. Optimal configuration of DCCBs' parameters can reduce these adverse impacts but cannot avoid them. Based on this, an adaptive reclosing scheme is proposed. Permanent and temporary faults are recognised according to the voltage characteristics at the beginning of the fault line as soon as the arresters are conducted. Extensive simulations on a four‐terminal DC grid in PSCAD/EMTDC show that the proposed method can eliminate the potential adverse impacts and is robust to fault resistance.
Generalized frequency estimator with rational combination of three spectrum lines
Authors: Gigleux Benjamin , Vincent François , Chaumette Eric 
Published: March 2022

AbstractThe popular Discrete Fourier Transform (DFT) is known to be a sub‐optimal frequency estimation technique for a finite transform length. In order to approach the Cramer‐Rao Lower Bound (CRLB), many refinement techniques have been considered, but little considering both zero padding or tapering, also known as windowing or apodisation. In this paper, a frequency estimator with closed‐form combination of three DFT samples is generalized to zero padding and tapered data within the class of cosine windowing. Root Mean Squared Error (RMSE) is shown to approach the CRLB in the case of a single tone signal with additive white Gaussian noise. Compared to state‐of‐the‐art techniques, the proposed algorithm improves the frequency RMSE up to 1 dB when using significant zero‐padding lengths (K ≥ 2 N) and for small to moderate SNR, which is the most challenging case for practical radar applications.
Prosumer‐centric energy storage system and high voltage distribution network topology Co‐optimisation for urban grid congestion management
Authors: Zhang Xi , Liu Youbo , Zeng Yu , Wu Gang , Liu Junyong 
Published: March 2022

AbstractThe surge of electricity load and the growing number of renewable energy sources (RESs) in urban power grid result in severe and frequent congestion for transmission networks. This causes the transmission system operators (TSOs) perform the high voltage distribution network (HVDN) reconfiguration frequently. The frequent reconfiguration of the HVDN will cause the fast degradation of high voltage circuit breakers, high maintenance and operational cost, and stability problems. To reduce the frequency of HVDN reconfiguration, this paper proposes a prosumer‐centric energy storage system (ESS) and HVDN topology co‐optimisation for transmission congestion management. Numerical results show that with the assist of ESS operational strategy, the reconfiguration frequency of HVDN can be significantly reduced while mitigating the transmission congestion.
Themes of climate change agency: a qualitative study on how people construct agency in relation to climate change
Authors: Toivonen Heidi 
Published: March 2022

AbstractThis study analyzes how people discursively construct their (non)agency—how they display abilities and capacities to act, or the lack thereof—vis-à-vis climate change. The paper presents the results of a detailed discursive and thematic analysis of 28 interview transcripts: 12 broad agency themes representing different ways of constructing human (non)agency in relation to climate change. The most common agency theme was Collective, followed by Individual, Critical, and Threatened agency. Climate change skepticism was displayed mostly within Critical agency, where the speakers presented themselves as intellectual and critically thinking individuals, drawing from scientific rhetoric while criticizing and misrepresenting climate science. The constructions of Collective agency emerged as a form of agency that displays a sense of meaningfulness related to socially embedded actions. The construction of agency in relation to climate change is very detailed discursive work, as people draw from multiple societal discourses to craft varied discursive positions of experiencing, knowing, and doing in relation to it. The paper suggests ways for climate communications to take into account these multiple themes of agency.
Risk factors and missing persons: advancing an understanding of ‘risk’
Authors: Ferguson Lorna 
Published: March 2022

AbstractThis study seeks to advance an understanding of ‘risk’ for persons going missing—a phenomenon also known as missingness. There is a need to clarify terms used to describe correlations or statistical associations between variables that are identified as risk factors for missing person incidents to understand the mechanisms influencing this phenomenon. Without such research, policies and preventative strategies cannot be adequately offered to begin to reduce missingness. To do so, a review is first provided of the current risk factors identified internationally for missing persons. Then, the Kraemer and colleagues (Arch Gen Psychiatry 54:337–343, 1997; Kraemer et al., Am J Psychiatry 158:848–856, 2001) risk factor classification system and MacArthur framework are applied to the risk factors to identify the ways in which these may be overlapping, proxy, mediating, and/or moderating factors. This clarification on risk terminology attempts to offer a common language for communicating about risk factors associated with missing persons. Suggestions are then provided for how these factors may overlap and/or work together to form risk pathways. The application of this framework highlights that ‘going missing’ may have multiple risk pathways that transgress the current risk factor categorical boundaries. The article then concludes that consistent use of terms and additional research on risk factors will enhance investigations of missing persons and understandings of low- and high-risk groups.
Stancetaking in Hong Kong political discourse
Authors: Liu Ming 
Published: March 2022

AbstractThis study gives a corpus-assisted discourse study (CADS) of stancetaking in the public speeches of three former Chief Executives. Three large corpora have been built by collecting all the public speeches of the three former Chief Executives. It combines automatic semantic tagging with the tripartite analysis of stancetaking in terms of evaluation, positioning and (dis)alignment. The findings not only reveal their preferential ways of stancetaking but also the changing socio-political contexts behind their particular ways of stancetaking. It is argued that a combination of the methods and theories in critical discourse analysis, stancetaking, and corpus linguistics can generate more illuminating findings concerning stancetaking in political discourse.