bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing calmodulin binding to ryanodine receptor is crucial to limit neuronal cell loss in Alzheimer disease
Authors: Nakamura Yoshihide , Yamamoto Takeshi , Xu Xiaojuan , Kobayashi Shigeki , Tanaka Shinji , Tamitani Masaki , Saito Takashi , Saido Takaomi C. , Yano Masafumi 
Published: March 2021

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive neuronal cell loss. Recently, dysregulation of intracellular Ca2+homeostasis has been suggested as a common proximal cause of neural dysfunction in AD. Here, we investigated (1) the pathogenic role of destabilization of ryanodine receptor (RyR2) in endoplasmic reticulum (ER) upon development of AD phenotypes inAppNL-G-Fmice, which harbor three familial AD mutations (Swedish, Beyreuther/Iberian, and Arctic), and (2) the therapeutic effect of enhanced calmodulin (CaM) binding to RyR2. In the neuronal cells fromAppNL-G-Fmice, CaM dissociation from RyR2 was associated with AD-related phenotypes, i.e. Aβ accumulation, TAU phosphorylation, ER stress, neuronal cell loss, and cognitive dysfunction. Surprisingly, either genetic (by V3599K substitution in RyR2) or pharmacological (by dantrolene) enhancement of CaM binding to RyR2 reversed almost completely the aforementioned AD-related phenotypes, except for Aβ accumulation. Thus, destabilization of RyR2 due to CaM dissociation is most likely an early and fundamental pathogenic mechanism involved in the development of AD. The discovery that neuronal cell loss can be fully prevented simply by stabilizing RyR2 sheds new light on the treatment of AD.
An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
Authors: Rawls Eric , Kummerfeld Erich , Zilverstand Anna 
Published: March 2021

AbstractAlcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others.
Evidence for continuity of interstitial spaces across tissue and organ boundaries in humans
Authors: Cenaj Odise , Allison Douglas H. R. , Imam Rami , Zeck Briana , Drohan Lilly M. , Chiriboga Luis , Llewellyn Jessica , Liu Cheng Z. , Park Young Nyun , Wells Rebecca G. , Theise Neil D. 
Published: March 2021

AbstractBodies have continuous reticular networks, comprising collagens, elastin, glycosaminoglycans, and other extracellular matrix components, through all tissues and organs. Fibrous coverings of nerves and blood vessels create structural continuity beyond organ boundaries. We recently validated fluid flow through human fibrous tissues, though whether these interstitial spaces are continuous through the body or discontinuous, confined within individual organs, remains unclear. Here we show evidence for continuity of interstitial spaces using two approaches. Non-biological particles (tattoo pigment, colloidal silver) were tracked within colon and skin interstitial spaces and into adjacent fascia. Hyaluronic acid, a macromolecular component of interstitial spaces, was also visualized. Both techniques demonstrate interstitial continuity within and between organs including within perineurium and vascular adventitia traversing organs and the spaces between them. We suggest that there is a body-wide network of fluid-filled interstitial spaces that has significant implications for molecular signaling, cell trafficking, and the spread of malignant and infectious disease.
Iodine(III) promotes cross-dehydrogenative coupling of N-hydroxyphthalimide and unactivated C(sp3)–H bonds
Authors: Wu Fufang , Han Xuanzhen , Li Xuejian , Shen Xiaobao , Wang Chang , Tian Zhimei , Cheng Bin , Zhang Jingbin , Sheng Liangquan , Zhai Hongbin 
Published: March 2021

AbstractCross-dehydrogenative coupling reactions provide a method to construct new chemical bonds by direct C–H activation without any pre-functionalization. Compared to functionalization of a C–H bond α- to ether oxygen, α- to carbonyl, or at a benzylic position, functionalization of unactivated hydrocarbons is difficult and often requires high temperatures, a transition-metal catalyst, or a superstoichiometric quantity of volatile, toxic, and explosive tert-butylhydroperoxide. Here, a cross-dehydrogenative C–O coupling reaction of N-hydroxyphthalimide with unactivated alkanes, nitriles, ethers, and thioethers has been realized by using iodobenzene diacetate as the radical initiator. The current protocol enables efficient functionalization of unactivated hydrocarbons and nitriles through inert C(sp3)–H bond activation under mild reaction conditions. O-substituted NHPI derivatives are generated in good yields under metal-free conditions.
Inverse design and flexible parameterization of meta-optics using algorithmic differentiation
Authors: Colburn Shane , Majumdar Arka 
Published: March 2021

AbstractUltrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the adjoint method, this requires explicitly deriving gradients, which is sometimes challenging for certain photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each focused in scope and scatterer type. Here, we leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the cost of two forward simulations typical of adjoint methods, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design.
Directed self-assembly of soft 3D photonic crystals for holograms with omnidirectional circular-polarization selectivity
Authors: Cho SeongYong , Takahashi Misaki , Fukuda Jun-ichi , Yoshida Hiroyuki , Ozaki Masanori 
Published: March 2021

AbstractControlling the crystallographic orientation of 3D photonic crystals is important as it determines the behavior of light propagating through the device. Blue phases self-assemble into unique soft 3D photonic crystals with chiral structures for circular-polarization selectivity, but it has remained a challenge to control its 3D orientation. Here, we show that the orientation of blue phases can be precisely controlled to follow a predefined pattern imprinted on a substrate by exploiting field-induced phase transitions. Obtaining the blue phase through the field-induced chiral nematic phase and tetragonal blue phase X results in a highly oriented blue phase I with the crystallographic [001] direction aligned along the surface anchoring. Our approach is applied to fabricating a Bragg-Berry hologram with omnidirectional circular-polarization selectivity, where the hologram is visible only for one circular-polarization under all incident angles. Such devices are difficult to fabricate using conventional optical materials, thereby demonstrating the potential of self-organizing soft matter for photonics.
Visible light-promoted NH-halogenation of sulfoximines with dichloromethane or dibromomethane
Authors: Wang Chenyang , Shi Peng , Bolm Carsten 
Published: March 2021

In the presence of a hypervalent iodine reagent, N-halo sulfoximines are obtained by catalyst-free visible light-promoted halogenations of their NH-counterparts with dichloro- or dibromomethane as halogen source.
Processing of Code-Switched Sentences in Noise by Bilingual Children
Authors: Gross Megan C. , Patel Haliee , Kaushanskaya Margarita 
Published: March 2021

Purpose
The purpose of the current study was to examine the effects of code-switching on bilingual children's online processing and offline comprehension of sentences in the presence of noise. In addition, the study examined individual differences in language ability and cognitive control skills as moderators of children's ability to process code-switched sentences in noise.

Method
The participants were 50 Spanish–English bilingual children, ages 7;0–11;8 (years;months). Children completed an auditory moving window task to examine whether they processed sentences with code-switching more slowly and less accurately than single-language sentences in the presence of noise. They completed the Dimensional Change Card Sort task to index cognitive control and standardized language measures in English and Spanish to index relative language dominance and overall language ability.

Results
Children were significantly less accurate in answering offline comprehension questions about code-switched sentences presented in noise compared to single-language sentences, especially for their dominant language. They also tended to exhibit slower processing speed, but costs did not reach significance. Language ability had an overall effect on offline comprehension but did not moderate the effects of code-switching. Cognitive control moderated the extent to which offline comprehension costs were affected by language dominance.

Conclusions
The findings of the current study suggest that code-switching, especially in the presence of background noise, may place additional demands on children's ability to comprehend sentences. However, it may be the processing of the nondominant language, rather than code-switching per se, that is especially difficult in the presence of noise.
Changes in Relative Fundamental Frequency Under Increased Cognitive Load in Individuals With Healthy Voices
Authors: Dahl Kimberly L. , Stepp Cara E. 
Published: March 2021

Purpose
The purpose of this study was to determine the effect of cognitive load on relative fundamental frequency (RFF) in individuals with healthy voices.

Method
Twenty adults with healthy voices read sentences under different cognitive load conditions. Each sentence contained color terms printed in colored ink, creating an embedded Stroop task. Participants read the ink color in which a word was printed, rather than the color term itself. Sentences with mismatched ink colors and printed words constituted an increased cognitive load. RFF, an acoustic correlate of laryngeal tension, was calculated for the 10 voicing cycles preceding (i.e., offset) and following (i.e., onset) voiceless consonants. Repeated measures analyses of variance were constructed to assess the effects of RFF cycle, cognitive load, and their interaction on mean RFF offset and onset.

Results
There was a significant effect of cognitive load condition on RFF offset. There was no significant effect of condition on RFF onset nor significant interaction between cycle and condition on RFF onset or offset values.

Conclusion
Reduced mean RFF offset may indicate an increase in laryngeal muscle tension during a cognitively demanding task.
Why AI still doesn’t have consciousness?
Authors: Li Deyi , He Wen , Guo Yike 
Published: March 2021

AbstractConsciousness is one of the unique features of creatures, and is also the root of biological intelligence. Up to now, all machines and robots haven’t had consciousness. Then, will the artificial intelligence (AI) be conscious? Will robots have real intelligence without consciousness? The most primitive consciousness is the perception and expression of self‐existence. In order to perceive the existence of the concept of ‘I’, a creature must first have a perceivable boundary such as skin to separate ‘I’ from ‘non‐I’. For robots, to have the self‐awareness, they also need to be wrapped by a similar sensory membrane. Nowadays, as intelligent tools, AI systems should also be regarded as the external extension of human intelligence. These tools are unconscious. The development of AI shows that intelligence can exist without consciousness. When human beings enter into the era of life intelligence from AI, it is not the AI became conscious, but that conscious lives will have strong AI. Therefore, it becomes more necessary to be careful on applying AI to living creatures, even to those lower‐level animals with only consciousness. The subversive revolution of such application may produce more careful thinking.
A predictive control method to improve pressure tracking precision and reduce valve switching for pneumatic brake systems
Authors: Zhang Rui , Peng Jun , Li Heng , Chen Bin , Liu Weirong , Huang Zhiwu , Wang Jing 
Published: March 2021

AbstractPneumatic brake systems are crucial for the operation of trains. However, due to switching characteristics of on/off solenoid valves, the precise pressure control and low switching activities of valves are difficult to guarantee simultaneously during braking. To address the issue, a hybrid model predictive control (MPC) method is proposed for implementing the multi‐objective optimisation in this paper, i.e. the precise pressure tracking and the valve switching reduction. In order to model the hybrid behaviour caused by continuous dynamics of the pressure and discrete features of solenoid valves, the mixed logic dynamical system theory is applied. Then, the braking control problem is recast as a hybrid MPC problem, which jointly optimises the pressure tracking precision and the valve switching. A mixed integer linear program is formulated to solve the MPC problem in an efficient way. Both simulation and experiment results show that the accurate pressure control and low switching activities of valves can be achieved by the proposed method when compared with existing methods.
Micro‐expression recognition by two‐stream difference network
Authors: Pan Hang , Xie Lun , Li Juan , Lv Zeping , Wang Zhiliang 
Published: March 2021

AbstractFacial micro‐expression is a superposition of micro‐expression features and identity information of a subject. For the problem of identity information interference in micro‐expression recognition, this study proposes a new method for facial micro‐expression recognition by de‐identity information, called two‐stream difference network (TSDN). First, a two‐stream encoder‐decoder network is trained by a convolutional neural network, where the input of the micro‐expression stream is a micro‐expression image, and the identity stream is a facial identity image. The micro‐expression image is the apex image, and the identity image is the onset image in the micro‐expression sequence. The identity information and micro‐expression features are recorded in the intermediate layer of the micro‐expression stream, while the intermediate layer of the identity stream contains only the identity information of a subject. Then, the identity information is removed by the difference network, but micro‐expression features are stored in the intermediate layer of the micro‐expression stream. Given the sequence of the micro‐expressions, the TSDN model of de‐identity information learns the difference that stores in the expression stream. Two public spontaneous facial micro‐expression data sets (SMIC and CASME II) are employed in our experiments. The experiment results show that our model can achieve a superior performance in micro‐expression recognition.
Pop‐net: A self‐growth network for popping out the salient object in videos
Authors: Yin Hui , Chen Ning , Yang Lin , Wan Jin 
Published: March 2021

AbstractIt is a big challenge for unsupervised video segmentation without any object annotation or prior knowledge. In this article, we formulate a completely unsupervised video object segmentation network which can pop out the most salient object in an input video by self‐growth, called Pop‐Net. Specifically, in this article, a novel self‐growth strategy which helps a base segmentation network to gradually grow to stick out the salient object as the video goes on, is introduced. To solve the sample generation problem for the unsupervised method, the sample generation module which fuses the appearance and motion saliency is proposed. Furthermore, the proposed sample optimization module improves the samples by using contour constrains for each self‐growth step. Experimental results on several datasets (DAVIS, DAVSOD, VideoSD, Segtrack‐v2) show the effectiveness of the proposed method. In particular, the state‐of‐the‐art methods on completely unfamiliar datasets (no fine‐tuned datasets) are performed.
Object tracking using temporally matching filters
Authors: Robeson Brendan , Javanmardi Mohammadreza , Qi Xiaojun 
Published: March 2021

AbstractOne of the primary challenges of visual tracking is the variable appearance of the target object. As tracking proceeds, the target object can change its appearance due to illumination changes, rotations, deformations etc. Modern trackers incorporate online updating to learn how the target changes over time. However, they do not use the history of target appearance. To address this shortcoming, we uniquely use domain adaptation with the target appearance history to efficiently learn a temporally matching filter (TMF) during online updating. This TMF emphasizes the persistent features found in different appearances of the target object. It also improves the classification accuracy of the convolutional neural network by assisting the training of the classification layers without incurring the runtime overhead of updating the convolutional layers. Extensive experimental results demonstrate that the proposed TMF‐based tracker, which incorporates domain adaptation with the target appearance history, improves tracking performance on three benchmark video databases (OTB‐50, OTB‐100 and VOT2016) over other online learning trackers. Specifically, it improves the overlap success of VITAL and MDNet by 0.44 % and 1.03 % on the OTB‐100 dataset and improves the accuracy of VITAL and MDNet by 0.55 % and 0.06 % on the VOT2016 dataset, respectively.
Point cloud super‐resolution based on geometric constraints
Authors: Li Xiaoqiang , Liu Jitao , Dai Songmin 
Published: March 2021

AbstractAmong all digital representations we have for real physical objects, three‐dimensional (3D) is arguably the most expressive encoding. But due to the limitations of 3D scanning equipment, point cloud often becomes sparse or partially missing. A point cloud super‐resolution (PCSR) method based on geometric constraints is proposed to solve the sparse problem of point clouds: it allows dense point clouds to be generated by sparse point clouds. The method is based on the conditional generative adversarial network including redesigned generator and discriminator for point cloud data specially. Moreover, the method can maintain the shape of the dense point cloud by adding geometric constraints. The contributions of our work are as follows: (1) a PCSR method based on geometric constraints is proposed; (2) add a module for obtaining point cloud neighbourhood information in the generator, called K‐nn operation module; and (3) feature aggregation is performed using the weighted pooling to process the neighbourhood information obtained by the K‐nn operation module. Extensive experimental results demonstrate the effectiveness of the proposed method.
A novel partial discharge localization method for GIL based on the 3D optical signal irradiance fingerprint and bagging‐KELM
Authors: Zang Yiming , Qian Yong , Zhou Xiaoli , Xu Antian , Sheng Gehao , Jiang Xiuchen 
Published: March 2021

AbstractPartial discharge (PD) is one of the main reasons that endanger the safe operation of power equipment. The effective detection and localization of PD in a gas‐insulated transmission line (GIL) is essential for timely finding the insulation defects to improve maintenance efficiency. Therefore, this paper proposes a method based on the three‐dimensional optical signal irradiance fingerprint and bagging‐kernel extreme learning machine (Bagging‐KELM), which introduces optical simulation into the PD localization. Using a simulation model with the same size and optical sensor arrangement as the experimental GIL, optical simulation signals emitted from different PD sources are collected. Principal component analysis is used to extract the signal features to construct optical PD fingerprints that correspond to PD source locations. This paper builds all the simulated PD fingerprints into a PD fingerprint database. Afterwards, the Bagging‐KELM is used to match PD optical fingerprints detected on site with the fingerprint database to identify the location of the PD sources. The experimental results show that the average localization error of this method is 0.93 cm, with 93.75% of the errors being less than 1.5 cm. The proposed method has better performance than the traditional KELM and BPNN, which can achieve precise PD localization in GIL.
A weighted graph‐based method for detection of data integrity attacks in electricity markets
Authors: Moslemi Ramin , Mesbahi Afshin , Mohammadpour Velni Javad 
Published: March 2021

AbstractThe negative impacts of data integrity attacks against multi‐settlement electricity markets have been recently studied. It has been shown that adversaries could launch profitable cyber attacks by casting an incorrect image of transmission lines' congestion pattern while simultaneously submitting bogus bids in electricity markets. Although a variety of countermeasures have been proposed in the literature to detect such data integrity attacks, none of those approaches has been designed specifically to ensure data integrity of multi‐settlement electricity markets. The existing methods may even fail to detect complicated types of attacks (e.g. unobservable false data injection attacks) or fail to address practical constraints and requirements of the electricity markets. As an example, most of the attack detection approaches have been established based on the DC economic dispatch, while the modern electricity markets may use AC models. This paper proposes a new anomaly detection tool based on graph theory using so‐called egonets, specifically designed to detect false data injection attacks against electricity markets. In contrast with the conventional detection methods, the malicious intention of the adversaries is exploited in the design procedure. Therefore, the proposed approach can detect false data injection attacks, regardless of the attack type and power flow model.